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\Volume{31}
\Number{1}
\Month{March}
\Year{2024}
%%%%\Issuetitle{「　　　　」特集号}

\received{2023}{5}{25}
\revised{2023}{9}{26}
\accepted{2023}{11}{17}

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\esubject{General Paper}
\etitle{Prefix Alignment for Training Simultaneous Machine Translation}
\eauthor{Yasumasa Kano\affiref{Author_1} \and Katsuhito Sudoh\affiref{Author_1} \and
Satoshi Nakamura\affiref{Author_1}}
\eabstract{%
Simultaneous translation is a task that starts translation even before the speaker has finished speaking.
This study focuses on prefix-to-prefix translation and proposes a method to align prefixes in a bilingual sentence pair iteratively to train a machine translation model to work with prefix-to-prefix.
In the experiments, the proposed method demonstrated higher BLEU than those of the baseline methods in low latency ranges on the IWSLT simultaneous translation benchmark.
However, the proposed method degraded the performance in high latency ranges in the English-to-Japanese experiments; thus, we analyzed it in length ratios and prefix boundary prediction accuracies. The obtained results suggested that the degraded performance was due to the large word order difference between English and Japanese.
}

\ekeywords{Neural Machine Translation, Simultaneous Translation}

\headauthor{Kano et al.}
\headtitle{Prefix Alignment for Training Simultaneous Machine Translation}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\affilabel{Author_1}{}{Nara Institute of Science and Technology}


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

\maketitle

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}
Simultaneous machine translation (SimulMT) is a task used to translate input text or speech incrementally even before observing its explicit boundaries, such as the end of sentences and pauses.
It has to determine when to translate a partially-observed input (\emph{input prefix}) in real-time and also when to generate an appropriate partial output (\emph{output prefix}) corresponding to the partial input.
This requirement poses a serious difficulty compared to standard full-sentence translation because the remaining \emph{unobserved} input cannot be used as context information.
The SimulMT quality should improve using a longer input at the cost of a long delay, and vice versa.
This trade-off is controlled by a \emph{policy} to select the next action between \emph{read} (waiting for further partial input) and \emph{write} (emitting partial output) according to the given input and generated output prefixes \cite{gu-etal-2017-learning}.

Wait-k \cite{ma-etal-2019-stacl} is one of the most commonly used baseline SimulMT models that waits for k tokens before starting translation.
There are also more adaptive models that determine whether to  \emph{read} or \emph{write} every time a new input token becomes available \cite{chiu2018monotonic,raffel2017online,arivazhagan-etal-2019-monotonic}.
These methods train the SimulMT model with the given policy to learn the prefix-to-prefix translation.
The latency amount of these SimulMT models is provided as a hyperparameter of training; thus it is difficult to adjust latency at the inference step.

In contrast, there are SimulMT methods that can share one pre-trained NMT model for any latency and the hyperparameter of latency is provided at the inference step.
For instance, \citeA{zhang-etal-2020-learning-adaptive}  proposed to segment the input sequence with a unit known as Meaningful Unit (MU) and translate the partial input.
The translation is conducted with a standard NMT model pre-trained with full sentences in their method.
This causes serious over-translation because the NMT model translates prefixes that is shorter than full sentences at the inference step of SimulMT \cite{kano-etal-2021-simultaneous}.


In this study, we propose to make the NMT model learn prefix-to-prefix translation by fine-tuning a standard NMT model with data augmentation called \emph{Prefix Alignment}.
We use a pre-trained full-sentence NMT model to find bilingual prefix pairs from a bilingual training corpus through the iteration of prefix translation and the matching of common prefixes between the prefix translation result and the corresponding reference in the training corpus.
The NMT model fine-tuned with the collected bilingual prefix pairs can be used with any segmentation methods.
The proposed fine-tuning method improved the SimulMT models with several segmentation methods in low latency ranges in our SimulMT experiments using IWSLT English-to-Japanese (En-Ja) and English-to-German  (En-De) datasets.
We also analyzed the effect of word order difference to explain the degradation by the proposed method in high latency ranges in En-Ja.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Related Work}
The problem of SimulMT has been tackled for a decade.
In previous attempts using statistical machine translation,
decision policies were combined with the beam search decoding \cite{sankaran-etal-2010-incremental,bangalore-etal-2012-real}.
\citeA{fujita13_interspeech} used phrase reordering probabilities employed in phrase-based statistical machine translation for their decision policy.
In later years, feature-based learned policies were proposed.
\citeA{oda-etal-2014-optimizing} proposed a feature-based policy optimization to maximize BLEU.
Syntactic features were also successfully used for the policies \cite{rangarajan-sridhar-etal-2013-segmentation,oda-etal-2015-syntax}.


Recently, most SimulMT studies are based on NMT, and such methods can output more fluent translations than before.
\citeA{cho-2016} proposed greedy decoding with policies conditioned by the prediction of the decoder, known as \emph{Wait-If-Worse} and \emph{Wait-If-Diff}.
\citeA{ma-etal-2019-stacl}  proposed a simple policy known as test-time wait-k.
This is a very simple fixed policy that waits for \textit{k} input tokens, and thereafter writes one target token and reads one source token alternately.
The latency hyperparameter \textit{k} of test-time wait-k is provided at the inference step.
To learn the policy of READ/WRITE decision from the bilingual corpus, reinforcement learning-based methods were proposed \cite{grissom-ii-etal-2014-dont,satija-2016,gu-etal-2017-learning,alinejad-etal-2018-prediction}.
It is a straightforward way to optimize latency and accuracy jointly, but its training process is relatively complex and sometimes unstable.
Therefore, \citeA{zheng-etal-2019-simpler} proposed a simpler method to find oracle read and write actions using a pre-trained NMT model.
These previous NMT-based SimulMT models used a standard NMT model pre-trained for full-sentence translation.
However, at the inference step of SimulMT, the model needed to translate partial input; thus there was a gap between training and inference.

Therefore, \citeA{ma-etal-2019-stacl}  proposed a wait-k that uses prefix-to-prefix training of a SimulMT model with full-sentence parallel corpus.
The policy of wait-k is the same as that of test-time wait-k, but the latency hyperparameter k of wait-k is provided in the training step.
\citeA{zheng-etal-2020-simultaneous} proposed an ensemble of different wait-k-based models for adaptive SimulMT.
There are other approaches for training a SimulMT model that constrain the attention to the last part of the partial input \cite{chiu2018monotonic,raffel2017online}.
The attentions that are extended to all the tokens in the partial input and latency-augmented loss functions are proposed to jointly optimize translation quality and latency \cite{arivazhagan-etal-2019-monotonic,xutaima-2020}. 
The latency amount of these SImulMT models is provided as a training hyperparameter similar to wait-k.
These approaches make the conditions of training and inference closer, but we cannot adjust the latency amount at the inference step and need to train the SimulMT model for each latency.

\citeA{zhang-etal-2020-learning-adaptive} proposed a method to segment the input sequence with a unit known as MU.
In their SimulMT model, the standard pre-trained full-sentence NMT model translates the partial input every time a boundary of MU is detected.
Their method can adjust latency at the inference step by a hyperparameter and outperformed wait-k and MILk \cite{arivazhagan-etal-2019-monotonic}.
\citeA{kano-etal-2021-simultaneous} proposed incremental prediction of the syntactic constituents to improve the segmentation.
These methods \cite{zhang-etal-2020-learning-adaptive,kano-etal-2021-simultaneous} use the NMT model pre-trained with full sentences; thus, there is a gap between training and inference.

There are some approaches to mitigate the gap between training and inference by fine-tuning the NMT model.
\citeA{zhang-etal-2020-learning-adaptive} proposed a refined version of MU in which they fine-tuned the pre-trained NMT model with synthetic monotonic translation sentences.
However, even though the word order of the source and target becomes similar by their method, the synthetic data are sentence-length which is longer than partial inputs.
\citeA{dalvi-etal-2018-incremental} attempted to fine-tune the NMT model with bilingual prefix pairs. 
However, they reported that the fine-tuning largely degraded the performance in the simultaneous translation experiment.
To generate the bilingual prefix pairs, they extracted the first N words, N + M words, N + 2M words,… from each source sentence as prefixes. 
Thereafter they found the corresponding target prefixes from each target sentence.


Our proposed method uses a more sophisticated technique to generate bilingual prefix pairs utilizing a pre-trained NMT model and improves the NMT model with any segmentation methods.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Simultaneous Machine Translation}
Before explaining the proposed method, we first introduce the basics of simultaneous translation.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Formulation}
A sentence-level NMT finds the best translation that maximizes the conditional probability formulated as follows,
letting $\bm{x} = x_1,x_2,...,x_n$ be an input sentence and $\bm{y} = y_1,y_2,...,y_m$ be its translation:
\begin{equation}
p(\bm{y}|\bm{x}) = \prod_{t=1}^{m} P(y_t | \bm{x}, \bm{y}_{<t}).
\end{equation}
In SimulMT, we take a prefix of the input for its incremental decoding as follows:
\begin{equation}
p(\bm{y}|\bm{x}) = \prod_{t=1}^{m} P(y_t | \bm{x}_{\leq g(t)},\bm{y}_{<t}),
\end{equation}
where $g(t)$ is a monotonic non-decreasing function representing the number of input tokens to read until the $t$-th step.
$\bm{x}_{\leq g(t)}$ denotes an input prefix read so far, and $\bm{y}_{<t}$ is the prefix translation written until the previous step.
This implies that we can align the prefix translation $\bm{y}_{\leq t}$ with the input prefix $\bm{x}_{\leq g(t)}$ as a pair of input-output prefixes.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t]
%%%%% \centerline{\includegraphics[width=7.8cm]{latex/figs/overview2.pdf}}
\begin{center}
\includegraphics{31-1ia3f1.pdf}
\end{center}
 \hangcaption{Example of the translation process of MU from English to Japanese. The threshold of boundary probability is 0.5 in this case. The underlined part is the output prefix used in forced decoding.}
\label{fig:overview}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Inference of MU}\label{subsec:incremental_decoding}\label{inference_mu}

Figure \ref{fig:overview} shows the inference process of MU.
Suppose we have a SimulMT policy based on boundary prediction to start a \emph{write} action, and a binary boundary predictor gives the probability of a boundary at every step.
In Step 1, the boundary predictor identifies a boundary for the given input of ``I'', and we obtain its corresponding translation ``私は''.
In Steps 2 and 3, it does not predict a boundary; thus, we do not update the translation.
Next, in Step 4, it finds a boundary after the observation of ``I bought a pen'', and we translate the input \emph{with the output prefix constraint} ``私は'' and obtain ``私はペンを買った''.
Finally, in Step 5, we translate the entire input \emph{with the output prefix constraint} ``私はペンを買った'' and obtain ``私はペンを買った。''.
We put underlines for the prefix constraints in Figure \ref{fig:overview}, which are used as the forced output prefixes in decoding.
The inference processes of MU can be used with different boundary prediction methods using surface- or length-based heuristics \cite{kano-etal-2021-simultaneous}, a statistical model, and so on.
The translation process of our proposed method is also the same as MU, but we use the NMT model fine-tuned with bilingual prefix pairs.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{MU-based boundary-predictor}\label{subsec:meaningful_unit}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure*}[t]
\begin{center}
\includegraphics{31-1ia3f2.pdf}
\end{center}
 \hangcaption{Process of generating training dataset for MU-based boundary predictor. The underlined part is the prefix translation included in the full-sentence translation. The boundary label  becomes 1 when such underlines exist.}
\label{fig:meaningful_unit}
\end{figure*}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


Figure \ref{fig:meaningful_unit} shows the example process of generating the training dataset for an MU-based boundary predictor.
First, the prefixes of source sentences in the training dataset are translated by a pre-trained full-sentence NMT model.
If the entire source prefix translation is included in the full-sentence translation, it is regarded that it is a boundary of MU.
In the fourth step of Figure \ref{fig:meaningful_unit}, ``私はペンを買った'' is the first source prefix translation included in the full-sentence translation, and the label of the boundary becomes 1.
Once a boundary is detected, the prefix translation output is used for forced decoding in the next prefix translation.
In Figure \ref{fig:meaningful_unit}, the fourth prefix translation ``私はペンを買った'' is used for forced-decoding when the fifth source prefix ``I bought a pen.'' is translated.

After this process, pairs of a source prefix and boundary label such as (``I'', 0), (``I bought'', 0), ... are obtained.
Thereafter, these pairs are used to train an end-to-end binary classifier.
This classifier is used as described in Section \ref{subsec:incremental_decoding}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Proposed Method: Prefix Alignment}
We fine-tune the pre-trained NMT model with bilingual prefix pairs to mitigate the gap between training and inference. 
To generate such prefix pairs, we propose a method called \emph{prefix alignment}, which is shown in Figure \ref{fig:prefix_alignment}.
The procedure is as follows.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure*}[b]
%%%%%\centerline{\includegraphics[width=13cm]{latex/figs/prefix_alignment.pdf}}
\begin{center}
\includegraphics{31-1ia3f3.pdf}
\end{center}
 \hangcaption{Proposed method to extract the pairs of the source prefix and the corresponding target translation prefix. Underlines represent the longest common prefixes.
We perform forced decoding with the extracted target prefix for both source prefix and full-sentence translation.
Therefore, the full-sentence translation in the second row changes from that in the first row because the beam search result is different owing to forced decoding.
The label of the boundary is 1 when the target prefix is extracted.}
\label{fig:prefix_alignment}
\end{figure*}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Generating Prefix Translation Pairs}\label{subsec:bilingual_alignment}


First, we generate pairs of the source prefix and the corresponding prefix translation using the pre-trained full-sentence NMT model.
Let $\bm{x}$ be a source language sentence and $\bm{y}$ be the corresponding translation result in the target language obtained using the pre-trained NMT model.

Thereafter, we translate a prefix of $\bm{x}$ with one word,\footnote{We use the word-based prefix length even though we use subwords for simplicity, and $\bm{x}_{|w|\leq 1}$ can comprise one or more subwords.} $\bm{x}_{|w|\leq 1}$, into a target language prefix $\bm{\bar{y}}^{(1)}$.
Here, if the \emph{longest common prefix} $\bm{\bar{y}}_{lcp}^{(1)}$ between $\bm{y}$ and $\bm{\bar{y}}^{(1)}$ is not empty, we extract ($\bm{x}_{|w|\leq 1}$, $\bm{\bar{y}}_{lcp}^{(1)}$) as a prefix translation pair.
Taking the longest common prefix is motivated by the local agreement \cite{liu2020lowlatency}, which verifies the stability of a prefix translation based on the agreement between prefix translation results for growing input prefixes.
We iterate this process by growing the input prefix individually;
when we translate the \textit{m}-word input prefix $\bm{x}_{|w|\leq m}$ into $\bm{\bar{y}}^{(m)}$, we find the longest common prefix $\bm{\bar{y}}_{lcp}^{(m)}$ between $\bm{y}$ and $\bm{\bar{y}}^{(m)}$.
In the prefix translation, we constrain the decoding to follow the output prefixes found thus far for the same bilingual sentence pair by forced decoding, similar to chunk-based incremental decoding described in Section \ref{subsec:incremental_decoding}, to find consistent prefix translation pairs.
The forced decoding avoids possible changes in the output prefix caused by a beam search.
Furthermore, once we extract a prefix translation pair ($\bm{x}_{|w|\leq i}$, $\bm{\bar{y}}_{lcp}^{(i)}$), we update the full-sentence translation $\bm{y}$ by forced decoding using $\bm{\bar{y}}_{lcp}^{(i)}$ as an output prefix constraint and use the updated $\bm{y}$ for later steps $\bm{\bar{y}}^{(j)}$ ($j > i$).
If we obtain the same longest common prefix for different source language prefixes, we only take the first appearance (i.e., the pair from the smallest $m$) and ignore the remainder to avoid inconsistency among the prefix translation pairs.
This prefix extraction strategy is different from that used for finding an MU \cite{zhang-etal-2020-learning-adaptive},
in which the entire prefix translation $\bm{\bar{y}}^{(i)}$ should be a prefix of the full-sentence translation $\bm{y}$ without taking the longest common prefix as in this study.
Additionally, our strategy works flexibly by the possible update of the full-sentence translation by extracted output prefixes.

Figure \ref{fig:prefix_alignment} shows an example of the proposed method.
The first prefix translation ends with a punctuation mark ``。''
Thus, MU \cite{zhang-etal-2020-learning-adaptive} cannot extract the first prefix as the pair because the punctuation mark does not match the end of the prefix of full-sentence translation as shown in Figure \ref{fig:meaningful_unit}.
In contrast, the proposed method can extract the common output prefix by ignoring the latter part of the prefix translation.
Therefore, the proposed method identifies more boundaries than the MU.

The extracted target prefixes are a part of source prefix translations.
Therefore, we can fine-tune the NMT model with the extracted prefix pairs to prevent outputting unnecessary end of sentence expressions such as ``。''.
In contrast, the main purpose of the MU is only to find the boundaries.
The target prefixes with boundaries are already entirely included in the full-sentence translation; thus, they do not need to use the target prefixes for fine-tuning.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Prefix Alignment with References}\label{subsec:monolingual_alignment}

The prefix translations obtained through the described process are NMT results and are different from their references ($\bm{\hat{y}}$) in general.
We obtain reference-based prefix alignment by leveraging the extracted bilingual prefix pairs.
We use BERTScore \cite{bert-score} to find the correspondence between an NMT-based target prefix and a reference prefix, varying the prefix window over the reference.
We select the reference prefix $\bm{\hat{y}}^{(i)}$ with the largest BERTScore (in F-measure) against a given NMT-based target prefix $\bm{\bar{y}}_{lcp}^{(i)}$ and extract ($\bm{x}_{|w|\leq i}$, $\bm{\hat{y}}^{(i)}$) as a reference-based bilingual prefix pair.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Fine-Tuning of a Simultaneous Machine Translation Model}\label{subsubsec:fine-tuning}
We fine-tune the pre-trained NMT model for the SimulMT task using the extracted bilingual prefix pairs.
The model works to translate an input prefix incrementally in the chunk-based decoding as presented in Section \ref{subsec:incremental_decoding}.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Experimental Setup}
We conducted experiments on En-De and En-Ja simultaneous translation to compare the performance of the proposed method with the baselines in the quality-latency trade-off.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Compared Methods}

The baseline is the NMT model trained with full-sentences (full-sentence NMT model), and we compared it with the NMT model fine-tuned with prefix pairs extracted by the proposed method.
We used several boundary prediction methods for both models in the experiments.
The compared boundary predictors are as follows.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{itemize}
    \item \textbf{Meaningful Unit (\texttt{MU})}:
We implemented the standard version of the MU boundary predictor \cite{zhang-etal-2020-learning-adaptive} with the NMT model trained with full-sentences.\footnote{
We attempted to implement the refined version of MU \cite{zhang-etal-2020-learning-adaptive}.
In the refined version, monotonic translations with pre-trained NMT were first generated using the training dataset.
Thereafter, the NMT model was fine-tuned  with the monotonic translations.
However, in our experiment, the BLEU score of the NMT model fine-tuned with monotonic translations was extremely low because it was difficult to generate high-quality monotonic translations based on attention following a previous study.
Therefore, we used the standard version in our experiments.}
We can adjust the SimulMT latency by the detection threshold for boundary probabilities given by the boundary predictor.
We used 0.5 as its default value but also attempted the following values for further investigation: $\{0.1, 0.15,..., 0.95\}$, $\{0.99, 0.991, 0.992,..., 0.999\}$ and $\{0.9991, 0.9992,..., 0.9999\}$.
We used three ranges because it is not clear how much the difference of hyperparameters changes the latency in the experiment.
    \item \textbf{Incremental Constituent Label Prediction (\texttt{ICLP})}:
Following our previous study \cite{kano-etal-2021-simultaneous}, we used a one-look-ahead label predictor.
We segmented the input sequence based on the rules with predicted labels \texttt{VP} and \texttt{S}.
We varied the minimum segment length to adjust latency among $\{1, 2, 3, ..., 29\}$.
    \item \textbf{Fixed-length segmentation (\texttt{Fixed})}:
We also attempted simple fixed-length segmentation with a length hyperparameter $L$, implying that the boundary comes to every $L$ words.
We varied $L$ among $\{2, 4, 6,..., 30\}$.
\end{itemize}
The translation process is the same with all the boundary predictors as described in Section \ref{inference_mu}.

We additionally compared wait-k as another baseline that has a hyperparameter of latency provided at the training step different from the described three methods.
We trained the wait-k model for each latency hyperparameter $k$ among $\{2, 4, 6, ..., 30\}$.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[b]
\input{03table01.tex}
\hangcaption{Number of sentence pairs in the dataset. For the dev dataset, we used IWSLT dev2010, tst2010, tst2011 and tst2012 for En-De, and IWSLT dev2010, tst2011, tst2012, and tst2013 for En-Ja.
PA is short for prefix alignment.}
\label{tb:dataset}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Dataset and Preprocessing}


Table \ref{tb:dataset} lists the dataset that we used in the experiment.
First all the translation models were trained with pre-training dataset. Next we fine-tuned these models with the IWSLT training dataset.
Simultaneous translation is basically used for spoken language. 
The IWSLT training dataset is a spoken language corpus, but it is relatively small. 
Therefore, we first pre-trained the NMT model with a large training dataset from WMT, and subsequently fine-tuned it with the IWSLT training dataset.
This NMT model is the baseline.
For the proposed method, we generated bilingual prefix pairs only from the fine-tuning dataset, because extracting prefix pairs from a large training corpus requires a significant amount of time.
Thereafter, we fine-tuned the baseline NMT model with the prefix pairs generated after the process of prefix alignment with references explained in Section \ref{subsec:monolingual_alignment}.


We tokenized Japanese sentences using MeCab \cite{kudo2005} and English and German sentences using \texttt{tokenizer.perl} in Moses \cite{koehn-etal-2007-moses}, followed by the subword tokenization based on Byte Pair Encoding (BPE) \cite{sennrich-etal-2016-neural} with a shared subword vocabulary of 16K entries.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{NMT Models}

All the NMT models were based on the transformer-base \cite{vaswani-2017} implementation in fairseq \cite{ott-etal-2019-fairseq}. 
The hyperparameter settings basically followed the official baseline for IWSLT 2021,\footnote{\url{https://github.com/pytorch/fairseq/blob/master/examples/simultaneous_translation/docs/enja-waitk.md}, \url{https://github.com/pytorch/fairseq/issues/346}} for both pre-training and fine-tuning.
The models were saved on checkpoints in every 5,000 updates for pre-training and every 200 updates for fine-tuning. 
We applied early stopping with patience for four checkpoints, based on the loss on the development set. 
We set the learning rate to 0.0007, and the minibatch size to 4,096 with the parameter update frequency of 4.
We applied a chunk-based beam search described in Section \ref{subsec:incremental_decoding} to the methods except wait-k.
We used a greedy decoding for wait-k, because of the nature of its model.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Evaluation Metrics}
We used BLEU \cite{papineni-etal-2002-bleu} to evaluate translation quality and evaluated the latency of simultaneous translation with Average Lagging (AL) \cite{ma-etal-2019-stacl} and Average Token Delay (ATD) \cite{kano2023average}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Average Lagging (AL)}
\citeA{ma-etal-2019-stacl} proposed Average Lagging (AL).
Suppose we have an input $\bm{x} = x_1, ..., x_{|\bm{x}|}$ and an output $\bm{y} = y_1, ..., y_{|\bm{y}|}$.
AL is denoted as follows:
\begin{equation}
AL_g(\bm{x}, \bm{y}) = \frac{1}{\tau_g(|\bm{x}|)} \sum_{t =1}^{\tau_g(|\bm{x}|)} \left( g(t) - \frac{t -1}{r} \right),
\label{eqn:AL_g}
\end{equation}
where $r$ denotes the length ratio defined as $|\bm{y}|/|\bm{x}|$ and $g(t)$ represents the number of source tokens already read to output the $t$ th target token.
$\tau_g(|\bm{x}|)$ denotes the cut-off step defined as follows:
\begin{equation}
\tau_g(|\bm{x}|) = \min\{t \mid g(t) = |\bm{x}|\},
\label{eqn:tau_g}
\end{equation}
implying the index of the output token predicted right after the observation of the entire source sentence.

AL is the most commonly used latency metric that focuses on when to start the translation.
However, AL has the problem that the longer the segment translation becomes, the smaller the latency becomes although long segment translation should delay the latter translation in actual situations.
Therefore, AL is compatible with wait-k that outputs translation tokens individually, but not with chunk-based models that output multiple tokens instantly.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Average Token Delay (ATD)}
ATD is a metric that adequately considers when to finish the translation. 
It includes the delay at the end of segment translation for that of simultaneous translation.
Therefore, we also used ATD to appropriately evaluate chunk-based translation models.

Suppose $\bm{x}$ and $\bm{y}$ are segmented into $C$ chunks $\bm{x} = \bm{x}^{1}, \bm{x}^{2}, ..., \bm{x}^{C}$ and $\bm{y} = \bm{y}^{1}, \bm{y}^{2}, ..., \bm{y}^{C}$, respectively.
An input chunk $\bm{x}^{c}$ is observed after the previous prefix translation $\bm{y}^{c-1}$ and used to predict $\bm{y}^{c}$.

ATD is defined as follows:
\begin{equation}
\textrm{ATD}(\bm{x}, \bm{y})= \frac{1}{|\bm{y}|}\sum_{t=1}^{|\bm{y}|} \left( T(y_{t}) - T(x_{a(t)}) \right)
\label{eqn:atd_definition}
\end{equation}
where

\begin{equation}
\label{s_condition}
a(t) = \begin{cases}s(t) & \textrm{if}\ s(t) \leq L_{acc}(\bm{x}^{c(t)}) \\
L_{acc}( \bm{x}^{c(t)}) & \textrm{otherwise}\end{cases}
\end{equation}
\begin{equation}
\label{max_condition}
s(t) = t - \max(L_{acc}(\bm{y}^{c(t) -1}) - L_{acc}(\bm{x}^{c(t) -1}),0)
\end{equation}

$T(\cdot)$ in Eq. (\ref{eqn:atd_definition}) denotes the ending time of each token and $a(t)$ denotes the index of the input token corresponding to $y_{t}$.
$L_{acc} (\bm{x}^{c}) = \sum_{j =1}^{c} |\bm{x}^{j}| $ is the cumulative length up to the $c$-th chunk, and  $L_{acc} (\bm{x}^{0}) = 0$. 
$L_{acc} (\bm{y}^{c})$ is defined similarly.
$c(t)$ denotes the chunk number $c$ to which $y_t$ belongs.

AL and ATD were calculated using SimulEval \cite{ma-etal-2020-simuleval} and drawn in scatterplots to show the quality-latency trade-off.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Result}\label{sec:results}

Figures \ref{fig:result_de}a to \ref{fig:result_de}d compare the baselines with the PA (prefix alignment)-based methods.
Here, \texttt{full} implies that the NMT model was trained only with full sentences.
\texttt{PA} implies that the NMT model was fine-tuned with prefix pairs of the source and reference in the fine-tuning dataset.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[b]
\begin{center}
\includegraphics{31-1ia3f4.pdf}
\end{center}
\hangcaption{Each point represents each value of latency hyperparameter.
For instance, in Figure \ref{fig:result_de}, the leftmost black dot of wait-k represents k = 2.
The x-axis represents delay and the delay is larger on the right side.
The y-axis represents quality and the quality is better on the upper side.
Therefore, the dot near the upper left corner is better in quality-latency trade-off.}
\label{fig:result_de}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



The AL results in En-De shown in Figure \ref{fig:result_de}a indicate that the PA-based models demonstrated higher performance than the others in all latency ranges.
In contrast, those in En-Ja exhibited the advantage only in an extremely low latency range (AL $<$ 3), as shown in Figure \ref{fig:result_de}b.


The ATD results in Figure \ref{fig:result_de}c show that PA-based models also worked well in most latency ranges for En-De, except for wait-k in the lowest latency range ($k=2,4$).
ATD is a latency metric considering the delay caused by long translation outputs in previous steps; thus, so this result reflects a possible advantage of wait-k in ATD;
Its alternating actions of reading and writing one token at a step are advantageous in the ATD measurement.
In En-Ja, the PA-based model worked well in the low-middle latency ranges in Figure \ref{fig:result_de}d.
The gap between PA-based models and full-sentence-based models, particularly in low latency is larger in ATD than in AL.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Analyses}
We performed the following analyses to investigate the characteristics of the proposed method in detail.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[b]
\begin{center}
\includegraphics{31-1ia3f5.pdf}
\end{center}
\caption{Length ratio and Average Lagging.}
\label{fig:result_ja_al}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Length-Related Issues}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Length Ratio}
First, we analyzed the length ratios of the translation outputs and references to investigate over-translation phenomena in prefix-to-prefix translation.
Figures \ref{fig:result_ja_al}a and \ref{fig:result_ja_al}b show the scatterplots of AL and the length ratio for En-De and En-Ja, respectively.
Fine-tuning using prefix alignment pairs decreased the length ratio toward 1.0.
This implies that the translation outputs had fewer unnecessary words, resulting in higher BLEU by increasing n-gram precisions.
From the perspective of the latency in ATD, this length ratio reduction derives better ATD because it considers delays caused by the outputs.
In contrast, the \texttt{full-MU} exhibited extremely large length ratios ($>$ 1.2) with their hyperparameter settings for smaller AL.
Such large length ratios increased the ATD as shown in the bottom part of Figures \ref{fig:result_de}c and \ref{fig:result_de}d.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[b]
\input{03table02.tex}
\caption{Length ratio of source to target.}
\label{tb:ratio_src_tgt}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[b]
\begin{center}
\includegraphics{31-1ia3f6.pdf}
\end{center}
 \caption{Example of Prefix Alignment (En-De).}
\label{fig:prefix_alignment_de}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[b]
\begin{center}
\includegraphics{31-1ia3f7.pdf}
\end{center}
 \caption{Example of Prefix Alignment (En-Ja).}
\label{fig:prefix_alignment_ja}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Degradation in high latency for En-Ja}
The degradation of PA-based models in high latency for En-Ja can be explained by word order difference and the quality of the full-sentence NMT model.
From Figures \ref{fig:result_ja_al}a and \ref{fig:result_ja_al}b, we find that the length ratios by the PA-based methods were smaller than those by the full-sentence translation, and they became less than 1.0 in En-Ja.
We investigated the difference between the language pairs on the training data.
Table \ref{tb:ratio_src_tgt} lists the length ratios of the source to target in the IWSLT training dataset for the original sentence pairs and the prefix pairs extracted by Prefix Alignment.
We found that the length ratio of the prefix pairs of En-Ja was much smaller than En-De, while it was almost the same in the sentence level.
This is probably because of the larger word order difference between English and Japanese.
Figures \ref{fig:prefix_alignment_de} and \ref{fig:prefix_alignment_ja} show examples of the Prefix Alignment for an English sentence ``I like playing soccer in the park,'' with a German translation ``Ich spiele gerne Fußball im Park,'' and a Japanese translation ``私はサッカーを公園でするのが好き。''.
For an English prefix ``I like playing soccer'', we extract a semantically equivalent German prefix ``Ich spiele gerne Fußball'' but obtain a Japanese prefix ``私はサッカーを'' that misses the counterparts of ``like (好き)'' and ``playing (する)''.
This is because Prefix Alignment does not guarantee the semantic equivalence of an extracted prefix pair.
Our prefix pair extraction described in Figure \ref{fig:prefix_alignment} finds the longest common prefix between a prefix and full-sentence translations.
The remaining part of the prefix translation is discarded and is missing in the extracted prefix pair, and this causes the aforementioned semantic inequivalence.
For a given source language prefix, Prefix Alignment gives possible translation prefixes that \emph{can be determined} at the time of the observation of the source language prefix and does not guarantee these prefixes cover the contents of the entire source language prefix.
Such an issue happens mainly because of word order differences because some parts in a source language prefix can be translated only after the translation of later parts that are not observed thus far.
For instance, in the translation from a Subject-Verb-Object language (English) to a Subject-Object-Verb language (Japanese), a verb should be translated after the translation of its object.
Consequently, the PA-based models derived short translations as shown in Figure \ref{fig:result_ja_al}b and resulted in worse BLEU for En-Ja.

From the perspective of the quality of prefix pairs, Table \ref{tb:BERTscore_tgt_ref} lists the similarity between the hypothesis- and reference-based prefix measured by BERTScore \cite{bert-score}.
We observed a lower score in En-Ja than in En-De.
This is because of the difference in the translation quality by the full-sentence NMT model as shown in Figures \ref{fig:result_de}a and \ref{fig:result_de}b.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[b]
\input{03table03.tex}
\caption{BERTScore of target prefix and reference prefix.}
\label{tb:BERTscore_tgt_ref}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



Therefore, the degradation of PA-based models in high latency for En-Ja is caused by the word omission in the extracted target prefix and low quality of extracted prefix pairs.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Length Distribution in the Training Set}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[b]
\input{03table04.tex}
\caption{Statistics of the training data.}
\label{tb:comp_dataset}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[b]
\begin{center}
\includegraphics{31-1ia3f8.pdf}
\end{center}
 \caption{Source prefix length distribution in the IWSLT training data.}
\label{fig:prefix_len_dist_both}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


Table \ref{tb:comp_dataset} summarizes statistics from the IWSLT training set in the number of extracted prefix pairs and words in the entire dataset.
Even though the number of words was almost similar, that of prefix pairs in En-De was almost two times larger than that in En-Ja.
This is also due to the large word order difference discussed above because we have to wait for the later source language input to \emph{grow} a prefix translation in the target language.
Figure \ref{fig:prefix_len_dist_both} shows the length distributions of the source language portions of the prefix pairs.
The numbers of short source language prefixes in En-Ja were much smaller than those in En-De.
Figures \ref{fig:train_len_dist_en_de} and \ref{fig:train_len_dist_en_ja} show the length distributions from the entire training data with and without Prefix Alignment.
Prefix alignment augmented much more prefix translation pairs for English-to-German than for English-to-Japanese.
These findings suggest that many short prefix pairs should contribute to improving SimulMT in En-De.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t]
\begin{center}
\includegraphics{31-1ia3f9.pdf}
\end{center}
\caption{Source sentence length distribution in the training data (En-De).}
\label{fig:train_len_dist_en_de}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t]
\begin{center}
\includegraphics{31-1ia3f10.pdf}
\end{center}
 \caption{Source sentence length distribution in the training data (En-Ja).}
\label{fig:train_len_dist_en_ja}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Comparison of Boundary Predictors}
Second, we investigated the effects of boundary prediction on the performance of the PA-based models by comparing different boundary prediction methods.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Boundary Predictors Based on Prefix Pairs}\label{subsubsec:boundary_prediction}
In addition to the existing segmentation methods, we tested a boundary predictor that is suitable to the NMT model trained with prefix pairs.
The predictor identifies a prefix boundary to start a \emph{write} action.
The training data for the boundary prediction model comprise pairs of a source language sentence prefix and the corresponding boundary label.
The label is set to 1 for a prefix included in the extracted prefix pairs and 0 otherwise, as shown in the rightmost column in Figure \ref{fig:prefix_alignment}.
The boundary prediction can also be extended to use a target language prefix as an additional clue.
The extended model takes a pair of input and output prefixes at the time of the boundary prediction and can be trained similarly.
For instance, at the fourth step in Figure \ref{fig:prefix_alignment}, it should identify a boundary with an input-output prefix pair of (``I bought a pen'', ``私は'') in the form of a concatenates string ``I bought a pen $\vert\vert\vert$ 私は''.

We trained the following boundary prediction models for comparison:
\begin{itemize}
    \item \texttt{bert-src-w}
    \item \texttt{ro-src-w}
    \item \texttt{ro-src-tgt-w}
    \item \texttt{ro-src-tgt}
\end{itemize}
where \texttt{bert} means the use of a pre-trained English BERT\textsubscript{base} model\footnote{\url{https://huggingface.co/bert-base-uncased}} \cite{devlin-etal-2019-bert}, and \texttt{ro} means the use of a pretrained XLM-RoBERTa\textsubscript{base} model\footnote{\url{https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr}}  \cite{conneau-etal-2020-unsupervised}.
\texttt{src} and \texttt{tgt} mean the use of the current prefix in the source and target languages, respectively.
\texttt{w} means the use of a weighted loss to mitigate the label imbalance for training boundary prediction models.

We also compared oracle segmentation to investigate the limitation of the PA-based boundary prediction. To obtain oracle segmentation boundaries, we applied Prefix Alignment to the test set and the end of source prefixes with label 1 are the oracle boundaries.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[b]
\input{03table05.tex}
\caption{Result of boundary prediction.}
\label{tb:boundary_prediction}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t]
\begin{center}
\includegraphics{31-1ia3f11.pdf}
\end{center}
\caption{Models fine-tuned using the proposed method with different boundary predictors.}
\label{fig:roberta_0.5}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Boundary Prediction Performance}

We evaluated their performance using the development set listed in Table \ref{tb:dataset} against the boundaries obtained through Prefix Alignment.


Table \ref{tb:boundary_prediction} summarizes the results. 
For both language pairs, \texttt{ro-src-tgt-w} demonstrated the highest F1 scores, and those of \texttt{ro-src-w} were slightly better than those of \texttt{bert-src-w}.
\texttt{ro-src-tgt} resulted in a higher precision and lower recall, particularly in En-Ja, but the F1 scores were almost the same as those of \texttt{ro-src-tgt-w}.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Comparison of PA-based Models}

Figures \ref{fig:roberta_0.5}a to \ref{fig:roberta_0.5}d show the quality-latency results of PA-based models in AL and ATD on En-De and En-Ja SimulMT experiments.
For PA-based boundary predictors, we used the same thresholds as MU to adjust the latency.
The AL results in Figures \ref{fig:roberta_0.5}a and \ref{fig:roberta_0.5}b show that \texttt{PA-ro-src-tgt-w} and \texttt{PA-ro-src-tgt} were the best in the middle and high latency ranges for both En-Ja and En-De.
In low latency ranges, \texttt{PA-bert-src-w} and \texttt{PA-MU} are the best for En-De and En-Ja respectively, and their curves are near the oracle segmentation. 
Surprisingly, \texttt{PA-fixed} was comparably effective in the low latency range.

The ATD results in Figures \ref{fig:roberta_0.5}c and \ref{fig:roberta_0.5}d show that  \texttt{PA-ro-src-tgt-w} and \texttt{PA-ro-src-tgt} were slightly better than the other models in the high latency range.
In the low latency range, \texttt{PA-fixed} worked the best.
The fixed-length heuristics ran much faster than the other segmentation models requiring more computations; thus, we may prefer it in practice.
Although \texttt{ICLP} has a limitation on the number of segmentation because of the number of verbs, the first few points in low latency have achieved the best performance in ATD for En-Ja.
When we observe oracle segmentation, it is not the best in ATD.

In conclusion, the curves by \texttt{PA-ro-src-tgt} are very similar to those by \texttt{PA-ro-src-tgt-w} despite the difference in the precision-recall trade-offs shown in Table \ref{tb:boundary_prediction}.
In the high latency range, \texttt{PA-ro-src-tgt} and \texttt{PA-ro-src-tgt-w} were the best with high thresholds because they are compatible with the NMT model fine-tuned with PA and use the richer input feature for prediction compared to other PA-based boundary predictors.
These models also have the highest precision and recall as listed in  Table \ref{tb:boundary_prediction}.
In the low latency range, mixed results were obtained and the PA-based boundary predictors were not always the best for low thresholds.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\addtocounter{table}{-1}
\begin{table}[b]
\input{03table06.tex}
\hangcaption{Total number of input prefixes and frequencies of those that resulted in null outputs by \texttt{PA-fixed} in the test set translation.}
\label{tb:num_none}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Analysis of Fixed-size Segmentation}
Fixed-size segmentation (\texttt{PA-fixed}) worked comparably or sometimes better than model-based boundary predictions.
Tables \ref{tb:num_none}a and \ref{tb:num_none}b list the numbers of input prefixes that yield \emph{null outputs}, where the end-of-the-sentence (EOS) symbol was predicted at the beginning, on the test at different latency (in the number of input tokens) settings of \texttt{PA-fixed}.
As summarized in these tables, the prefix-to-prefix translation often generated null outputs in the low latency settings.
The null outputs do not impose any constraints on the following prefix translations; thus, \texttt{PA-fixed} worked flexibly to wait for later inputs.
The tables also reveeal that it happened more frequently in En-Ja than in En-De.
This indicates that En-Ja translation requires a longer input prefix than En-De because of the larger word order difference.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Conclusion}
We proposed a method to train the neural SimulMT model by extracting bilingual prefix pairs through prefix alignment.
The proposed method outperformed the baselines in a quality-latency trade-off in En-De SimulMT, but the results in En-Ja were mixed.
We investigated the results in detail and found the performance degradation in En-Ja came from poor prefix pair extraction mainly because of the word order difference.
The proposed method can be extended to the speech-to-text translation by extracting a pair of a source language speech and target language text fragments.\footnote{We applied this method to our speech-to-text simultaneous machine translation system for the IWSLT 2022 Evaluation Campaign \cite{iwslt:2022,fukuda-etal-2022-naist}.}


In future work, we will tackle En-Ja problems and improve the method to work for more language pairs.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\acknowledgment
This paper is an extended version of our conference paper \cite{kano-etal-2022-simultaneous} with additional experiments and analyses.
Part of this work was supported by JSPS KAKENHI with Grant Numbers JP21H05054 and JP21H03500.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    \bibliographystyle{jnlpbbl_1.7}
\bibliography{03refs}



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

\bioauthor[:]{Yasumasa Kano}{%
He is a Ph.D. student at Nara Institute of Science and Technology. He received his bachelor's degree in economics from Yokohama National University in 2019 and his master's degree in engineering from Nara Institute of Science and Technology in 2021.
}

\bioauthor[:]{Katsuhito Sudoh}{%
He is an associate professor at Nara Institute of Science and Technology. He received his bachelor's degree in engineering in 2000 and his master's and Ph.D. degrees in informatics in 2002 and 2015, respectively, from Kyoto University. He worked with the NTT Communication Science Laboratories from 2002 to 2017. He currently works on machine translation and natural language processing. He is a member of ACL, ISCA, ANLP, IPSJ, ASJ, and JSAI.
}

\bioauthor[:]{Satoshi Nakamura}{%
He is a professor at Nara Institute of Science and Technology and an honorary professor at the Karlsruhe Institute of Technology, Germany. He received his B.S. from the Kyoto Institute of Technology in 1981 and a Ph.D. from the Kyoto University in 1992. He was the Director of ATR Spoken Language Communication Research Laboratories in the period 2000--2008 and Vice President of ATR in the period 2007--2008. He was the Director General of Keihanna Research Laboratories and Executive Director of Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology in 2009--2010. He is currently the director of the Augmented Human Communication laboratory and a full professor at the Data Science Center and Information Science Division, Graduate School of Science and Technology, Nara Institute of Science and Technology. He was an Elected Board Member of the International Speech Communication Association, ISCA, in the period June 2011--2019, and IEEE Signal Processing Magazine Editorial Board member in the period 2012--2015, 
 and IEEE SPS Speech and Language Technical Committee Member in the period 2013--2015. He is an ATR Fellow, IPSJ Fellow, IEEE Fellow, and ISCA Fellow.
}
\end{biography}

\biodate   %%%% Following settings will be edited by editors.


\end{document}

