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\Volume{29}
\Number{2}
\Month{June}
\Year{2022}
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\esubject{General Paper}
\etitle{Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing}
\eauthor{Yuki Yamamoto\affiref{Author_1} \and Yuji Matsumoto\affiref{Author_2} \and Taro Watanabe\affiref{Author_1}} 
\eabstract{
The syntax-based AMR parsing approach assumes a close mapping between syntactic and semantic structures. However, syntax-semantic mapping is not evident in complex sentences, causing parsers to fail to build the correct core structure of a tree. In this paper, as an aid to AMR parsing, we propose a dependency matching system that first detects complex sentence structures in a dependency parse tree of a sentence and then returns a corresponding AMR skeleton structure. We manually designed a dictionary of dependency patterns and the corresponding AMR skeletons for the types of complex sentence constructions that appear in the AMR corpus. A disambiguation step is necessary for certain types of constructions with semantically ambiguous subordinators. We show that the disambiguation can be formulated as sentence-pair classification using the fine-tuning approach of a pretrained BERT model. The classification models were trained on data derived from AMR and Wikipedia corpora, establishing a novel baseline for future research.
}
\ekeywords{AMR, Complex Sentence, Semantic Ambiguity}

\headauthor{Yamamoto et al.}
\headtitle{Dependency Patterns of Complex Sentences}

\affilabel{Author_1}{}{Nara Institute of Science and Technology (NAIST)}
\affilabel{Author_2}{}{RIKEN Center for Advanced Intelligence Project (AIP)}


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

\maketitle

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

Abstract Meaning Representation (AMR) is a sentence-level meaning representation based on a predicate argument structure \cite{amr}.
AMR parsing transforms a sentence into an AMR graph with nodes and edges, each representing a concept or relation.
Early studies \cite{jamr,camr,ccg,pust} used dependency parsers to integrate syntactic features into their models, 
while recent deep-neural-network-based approaches \cite{konstas,peng,zhang,cai} tend to encode the input sentence as a sequence without considering its syntactic structure. 
Based on the syntax-based approach, our study attempts to handle complex sentences that existing studies seem to have problems in capturing their structure.

In formal linguistics, particularly in the field of syntax--semantic interface \cite{partee}, structural mapping between syntax and semantics has been a concern for decades.
This structural mapping between syntax and semantics has been the basis for addressing semantic-related tasks in natural language processing.
For example, semantic role labeling (SRL) tasks can benefit from the similarity between dependency trees and SRL structures, because nominal subjects (\textit{nsubj}) and direct objects (\textit{dobj}) can be used interchangeably for agent (ARG0) and patient (ARG1) roles \cite{srl}.
As stated in a study on the dependency--AMR interface \cite{synsem}, the same relationship appears to apply to dependency trees and AMR graphs.
Thus, the syntax-based AMR parsing approach attempts to take advantage of the close mapping between the two structures.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 1
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f1.pdf}
\end{center}
\caption{Representations of \textit{as long as} construction.}
\label{aslongas}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

This similarity assumption between syntax and semantics seems reasonable for a simple sentence, which is esentially a matrix clause, comprising a predicate and its arguments.
However, for complex sentence constructions, we observe that the validity of this assumption is arguable.
Here, the term ``complex sentence'' is used to refer to a type of sentence comprising a matrix and subordinate clause(s) with subordinator(s) serving as linking word(s) between the main predicates of the clauses.
Fig.~\ref{aslongas} visualizes an example, which compares the dependency and AMR representations of a complex sentence with subordinator \textit{as long as}.
While variables \texttt{V}'s and \texttt{S}'s are interchangeable between the representations, 
the manner in which the \texttt{V}'s connect and subordinator itself expressed are quite different.\footnote{Throughout this paper, \texttt{V} and \texttt{S} are used to represent a predicate and its subject, respectively.}
Moreover, in contrast to the uniform structures of simple sentences, various types of complex sentence constructions are used in human languages.
These characteristics make it challenging for existing AMR parsers to capture the structures of complex sentences correctly.

Fig.~\ref{error}(a) presents a typical error encountered when parsing a complex sentence using CAMR \cite{camr}.
CAMR is a syntax-based AMR parser that directly transforms the output of a dependency parser into an AMR graph.
The results show that this type of parser has difficulty capturing the relation between the matrix and subordinate clauses of the input sentence, misinterpreting the semantic relation between the main predicates of the clauses.
In this case, CAMR predicts the relationship between \texttt{believe-01} and \texttt{seem-01} as ARG1, although it should be represented as a causal relation with a predicative frame \texttt{cause-01}, as shown in Fig.~\ref{error}(b).
This error is crucial because it leads to an incorrect core structure in the output AMR graph.
We believe that training the parser on larger annotated datasets may be a solution; however, annotating AMRs is time consuming and expensive.
A more reasonable approach is to provide the parser with the core structure of the input sentence in advance. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 2
\begin{figure}[t]
\begin{center}
\includegraphics{29-2ia10f2.pdf}
\end{center}
\caption{Result of parsing ``As the boy seemed reliable, the girl believed him.'' using CAMR.}
\label{error}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

To this end, we present a dependency-based pattern-matching method that retrieves the occurrences of complex sentence constructions that appear in the AMR corpus. 
To the best of our knowledge, no comprehensive resource that provides structural information on the relationships between clauses, particularly in the AMR framework, is currently available.
Therefore, we developed a pattern dictionary of English complex sentence constructions together with the corresponding AMRs that represent the core structures of sentences (hereinafter referred to as ``skeletal AMRs''). 
In this study, we focus on constructions that contain the following types of subordinators that connect the head predicates of clauses \cite{quirk,koubun}.
\begin{itemize}
\item Simple: Those with a single subordinate conjunction (e.g., {\it after}, {\it if}, {\it once}, {\it since})
\item Complex: Those with a multi-word expression functioning as a subordinate conjunction (e.g., {\it as far as}, {\it in case}, {\it so that})
\item Correlate: Those with two separate markers of subordination (e.g., {\it barely ... when}, {\it no sooner ... than})
\end{itemize}

Note that tree matching encounters the problem of syntactic ambiguity when a complex sentence has multiple subordinate clauses.
Consider the following sentence with two subordinate clauses (i.e., ${SUB1}$ and ${SUB2}$), each with two dependency candidates.
\begin{enumerate}
\setcounter{enumi}{0}
\renewcommand{\labelenumi}{(\arabic{enumi})}
\item ... $[$\underline{if} you wish to look at the comparative risks$]_{SUB1}$ $[$\underline{if} we do not confront terrorist organizations in their staging areas$]_{SUB2}$, $[$how many people could die as a result of weapons of mass destruction at some point in the not too distant future$]_{MAT}$?
(AMR: bolt-eng-DF-199-192783-6849434\_0102.3)
\end{enumerate}
\noindent
While a study has regarded the syntactic disambiguation of dependencies between subordinate clauses \cite{utsuro}, this problem is beyond the scope of this study. 
Here, the output of the dependency parser provided in Stanza \cite{stanza} is employed in the pattern-matching system to determine which pair of clauses are syntactically related.\footnote{The reported scores are 86.22 and 83.59 for UAS and LAS, respectively.}

Meanwhile, complex sentence constructions can be semantically ambiguous if the subordinator carries multiple senses.
As an example, the sentences in (2) show the usage of the subordinator \textit{since}, which is semantically ambiguous in connecting clauses with (a) causal or (b) temporal relations. 

\begin{enumerate}
\setcounter{enumi}{1}
\renewcommand{\labelenumi}{(\arabic{enumi})}
\item
	\begin{enumerate}
	\renewcommand{\labelenumii}{\alph{enumii}.}
	\item \underline{Since} there is responsibility, we are not afraid. 
	(AMR: bolt12\_6455\_6561.15)
	\item Also \underline{since} he turned 80, people had been paying more and more attention to Mao Zedong's birthday.
	(AMR: bolt12\_10511\_7302.5)
	\end{enumerate}
\end{enumerate}

To obtain correct skeletal AMRs as outputs, we regarded the disambiguation problem as a multi-class sentence-pair classification between two clauses.
We adopted a fine-tuning approach and a data augmentation method for our study.
With the support of weakly supervised data derived from Wikipedia, we achieved scores of 75.65\% and 83.94\% for macro and micro F1, respectively, 
thereby establishing a novel baseline for the semantic relation classification of complex sentence constructions in the AMR framework.

To summarize, the contributions of this study are as follows:
\begin{itemize}
\item A dictionary of complex sentence constructions was created.
\item A pattern matching system that takes a sentence as input and returns a corresponding skeletal AMR was developed.
\item A novel baseline for the semantic disambiguation of complex sentence constructions in the AMR framework was established. 
\end{itemize}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Related Works}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Tree Matching}

Early studies have proposed open-source tools for searching phrase-structure annotations 
\pagebreak
in treebanks, such as the Penn Treebank (PTB; Marcus et al. 1993\nocite{ptb}).
For example, TGrep \cite{tgrep} is a tool that supports a query, or pattern language, with specification on nodes with parent--child relations and regular expressions.
TGrep2 and Tregex \cite{tgrep2,tregex} were later developed with additional features for pattern languages, which allowed users to search for further linguistic information on edge labels (e.g., case, grammatical function, headedness).

As dependency-style annotation became popular in recent years, tools that support dependency patterns have also been developed.
For example, Stanford CoreNLP's Semgrex \cite{semgrex} and SpaCy tookit's JSON-based format \cite{spacy} are publicly available.
These pattern languages support specification on nodes and edges with regular expressions for subsets of attributes specialized for dependency annotation (e.g., word, POS tag, dependency relation). 

The development of these tools and languages has allowed users to design flexible search patterns and perform tree matching with ease.
In this research, we employed the pattern language provided by \citeA{spacy} to design patterns for complex sentence constructions in dependency format.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Relation Classification of Clauses}

Thus far, few studies have dealt with semantic relations above clause level in the AMR framework.
These past research efforts included those by \citeA{tense}, in which AMR was enhanced through annotations of tense and aspect phenomena, and those by
\citeA{multi}, in which the relations of sentences were targeted, and annotations of co-reference were provided in a multi-sentence AMR corpus. 
However, no study has focused particluarly on tasks regarding semantic classification of clauses in this framework.

Meanwhile, various attempts have been made to capture relations between pairs of sentences or clauses \cite{easily,dis1,dis2,dis3} within the Penn Discourse Treebank (PDTB; Prasad et al. 2008\nocite{pdtb}) framework.
Most recently, \citeA{shi} presented a fine-tuning-based approach using the Bidirectional Encoder Representation from Transformers (BERT; Devlin et al. 2019\nocite{bert}).
With sentence and clause pairs as inputs, they designed their model to learn 11 discourse classes (e.g., Asynchronous, Synchronous, Cause) to achieve state-of-the-art performance on a relation classification task in the PDTB framework.
Their work was motivated by a method used to pre-train BERT which is called a ``next sentence prediction'' (NSP) task.
In the process of pre-training using NSP, the model is presented with pairs of sentences. 
The model then predicts whether the second sentence is an actual subsequent sentence. 
NSP combines a pair of sentences to enable BERT to represent sentences in a single sequence.

The annotation style of AMR and PDTB differs in that AMR extends and utilizes PropBank semantic roles \cite{prop} whereas PDTB utilizes its original classes.
As discussed by \citeA{prasad}, the two styles diverge in terms of coverage, where PDTB does not have classes corresponding to some PropBank semantic roles (e.g., ARG-MNR, ARG-PNC).
Additionally, \citeA{prasad2} noted that the temporal relation between PDTB and PropBank-style are not fully aligned.
Accounting for this, our research on the relation classification of clauses will be fully based on the AMR framework, while adopting the approach of \citeA{shi}.
Our approach can be interpreted as a testbed for other frameworks that utilize PropBank, such as the recent attempts of Universal Semantic Representation (USR; Zhu et al. 2019\nocite{usr}).


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Pattern Matching System}

In this section, the pattern matching system is described.
Fig.~\ref{flow} illustrates the workflow. 
The system takes a sentence as input, parses it into a dependency tree, and returns a skeletal AMR if it detects a complex sentence pattern.
The disambiguation module will be described later in Section 5.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 3
\begin{figure}[t]
\begin{center}
\includegraphics{29-2ia10f3.pdf}
\end{center}
\hangcaption{Pattern matching system workflow for sentence ``Since airline flights were disrupted, the company chartered a plane to fly the executives back to the West Coast.''}
\label{flow}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Dictionary of Dependency Patterns}

Here, we decribe the process of creating an entry for the dictionary of dependency patterns.
First, to cover a wide variety of complex sentence constructions, we referred to comprehensive studies on grammar \cite{quirk,koubun} which provided a typology of subordinators.
Particularly, constructions with simple, complex, and correlate subordinators were targeted, as mentioned in Section 1.
In addition, we incorporated constructions involving degree and quantity introduced to AMR by Bonial et al. (2018)\nocite{bonial}.
This includes {\it the X-er, the Y-er construction} and {\it comparing resemblance construction} that compare head predicates of clauses.\footnote{Generally, AMR is based on lexical semantics, meaning that a sentence meaning is represented by the composition of predicate frames.
However, a recent approach assigns degree/quantity constructions with unique construction-rolesets which are based on construction grammar \cite{goldberg}.
Accounting for these cases, constructional-rolesets were assigned as skeletal AMRs for degree/quantity constructions.}

Among the constructions initially targeted, we only selected those that occurred at least once in the AMR corpus.\footnote{\url{https://catalog.ldc.upenn.edu/LDC2020T02}}
More precisely, we created an entry only for constructions whose semantic relation is defined in the AMR Annotation Dictionary,\footnote{\url{https://amr.isi.edu/doc/amr-dict}} which provides semantic definitions for each topic (i.e., relation, special frameset, punctuation) along with typical examples in PENMAN notation \cite{penman}.
This ensured that skeletal AMRs fully originated from the definitions provided for the annotators.
With this process, seemingly rare usages of simple subordinators such as {\it directly}, {\it immediately} and correlate subordinators such as {\it scarcely ... when} were excluded.

Fig. \ref{if} provides an example of creating a skeletal AMR for the subordinator {\it if}.
For each example in the AMR Annotation Dictionary, the definition of the relation is highlighted in boldface. 
In this case, \texttt{:condition} that connects the two predicates, \texttt{cancel-01} and \texttt{rain-01}, is highlighted. 
We took a simple step to process example usage by replacing predicates with \texttt{V}'s while leaving the highlighted relation.
This provides us with a skeletal AMR, which is an abstracted version of example usage.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 4
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f4.pdf}
\end{center}
\hangcaption{Example usage and skeletal AMR for subordinator {\it if} in PENMAN notation. Example is given for the sentence ``The game will be canceled if it rains.''.}
\label{if}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

To design dependency patterns, we used the pattern language provided by \citeA{spacy}, which allows specification on both nodes and edges.
Our dependency pattern forms a tree based on the Universal Dependencies (UD; Nivre et al. 2020\nocite{udv2}) format.
Each node can contain a lemma and/or POS tag and each edge may optionally have a dependency relation label. 
Regular expressions are employed on nodes or edges to search for any form of a specific attribute. 
Each dependency patterns is provided with a corresponding skeletal AMR that describes the core structure of an input sentence with a set of variables used to align patterns and skeletons.

Finally, subordinators that share both the dependency pattern structure and skeletal AMR were reduced to a single entry. For example, {\it though} and {\it although} were packed as {\it though/although}.
Currently, our dictionary adds up to 70 distinct pairs of a patterns and skeletal AMRs.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 5
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f5.pdf}
\end{center}
\caption{Pattern dictionary entry of complex sentence construction with subordinator \textit{because}.}
\label{because}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Fig.~\ref{because} illustrates an example of a paired entry of a dependency pattern and skeletal AMR for the \textit{because} construction.
The dependency pattern shown in Fig.~\ref{because}(a) describes the simplest type of structure in our dictionary.
The nodes represented by the variables \texttt{v1} and \texttt{s1} capture a predicate and its argument in a matrix clause, whereas those represented by \texttt{v2} and \texttt{s2} capture a subordinate clause.\footnote{Note that the use of \texttt{s} is optional in actual matching to capture clauses with null-subjects.}
Subordinators are indicated by \texttt{a}'s, whereas their lemma forms are shown as cues.
\texttt{REGEX} operators are used on both nodes and edges to flexibly match the possible elements.

For example, the dependency relation of \texttt{\{REGEX:\^{}nsubj\}} between \texttt{v} and \texttt{s} enables us to handle subjects in both active (\texttt{nsubj}) and passive (\texttt{nsubj:pass}) voices.
In the case of \texttt{TAG: \{REGEX: \^{}V \}}, \texttt{REGEX} is used to represent any POS tag that starts from ``\texttt{V}'', meaning that it captures any form of a verb.
Fig.~\ref{because}(b) shows the corresponding skeletal AMR, which represents a core relation between the two predicates.
For all patterns in the dictionary, \texttt{V$_{1}$} and \texttt{V$_{2}$} act as slots for the predicates of the matrix and subordinate clauses.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Semantic Ambiguity}

A number of subordinators indicate more than one semantic relation.\footnote{Nine out of 70 entries in the pattern dictionary are semantically ambiguous.}
For example, Fig.~\ref{amb} illustrates an example with \textit{since}, where the subordinator presents either (a) causal or (b) temporal relations.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 6
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f6.pdf}
\end{center}
\caption{Skeletal AMRs of ambiguous \textit{since}.}
\label{amb}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
In our dictionary, we avoided the enumeration of skeletal AMRs for all candidates of semantic relations.
Instead, a single primitive structure of the skeletal AMR is described in accordance with the generative lexicon approach \cite{gl}.
More specifically, to accommodate all ambiguous relations, a vertical bar on the edge of a skeletal AMR is used to separate the candidates, as shown in Fig.~\ref{since}(b); we refer to the resulting structure as a ``primitive skeleton.''

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 7
\begin{figure}[t]
\begin{center}
\includegraphics{29-2ia10f7.pdf}
\end{center}
\caption{Pattern dictionary entry of complex sentence construction with subordinator \textit{since}.}
\label{since}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
To keep the descriptions of the skeletal AMRs as concise as possible, a number of representations were abbreviated to the extent permitted by the AMR guidelines.
For example, we used the semantic relation \texttt{:cause} instead of the predicative frame \texttt{cause-01} to describe causal relations.
The disambiguation step follows after the pattern matcher returns the primitive skeleton. Section 5 details this method.

Aside from semantic ambiguity, which was described earlier, structural ambiguity can occur when several types of structures exist within the same relation.
For example, in Fig.~\ref{amb}(b) and \ref{once}, both skeletal AMRs represent temporal relation but for different structures.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 8
\begin{figure}[t]
\begin{center}
\includegraphics{29-2ia10f8.pdf}
\end{center}
\caption{Skeletal AMR of \textit{as}, \textit{once}, etc. representing temporal relation.}
\label{once}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
Because the primitive skeleton describes only the simplest form, a recovery step is required to obtain the final outputs of skeletal AMRs for ambiguous subordinators.
Therefore, for each ambiguous subordinator, a possible interpretation (such as in Fig.~\ref{amb}(b) and \ref{once}) for each primitive skeleton was defined, to be referred to after the skeletal AMR is disambiguated.
The structural ambiguities of the skeletal AMRs were resolved following a list of definitions.\footnote{This idea is similar to the notion of \textit{meaning postulates} \cite{carnap,dowty}.}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Algorithm}

Our pattern matching system builds upon the dependency matching module introduced by \citeA{spacy}, which matches subtrees within a dependency tree $D$ of an input sentence $S = w_{1}, ... , w_{n}$.
At runtime, the system loads the pattern dictionary $P = p_{1}, ... , p_{m}$, where each $p_{i}$ is a dependency pattern, and performs naive matching on $D$.
If $p_{i}$ matches a subtree in $D$, then $p_{i}$ is appended to a matched list $M$. 
The matcher returns a list of skeletal AMRs corresponding to the matched patterns in $M$. 

Meanwhile, the original system can suffer from ``overlap'' when multiple patterns accidentally match a pair of clauses, and ``copula'' when the matcher fails to capture copular constructions because of the limitation in the expressiveness of pattern grammar.
Accounting for these problems, we extended the system by incorporating the following additional functions:

\noindent
{\bf ConvertCopula:}{\quad}This function detects copular constructions in $D$ and converts it to a copula-headed dependency tree $D^{cop}$ before matching on $P$. Verbs that show a relationship between subjects and objects are referred to as copulas (e.g., ``is'' for ``John is tall.''). Because the UD format refrains from using a copula as the head of its complement,\footnote{For ``John is tall.'' UD treats ``tall'' as the head of ``is''. } the matcher fails to detect complex sentence patterns in the UD trees.
To avoid redundancy in creating an additional pattern $p_{i}^{cop}$ for each entry in $P$, an additional pattern for copular construction is created as shown in Fig.~\ref{cop}(a).

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 9
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f9.pdf}
\end{center}
\caption{Dependency pattern of copular construction and example of conversion.}
\label{cop}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
With this function, our system first detects copular clauses in complex sentences and then converts them into copular-headed structures when matched, as illustrated in Fig.~\ref{cop}(b). In this manner, the matcher was enhanced to cover a wider variety of complex sentences.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 10
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f10.pdf}
\end{center}
\caption{Overlap of patterns for \textit{as} and \textit{as if}.}
\label{overlap}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
{\bf ResolveOverlap:}{\quad}This function resolves overlapping patterns in $M$ before the outputs are returned.
In some cases, multiple patterns are matched even within a single pair of clauses, because some patterns partially share the form. 
For example, when sentence (3) is given as an input, the matcher returns the following overlapping patterns of \textit{if} and \textit{as if} in Fig.~\ref{overlap}:
\begin{enumerate}
  \setcounter{enumi}{2}
  \renewcommand{\labelenumi}{(\arabic{enumi})}
  \item \underline{As if} he were still in his old job, Mr.~Wright enjoys a \$120,000 annual office expense allowance.
\end{enumerate}
This occurs mostly between patterns of simple and complex subordinators (e.g., \textit{as} and \textit{as far as}, \textit{if} and \textit{even if}).
To address this problem, an ID referring to the subordinator type was assigned to each entry in the pattern dictionary.
For example, \textit{as} was assigned ``\#1.6.1'', and \textit{as if} was assigned ``\#2.2.1''.
The first number in an ID represents the number of tokens in the subordinator, the second describes the sorted order, and the third is determined by the pattern structure.
When an overlap is detected in $M$, this function looks up the IDs and compares the first numbers in the IDs to select the pattern with the wider scope.
In the case of Fig.~\ref{overlap}, \textit{as if} is selected as a more desirable pattern, and \textit{as} is removed from $M$.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Corpus Investigation}

To observe how the proposed matcher works, we investigated the coverage of our pattern dictionary and matching performance on the Penn Treebank (Marcus et al., 1993\nocite{ptb}).
First, we explain how we collected subordinator candidates. 
In PTB, a subordinator can appear with an IN tag in a position under an SBAR structure, while being c-commanded by an S structure \cite{ouchi}.
Additionally, {\it wh-}adverbs can be observed under WHADVP instead of IN in this structure. 
Hence, in the extraction step, we used depth-first traversal on trees that satisfied these conditions to find subordinator candidates in the Wall Street Journal portion of PTB.
We then used Stanford Converter tool to convert the trees that contained the candidates from phrase-structure to dependency format.
Using this method, we collected 11,915 sentences with 233 subordinator candidates.
After recategorization of variants (e.g., {\it 'cause} into {\it because}, {\it a week after} into  {\it after}), we have obtained 56 types of candidates.
We show our investigation on PTB with the number of occurrences and matching ratio\footnote{Simply computed by (the number matched)$/$(the number of sampled sentence). 100 sentences are sampled at most for each type of subordinator candidate.} in Table \ref{in}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 1
\begin{table}[t]
\input{10table01.tex}
\hangcaption{Occurrences and matched ratio for subordinators candidates extracted from PTB. N means the pattern is absent in the dictionary.}
\label{in}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

From the results, we observe that 73.3\% of sentences with subordinate clauses randomly sampled from PTB can be covered by the pattern dictionary.
Here, we further analyze the collected data and discuss how the dictionary can be improved.

In terms of the type of subordinator, 85.7\% are covered with eight absent in the dictionary illustrated as N in Table \ref{in}.
The candidate {\it with} is a case that we did not considered as a subordinator, but was accidentally captured in the extraction process.
This is because the IN tag can be used for either a preposition or subordinate conjunction.
We observe that the usage in all 90 occurrences appears to be prepositional.

Most importantly, in other cases, some candidates with N are absent in the AMR corpus.
This includes the coordination of {\it wh-}adverbs (e.g., {\it how and when, where and how, where and when, when and where}).
Although {\it if and when} is already defined as a conditional relation in AMR, the cases of {\it wh-adverbs} appear to be different because each represents different relation when used alone.
As definitions are not provided, we leave room for further research on the design of semantic relations for these subordinators with coordination. 

Candidates that showed a high matching ratio were mostly used in our target constructions, where a subordinator connects the head predicates of clauses.
Sentences that did not match had either a sentence fragment, inconsistent lemma form of a subordinator, or an apparent error in POS tags.

Some subordinators can be both subordinate conjunctions and prepositions (e.g., {\it as}, {\it for}, {\it since}). 
Particularly, {\it for} was primarily used as a preposition in 48 unmatched sentences.
This causes the matched ratio for theses cases to be lower.
Considering less frequent cases, the occurrences of {\it whatever} was not used as a connective while being a subject of a nominal clause and {\it provide* that} was used in a sentence fragment.

Approximately half of {\it how} instances were used as a simple subordinator, whereas the other half was used in constructions regarding degree and quantity (e.g., {\it how much/many}) that do not express predicative relation.
The latter expressions do not provide the target structure for this research, but present potential extention of our approach with further types of constructions.
This is somewhat related to the case of {\it than} in which our matcher managed to find a comparison of two predicates in four sentences, but the remaining ones in which two nominals or adjectivals were compared were left.
Although new semantic frames were introduced for some constructions regarding comparison, degree, and quantity by \citeA{bonial}, it is unclear whether they can be used for these cases.
Hence, in extending the dictionary, further discussion is necessary to assign skeletal AMRs to the variants of constructions.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Semantic Disambiguation Experiment}

In this section, we discuss the disambiguation method of semantic relations between clauses in complex sentences.
As mentioned in Section 3.2, some subordinators exhibit ambiguity when connecting two clauses.
The sentence in Fig. \ref{flow} is an example of where disambiguation is required in the presence of an ambiguous subordinator \textit{since}:
\textit{Since} [airline flights were disrupted]$_{SUB}$, [the company chartered a plane to fly the executives back to the West Coast]$_{MAT}$.

Because each clause in a complex sentence can be regarded as a simple sentence, we can formulate the semantic disambiguation of clauses as a sentence-pair classification task.
While designing the pattern dictionary, we observed that semantic ambiguity can occur among causal, conditional, concessional, and temporal relations. 
This makes our task a 4-class setting, with \textsc{cause}, \textsc{cond}, \textsc{conc}, and \textsc{time} as class labels.

Throughout the experiment, the pre-trained \texttt{bert-base-uncased} model was used for fine-tuning.
BERT was pre-trained using next sentence prediction and masked language modeling.
During pre-training, the model predicted the next sentence from a pair of candidate sentences.
Thus, the model was expected to learn what the next sentence would look like.
By fine-tuning BERT on a set of pairs of clauses, the model was expected to further capture the semantic relations between the clauses.

The input was a separate pair of clauses with special tokens ``[CLS]'' and ``[SEP]''.
To benefit from the results of dependency matching, artificial tags ``$<$'' and ``$>$'' were added to indicate the position of the main predicate of each clause. 
Thus, the input format of an earlier example would look like: ``[CLS] airline flights $<$ were $>$ disrupted [SEP] the company $<$ chartered $>$ a plane to fly the executives back to the West Coast [SEP]''.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Experimental Setup}

\noindent
{\bf Dataset}{\quad}To create the dataset, we used the latest release of the AMR corpus (LDC2020T02), which contains 59.2k pairs of sentences and AMR graphs.
To extract complex sentence constructions from the corpus, pattern matching was performed with all patterns in the dictionary after processing with SpaCy and Stanza.  
To check whether the corresponding AMR graph described the semantic relation for our setting (i.e., causal, conditional, concessional, and temporal), we searched for alignments between the sentence tokens and AMR graph.\footnote{Only automatically generated alignments provided in the corpus are used.}${^{,}}$\footnote{Only simple target structures such as the one presented in Fig. \ref{once} are used with the assumption that they represent typical relations for each class.}
After checking, we divided each sentence into pairs of clauses and subordinators. 

While the data derived from the AMR corpus can be regarded as ``supervised,'' the size of the dataset is relatively small, with a total of 1,933 pairs of matrix and subordinate clauses.
Therefore, we adopted a weak supervision approach to augment the training data.
That is, we used unambiguous subordinators found in the AMR corpus as linguistic cues to obtain complex sentences from a larger corpus.
For data augmentation, raw data from Wikipedia were used to create ``weakly supervised'' data.
Table \ref{sub} lists the subordinators used in this method.
Table \ref{label} lists the labels statistics of these datasets, referred to as the AMR and WIKI datasets.
The data comprise (\textit{subordinate clause, matrix clause, subordinator, class label}) quadruplets.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 2
\begin{table}[b]
\input{10table02.tex}
\caption{Subordinators chosen to create the WIKI dataset.}
\label{sub}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 3
\begin{table}[b]
\input{10table03.tex}
\caption{Label statistics of the AMR and WIKI datasets.}
\label{label}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
{\bf Methods and Metrics}{\quad}Considering the size of the collected AMR dataset, the data were split into five sets for 5-fold cross validation.\footnote{Train:Dev:Test=3:1:1 for each split.}
When the models were trained on the WIKI dataset, the scores for five runs using different random seeds were averaged to ensure stability.
In addition, a grid search was performed for hyperparameter tuning over the options listed in Table \ref{hp}.
All models were evaluated using macro and micro F1 scores ($F_{M}$ and $F_{m}$, respectively). Note that variances are specified in parentheses.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 4
\begin{table}[t]
\input{10table04.tex}
\caption{Hyperparameter options for grid search.}
\label{hp}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\noindent
{\bf Baseline}{\quad}As a baseline, BERT was fine-tuned solely on the AMR dataset; this process is hereafter referred to as BERT$\rightarrow$AMR.
To make best use of the data, BERT$\rightarrow$AMR was compared with BERT$\rightarrow$AMRs (i.e., with subordinators as a feature). One of BERT's unused tags, ``[unused\_0]'', was placed to specify the position of a subordinator in an input sequence:\footnote{The unused tags are orthogonal to the brackets.} ``[CLS] [unused\_0] since [unused\_0] airline flights $<$ were $>$ disrupted [SEP] the company $<$ chartered $>$ a plane to fly the executives back to the West Coast [SEP]''.
Our preliminary experiments showed that using subordinators as explicit inputs does not work as indicated in Section 5, as opposed to a previously reported tendency that such tasks can benefit from the explicit usage of subordinators \cite{easily}.
This may have been caused by the model overfitting to certain subordinators.
Therefore, in the following experiments, BERT$\rightarrow$AMR was selected as the baseline.

Although using explicit information regarding the subordinators was demonstrated ineffective, taking advantage of this information was still reasonable as a restriction on the trained models to force comparisons between ambiguous labels.
First, a list of ambiguous subordinators, with corresponding possible labels (e.g., \textit{since} is ambiguous between \textsc{cause} and \textsc{time}), was created.
When a complex sentence is fed to a model, the list is searched for a subordinator to check for potential ambiguity.
If ambiguity is present, a restriction is applied to the softmax layer, and the probabilities of irrelevant class labels are reduced to zero. 
That is, the models are modified to search only for the possible labels defined in the list.
Section 5 presents a comparison of the results obtained without the restriction and the results after the restriction was applied.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Conditions}

In the experiments, several approaches were used to examine the effect of augmented data on the classification performance. 
For a direct comparison of the training data, BERT$\rightarrow$WIKI was fine-tuned solely on the WIKI dataset to determine whether the model would benefit from the larger, augmented, weakly supervised dataset.
BERT$\rightarrow$MIX was fine-tuned using a combined set of data consisting of AMR and additional WIKI data.
The WIKI dataset, whose size ranged from 2--20k, was created through the incremental addition of 2k sentences at a time.\footnote{The additional data comprises 0.5k sentences for each class label.} 
This approach was used with the expectation that the WIKI data would complement the imbalanced distribution of the AMR data and result in a better performance than that for the baseline or BERT$\rightarrow$WIKI.
Finally, the model identified as BERT$\rightarrow$WIKI$\rightarrow$AMR, which was first fine-tuned on the WIKI dataset, was evaluated and then further fine-tuned on the AMR data. 
Here, we hypothesized that ``pre-fine-tuning'' on the WIKI dataset would make the BERT model fit the defined task better than the original model, which was pre-trained on the next sentence prediction task.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 5
\begin{table}[b]
\input{10table05.tex}
\caption{Performance of each approach. For BERT$\rightarrow$MIX, only the best-performing model is presented.}
\label{eval}
\vspace{-0.5\Cvs}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Results and Analyses}

Table \ref{eval} presents the evaluation scores for all experimental conditions. 
The results show that training only on weakly supervised data did not improve the baseline. Furthermore, even when the dataset size was significantly larger than the AMR dataset, training with BERT$\rightarrow$WIKI harmed the performance in terms of $F_{M}$ and  $F_{m}$ by 16.39\% and 12.57\%, respectively.
This may be attributed to the different distribution of the WIKI and AMR datasets.
However, improvements of 3.06\% and 3.21\%, respectively, were achieved when 8k WIKI data points were added to the AMR data.
Fig.~\ref{plot} presents the changes in the scores as a function of the size of the added data.
Both $F_{M}$ and $F_{m}$ showed increases in performance when the AMR and WIKI data were combined, until peak performance was reached at 8k additional data points;
further addition of data led to reductions in the model performance.
This suggests that excess WIKI data can dilute the presence of supervised AMR data.
We predict that training on the full addition of the WIKI data would deteriorate the performance to a level equivalent to that of BERT$\rightarrow$WIKI. 
Among all models tested, BERT$\rightarrow$WIKI$\rightarrow$AMR achieved the best results, as expected, with 8.37\% and 6.93\% increases in performance, respectively.\footnote{In addition, checking whether the preprocessing step of adding artificial tags (``$<$'' and ``$>$'') for predicate heads mentioned in Section 4 helped the model's performance; it was determined that the untagged version of BERT$\rightarrow$WIKI$\rightarrow$AMR scored 71.21\% for $F_{M}$ and 80.47\% for $F_{m}$, with increases of 1.22\% and 0.75\%, respectively, for the tagged version. We find this step somewhat beneficial, considering that it is a by-product of dependency matching.}
These results prove the effectiveness of pre-fine-tuning on weakly supervised data when the size of the supervised dataset is small. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 11
\begin{figure}[b]
\begin{center}
\includegraphics{29-2ia10f11.pdf}
\end{center}
\hangcaption{Score transitions of BERT$\rightarrow$MIX with respect to number of WIKI data points added; letter ``k'' indicates $\times$ 1,000.}
\label{plot}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 6
\begin{table}[b]
\input{10table06.tex}
\caption{Performance with subordinator as restrictions on softmax layer.}
\label{rest}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Table \ref{rest} lists the results after the restriction was applied, indicated by +{\it r} in the model names.
Compared to the performance outlined in Table \ref{eval}, overall improvements in terms of $F_{M}$ by 3.05--3.64\% and $F_{m}$ by 2.72--3.02\% were achieved for all models.
Tables \ref{lab}(a) and \ref{lab}(b) present the precision, recall, and F1 of BERT$\rightarrow$AMR and BERT$\rightarrow$AMR+{\it r} with respect to the labels.
Based on the result, this approach of applying the restriction seems the most effective, except for \textsc{conc}, which resulted in a decrease in the precision by 5.75\%.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 7
\begin{table}[b]
\input{10table07.tex}
\caption{Performance with respect to label.}
\label{lab}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 8
\begin{table}[b]
\input{10table08.tex}
\caption{Confusion matrices of models trained on the first split of data for cross validation.}
\label{cm}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

With further analysis of the confusion matrices on the first split of data for cross-validation, as shown in Table \ref{cm}, the number of false-positive errors for \textsc{conc} was observed to increase.
This is because of an error in predicting sentences that have the word \textit{while}, which is regarded as ambiguous between \textsc{conc} and \textsc{time}.
When the correct label is \textsc{time}, the model first predicts the \textsc{cause} or \textsc{cond}.
Even after the restriction was applied, the model predicted \textsc{conc}.
Nonetheless, overall, the restriction helped the models reduce false-positive errors.

While we targeted four types of semantic relations, 36 relations are defined in the AMR framework\nocite{amr}.
Below is a list of relations that were not considered in our experiments.

\begin{quote}
:accompanier, :age, :beneficiary, :compared-to, :consist-of, :degree, :destination, :direction, :domain, :duration, :employed-by, :example, :extent, :frequency, :instrument, :li, :location, :manner, :medium,
:mod, :mode, :name, :part, :path, :polarity, :poss, :purpose, :source, :subevent, :subset, :topic, :value
\end{quote}

\noindent
We believe our approach is also applicable to semantic disambiguation involving the above relations by training models with different combination of classes.
Consider the case in which we want to disambiguate a {\it participial construction}, which is ambiguous in \texttt{:manner} and the presented four relations.
One possible way to create weakly supervised data for \texttt{:manner} would be to extract sentences with a marker {\it by V-ing} that describes the method of how something is done.
We leave room for further investigation on other possible combinations.


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

This study addressed the problem of handling complex sentences, in which syntax--semantics mappings are less evident than simple sentences.
For this purpose, we proposed a two-step process that captures the structures of complex sentence constructions in the AMR framework. The solution involved: 
(1) pattern matching of sentences to skeletal AMRs and (2) disambiguation of semantic relations between clauses.
In addition to the pattern matcher, we developed a dictionary of dependency patterns and their corresponding skeletal AMRs.
We then formulated the disambiguation problem as a multi-class sentence-pair classification task.
We applied the fine-tuning approach of pre-trained BERT models using supervised and weakly supervised data derived from AMR and Wikipedia corpora, respectively.
Our experiments showed that pre-fine-tuning on weakly supervised data effectively improved the performance of the classification models compared with those of models trained solely on small-sized supervised data.

In future research, we plan to extend the proposed method by accounting for syntactic disambiguation.
Currently, our pattern-matching system relies on the outputs of the dependency parser provided by Stanza.
Because this dependency parser was not specifically designed to resolve syntactic ambiguities of clauses, the outputs may potentially hurt the overall performance of the system.
We also plan to incorporate these methods into a full AMR parsing task.


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

Part of this work was presented at the 10th Joint Conference on Lexical and Computational Semantics, August, 2021, Online (Yamamoto et al., 2021).


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bibliographystyle{jnlpbbl_1.6.bst}
\begingroup
\addtolength{\baselineskip}{-0.5pt}
\bibliography{10refs}
\endgroup

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

Here, typical entries in the pattern dictionary that are characterized by different structures are presented with a sample complex sentence that can be matched.
The dependency patterns are presented in the figures to the left and the corresponding skeletal AMRs to the right.\\

\begin{itemize}
\item \textit{after}
\end{itemize}

\textit{After} he had returned to Beijing, the student movement in Tiananmen began.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. a1
\begin{figure}[h]
\vspace{-1\Cvs}
\begin{center}
\includegraphics{29-2ia10a1.pdf}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{itemize}
\item \textit{even though}
\end{itemize}

\textit{Even though} your anxiety levels would increase for a duration, they will gradually decrease.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. a2
\begin{figure}[h]
\vspace{-1\Cvs}
\begin{center}
\includegraphics{29-2ia10a2.pdf}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{itemize}
\item \textit{in case}
\end{itemize}

It is a good idea to have a survival kit in your car \textit{in case} a blizzard forces you to spend the night on the side of the road.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. a3
\begin{figure}[h]
\vspace{-1\Cvs}
\begin{center}
\includegraphics{29-2ia10a3.pdf}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\clearpage

\begin{itemize}
\item \textit{in order that}
\end{itemize}

They should have destinations at both ends \textit{in order that} you get maximum use out of your investment.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. a4
\begin{figure}[h]
\vspace{-1\Cvs}
\begin{center}
\includegraphics{29-2ia10a4.pdf}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{itemize}
\item \textit{no sooner ... than}
\end{itemize}

\textit{No sooner} did I post about it \textit{than} the TV program manifest their nonsense.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. a5
\begin{figure}[h]
\vspace{-1\Cvs}
\begin{center}
\includegraphics{29-2ia10a5.pdf}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{itemize}
\item \textit{despite the fact that}
\end{itemize}

\textit{Despite the fact that} her way didn't work, she will never admit it.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. a6
\begin{figure}[h]
\vspace{-1\Cvs}
\begin{center}
\includegraphics{29-2ia10a6.pdf}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


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

\bioauthor[:]{Yuki Yamamoto}{%
received his B.A. degree in language and culture from Osaka University in 2018 and M.E. degree from the Nara Institute of Science and Technology in 2020. 
He is currently a doctoral student at the Natural Language Processing Laboratory, Nara Institute of Science and Technology.
His research interests include syntactic parsing, semantic parsing, and lexical semantics.
}

\bioauthor[:]{Yuji Matsumoto}{%
received his B.E., M.E., and Ph.D. degrees in information science from Kyoto University in 1977, 1979, and 1989, respectively. 
He has experience as a researcher at the Machine Inference Section of Electrotechnical Laboratory, an academic visitor at the Imperial College of Science and Technology, a deputy chief of the First Laboratory at ICOT, an associate professor at Kyoto University, and a professor at the Nara Institute of Science and Technology.
He is currently the head of the Knowledge Acquisition Team at RIKEN Center for Advanced Intelligence Projects.
His main research interests are natural language understanding and machine learning.
}

\bioauthor[:]{Taro Watanabe}{%
received his B.E. and M.E. degrees in information science from Kyoto University in 1994 and 1997, respectively, and obtained his M.S. degree in language and information technologies from Carnegie Mellon University in 2000.
He then received his Ph.D. in informatics from Kyoto University in 2004.
He has experience as a researcher at ATR, NTT, and NICT, and a software engineer at Google.
He is currently a professor at Nara Institute of Science and Technology.
His main research interests are natural language processing, machine learning, and machine translation.
}

\end{biography}


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\biodate



\end{document}

