\documentclass[english]{jnlp_3.8}
\usepackage{jnlpbbl_2.3}
\usepackage[dvipdfmx]{graphicx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{url}
\usepackage{array}
\usepackage{hangcaption_jnlp}
\usepackage{booktabs}
\usepackage{multirow}
\allowdisplaybreaks


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% author's definition
%%%%\usepackage{color}
%%%%\usepackage{jumoline}
%%%%\usepackage{proofread}
%%%% \noproofreadmark
%%%%\usepackage{xcolor}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\newcommand{\addspan}[1]{#1}


\Volume{32}
\Number{4}
\Month{December}
\Year{2025}
%%%%\Issuetitle{Special Issue: XXXXXXXX}

\received{2025}{3}{14}
\revised{2025}{5}{30}
\accepted{2025}{6}{30}

\setcounter{page}{1030}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\esubject{General Paper}
\etitle{Adapting Spoken Dialogue Systems to Low-Resource User Groups through Data Augmentation with a Focus on Dialogue Act Flow and Style}
\eauthor{Zhiyang Qi\affiref{Author_1} \and Michimasa Inaba\affiref{Author_1}} 
\eabstract{%
Spoken dialogue systems (SDSs) often encounter significant challenges when interacting with users who exhibit unique conversational behaviors, such as minors, particularly in low-resource environments with limited data availability. To address these challenges, we present a novel data augmentation framework for enhancing SDS performance, particularly when handling such user groups. The framework employs large language models to extract and model speaker styles and leverages pre-trained language models to simulate diverse dialogue act (DA) histories, ultimately creating a rich and personalized set of training data. By focusing on both the unique speaking styles and distinctive dialogue behavior trajectories of users, the framework improves DA prediction accuracy, thereby guiding the SDS to more effectively adapt to low-resource users with specialized conversational characteristics. Extensive experiments conducted in low-resource settings validate the effectiveness of this approach, demonstrating its potential to improve SDS adaptability and foster the development of more inclusive and responsive systems.
}
\ekeywords{Data Augmentation, User Modeling, Spoken Dialogue Systems, Dialogue Act}

\headauthor{Qi and Inaba}
\headtitle{Adapting SDS to Low-Resource Users via Data Augmentation}

\affilabel{Author_1}{}{The University of Electro-Communications}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}

\maketitle

\renewcommand{\thefootnote}{}
\footnote[0]{This paper is an extended version of our previous work, which was presented at NLP2025 (Qi and Inaba 2025) and SIGDIAL2024 (Qi and Inaba, 2024), both of which contain the same content. Section 1 describes the differences between this study and previous publications.}
\renewcommand{\thefootnote}{\arabic{footnote}}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}

As an innovative technology at the forefront of AI and speech processing, spoken dialogue systems (SDSs) have attracted significant interest from both academia and industry \cite{Kawahara2018SpokenDS,Kim2021HowRR,Si2023SpokenWOZAL}. 
Despite the powerful capabilities of large language models (LLMs), traditional SDSs remain the focal point of research because of their superior control and interpretability \cite{rethinking}.
SDSs are predominantly trained using data from human-to-human interactions, which highlight varying speaking styles, such as clarity of intentions, as depicted in Figure \ref{fig:pic_intro}. This variability necessitates human speakers to adjust their dialogue strategies when engaging with different users. For instance, compared to adults, minors often exhibit less clarity in their intentions and provide ambiguous responses, requiring more confirmatory language or additional inquiries to better adapt to the unique speaking styles of younger users. This adaptive approach is crucial for enhancing the effectiveness and user-friendliness of SDSs in real-world scenarios.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 1
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f01.eps}
\end{center}
\hangcaption{Real human-to-human conversations. Speakers adopt various dialogue actions when interacting with users using diverse speaking styles.}
\label{fig:pic_intro}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

However, adapting SDSs to these distinctive speaking styles typically requires a wealth of annotated dialogue data, which can be challenging to obtain because of the minority status of users employing unique conversational strategies or behaviors. To enhance the performance of dialogue systems when engaging with data-scarce user groups exhibiting distinct speaking styles, targeted data augmentation is imperative to enable the system to better cater to their needs.

This study introduced a tailored data augmentation framework specifically designed for low-resource user groups that exhibit distinctive conversational behaviors. 
\addspan{Considering two key challenges---(1) the distinctive conversational behaviors of minors \cite{inaba-etal-2024-collection}, as illustrated in Figure \ref{fig:pic_intro}, and (2) the difficulty of collecting their data in real-world settings---our study conducted experiments employing dialogue data from minors to facilitate targeted data augmentation for this demographic.}

As depicted in Figure \ref{fig:pic_intro}, the unique speaking style of users directly influences the speaker's dialogue acts (DAs) and indirectly shapes response content. Therefore, our data augmentation framework focuses on the speaking styles of users and the trajectories of DAs. 

Specifically, we utilized LLM to extract the speaking styles of users and speakers interacting with them.
We then fine-tuned a pretrained language model (PLM) using all available data in a low-resource setting to create varied histories of DAs for speakers interacting with these user groups. The resulting speaker styles and DA histories were input into the LLM to produce customized training dialogue data for these users. The primary goal is to enhance the model's ability to predict DAs when interacting with low-resource groups with unique speaking styles, because controlling the content of generated responses through DAs is deemed effective \cite{kawano-etal-2019-neural}.

The contributions of the study are outlined below.

\begin{itemize}
\item We introduced a data augmentation method to enhance the performance of the DA prediction model when handling users with limited data and unique conversational behaviors and styles.
\item Through multiple experiments conducted in a low-resource setting, we discovered that the difficulty of DA prediction varies across different users and demonstrated the adaptability and effectiveness of the proposed method.
\end{itemize}

This paper forms an updated and rectified version of papers presented at SIGDIAL2024 \cite{qi-inaba-2024-data} and NLP2025 \cite{qi-nlp-2025}. The previous version verified the effectiveness of the proposed method using relatively small amounts of augmented data. 
In this study, we conducted additional experiments to assess whether generating large amounts of augmented data can lead to further performance improvements (Section \ref{sec:new_4}). In addition, we conducted ablation studies based on human evaluation to validate the effectiveness of the components within the proposed method (Sections \ref{sec:new_1}, \ref{sec:new_2}, and \ref{sec:new_3}).


\addspan{Note that in this study, we deliberately limited our scope to DA prediction, as it serves as a core planning component in SDSs and allows for more precise evaluation using metrics such as exact and partial match rates. Contrarily, response generation introduces greater linguistic variability and complexity, making it more difficult to isolate the impact of data augmentation. Therefore, we left response generation outside the scope of this study.}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Related Work}

The scarcity of annotated data and the challenge of data imbalance are persistent issues in various artificial intelligence domains \cite{Shorten2019ASO,rs12101688,AHMAD2021125834,hedderich-etal-2021-survey,kim-etal-2023-soda}. 
To address these issues effectively, data augmentation techniques have been employed, as demonstrated in prior research across different tasks \cite{feng-etal-2021-survey,10.1145/3544558}. 
For instance, \citeauthor{schick-schutze-2021-generating} \citeyear{schick-schutze-2021-generating} generated text-similarity datasets from scratch using a large PLM. 
Similarly, \citeauthor{liu-etal-2022-data} \citeyear{liu-etal-2022-data} and \citeauthor{chen-yang-2021-simple} \citeyear{chen-yang-2021-simple} enhanced data by manipulating individual utterances within dialogues---such as adding, deleting, changing their order, or regenerating them---while preserving the original meaning, which improved the model performance in dialogue summarization tasks. While the abovementioned methods focused on generating individual sentences, our study aimed to create coherent dialogues comprising multiple sentences tailored for specific target groups. 

\citeauthor{mohapatra-etal-2021-simulated-chats} \citeyear{mohapatra-etal-2021-simulated-chats} utilized GPT-2 \cite{Radford2019LanguageMA} to develop user and agent bots, generating comprehensive task-oriented dialogues through bot interactions, demonstrating notable enhancements in low-resource scenarios with datasets MultiWOZ \cite{budzianowski-etal-2018-multiwoz} and PersonaChat \cite{zhang-etal-2018-personalizing}. 
Recently, with the advanced text-generation capabilities of LLMs, researchers have begun using them for data augmentation \cite{pan-etal-2023-data,kim-etal-2023-soda,wang-etal-2023-improving-conversational}. For instance, \citeauthor{kim-etal-2023-soda} \citeyear{kim-etal-2023-soda} guided LLMs to generate a broad spectrum of social dialogues using social commonsense knowledge from a knowledge graph. 
\citeauthor{pan-etal-2023-data} \citeyear{pan-etal-2023-data} generated domain-specific, task-oriented dialogues by extracting dialogue paths from out-of-domain conversations. The concept of dialogue paths in their study aligns with the concept of DA history in our research. However, the key distinction is that, while they extract DA paths from existing data, we generate tailored DA histories based on existing data, specifically optimized for target user groups.

\addspan{DA prediction, which involves identifying the communicative intent behind each utterance, has been extensively explored in dialogue systems research \cite{shriberg-etal-2004-icsi,mezza-etal-2018-iso,saha-etal-2020-towards,kim-etal-2024-dual}. 
Various approaches have been proposed, including conditional random fields \cite{shang-etal-2020-speaker}, speaker turn modeling for DA classification \cite{he-etal-2021-speaker-turn}, and transformer-based methods \cite{zelasko-etal-2021-helps}. 
More recently, prompt-based approaches to dialogue act annotation have also been explored \cite{shan-education-2023,perkoff-etal-2024-keep}.
Building on these advancements, our study incorporates DA prediction to construct plausible DA histories and further leverages them as structural backbones to generate coherent, customized dialogues tailored to specific user groups.}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 2
\begin{figure}[b]
\begin{center}
\includegraphics{32-4ia1f02.eps}
\end{center}
\hangcaption{Our data augmentation framework is designed to improve the performance of the PLM in predicting DA when interacting with low-resource users who exhibit unique speaking styles. Beginning with dialogues that involve specific target users, we: (1) extract speaker styles, (2) generate DA histories of system interactions with these users, and (3) input this information into ChatGPT for tailored data augmentation.}
\label{fig:our_model}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{The Proposed Framework}

This study aims to enhance the DA prediction performance of the system when handling low-resource user groups that exhibit unique dialogue strategies by generating training data using the proposed data augmentation framework. Accurate DA prediction is crucial for the construction of SDSs because it facilitates dialogue state tracking and guides response generation, thereby reducing erroneous responses \cite{10.1145/3166054.3166058}. 
The task depicted on the left side of Figure \ref{fig:our_model} is defined as follows:
Assuming that the current turn of the dialogue is turn $t$, we utilize the dialogue history $H_d=(S_{t-n}, U_{t-n},...,S_{t-1}, U_{t-1})$ from the previous $n$ turns along with the system's DA history $H_a=(a_{t-n},...,a_{t-1})$ from these turns as the input. The output is the system DA $a_t$ for the current turn.

Because we predict the current turn's DA based on the dialogue and system's DA histories, it is crucial to generate dialogue and system DA histories that closely align with the target user group. To achieve this, we controlled the generation of dialogue data by capturing the speaking styles of dialogue participants and generating dialogue flows that mimic real human interactions with the target user group. The importance of this approach lies in the fact that the model can effectively understand and adapt to unique dialogue strategies only when the training data realistically simulates complex dialogue scenarios. In real human interactions, users with unique dialogue strategies are in the minority and exhibit considerable diversity. Owing to limitations in the data scale, traditional training datasets often fail to cover this diversity, which limits the adaptability and accuracy of the model when handling such users. By simulating the dialogue styles and processes of specific user groups, we can generate more diverse and precise training data, thereby enhancing the model's generalizability and adaptability to diverse users.

As illustrated in Figure \ref{fig:our_model}, our data augmentation framework comprises three components: (1) employing ChatGPT\footnote{\url{https://openai.com/blog/chatgpt}} to extract the speaker's styles $S$, (2) finetuning a pre-trained model to generate the system's DA history $H_a^{Aug}=(a_{t-n}^{Aug},...,a_{t-1}^{Aug})$, and (3) inputting the extracted speaking styles $S$ and the generated system's DA history $H_a^{Aug}$ into ChatGPT\footnote{The outputs from ChatGPT were used solely to generate training data for a dialogue act prediction model (i.e., a classifier), not for training a generative language model. This usage complies with OpenAI's terms of use, which prohibit using outputs to develop competing generative models.} to generate the training dialogue data $H_d^{Aug}=(S_{t-n}^{Aug}, U_{t-n}^{Aug},...,S_{t-1}^{Aug}, U_{t-1}^{Aug})$.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Speaker Styles Extraction}

As the unique speaking styles employed by the target user group significantly influence the content of conversations, it is crucial to capture the speaking styles of this group by comparing dialogues from the target user group with those from non-target groups. This helps guide the subsequent generation of dialogues that are specifically tailored to the target user group. To facilitate this, we employed ChatGPT to extract speaker styles from conversations involving target users. 

Specifically, we input a set of $m$ dialogues, half of which involve users from the target group and the other half from non-target user groups. This balanced approach allows for effective comparison, helping to identify and differentiate prominent speaking characteristics unique to the target group. 
Subsequently, ChatGPT was utilized to generate outputs representing the speaking style of the target users, as well as the speaking style of speakers when engaging with the target user group. 
\addspan{That is, we extracted the styles of both sides of the dialogue: the target users and their interlocutors (e.g., operators or staff).}
Notably, our primary focus is on extracting abstract styles, such as ``target users often exhibit ambiguous intentions toward destinations and activities.'' These styles are crucial because they significantly influence the direction of the dialogue, thereby enhancing the realism and relevance of the generated dialogue to actual human conversations. 
The prompt and extracted speaker styles are presented in Appendix \ref{sec:appendix_c}.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{DA History Generation}

As depicted in Figure \ref{fig:pic_intro}, the unique conversational strategies employed by the target group also significantly influence the DAs of those engaging with them. Our objective at this stage is to generate a diverse and realistic DA history $H_a^{Aug}$ that is specifically optimized for groups with distinctive speaking strategies. As shown in Figure \ref{fig:pic_da_gen}, this is achieved by finetuning a PLM using the existing data to generate the system's DA history $H_a^{Aug}$ for the previous $n$ turns.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 3
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f03.eps}
\end{center}
\hangcaption{DA History Generation. We conducted two rounds of finetuning: the first round using all available data, and the second round using only data from the target user group, to ensure the generated DA history more closely aligns with the target demographic.}
\label{fig:pic_da_gen}
\vspace{-0.75\Cvs}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

In particular, we utilized DA $a_t$ and utterance $S_t$  from the current turn $t$ as inputs, with DA history $H_a$ from the previous $n$ turns as the desired output, to establish the training data. These data were then divided into two sets: one for training and the other for generation. Initially, we fine-tuned the PLM using all available training data to capture DA histories that closely resembled real human conversations. Subsequently, we conducted secondary fine-tuning using the training data exclusively from the target user group. This dual fine-tuning approach ensures that the model can generate DA histories that closely mimic real human dialogues and align with the unique speaking strategies of the target users. The first fine-tuning, which employed a relatively large dataset, enabled the model to produce DA histories that mirror authentic human interactions. The second fine-tuning, which focused on a smaller dataset specific to the target user group, allowed the model to better tailor DA histories to their unique characteristics.

During the generation phase, we input DA $a_t$ and utterance $S_t$ from the current turn $t$ and generated the DA history $H_a^{Aug}$ from the previous $n$ turns. To ensure diversity, we simultaneously generated multiple outputs by selecting only the $(a_t,H_a^{Aug})$ combinations that were not previously observed.

%%%%\label{sec:dahg}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Dialogue Generation}

Having obtained speaker styles and DA histories tailored to users employing 
\pagebreak
unique dialogue strategies, our ultimate goal was to generate dialogues corresponding to these styles and histories to enrich the training data for DA prediction. 
At this stage, we leverage ChatGPT's powerful generation capabilities to create dialogue data for training.
Utilizing a few-shot prompt, we input the extracted speaking styles $S$ and DA histories $H_a^{Aug}$ into ChatGPT to generate dialogues $H_d^{Aug}$ that reflect the conversational styles of the target users. Subsequently, we used the generated dialogues $H_d^{Aug}$ and DA histories $H_a^{Aug}$ as inputs, with $a_t$ as the gold standard answer, to construct the training data. The prompts used to generate these dialogues are detailed in Appendix \ref{sec:appendix_d}.

This approach aimed to enhance the ability of the model to predict DAs when interacting with target users exhibiting unique conversational strategies. This effectively addresses the challenge of data scarcity through data augmentation.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Experiment}

To evaluate the effectiveness of the proposed data augmentation framework, 
\pagebreak
we experimented using data from minors who employed unique conversational styles and strategies in actual dialogues within the dataset. These experiments were conducted in a low-resource setting across multiple splits, each utilizing different subsets of data from minors. We trained multiple DA prediction models on datasets of varying sizes, including models trained using augmented data added to existing datasets.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Dataset}

This study utilized a multimodal dialogue Japanese dataset known as the ``Travel Agency Task Dialogue Corpus''\footnote{\addspan{The dataset does not contain speaker style annotations, making it infeasible to directly evaluate the accuracy of the extracted styles. Instead, we assessed their effectiveness through ablation studies and human evaluations.}} \cite{inaba-etal-2022-collection,inaba-etal-2024-collection}, which features conversations from users of various age groups, with detailed annotations of DAs.
This dataset contains 115 hours of dialogue, spanning 330 conversations, with each averaging approximately 20 minutes. The dialogues were facilitated by Zoom video calls, involving six operators and 55 customers, including 20 minors (ages 7--17), 25 adults (ages 20--60), and 10 seniors (ages 65--72). Each customer participated in six dialogue sessions.

The dialogues revolve around recommending travel destinations to users across various age groups. The dataset authors employed a Hidden Markov Model (HMM) \cite{18626} to analyze the transitions in dialogues among different age groups using sequences of DAs. 
\addspan{Moreover, the authors observed that, contrary to adults, minors tended to display unique conversational patterns characterized by fewer independently expressed opinions, thereby necessitating more proactive engagement from the operator to elicit their preferences.}
The annotation of DAs was performed using a functional segment, which is a unit smaller than the utterance. Each operator's segment was annotated as one of the 28 predefined DAs related to travel destination recommendations, or as ``None.'' Examples of these DAs include asking about the travel season (SeasonQuestion) and summarizing the travel plan (TravelSummary), all of which are detailed in Appendix \ref{sec:appendix_a}.
Because segments labeled ``None'' primarily consisted of non-informative responses such as ``Yeah'' or ``Uh-huh,'' and our objective was to guide the system to generate accurate and meaningful responses using DA tags, we selectively included only those training instances where the gold-standard responses were not labeled ``None'' in this study. In addition, we employed text-based human transcriptions rather than audio recordings.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Low-Resource Setting}

We trained five DA prediction models using datasets of various scales: 
\pagebreak
\textbf{Minors-Only}, \textbf{Zero-Shot}, \textbf{Low-Resource}, \textbf{Full-Resource}, and \textbf{Low-Resource + Aug(mentation) (Ours)}. To simulate low-resource conditions for specific user demographics, we used dialogue data from only three minors out of a group of 20, totaling 18 dialogues for training. For the evaluation, we used 60 dialogues from ten minors.

\begin{itemize}
\item \textbf{Minors-Only}: Employed only 18 dialogues from 3 minors.

\item \textbf{Zero-Shot}: Utilized all data from adults and seniors, amounting to 210 dialogues.

\item \textbf{Low-Resource}: Combined the 18 dialogues from the Minors-Only with all 210 dialogues from adults and seniors, totaling 228 dialogues.

\item \textbf{Full-Resource}: Included dialogues from 10 minors (60 dialogues), encompassing those from the 3 minors in the low-resource setting, plus all 210 dialogues from adults and seniors, totaling 270 dialogues.

\item \textbf{Low-Resource + Aug(mentation) (Ours)}: Used the 228 dialogues from the Low-Resource and supplemented them using our proposed augmentation framework. Additional data was generated until the dataset size matched that of the Full-Resource for a direct comparison.

\end{itemize}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Setup and Details}

In the process of extracting speaker styles, we fed $m=6$ dialogues into GPT-4-0125-preview, where three were from minors in a low-resource setting and the other three involved different adults or seniors. For generating the training dialogues, GPT-3.5-turbo-0125 was employed.

During the DA history generation phase, we utilized the Japanese T5-Large\footnote{\url{https://huggingface.co/retrieva-jp/t5-large-long}} as the PLM. We conducted two rounds of fine-tuning to ensure that the model could generate DA histories that closely mimicked real human conversations and aligned with the unique conversational strategies of minors during interactions. During the first training phase, the learning rate was set to $1\text{e}-4$, and for the subsequent phase, exclusively involving data from minors, it was set to $5\text{e}-5$. 
We used 210 adult and elderly conversations to generate DA histories, dividing them into 120 for training and 90 for generational purposes. To ensure data diversity and novelty, we retained only those $(a_t,H_a^{Aug})$ combinations that did not previously exist; all 18 dialogues from the three minors were included in both the training and generation phases. To ensure diversity, we set num\_return\_sequences=3 when generating DA histories, meaning that for each data point, three DA histories were generated simultaneously.

In the DA prediction phase, Japanese T5-base\footnote{\url{https://huggingface.co/retrieva-jp/t5-base-long}} and Japanese GPT-NeoX\footnote{\url{https://huggingface.co/stockmark/gpt-neox-japanese-1.4b}} were used as PLMs to validate the effectiveness of the generated data. We reconstructed the training and evaluation sets for the same DA prediction task to optimize hyperparameters; specific details are provided in Appendix \ref{sec:appendix_b}.
Regarding the distribution of the training and validation sets, the validation sets for all settings, except Minors-Only, were identical, comprising 21 dialogues from adults and seniors. The minor-only validation set consisted of three dialogues from minors in the low-resource scenario. 
To validate the generalizability of our method, we experimented across four splits, each using data from three different minors for training in a low-resource setting while varying the test data. 
Details on the data points for each split, after removing entries with a gold-standard answer of ``None,'' are outlined in Table \ref{tab:5_splits}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 1
\begin{table}[b]
\input{01table01.tex}%
\hangcaption{Training data quantity for DA prediction across four splits: MO (Minors-Only), ZS (Zero-Shot), LR (Low-Resource), FR (Full-Resource)}
\label{tab:5_splits}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Considering that a single utterance may consist of multiple segments (see Figures \ref{fig:pic_intro} and \ref{fig:our_model}), each potentially labeled with a different DA, there may be more than one gold-standard DA label for the current turn. Therefore, we employed both the \textbf{exact and partial match rates} as evaluation metrics. 
The exact match rate is a strict metric that requires the predicted set of labels to align completely with the true set of gold labels, measuring the ability of the model to fully grasp the dialogue context and predict all relevant DA labels accurately. The partial match rate assesses the performance of the model in predicting the correct labels. This metric is more lenient, recognizing that in real conversations, capturing the main intent or action of the dialogue, even if every label is not precisely predicted, is still valuable. Therefore, the partial match rate helps understand the robustness of the model in practical use. Combined, these two metrics offer a balanced approach for evaluating a model's DA prediction capabilities, providing a more accurate reflection of the model's performance.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 2
\begin{table}[b]
\input{01table02.tex}%
\hangcaption{Results of DA prediction: mean exact and partial match rates ($\pm$ standard deviation) across four data splits, based on five runs with random seeds from 1 to 5 under different training settings.}
\label{tab:result_each}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Results and Analysis}

\addspan{Table \ref{tab:result_each} presents the results of DA prediction, reporting the mean and standard deviation of both exact match and partial match rates across five runs with different random seeds (from one to five) under four different data splits, using both Japanese GPT-NeoX and T5-base models.}
Although \textbf{Minors-Only} solely comprised data from minors, its performance was inferior to that of the \textbf{Zero-Shot} model trained only with adult and elderly dialogue data because of the limited amount of training data. Therefore, we used all the available adult and elderly dialogue data from other setups to enhance the generalizability of the model. 

Additionally, because \textbf{Zero-Shot} does not use minor dialogues, the training data remained consistent across the four different splits. The variation in the performance of \ textbf{Zero-Shot} across the splits further underscores the differences in the adaptability of the model to different minors, with the third split proving to be the most challenging. 

Across the four splits, the performance of the proposed data augmentation framework, \textbf{Low-Resource + Aug (Ours)}, almost surpassed that of \textbf{Low-Resource} for both T5 and GPT-NeoX in terms of the mean exact and partial match rates. This demonstrates that even in a low-resource setting, our method successfully captures the characteristics of minor speakers and generates dialogue flows that align with minor speaking behaviors, thereby guiding the generation of a training dialog.

However, although we augmented the data to match the quantity of \textbf{Full-Resource} in each split, \textbf{Full-Resource} typically exhibited superior performance. A possible explanation is the lack of quality control, which means that subpar data were not filtered out, leading to poorer adaptation compared to \textbf{Full-Resource}, which used data exclusively from real human conversations. 
In addition, the ``Travel Agency Task Dialogue Corpus,'' which is derived from video calls and is manually transcribed, may contain colloquial filler words and other informal elements in its complete utterances. Conversely, ChatGPT-generated dialogues tended to be more structured and fluid. This stylistic difference could also contribute to the observed performance disparity between \textbf{Low-Resource + Aug (Ours)} and \textbf{Full-Resource}.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Ablation}

To evaluate the individual effectiveness of the components in our proposed framework, we conducted ablation experiments using the Japanese GPT-NeoX across four splits:

\begin{itemize}
\item \textbf{w/o DA History Gen}: In this model, we omitted the generation of new DA histories and instead randomly selected DA histories from the Low-Resource for data generation.

\item \textbf{DA History Gen w/o Second Finetune}: This variant involved finetuning the DA history generation model only once, without a second round of finetuning tailored specifically for minors.

\item \textbf{w/o Speaker Style}: This model utilized the same DA histories as our complete method but did not use extracted speaker styles during dialogue data generation.
\end{itemize}

Table \ref{tab:ablation} lists the average results across the four splits, conducting five training sessions for each model in every split, with seed values ranging from 1--5. The findings indicate that both the \textbf{w/o DA History Gen} and \textbf{w/o Speaker Style} variants achieved higher mean exact and partial match rates than \textbf{Low-Resource}. This demonstrates that the training data generated through the independent use of style extraction and DA history generation components can also significantly improve performance.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 3
\begin{table}[t]
\input{01table03.tex}%
\hangcaption{Average results of the ablation experiments on DA prediction across four splits using the Japanese GPT-NeoX model.}
\label{tab:ablation}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Furthermore, although \textbf{DA History Gen w/o Second Finetune} did not use data from the target user group for a second fine-tuning step during the training of the DA history generation model, its performance still surpassed that of \textbf{w/o DA History Gen}. This indicates that in generating DA history, even without a second fine-tuning step to optimize the PLM for minors, the new $(a_t, H_a^{Aug})$ combinations generated by a PLM trained with all available data can still enhance the performance. 
Finally, \textbf{Ours}\footnote{\addspan{\textbf{Ours} denotes the \textbf{Low-Resource + Aug (Ours)}.}} achieved the highest rates for both exact and partial match rates, indicating that the combination of speaker style extraction and DA history generation is the most effective, and underscores the necessity of targeted age-specific second fine-tuning when training the DA history generation model.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Why Did the Speaker Style Work?}

Figure \ref{fig:f_ablation} shows the dialogues generated by \textbf{w/o Speaker Style} and \textbf{Ours} using the same DA history. The DA history consists of first asking the user for a travel-related request (RequestQuestion), then confirming the request (RequestConfirm), and finally indicating the content to be searched (SearchConditionInform). 
We observed that without the speaker style, the user in the \textbf{w/o Speaker Style} provided specific travel requirements and the dialogue progressed smoothly. However, the user in \textbf{Ours} did not exhibit a clear intent. This indicates that the speaker style is effective, resulting in dialogues that closely match the speaking styles of minors and align more closely with real human conversations.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 4
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f04.eps}
\end{center}
\caption{Dialogues generated by the variant without speaker styles and our approach.}
\label{fig:f_ablation}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Human evaluation}
\label{sec:new_1}

To further evaluate the effectiveness of the speaker-style extraction, we conducted a human evaluation. We also compared the performance of the proposed method with that of the original dataset. We randomly selected 100 dialogue data samples from minors in the original training dataset, with each sample containing three rounds of dialogue history between the operator and customer, along with the operator's DA history. Although our proposed enhancement framework extends the training data by generating new DA histories, for direct comparison, we used the original DA history of these 100 data points to generate dialogue content and assess the quality of the generated dialogues.

We conducted comparisons between \textbf{Ours} and \textbf{w/o Speaker Style} as well as between \textbf{Ours} and the \textbf{original dataset}. Specifically, before the evaluation began, we provided three crowdsourced evaluators with two complete dialogue examples from minor customers to help them familiarize themselves with and understand the speaking styles of minors and the operator's speaking style when interacting with them. During the evaluation process, each set of evaluation data simultaneously presented two dialogue samples to the evaluators, both of which had the same DA history but were generated from different sources. The evaluators were asked to choose which dialogue was closer to the previously provided examples, or to select ``both are the same'' if they could not determine a preference. To encourage evaluators to make a clear choice, we emphasized that they should avoid choosing ``the same'' unless it was particularly difficult to decide. \addspan{Importantly, the evaluators were not informed of which model generated each dialogue, and the presentation order of the samples was randomized}. Additionally, to allow evaluators to focus more on the speaker styles reflected in the dialogue content, we omitted the DA history during the evaluation process.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Human evaluation results}
\label{sec:new_2}

Table \ref{tab:human} presents the average evaluation results from the three crowd-sourced evaluators, along with the Fleiss' Kappa consistency metric \cite{Fleiss1971MeasuringNS}. Compared with \textbf{w/o Speaker Style}, the dialogue data generated using the proposed framework more closely matched the speaker styles in the real dataset 84\% of the time, with a Fleiss' Kappa value of 0.5303, indicating moderate agreement. This further highlights the advantages of our proposed framework. This suggests that although we used a few-shot prompt to guide the LLM with some real dialogue examples, incorporating the extracted speaker styles led to the generation of dialogues that were more similar to the real data.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 4
\begin{table}[b]
\input{01table04.tex}%
\hangcaption{Average results of the human evaluation comparing \textbf{Ours} with \textbf{w/o Speaker Style} and the \textbf{Original Dataset}. Three crowdsourced evaluators determined which dialogue better matched the speaking styles of minors and the operator, given similar DA history. Fleiss' Kappa indicates inter-annotator agreement.}
\label{tab:human}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Moreover, compared with the \textbf{original dataset}, the dialogue data generated using the proposed framework more closely matched the speaker styles in the real dataset (28\% of the time). Although this result was anticipated, because, even in automatic evaluation, increasing the data volume to match the full-resource level usually does not outperform full-resource performance, it nonetheless demonstrates the competitiveness of our enhancement framework. It effectively generated high-quality dialogue data that aligns with the unique speaking styles of specific groups. Additionally, the Fleiss' Kappa value was 0.2645, indicating fair agreement. Although the consistency was not high, this suggests that the evaluators found it difficult to consistently determine which was better between the generated and original data, reflecting the competitive performance of the proposed framework.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 5
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f05.eps}
\end{center}
\caption{Case study.}
\label{fig:case_study}
\vspace{-0.5\Cvs}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Case study}
\label{sec:new_3}

Figure \ref{fig:case_study} illustrates two examples of comparisons 
\pagebreak
between \textbf{Ours} and the \textbf{original dataset}. In the top example, all three evaluators unanimously agreed that the dialogue generated using the proposed framework was superior to the original data. This may be because, in real complete dialogues, user responses are often brief, such as ``yes'' or ``okay.'' As shown in Figure \ref{fig:pic_prompt_ex} in Appendix \ref{sec:appendix_c}, the extracted speaker styles reflected this tendency. In such cases, the other party should provide more information to ensure that the dialogue progresses smoothly. Our proposed framework successfully captures this interaction pattern and generates high-quality dialogue. However, in the bottom example, the opinions of the evaluators are divided, each providing a different assessment. This discrepancy highlights the effectiveness of the proposed framework.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Why Did the DA History Generation Work?}

We compared the performances in generating DA histories between \textbf{DA History Gen w/o Second Finetune} and \textbf{Ours} on Split 1. 

For direct comparison, we used 9,999 data points $(a_t,S_t)$ from dialogues involving 90 adults and the elderly to generate DA histories $H_a^{Aug}$, resulting in three DA histories per data point. This generation was conducted using the following settings: top\_k=50, top\_p=0.9, and temperature=0.9.
After removing the duplicates $(a_t,H_a^{Aug})$, \textbf{DA History Gen w/o Second Finetune} produced 7,677 new $(a_t,H_a^{Aug})$, whereas \textbf{Ours} generated 10,412.
We assessed how many of these combinations appeared in dialogues involving 17 minors (excluding those from \textbf{Low-Resource}), finding 908 for \textbf{DA History Gen w/o Second Finetune} and 956 for \textbf{Ours}. Referring to Table \ref{tab:ablation}, we can infer that compared with \textbf{w/o DA History Gen}, which relied solely on existing DA histories, both \textbf{DA History Gen w/o Second Finetune} and \textbf{Ours} generated DAs that were present in the target user group, leading to improved performance. Notably, \textbf{Ours}, which underwent secondary fine-tuning for the target users, produced more DA histories that were closely aligned with the target group, thereby enhancing the performance.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Whether Generating Large Amounts of Augmented Data Can Lead to Further Performance Improvements?}
\label{sec:new_4}

Through the experiments described in Section 4, we demonstrate that the data generated using our proposed framework can be effective in low-resource scenarios, although they are less efficient than real data. This is evident because with the same amount of data, \textbf{Ours} typically underperformed \textbf{Full-Resource}. This raises the question: Can generating large amounts of data for training further enhance DA prediction performance?


To address this question, we conducted experiments using Split 3 as shown in Table \ref{tab:result_each}. Among the four splits, the model performed the worst in Split 3, indicating that it was the most challenging. In the previous experiments, we set num\_return\_sequences=3; this time, we increased it to 10 to generate more DA histories, thus creating more training dialogue data. The generation process utilized the GPT-3.5-turbo-0125 model. Based on the \textbf{Low-Resource} setup, we incrementally added 2,000 data points per iteration and trained it five times with five different seed values, averaging the results.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 6
\begin{figure}[b]
\begin{center}
\includegraphics{32-4ia1f06.eps}
\end{center}
\hangcaption{Trend in both exact and partial match rates as data augmentation progresses. The top graph represents the exact match rate, whereas the bottom graph represents the partial match rate. The green line indicates the \textbf{Full-Resource} performance, whereas the red line indicates the \textbf{Low-Resource} performance.}
\label{fig:lots_of}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Figure \ref{fig:lots_of} illustrates the trends in the exact and partial match rates for the model as data augmentation progresses. Although the overall performance improved with the data augmentation, the improvement was not entirely stable. This suggests that the proposed framework can generate dialogue data aligned with the target dialogue style, thereby enhancing the model's DA prediction performance, particularly in low-resource scenarios where users employ specific dialogue strategies.

\textbf{Improvement in Exact Match Rate}: We observed that, as the volume of training data increased, the model's exact match rate even surpassed \textbf{Full-Resource} performance at certain points. This phenomenon may be because the generated DA histories $H_a^{Aug}$ captured broader patterns in the dialogues, including some DA histories that were absent in the low-resource dataset but present in the test data. This allows the model to better approximate the correct DA when making predictions. Another possible reason for the exact match rate improvement is that the generated data encompassed a wider range of dialogue scenarios and strategies than the original data, enabling the model to be generalized more effectively to real-world dialogue situations.

\textbf{Limited Improvement in Partial Match Rate}: The improvement in partial match rate consistently fell short of the \textbf{Full-Resource} level. This may be related to the lack of diversity in the generated data. Although we generated more DA histories and dialogue content through augmentation, the ground-truth DAs $a_t$ were still extracted from the original low-resource dataset. This implies that the proposed framework does not introduce diversity into the ground truth DAs. Although a large volume of data was generated, if these data remained closely centered around the limited ground-truth DAs from the original low-resource dataset during generation, the predictive capabilities of the model would be constrained. Consequently, the lack of diversity in the ground-truth DAs limited the model's flexibility in adapting to slightly varied or diversified dialogue strategies, which, in turn, hampered more significant improvements in the partial match rate.

To illustrate this further, we compared the \textbf{Ours} model, which was trained with an additional 26,000 data points using the \textbf{Full-Resource} model, as shown in Figure \ref{fig:lots_of}. We first examined the model outputs. Figure \ref{fig:case_study_2} displays two examples in which \textbf{Ours} failed to make the correct prediction, whereas \textbf{Full-Resource} partially succeeded in predicting multiple DAs. We then analyzed the number of DA labels predicted by \textbf{Ours} and \textbf{Full-Resource} across 5,667 test instances, and the results are shown in Figure \ref{fig:output_check}. Evidently, \textbf{Full-Resource} was more likely to output multiple labels than \textbf{Ours}, which explains why \textbf{Full-Resource} had better partial prediction accuracy, as observed in the examples in Figure \ref{fig:case_study_2} and the results in Figure \ref{fig:lots_of}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 7
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f07.eps}
\end{center}
\caption{Comparing two examples of \textbf{Ours} and \textbf{Full-Resource} outputs.}
\label{fig:case_study_2}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 8
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f08.eps}
\end{center}
\hangcaption{Distribution of DA quantities output by \textbf{Ours} and \textbf{Full-Resource} on the test set. The \textbf{Full-Resource} tends to output multiple DA labels.}
\label{fig:output_check}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 9
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f09.eps}
\end{center}
\hangcaption{Comparison between the number of unique combinations and the number of DAs in the ground truth.}
\label{fig:train_check}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

To further explore why \textbf{Full-Resource} tended to output more labels, we compared the number of unique combinations of ground-truth DAs in the training data of \textbf{Ours} and \textbf{Full-Resource}. \textbf{Ours} had 480 types, while \textbf{Full-Resource} had 577 types. We also analyzed the number of unique combinations for various numbers of DAs within a single ground truth, as shown in Figure \ref{fig:train_check}. Although \textbf{Ours} had a larger quantity of training data, its variety of ground-truth DAs was less than that of \textbf{ Full Resource}. \textbf{Full-Resource} exhibited more variety in ground truths with the same number of DAs. Consequently, because the correct DAs $a_t$ were extracted from the original dataset and lacked diversity, \textbf{Ours} was unable to match the partial match rate of \textbf{Full-Resource}.


Furthermore, simply increasing the data volume does not always result in a significant performance improvement. This may be because, while we generated a large amount of data, there was no sufficient filtering mechanism to remove data that were unsuitable or deviated from the target user group's strategies. Consequently, the generated data included irrelevant or noisy data, thereby diluting the effect of high-quality data. Therefore, although both the exact and partial match rates improved, progress was not entirely stable.

In conclusion, increasing the amount of training data can boost the model's DA prediction performance in low-resource scenarios, particularly in terms of the exact match rate. However, the lack of an effective data-filtering mechanism and limitations in data diversity indicate that the proposed framework has room for improvement.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Conclusion}

In this study, we propose a data augmentation framework designed to enhance SDS capabilities by improving DA prediction performance, particularly when handling users in low-resource settings who exhibit unique conversational behaviors. Given the difficulty of obtaining sufficient annotated data for such groups, our approach leverages LLM to extract speaker styles and PLM to generate DA histories, thereby creating customized training data.

Our experiments demonstrated that the proposed framework effectively generated training data, enabling the model to adapt to different user-speaking styles and improve DA prediction accuracy. Moreover, ablation studies and human evaluations validated the effectiveness of each component of the proposed method. Furthermore, we showed through experiments that increasing the data volume can further enhance performance.

\addspan{Although our experiments focused specifically on minors as a representative low-resource user group, the proposed data augmentation framework is not limited to this demographic. In principle, the same methodology can be applied to other low-resource groups characterized by distinct conversational behaviors, such as elderly users, individuals with cognitive impairments, and speakers from underrepresented dialectal or cultural backgrounds. We leave the exploration of broader applications for future work.}

However, several challenges remain unresolved. The lack of an efficient data-filtering mechanism and limitations in data diversity indicate that there is room for further refinement of the framework.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\acknowledgment

This work was supported by JSPS KAKENHI Grant Number 19H05692.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bibliographystyle{jnlpbbl_1.8}
\bibliography{01refs}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\appendix

\section{DA tags in Travel Agency Task Dialogue Corpus}
\label{sec:appendix_a}

In this study, we utilized the ``Travel Agency Task Dialogue Corpus'' collected by \citeauthor{inaba-etal-2024-collection} \citeyear{inaba-etal-2024-collection}, which includes task specific DA annotations. The dataset defined DA tags for operators and customers in travel agency conversations, with 28 tags for operators and eight tags for customers. In this study, only the tags of the operator were used, as listed in Table \ref{tab:dialog_tag}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 5
\begin{table}[p]
\input{01table05.tex}%
\caption{Task Specific Dialogue Act Tags for Operator Segments.}
\label{tab:dialog_tag}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Hyperparameter Optimization}
\label{sec:appendix_b}


Hyperparameter optimization was performed during our experiments.

For T5-base, we conducted a grid search with batch sizes of \{8, 16, 32, 64\}, warmup ratios of \{0, 0.1, 0.2\}, and learning rates of \{$3\text{e}-3$, $2\text{e}-3$, $1\text{e}-3$, $9\text{e}-4$, $8\text{e}-4$\}. The optimal configuration was identified as a batch size of 64, warmup ratio of 0.1, and learning rate of $1\text{e}-3$.

Similarly, for GPT-NeoX, we conducted a grid search with batch sizes of \{4, 8, 16\}, warmup ratios of \{0.1, 0.2, 0.3\}, and learning rates of \{$3\text{e}-4$, $2\text{e}-4$, $1\text{e}-4$, $9\text{e}-5$, $8\text{e}-5$, $7\text{e}-5$, $6\text{e}-5$, $5\text{e}-5$, $4\text{e}-5$\}. The best settings were determined to be a batch size of 8, a warmup ratio of 0.1, and a learning rate of $9\text{e}-5$.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Details for Speaker Styles Extraction}
\label{sec:appendix_c}

We utilized the prompts shown in Figure \ref{fig:pic_prompt_1} to extract speaker styles using the GPT-4-0125-preview model with six dialogues from different users, three from the target user group, and three from a non-target user group. As the extraction was conducted with the default temperature setting (i.e., temperature=1), the generated results were diverse. We perform multiple extractions and manually combine the extracted speaker styles. Consolidated speaker styles, as illustrated in Figure \ref{fig:pic_prompt_ex}, were used for subsequent dialogue data generation.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Prompt used for Training Dialogue Generation}
\label{sec:appendix_d}

The prompt shown in Figure \ref{fig:pic_prompt} was employed to instruct GPT-3.5-turbo-0125 in generating dialogue data for training. Seven examples were included in the prompt to control the quality of generation. All examples originated from real conversations with the target user group in the ``Travel Agency Task Dialogue Corpus'' \cite{inaba-etal-2024-collection}.

\clearpage

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 10
\begin{figure}[p]
\begin{center}
\includegraphics{32-4ia1f10.eps}
\end{center}
\caption{Prompt for Speaker Styles Extraction.}
\label{fig:pic_prompt_1}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 11
\begin{figure}[p]
\begin{center}
\includegraphics{32-4ia1f11.eps}
\end{center}
\caption{Extracted Speaker Styles. They are utilized for subsequent dialogue generation.}
\label{fig:pic_prompt_ex}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\clearpage

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 12
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia1f12.eps}
\end{center}
\caption{Prompt for Dialogue Generation. Red indicates the condition generated in previous steps.}
\label{fig:pic_prompt}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



\clearpage
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{biography}

\bioauthor[:]{Zhiyang Qi}{%
Z. Qi received his Master's degree in Informatics from the University of Electro-Communications, Japan, in 2023. He is currently pursuing a Ph.D. degree at the same university. His research interests include dialogue systems and natural language processing. He is a member of ANLP.
}

\bioauthor[:]{Michimasa Inaba}{%
M. Inaba received his Ph.D. in Information Science from Nagoya University, Japan, in 2012. He then worked as an assistant professor at Hiroshima City University. Since 2019, he has been an associate professor at the Artificial Intelligence eXploration Research Center at the University of Electro-Communications. His research interests include dialogue systems and discourse processing. He is a member of IEICE, JSAI, ANLP, and ACL.
}
\end{biography}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\biodate




\end{document}


