Yusuke Miyao

2021

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Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes
Takahiro Kondo | Koki Washio | Katsuhiko Hayashi | Yusuke Miyao
Proceedings of the 8th Workshop on Argument Mining

We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes. We express the meaning of text segments using predicate logic and map the boolean values of predicate logic expressions to nodes in a Bayesian network. The reasoning structure among text segments is described with a directed acyclic graph. While our formalism is highly expressive and capable of describing the informal logic of human arguments, it is too open-ended to actually build a network for an argument. It is not at all obvious which segment of argumentative text should be considered as a node in a Bayesian network, and how to decide the dependencies among nodes. To alleviate the difficulty, we provide abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes. The network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination. We define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them. We also create a dataset consisting of pairs of an argumentative text and a corresponding Bayesian network. Our dataset contains about 2,400 pairs, which is large in the research area of argumentation schemes.

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Talking with the Theorem Prover to Interactively Solve Natural Language Inference
Atsushi Sumita | Yusuke Miyao | Koji Mineshima
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

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Leveraging Partial Dependency Trees to Control Image Captions
Wenjie Zhong | Yusuke Miyao
Proceedings of the Second Workshop on Advances in Language and Vision Research

Controlling the generation of image captions attracts lots of attention recently. In this paper, we propose a framework leveraging partial syntactic dependency trees as control signals to make image captions include specified words and their syntactic structures. To achieve this purpose, we propose a Syntactic Dependency Structure Aware Model (SDSAM), which explicitly learns to generate the syntactic structures of image captions to include given partial dependency trees. In addition, we come up with a metric to evaluate how many specified words and their syntactic dependencies are included in generated captions. We carry out experiments on two standard datasets: Microsoft COCO and Flickr30k. Empirical results show that image captions generated by our model are effectively controlled in terms of specified words and their syntactic structures.The code is available on GitHub.

We propose the task of automatically generating commentaries for races in a motor racing game, from vision, structured numerical, and textual data. Commentaries provide information to support spectators in understanding events in races. Commentary generation models need to interpret the race situation and generate the correct content at the right moment. We divide the task into two subtasks: utterance timing identification and utterance generation. Because existing datasets do not have such alignments of data in multiple modalities, this setting has not been explored in depth. In this study, we introduce a new large-scale dataset that contains aligned video data, structured numerical data, and transcribed commentaries that consist of 129,226 utterances in 1,389 races in a game. Our analysis reveals that the characteristics of commentaries change over time or from viewpoints. Our experiments on the subtasks show that it is still challenging for a state-of-the-art vision encoder to capture useful information from videos to generate accurate commentaries. We make the dataset and baseline implementation publicly available for further research.

2020

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Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser
Binyam Ephrem Seyoum | Yusuke Miyao | Baye Yimam Mekonnen
Proceedings of the first workshop on Resources for African Indigenous Languages

In this paper, we compare four state-of-the-art neural network dependency parsers for the Semitic language Amharic. As Amharic is a morphologically-rich and less-resourced language, the out-of-vocabulary (OOV) problem will be higher when we develop data-driven models. This fact limits researchers to develop neural network parsers because the neural network requires large quantities of data to train a model. We empirically evaluate neural network parsers when a small Amharic treebank is used for training. Based on our experiment, we obtain an 83.79 LAS score using the UDPipe system. Better accuracy is achieved when the neural parsing system uses external resources like word embedding. Using such resources, the LAS score for UDPipe improves to 85.26. Our experiment shows that the neural networks can learn dependency relations better from limited data while segmentation and POS tagging require much data.

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.

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Utterance-Unit Annotation for the JSL Dialogue Corpus: Toward a Multimodal Approach to Corpus Linguistics
Mayumi Bono | Rui Sakaida | Tomohiro Okada | Yusuke Miyao
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives

This paper describes a method for annotating the Japanese Sign Language (JSL) dialogue corpus. We developed a way to identify interactional boundaries and define a ‘utterance unit’ in sign language using various multimodal features accompanying signing. The utterance unit is an original concept for segmenting and annotating sign language dialogue referring to signer’s native sense from the perspectives of Conversation Analysis (CA) and Interaction Studies. First of all, we postulated that we should identify a fundamental concept of interaction-specific unit for understanding interactional mechanisms, such as turn-taking (Sacks et al. 1974), in sign-language social interactions. Obviously, it does should not relying on a spoken language writing system for storing signings in corpora and making translations. We believe that there are two kinds of possible applications for utterance units: one is to develop corpus linguistics research for both signed and spoken corpora; the other is to build an informatics system that includes, but is not limited to, a machine translation system for sign languages.

End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.

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Towards Grounding of Formulae
Takuto Asakura | André Greiner-Petter | Akiko Aizawa | Yusuke Miyao
Proceedings of the First Workshop on Scholarly Document Processing

A large amount of scientific knowledge is represented within mixed forms of natural language texts and mathematical formulae. Therefore, a collaboration of natural language processing and formula analyses, so-called mathematical language processing, is necessary to enable computers to understand and retrieve information from the documents. However, as we will show in this project, a mathematical notation can change its meaning even within the scope of a single paragraph. This flexibility makes it difficult to extract the exact meaning of a mathematical formula. In this project, we will propose a new task direction for grounding mathematical formulae. Particularly, we are addressing the widespread misconception of various research projects in mathematical information retrieval, which presume that mathematical notations have a fixed meaning within a single document. We manually annotated a long scientific paper to illustrate the task concept. Our high inter-annotator agreement shows that the task is well understood for humans. Our results indicate that it is worthwhile to grow the techniques for the proposed task to contribute to the further progress of mathematical language processing.

Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., “Nikkei gains” is generated when “Nikkei drops” is expected. We investigate models trained on contrastive examples i.e., incorrect sentences or terms, in addition to correct ones to reduce such errors. We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as “gain” or “drop”. We then use learning methods with several losses that exploit contrastive examples. Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability of generating sentences with better lexical choice, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances on different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance. Human evaluation also supports the effectiveness of using contrastive examples.

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An empirical analysis of existing systems and datasets toward general simple question answering
Namgi Han | Goran Topic | Hiroshi Noji | Hiroya Takamura | Yusuke Miyao
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a model on a single dataset, and recent studies suggest that SimpleQuestions, the most popular and largest dataset, is nearly solved under this setting. However, this common setting does not evaluate the robustness of the systems outside of the distribution of the used training data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering. We discuss a possible future direction toward this goal.

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Analyzing Word Embedding Through Structural Equation Modeling
Namgi Han | Katsuhiko Hayashi | Yusuke Miyao
Proceedings of the 12th Language Resources and Evaluation Conference

Many researchers have tried to predict the accuracies of extrinsic evaluation by using intrinsic evaluation to evaluate word embedding. The relationship between intrinsic and extrinsic evaluation, however, has only been studied with simple correlation analysis, which has difficulty capturing complex cause-effect relationships and integrating external factors such as the hyperparameters of word embedding. To tackle this problem, we employ partial least squares path modeling (PLS-PM), a method of structural equation modeling developed for causal analysis. We propose a causal diagram consisting of the evaluation results on the BATS, VecEval, and SentEval datasets, with a causal hypothesis that linguistic knowledge encoded in word embedding contributes to solving downstream tasks. Our PLS-PM models are estimated with 600 word embeddings, and we prove the existence of causal relations between linguistic knowledge evaluated on BATS and the accuracies of downstream tasks evaluated on VecEval and SentEval in our PLS-PM models. Moreover, we show that the PLS-PM models are useful for analyzing the effect of hyperparameters, including the training algorithm, corpus, dimension, and context window, and for validating the effectiveness of intrinsic evaluation.

2019

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our proposed model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generations. Experimental results show that our proposed model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, because depending on users, it differs what they are interested in, so it is necessary to develop a method to generate various summaries according to users’ interests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation.

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Does My Rebuttal Matter? Insights from a Major NLP Conference
Yang Gao | Steffen Eger | Ilia Kuznetsov | Iryna Gurevych | Yusuke Miyao
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Peer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial reviews and author responses. Although author responses do have a marginal (and statistically significant) influence on the final scores, especially for borderline papers, our results suggest that a reviewer’s final score is largely determined by her initial score and the distance to the other reviewers’ initial scores. In this context, we discuss the conformity bias inherent to peer reviewing, a bias that has largely been overlooked in previous research. We hope our analyses will help better assess the usefulness of the rebuttal phase in NLP conferences.

2018

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Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Iryna Gurevych | Yusuke Miyao
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Iryna Gurevych | Yusuke Miyao
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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An Empirical Investigation of Error Types in Vietnamese Parsing
Quy Nguyen | Yusuke Miyao | Hiroshi Noji | Nhung Nguyen
Proceedings of the 27th International Conference on Computational Linguistics

Syntactic parsing plays a crucial role in improving the quality of natural language processing tasks. Although there have been several research projects on syntactic parsing in Vietnamese, the parsing quality has been far inferior than those reported in major languages, such as English and Chinese. In this work, we evaluated representative constituency parsing models on a Vietnamese Treebank to look for the most suitable parsing method for Vietnamese. We then combined the advantages of automatic and manual analysis to investigate errors produced by the experimented parsers and find the reasons for them. Our analysis focused on three possible sources of parsing errors, namely limited training data, part-of-speech (POS) tagging errors, and ambiguous constructions. As a result, we found that the last two sources, which frequently appear in Vietnamese text, significantly attributed to the poor performance of Vietnamese parsing.

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Inducing Temporal Relations from Time Anchor Annotation
Fei Cheng | Yusuke Miyao
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Recognizing temporal relations among events and time expressions has been an essential but challenging task in natural language processing. Conventional annotation of judging temporal relations puts a heavy load on annotators. In reality, the existing annotated corpora include annotations on only “salient” event pairs, or on pairs in a fixed window of sentences. In this paper, we propose a new approach to obtain temporal relations from absolute time value (a.k.a. time anchors), which is suitable for texts containing rich temporal information such as news articles. We start from time anchors for events and time expressions, and temporal relation annotations are induced automatically by computing relative order of two time anchors. This proposal shows several advantages over the current methods for temporal relation annotation: it requires less annotation effort, can induce inter-sentence relations easily, and increases informativeness of temporal relations. We compare the empirical statistics and automatic recognition results with our data against a previous temporal relation corpus. We also reveal that our data contributes to a significant improvement of the downstream time anchor prediction task, demonstrating 14.1 point increase in overall accuracy.

This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean. UD applies a strict principle that makes the head of coordination the left-most conjunct. However, the guideline may produce syntactic trees which are difficult to accept in head-final languages. This paper describes the status in the current Japanese and Korean corpora and proposes alternative designs suitable for these languages.

Comments on a stock market often include the reason or cause of changes in stock prices, such as “Nikkei turns lower as yen’s rise hits exporters.” Generating such informative sentences requires capturing the relationship between different resources, including a target stock price. In this paper, we propose a model for automatically generating such informative market comments that refer to external resources. We evaluated our model through an automatic metric in terms of BLEU and human evaluation done by an expert in finance. The results show that our model outperforms the existing model both in BLEU scores and human judgment.

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Universal Dependencies for Amharic
Binyam Ephrem Seyoum | Yusuke Miyao | Baye Yimam Mekonnen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Learning to Generate Market Comments from Stock Prices
Soichiro Murakami | Akihiko Watanabe | Akira Miyazawa | Keiichi Goshima | Toshihiko Yanase | Hiroya Takamura | Yusuke Miyao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents a novel encoder-decoder model for automatically generating market comments from stock prices. The model first encodes both short- and long-term series of stock prices so that it can mention short- and long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or rounding, and applying it to the input stock prices. Empirical experiments show that our best model generates market comments at the fluency and the informativeness approaching human-generated reference texts.

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Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths
Fei Cheng | Yusuke Miyao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a “common root” assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.).

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Proceedings of the 15th International Conference on Parsing Technologies
Yusuke Miyao | Kenji Sagae
Proceedings of the 15th International Conference on Parsing Technologies

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Evaluation Metrics for Automatically Generated Metaphorical Expressions
Akira Miyazawa | Yusuke Miyao
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

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On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
Pascual Martínez-Gómez | Koji Mineshima | Yusuke Miyao | Daisuke Bekki
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts need to be explicitly stated. However, recognising subsentential semantic relations is not trivial. We address this problem by monitoring the proof of the theorem and detecting unprovable sub-goals that share predicate arguments with logical premises. If a linguistic relation exists, then an appropriate axiom is constructed on-demand and the theorem proving continues. Experiments show that this approach is effective and precise, producing a system that outperforms other logic-based systems and is competitive with state-of-the-art statistical methods.

2016

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Paraphrase for Open Question Answering: New Dataset and Methods
Ying Xu | Pascual Martínez-Gómez | Yusuke Miyao | Randy Goebel
Proceedings of the Workshop on Human-Computer Question Answering

Automatic video description generation has recently been getting attention after rapid advancement in image caption generation. Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames. Most of the work relied on Recurrent Neural Network (RNN) and recently attentional mechanisms have also been applied to make the model learn to focus on some frames of the video while generating each word in a describing sentence. In this paper, we focus on a sequence-to-sequence approach with temporal attention mechanism. We analyze and compare the results from different attention model configuration. By applying the temporal attention mechanism to the system, we can achieve a METEOR score of 0.310 on Microsoft Video Description dataset, which outperformed the state-of-the-art system so far.

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Video Event Detection by Exploiting Word Dependencies from Image Captions
Sang Phan | Yusuke Miyao | Duy-Dinh Le | Shin’ichi Satoh
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Video event detection is a challenging problem in information and multimedia retrieval. Different from single action detection, event detection requires a richer level of semantic information from video. In order to overcome this challenge, existing solutions often represent videos using high level features such as concepts. However, concept-based representation can be confusing because it does not encode the relationship between concepts. This issue can be addressed by exploiting the co-occurrences of the concepts, however, it often leads to a very huge number of possible combinations. In this paper, we propose a new approach to obtain the relationship between concepts by exploiting the syntactic dependencies between words in the image captions. The main advantage of this approach is that it significantly reduces the number of informative combinations between concepts. We conduct extensive experiments to analyze the effectiveness of using the new dependency representation for event detection on two large-scale TRECVID Multimedia Event Detection 2013 and 2014 datasets. Experimental results show that i) Dependency features are more discriminative than concept-based features. ii) Dependency features can be combined with our current event detection system to further improve the performance. For instance, the relative improvement can be as far as 8.6% on the MEDTEST14 10Ex setting.

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Rule Extraction for Tree-to-Tree Transducers by Cost Minimization
Pascual Martínez-Gómez | Yusuke Miyao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Using Left-corner Parsing to Encode Universal Structural Constraints in Grammar Induction
Hiroshi Noji | Yusuke Miyao | Mark Johnson
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser
Koji Mineshima | Ribeka Tanaka | Pascual Martínez-Gómez | Yusuke Miyao | Daisuke Bekki
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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ccg2lambda: A Compositional Semantics System
Pascual Martínez-Gómez | Koji Mineshima | Yusuke Miyao | Daisuke Bekki
Proceedings of ACL-2016 System Demonstrations

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Jigg: A Framework for an Easy Natural Language Processing Pipeline
Hiroshi Noji | Yusuke Miyao
Proceedings of ACL-2016 System Demonstrations

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Challenges and Solutions for Consistent Annotation of Vietnamese Treebank
Quy Nguyen | Yusuke Miyao | Ha Le | Ngan Nguyen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Treebanks are important resources for researchers in natural language processing, speech recognition, theoretical linguistics, etc. To strengthen the automatic processing of the Vietnamese language, a Vietnamese treebank has been built. However, the quality of this treebank is not satisfactory and is a possible source for the low performance of Vietnamese language processing. We have been building a new treebank for Vietnamese with about 40,000 sentences annotated with three layers: word segmentation, part-of-speech tagging, and bracketing. In this paper, we describe several challenges of Vietnamese language and how we solve them in developing annotation guidelines. We also present our methods to improve the quality of the annotation guidelines and ensure annotation accuracy and consistency. Experiment results show that inter-annotator agreement ratios and accuracy are higher than 90% which is satisfactory.

We present an attempt to port the international syntactic annotation scheme, Universal Dependencies, to the Japanese language in this paper. Since the Japanese syntactic structure is usually annotated on the basis of unique chunk-based dependencies, we first introduce word-based dependencies by using a word unit called the Short Unit Word, which usually corresponds to an entry in the lexicon UniDic. Porting is done by mapping the part-of-speech tagset in UniDic to the universal part-of-speech tagset, and converting a constituent-based treebank to a typed dependency tree. The conversion is not straightforward, and we discuss the problems that arose in the conversion and the current solutions. A treebank consisting of 10,000 sentences was built by converting the existent resources and currently released to the public.

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Typed Entity and Relation Annotation on Computer Science Papers
Yuka Tateisi | Tomoko Ohta | Sampo Pyysalo | Yusuke Miyao | Akiko Aizawa
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We describe our ongoing effort to establish an annotation scheme for describing the semantic structures of research articles in the computer science domain, with the intended use of developing search systems that can refine their results by the roles of the entities denoted by the query keys. In our scheme, mentions of entities are annotated with ontology-based types, and the roles of the entities are annotated as relations with other entities described in the text. So far, we have annotated 400 abstracts from the ACL anthology and the ACM digital library. In this paper, the scheme and the annotated dataset are described, along with the problems found in the course of annotation. We also show the results of automatic annotation and evaluate the corpus in a practical setting in application to topic extraction.

We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP~2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.

2015

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Incorporating Complementary Annotation to a CCGbank for Improving Derivations for Japanese
Sumire Uematsu | Yusuke Miyao
Proceedings of the 14th International Conference on Parsing Technologies

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Optimal Shift-Reduce Constituent Parsing with Structured Perceptron
Le Quang Thang | Hiroshi Noji | Yusuke Miyao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Discriminative Preordering Meets Kendall’s 𝜏 Maximization
Sho Hoshino | Yusuke Miyao | Katsuhito Sudoh | Katsuhiko Hayashi | Masaaki Nagata
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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A Lexicalized Tree Kernel for Open Information Extraction
Ying Xu | Christoph Ringlstetter | Mi-Young Kim | Grzegorz Kondrak | Randy Goebel | Yusuke Miyao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Paraphrase Detection Based on Identical Phrase and Similar Word Matching
Hoang-Quoc Nguyen-Son | Yusuke Miyao | Isao Echizen
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Higher-order logical inference with compositional semantics
Koji Mineshima | Pascual Martínez-Gómez | Yusuke Miyao | Daisuke Bekki
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Annotation of Computer Science Papers for Semantic Relation Extrac-tion
Yuka Tateisi | Yo Shidahara | Yusuke Miyao | Akiko Aizawa
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We designed a new annotation scheme for formalising relation structures in research papers, through the investigation of computer science papers. The annotation scheme is based on the hypothesis that identifying the role of entities and events that are described in a paper is useful for intelligent information retrieval in academic literature, and the role can be determined by the relationship between the author and the described entities or events, and relationships among them. Using the scheme, we have annotated research abstracts from the IPSJ Journal published in Japanese by the Information Processing Society of Japan. On the basis of the annotated corpus, we have developed a prototype information extraction system which has the facility to classify sentences according to the relationship between entities mentioned, to help find the role of the entity in which the searcher is interested.

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Overview of Todai Robot Project and Evaluation Framework of its NLP-based Problem Solving
Akira Fujita | Akihiro Kameda | Ai Kawazoe | Yusuke Miyao
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We introduce the organization of the Todai Robot Project and discuss its achievements. The Todai Robot Project task focuses on benchmarking NLP systems for problem solving. This task encourages NLP-based systems to solve real high-school examinations. We describe the details of the method to manage question resources and their correct answers, answering tools and participation by researchers in the task. We also analyse the answering accuracy of the developed systems by comparing the systems answers with answers given by human test-takers.

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Efficient Logical Inference for Semantic Processing
Ran Tian | Yusuke Miyao | Takuya Matsuzaki
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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Significance of Bridging Real-world Documents and NLP Technologies
Tadayoshi Hara | Goran Topić | Yusuke Miyao | Akiko Aizawa
Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT

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Japanese to English Machine Translation using Preordering and Compositional Distributed Semantics
Sho Hoshino | Hubert Soyer | Yusuke Miyao | Akiko Aizawa
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

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Logical Inference on Dependency-based Compositional Semantics
Ran Tian | Yusuke Miyao | Takuya Matsuzaki
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Encoding Generalized Quantifiers in Dependency-based Compositional Semantics
Yubing Dong | Ran Tian | Yusuke Miyao
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

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In-House: An Ensemble of Pre-Existing Off-the-Shelf Parsers
Yusuke Miyao | Stephan Oepen | Daniel Zeman
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Left-corner Transitions on Dependency Parsing
Hiroshi Noji | Yusuke Miyao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning
Yo Ehara | Yusuke Miyao | Hidekazu Oiwa | Issei Sato | Hiroshi Nakagawa
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Improvements to the Bayesian Topic N-Gram Models
Hiroshi Noji | Daichi Mochihashi | Yusuke Miyao
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Effects of Parsing Errors on Pre-Reordering Performance for Chinese-to-Japanese SMT
Dan Han | Pascual Martínez-Gómez | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27)

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Utilizing State-of-the-art Parsers to Diagnose Problems in Treebank Annotation for a Less Resourced Language
Quy Nguyen | Ngan Nguyen | Yusuke Miyao
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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Relation Annotation for Understanding Research Papers
Yuka Tateisi | Yo Shidahara | Yusuke Miyao | Akiko Aizawa
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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Using unlabeled dependency parsing for pre-reordering for Chinese-to-Japanese statistical machine translation
Dan Han | Pascual Martínez-Gómez | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Second Workshop on Hybrid Approaches to Translation

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Deep Context-Free Grammar for Chinese with Broad-Coverage
Xiangli Wang | Yi Zhang | Yusuke Miyao | Takuya Matsuzaki | Junichi Tsujii
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing

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Alignment-based Annotation of Proofreading Texts toward Professional Writing Assistance
Ngan Nguyen | Yusuke Miyao
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Two-Stage Pre-ordering for Japanese-to-English Statistical Machine Translation
Sho Hoshino | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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University Entrance Examinations as a Benchmark Resource for NLP-based Problem Solving
Yusuke Miyao | Ai Kawazoe
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Integrating Multiple Dependency Corpora for Inducing Wide-coverage Japanese CCG Resources
Sumire Uematsu | Takuya Matsuzaki | Hiroki Hanaoka | Yusuke Miyao | Hideki Mima
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Building Japanese Textual Entailment Specialized Data Sets for Inference of Basic Sentence Relations
Kimi Kaneko | Yusuke Miyao | Daisuke Bekki
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing
Hiroyuki Shindo | Yusuke Miyao | Akinori Fujino | Masaaki Nagata
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Incremental Joint Approach to Word Segmentation, POS Tagging, and Dependency Parsing in Chinese
Jun Hatori | Takuya Matsuzaki | Yusuke Miyao | Jun’ichi Tsujii
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Comparing Different Criteria for Vietnamese Word Segmentation
Quy T. Nguyen | Ngan L.T. Nguyen | Yusuke Miyao
Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing

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Answering Yes/No Questions via Question Inversion
Hiroshi Kanayama | Yusuke Miyao | John Prager
Proceedings of COLING 2012

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Framework of Semantic Role Assignment based on Extended Lexical Conceptual Structure: Comparison with VerbNet and FrameNet
Yuichiroh Matsubayashi | Yusuke Miyao | Akiko Aizawa
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Annotating Factive Verbs
Alvin Grissom II | Yusuke Miyao
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We have created a scheme for annotating corpora designed to capture relevant aspects of factivity in verb-complement constructions. Factivity constructions are a well-known linguistic phenomenon that embed presuppositions about the state of the world into a clause. These embedded presuppositions provide implicit information about facts assumed to be true in the world, and are thus potentially valuable in areas of research such as textual entailment. We attempt to address both clear-cut cases of factivity and non-factivity, as well as account for the fluidity and ambiguous nature of some realizations of this construction. Our extensible scheme is designed to account for distinctions between claims, performatives, atypical uses of factivity, and the authority of the one making the utterance. We introduce a simple XML-based syntax for the annotation of factive verbs and clauses, in order to capture this information. We also provide an analysis of the issues which led to these annotative decisions, in the hope that these analyses will be beneficial to those dealing with factivity in a practical context.

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Building Japanese Predicate-argument Structure Corpus using Lexical Conceptual Structure
Yuichiroh Matsubayashi | Yusuke Miyao | Akiko Aizawa
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper introduces our study on creating a Japanese corpus that is annotated using semantically-motivated predicate-argument structures. We propose an annotation framework based on Lexical Conceptual Structure (LCS), where semantic roles of arguments are represented through a semantic structure decomposed by several primitive predicates. As a first stage of the project, we extended Jackendoff 's LCS theory to increase generality of expression and coverage for verbs frequently appearing in the corpus, and successfully created LCS structures for 60 frequent Japanese predicates in Kyoto university Text Corpus (KTC). In this paper, we report our framework for creating the corpus and the current status of creating an LCS dictionary for Japanese predicates.

We present the UOT Machine Translation System that was used in the IWSLT-09 evaluation campaign. This year, we participated in the BTEC track for Chinese-to-English translation. Our system is based on a string-to-tree framework. To integrate deep syntactic information, we propose the use of parse trees and semantic dependencies on English sentences described respectively by Head-driven Phrase Structure Grammar and Predicate-Argument Structures. We report the results of our system on both the development and test sets.

2008

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GENIA-GR: a Grammatical Relation Corpus for Parser Evaluation in the Biomedical Domain
Yuka Tateisi | Yusuke Miyao | Kenji Sagae | Jun’ichi Tsujii
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We report the construction of a corpus for parser evaluation in the biomedical domain. A 50-abstract subset (492 sentences) of the GENIA corpus (Kim et al., 2003) is annotated with labeled head-dependent relations using the grammatical relations (GR) evaluation scheme (Carroll et al., 1998) ,which has been used for parser evaluation in the newswire domain.

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Evaluating the Effects of Treebank Size in a Practical Application for Parsing
Kenji Sagae | Yusuke Miyao | Rune Saetre | Jun’ichi Tsujii
Software Engineering, Testing, and Quality Assurance for Natural Language Processing