Tsutomu Hirao


2021

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Improving Neural RST Parsing Model with Silver Agreement Subtrees
Naoki Kobayashi | Tsutomu Hirao | Hidetaka Kamigaito | Manabu Okumura | Masaaki Nagata
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.

2020

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Sequential Span Classification with Neural Semi-Markov CRFs for Biomedical Abstracts
Kosuke Yamada | Tsutomu Hirao | Ryohei Sasano | Koichi Takeda | Masaaki Nagata
Findings of the Association for Computational Linguistics: EMNLP 2020

Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers’ information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score.

2019

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NTT’s Machine Translation Systems for WMT19 Robustness Task
Soichiro Murakami | Makoto Morishita | Tsutomu Hirao | Masaaki Nagata
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes NTT’s submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news. Our submission combined techniques including utilization of a synthetic corpus, domain adaptation, and a placeholder mechanism, which significantly improved over the previous baseline. Experimental results revealed the placeholder mechanism, which temporarily replaces the non-standard tokens including emojis and emoticons with special placeholder tokens during translation, improves translation accuracy even with noisy texts.

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Split or Merge: Which is Better for Unsupervised RST Parsing?
Naoki Kobayashi | Tsutomu Hirao | Kengo Nakamura | Hidetaka Kamigaito | Manabu Okumura | Masaaki Nagata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Rhetorical Structure Theory (RST) parsing is crucial for many downstream NLP tasks that require a discourse structure for a text. Most of the previous RST parsers have been based on supervised learning approaches. That is, they require an annotated corpus of sufficient size and quality, and heavily rely on the language and domain dependent corpus. In this paper, we present two language-independent unsupervised RST parsing methods based on dynamic programming. The first one builds the optimal tree in terms of a dissimilarity score function that is defined for splitting a text span into smaller ones. The second builds the optimal tree in terms of a similarity score function that is defined for merging two adjacent spans into a large one. Experimental results on English and German RST treebanks showed that our parser based on span merging achieved the best score, around 0.8 F1 score, which is close to the scores of the previous supervised parsers.

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Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints
Masaaki Nishino | Sho Takase | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, “Trims cash” is an anagram of “Christmas”. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.

2018

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Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries
Tsutomu Hirao | Hidetaka Kamigaito | Masaaki Nagata
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper tackles automation of the pyramid method, a reliable manual evaluation framework. To construct a pyramid, we transform human-made reference summaries into extractive reference summaries that consist of Elementary Discourse Units (EDUs) obtained from source documents and then weight every EDU by counting the number of extractive reference summaries that contain the EDU. A summary is scored by the correspondences between EDUs in the summary and those in the pyramid. Experiments on DUC and TAC data sets show that our methods strongly correlate with various manual evaluations.

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Higher-Order Syntactic Attention Network for Longer Sentence Compression
Hidetaka Kamigaito | Katsuhiko Hayashi | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

A sentence compression method using LSTM can generate fluent compressed sentences. However, the performance of this method is significantly degraded when compressing longer sentences since it does not explicitly handle syntactic features. To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states. Furthermore, to avoid the influence of incorrect parse results, we trained HiSAN by maximizing jointly the probability of a correct output with the attention distribution. Experimental results on Google sentence compression dataset showed that our method achieved the best performance on F1 as well as ROUGE-1,2 and L scores, 83.2, 82.9, 75.8 and 82.7, respectively. In human evaluation, our methods also outperformed baseline methods in both readability and informativeness.

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Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function
Shinsaku Sakaue | Tsutomu Hirao | Masaaki Nishino | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Submodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular objective functions, the efficiency of the greedy algorithm, and the provable performance guarantee. However, when it comes to compressive summarization, we are currently missing a counterpart of the extractive method based on submodularity. In this paper, we propose a fast greedy method for compressive summarization. Our method is applicable to any monotone submodular objective function, including many functions well-suited for document summarization. We provide an approximation guarantee of our greedy algorithm. Experiments show that our method is about 100 to 400 times faster than an existing method based on integer-linear-programming (ILP) formulations and that our method empirically achieves more than 95%-approximation.

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Pruning Basic Elements for Better Automatic Evaluation of Summaries
Ukyo Honda | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.

2017

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Enumeration of Extractive Oracle Summaries
Tsutomu Hirao | Masaaki Nishino | Jun Suzuki | Masaaki Nagata
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.

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Supervised Attention for Sequence-to-Sequence Constituency Parsing
Hidetaka Kamigaito | Katsuhiko Hayashi | Tsutomu Hirao | Hiroya Takamura | Manabu Okumura | Masaaki Nagata
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT). Recently, MT performances were improved by incorporating supervised attention into the model. In this paper, we introduce supervised attention to constituency parsing that can be regarded as another translation task. Evaluation results on the PTB corpus showed that the bracketing F-measure was improved by supervised attention.

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Oracle Summaries of Compressive Summarization
Tsutomu Hirao | Masaaki Nishino | Masaaki Nagata
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper derives an Integer Linear Programming (ILP) formulation to obtain an oracle summary of the compressive summarization paradigm in terms of ROUGE. The oracle summary is essential to reveal the upper bound performance of the paradigm. Experimental results on the DUC dataset showed that ROUGE scores of compressive oracles are significantly higher than those of extractive oracles and state-of-the-art summarization systems. These results reveal that compressive summarization is a promising paradigm and encourage us to continue with the research to produce informative summaries.

2016

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Exploring Text Links for Coherent Multi-Document Summarization
Xun Wang | Masaaki Nishino | Tsutomu Hirao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.

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Neural Headline Generation on Abstract Meaning Representation
Sho Takase | Jun Suzuki | Naoaki Okazaki | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Empirical comparison of dependency conversions for RST discourse trees
Katsuhiko Hayashi | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2015

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Hybrid Approach to PDTB-styled Discourse Parsing for CoNLL-2015
Yasuhisa Yoshida | Katsuhiko Hayashi | Tsutomu Hirao | Masaaki Nagata
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization
Masaaki Nishino | Norihito Yasuda | Tsutomu Hirao | Shin-ichi Minato | Masaaki Nagata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Dependency-based Automatic Enumeration of Semantically Equivalent Word Orders for Evaluating Japanese Translations
Hideki Isozaki | Natsume Kouchi | Tsutomu Hirao
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Dependency-based Discourse Parser for Single-Document Summarization
Yasuhisa Yoshida | Jun Suzuki | Tsutomu Hirao | Masaaki Nagata
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Single Document Summarization based on Nested Tree Structure
Yuta Kikuchi | Tsutomu Hirao | Hiroya Takamura | Manabu Okumura | Masaaki Nagata
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model
Hitoshi Nishikawa | Kazuho Arita | Katsumi Tanaka | Tsutomu Hirao | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Single-Document Summarization as a Tree Knapsack Problem
Tsutomu Hirao | Yasuhisa Yoshida | Masaaki Nishino | Norihito Yasuda | Masaaki Nagata
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Latent Semantic Matching: Application to Cross-language Text Categorization without Alignment Information
Tsutomu Hirao | Tomoharu Iwata | Masaaki Nagata
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Text Summarization Model based on Redundancy-Constrained Knapsack Problem
Hitoshi Nishikawa | Tsutomu Hirao | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2012: Posters

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Sentence Compression with Semantic Role Constraints
Katsumasa Yoshikawa | Ryu Iida | Tsutomu Hirao | Manabu Okumura
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2010

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Divide and Translate: Improving Long Distance Reordering in Statistical Machine Translation
Katsuhito Sudoh | Kevin Duh | Hajime Tsukada | Tsutomu Hirao | Masaaki Nagata
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Automatic Evaluation of Translation Quality for Distant Language Pairs
Hideki Isozaki | Tsutomu Hirao | Kevin Duh | Katsuhito Sudoh | Hajime Tsukada
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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A Syntax-Free Approach to Japanese Sentence Compression
Tsutomu Hirao | Jun Suzuki | Hideki Isozaki
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

2005

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Kernel-based Approach for Automatic Evaluation of Natural Language Generation Technologies: Application to Automatic Summarization
Tsutomu Hirao | Manabu Okumura | Hideki Isozaki
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Evaluation Measures Considering Sentence Concatenation for Automatic Summarization by Sentence or Word Extraction
Chiori Hori | Tsutomu Hirao | Hideki Isozaki
Text Summarization Branches Out

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A Deterministic Word Dependency Analyzer Enhanced With Preference Learning
Hideki Isozaki | Hideto Kazawa | Tsutomu Hirao
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Dependency-based Sentence Alignment for Multiple Document Summarization
Tsutomu Hirao | Jun Suzuki | Hideki Isozaki | Eisaku Maeda
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Corpus and Evaluation Measures for Multiple Document Summarization with Multiple Sources
Tsutomu Hirao | Takahiro Fukusima | Manabu Okumura | Chikashi Nobata | Hidetsugu Nanba
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data
Jun Suzuki | Tsutomu Hirao | Yutaka Sasaki | Eisaku Maeda
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Japanese Zero Pronoun Resolution based on Ranking Rules and Machine Learning
Hideki Isozaki | Tsutomu Hirao
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

2002

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Extracting Important Sentences with Support Vector Machines
Tsutomu Hirao | Hideki Isozaki | Eisaku Maeda | Yuji Matsumoto
COLING 2002: The 19th International Conference on Computational Linguistics