Yutaka Matsuo


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Target-Aware Contextual Political Bias Detection in News
Iffat Maab | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Evaluating Large Language Models’ Understanding of Financial Terminology via Definition Modeling
James Jhirad | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop

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Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text
Qi Cao | Takeshi Kojima | Yutaka Matsuo | Yusuke Iwasawa
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

While Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear. In this study, we present novel experimental insights into the resilience of LLMs, particularly GPT-4, when subjected to extensive character-level permutations. To investigate this, we first propose the Scrambled Bench, a suite designed to measure the capacity of LLMs to handle scrambled input, in terms of both recovering scrambled sentences and answering questions given scrambled context. The experimental results indicate that multiple advanced LLMs demonstrate the capability akin to typoglycemia, a phenomenon where humans can understand the meaning of words even when the letters within those words are scrambled, as long as the first and last letters remain in place. More surprisingly, we found that only GPT-4 nearly flawlessly processes inputs with unnatural errors, a task that poses significant challenges for other LLMs and often even for humans. Specifically, GPT-4 can almost perfectly reconstruct the original sentences from scrambled ones, decreasing the edit distance by 95%, even when all letters within each word are entirely scrambled. It is counter-intuitive that LLMs can exhibit such resilience despite severe disruption to input tokenization caused by scrambled text.

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Towards Better Evaluation for Formality-Controlled English-Japanese Machine Translation
Edison Marrese-Taylor | Pin Chen Wang | Yutaka Matsuo
Proceedings of the Eighth Conference on Machine Translation

In this paper we propose a novel approach to automatically classify the level of formality in Japanese text, using three categories (formal, polite, and informal). We introduce a new dataset that combine manually-annotated sentences from existing resources, and formal sentences scrapped from the website of the House of Representatives and the House of Councilors of Japan. Based on our data, we propose a Transformer-based classification model for Japanese, which obtains state-of-the-art results in benchmark datasets. We further propose to utilize our classifier to study the effectiveness of prompting techniques for controlling the formality level of machine translation (MT) using Large Language Models (LLM). Our experimental setting includes a large selection of such models and is based on an En->Ja parallel corpus specifically designed to test formality control in MT. Our results validate the robustness and effectiveness of our proposed approach and while also providing empirical evidence suggesting that prompting LLMs is a viable approach to control the formality level of En->Ja MT using LLMs.

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An Effective Approach for Informational and Lexical Bias Detection
Iffat Maab | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)

In this paper we present a thorough investigation of automatic bias recognition on BASIL, a dataset of political news which has been annotated with different kinds of biases. We begin by unveiling several inconsistencies in prior work using this dataset, showing that most approaches focus only on certain task formulations while ignoring others, and also failing to report important evaluation details. We provide a comprehensive categorization of these approaches, as well as a more uniform and clear set of evaluation metrics. We argue about the importance of the missing formulations and also propose the novel task of simultaneously detecting different kinds of biases in news. In our work, we tackle bias on six different BASIL classification tasks in a unified manner. Eventually, we introduce a simple yet effective approach based on data augmentation and preprocessing which is generic and works very well across models and task formulations, allowing us to obtain state-of-the-art results. We also perform ablation studies on some tasks to quantify the strength of data augmentation and preprocessing, and find that they correlate positively on all bias tasks.


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A Parallel Corpus and Dictionary for Amis-Mandarin Translation
Francis Zheng | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities

Amis is an endangered language indigenous to Taiwan with limited data available for computational processing. We thus present an Amis-Mandarin dataset containing a parallel corpus of 5,751 Amis and Mandarin sentences and a dictionary of 7,800 Amis words and phrases with their definitions in Mandarin. Using our dataset, we also established a baseline for machine translation between Amis and Mandarin in both directions. Our dataset can be found at https://github.com/francisdzheng/amis-mandarin.

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On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing
Itsuki Okimura | Machel Reid | Makoto Kawano | Yutaka Matsuo
Proceedings of the Third Workshop on Insights from Negative Results in NLP

With in the broader scope of machine learning, data augmentation is a common strategy to improve generalization and robustness of machine learning models. While data augmentation has been widely used within computer vision, its use in the NLP has been been comparably rather limited. The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear. In this paper, we look to tackle this by evaluating the impact of 12 data augmentation methods on multiple datasets when finetuning pre-trained language models. We find minimal improvements when data sizes are constrained to a few thousand, with performance degradation when data size is increased. We also use various methods to quantify the strength of data augmentations, and find that these values, though weakly correlated with downstream performance, correlate negatively or positively depending on the task. Furthermore, we find a glaring lack of consistently performant data augmentations. This all alludes to the difficulty of data augmentations for NLP tasks and we are inclined to believe that static data augmentations are not broadly applicable given these properties.

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Improving Jejueo-Korean Translation With Cross-Lingual Pretraining Using Japanese and Korean
Francis Zheng | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 9th Workshop on Asian Translation

Jejueo is a critically endangered language spoken on Jeju Island and is closely related to but mutually unintelligible with Korean. Parallel data between Jejueo and Korean is scarce, and translation between the two languages requires more attention, as current neural machine translation systems typically rely on large amounts of parallel training data. While low-resource machine translation has been shown to benefit from using additional monolingual data during the pretraining process, not as much research has been done on how to select languages other than the source and target languages for use during pretraining. We show that using large amounts of Korean and Japanese data during the pretraining process improves translation by 2.16 BLEU points for translation in the Jejueo → Korean direction and 1.34 BLEU points for translation in the Korean → Jejueo direction compared to the baseline.


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AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages
Machel Reid | Junjie Hu | Graham Neubig | Yutaka Matsuo
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource or well-represented languages. Despite an increasing interest in low-resource machine translation, there are no standardized reproducible benchmarks for many African languages, many of which are used by millions of speakers but have less digitized textual data. To tackle these challenges, we propose AfroMT, a standardized, clean, and reproducible machine translation benchmark for eight widely spoken African languages. We also develop a suite of analysis tools for system diagnosis taking into account the unique properties of these languages. Furthermore, we explore the newly considered case of low-resource focused pretraining and develop two novel data augmentation-based strategies, leveraging word-level alignment information and pseudo-monolingual data for pretraining multilingual sequence-to-sequence models. We demonstrate significant improvements when pretraining on 11 languages, with gains of up to 2 BLEU points over strong baselines. We also show gains of up to 12 BLEU points over cross-lingual transfer baselines in data-constrained scenarios. All code and pretrained models will be released as further steps towards larger reproducible benchmarks for African languages.

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Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training
Takeshi Kojima | Yusuke Iwasawa | Yutaka Matsuo
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

Generating diverse texts is an important factor for unsupervised text generation. One approach is to produce the diversity of texts conditioned by the sampled latent code. Although several generative adversarial networks (GANs) have been proposed thus far, these models still suffer from mode-collapsing if the models are not pre-trained. In this paper, we propose a GAN model that aims to improve the approach to generating diverse texts conditioned by the latent space. The generator of our model uses Gumbel-Softmax distribution for the word sampling process. To ensure that the text is generated conditioned upon the sampled latent code, reconstruction loss is introduced in our objective function. The discriminator of our model iteratively inspects incomplete partial texts and learns to distinguish whether they are real or fake by using the standard GAN objective function. Experimental results using the COCO Image Captions dataset show that, although our model is not pre-trained, the performance of our model is quite competitive with the existing baseline models, which requires pre-training.

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Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining
Francis Zheng | Machel Reid | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Asháninka, Guaraní, Wixarika, Náhuatl, Hñähñu, Quechua, Shipibo-Konibo, and Rarámuri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.

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Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
Machel Reid | Edison Marrese-Taylor | Yutaka Matsuo
Findings of the Association for Computational Linguistics: EMNLP 2021

Transformers have shown improved performance when compared to previous architectures for sequence processing such as RNNs. Despite their sizeable performance gains, as recently suggested, the model is computationally expensive to train and with a high parameter budget. In light of this, we explore parameter-sharing methods in Transformers with a specific focus on generative models. We perform an analysis of different parameter sharing/reduction methods and develop the Subformer. Our model combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). Experiments on machine translation, abstractive summarization and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters.


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Learning to Describe Editing Activities in Collaborative Environments: A Case Study on GitHub and Wikipedia
Edison Marrese-Taylor | Pablo Loyola | Jorge A. Balazs | Yutaka Matsuo
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation

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A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Edison Marrese-Taylor | Cristian Rodriguez | Jorge Balazs | Stephen Gould | Yutaka Matsuo
Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.

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VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling
Machel Reid | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way. To tackle this issue we propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition. We rely on variational inference for estimation and leverage contextualized word embeddings for improved performance. Our approach is evaluated on four existing challenging benchmarks with the addition of two new datasets, “Cambridge” and the first non-English corpus “Robert”, which we release to complement our empirical study. Our Variational Contextual Definition Modeler (VCDM) achieves state-of-the-art performance in terms of automatic and human evaluation metrics, demonstrating the effectiveness of our approach.


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An Edit-centric Approach for Wikipedia Article Quality Assessment
Edison Marrese-Taylor | Pablo Loyola | Yutaka Matsuo
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques. Our model consists of a main classifier equipped with an auxiliary generative module which, for a given edit, jointly provides an estimation of its quality and generates a description in natural language. We performed an empirical study to assess the feasibility of the proposed model and its cost-effectiveness in terms of data and quality requirements.

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Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study
Jorge Balazs | Yutaka Matsuo
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling characters improves the learned representations at the word and sentence levels, and that doing so is particularly useful when representing less frequent words. We further show that a feature-wise sigmoid gating mechanism is a robust method for creating representations that encode semantic similarity, as it performed reasonably well in several word similarity datasets. Finally, our findings suggest that properly capturing semantic similarity at the word level does not consistently yield improved performance in downstream sentence-level tasks.


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Learning to Automatically Generate Fill-In-The-Blank Quizzes
Edison Marrese-Taylor | Ai Nakajima | Yutaka Matsuo | Ono Yuichi
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each. We present an empirical study based on data obtained from a language learning platform showing that both of our proposed settings offer promising results.

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Deep contextualized word representations for detecting sarcasm and irony
Suzana Ilić | Edison Marrese-Taylor | Jorge Balazs | Yutaka Matsuo
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.

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IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Jorge Balazs | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2nd place out of 30 teams with a test macro F1 score of 0.710. The system is composed of a single pre-trained ELMo layer for encoding words, a Bidirectional Long-Short Memory Network BiLSTM for enriching word representations with context, a max-pooling operation for creating sentence representations from them, and a Dense Layer for projecting the sentence representations into label space. Our official submission was obtained by ensembling 6 of these models initialized with different random seeds. The code for replicating this paper is available at https://github.com/jabalazs/implicit_emotion.

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Content Aware Source Code Change Description Generation
Pablo Loyola | Edison Marrese-Taylor | Jorge Balazs | Yutaka Matsuo | Fumiko Satoh
Proceedings of the 11th International Conference on Natural Language Generation

We propose to study the generation of descriptions from source code changes by integrating the messages included on code commits and the intra-code documentation inside the source in the form of docstrings. Our hypothesis is that although both types of descriptions are not directly aligned in semantic terms —one explaining a change and the other the actual functionality of the code being modified— there could be certain common ground that is useful for the generation. To this end, we propose an architecture that uses the source code-docstring relationship to guide the description generation. We discuss the results of the approach comparing against a baseline based on a sequence-to-sequence model, using standard automatic natural language generation metrics as well as with a human study, thus offering a comprehensive view of the feasibility of the approach.

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IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
Edison Marrese-Taylor | Suzana Ilic | Jorge Balazs | Helmut Prendinger | Yutaka Matsuo
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-layered bidirectional LSTM, without using external features that provide additional semantic information. Although our model is able to outperform the baseline in the validation set, our results show limited generalization power over the test set. Given the limited size of the dataset, we think the usage of more pre-training schemes would greatly improve the obtained results.


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Extractive Summarization Using Multi-Task Learning with Document Classification
Masaru Isonuma | Toru Fujino | Junichiro Mori | Yutaka Matsuo | Ichiro Sakata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems.

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Replication issues in syntax-based aspect extraction for opinion mining
Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics

Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.

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Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
Edison Marrese-Taylor | Jorge Balazs | Yutaka Matsuo
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Video reviews are the natural evolution of written product reviews. In this paper we target this phenomenon and introduce the first dataset created from closed captions of YouTube product review videos as well as a new attention-RNN model for aspect extraction and joint aspect extraction and sentiment classification. Our model provides state-of-the-art performance on aspect extraction without requiring the usage of hand-crafted features on the SemEval ABSA corpus, while it outperforms the baseline on the joint task. In our dataset, the attention-RNN model outperforms the baseline for both tasks, but we observe important performance drops for all models in comparison to SemEval. These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and written text, which have been discussed extensively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.

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EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a representation learning approach based on inner attention on top of an RNN. Results show that our model offers good capabilities and is able to successfully identify emotion-bearing words to predict intensity without leveraging on lexicons, obtaining the 13t place among 22 shared task competitors.

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Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Jorge Balazs | Edison Marrese-Taylor | Pablo Loyola | Yutaka Matsuo
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refined representations of both sentences into a single vector to be used for classification. With this model we obtained test accuracies of 72.057% and 72.055% in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM). When using an ensemble both accuracies increased to 72.247% and 72.827% respectively.

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A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
Pablo Loyola | Edison Marrese-Taylor | Yutaka Matsuo
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.


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Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users
Rahul Kamath | Masanao Ochi | Yutaka Matsuo
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation


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Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web
Yulan Yan | Naoaki Okazaki | Yutaka Matsuo | Zhenglu Yang | Mitsuru Ishizuka
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web
Danushka Bollegala | Yutaka Matsuo | Mitsuru Ishizuka
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing


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A Co-occurrence Graph-based Approach for Personal Name Alias Extraction from Anchor Texts
Danushka Bollegala | Yutaka Matsuo | Mitsuru Ishizuka
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II


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An Integrated Approach to Measuring Semantic Similarity between Words Using Information Available on the Web
Danushka Bollegala | Yutaka Matsuo | Mitsuru Ishizuka
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Subtree Mining for Relation Extraction from Wikipedia
Dat P.T. Nguyen | Yutaka Matsuo | Mitsuru Ishizuka
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers


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Extracting Key Phrases to Disambiguate Personal Name Queries in Web Search
Danushka Bollegala | Yutaka Matsuo | Mitsuru Ishizuka
Proceedings of the Workshop on How Can Computational Linguistics Improve Information Retrieval?

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Graph-based Word Clustering using a Web Search Engine
Yutaka Matsuo | Takeshi Sakaki | Kôki Uchiyama | Mitsuru Ishizuka
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing


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Improving Chronological Sentence Ordering by Precedence Relation
Naoaki Okazaki | Yutaka Matsuo | Mitsuru Ishizuka
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics