Kotaro Funakoshi


2021

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Generating Weather Comments from Meteorological Simulations
Soichiro Murakami | Sora Tanaka | Masatsugu Hangyo | Hidetaka Kamigaito | Kotaro Funakoshi | Hiroya Takamura | Manabu Okumura
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.

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Towards Table-to-Text Generation with Numerical Reasoning
Lya Hulliyyatus Suadaa | Hidetaka Kamigaito | Kotaro Funakoshi | Manabu Okumura | Hiroya Takamura
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent neural text generation models have shown significant improvement in generating descriptive text from structured data such as table formats. One of the remaining important challenges is generating more analytical descriptions that can be inferred from facts in a data source. The use of a template-based generator and a pointer-generator is among the potential alternatives for table-to-text generators. In this paper, we propose a framework consisting of a pre-trained model and a copy mechanism. The pre-trained models are fine-tuned to produce fluent text that is enriched with numerical reasoning. However, it still lacks fidelity to the table contents. The copy mechanism is incorporated in the fine-tuning step by using general placeholders to avoid producing hallucinated phrases that are not supported by a table while preserving high fluency. In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.

2018

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A POS Tagging Model Adapted to Learner English
Ryo Nagata | Tomoya Mizumoto | Yuta Kikuchi | Yoshifumi Kawasaki | Kotaro Funakoshi
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

There has been very limited work on the adaptation of Part-Of-Speech (POS) tagging to learner English despite the fact that POS tagging is widely used in related tasks. In this paper, we explore how we can adapt POS tagging to learner English efficiently and effectively. Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this. Considering the previous findings and the discussion, we introduce the design of our model based on bidirectional Long Short-Term Memory. In addition, we describe how to adapt it to a wide variety of native languages (potentially, hundreds of them). In the evaluation section, we empirically show that it is effective for POS tagging in learner English, achieving an accuracy of 0.964, which significantly outperforms the state-of-the-art POS-tagger. We further investigate the tagging results in detail, revealing which part of the model design does or does not improve the performance.

2016

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Nonparametric Bayesian Models for Spoken Language Understanding
Kei Wakabayashi | Johane Takeuchi | Kotaro Funakoshi | Mikio Nakano
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics
Ryuichiro Higashinaka | Kotaro Funakoshi | Yuka Kobayashi | Michimasa Inaba
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Dialogue breakdown detection is a promising technique in dialogue systems. To promote the research and development of such a technique, we organized a dialogue breakdown detection challenge where the task is to detect a system’s inappropriate utterances that lead to dialogue breakdowns in chat. This paper describes the design, datasets, and evaluation metrics for the challenge as well as the methods and results of the submitted runs of the participants.

2015

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Fatal or not? Finding errors that lead to dialogue breakdowns in chat-oriented dialogue systems
Ryuichiro Higashinaka | Masahiro Mizukami | Kotaro Funakoshi | Masahiro Araki | Hiroshi Tsukahara | Yuka Kobayashi
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Towards Taxonomy of Errors in Chat-oriented Dialogue Systems
Ryuichiro Higashinaka | Kotaro Funakoshi | Masahiro Araki | Hiroshi Tsukahara | Yuka Kobayashi | Masahiro Mizukami
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2013

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A Robotic Agent in a Virtual Environment that Performs Situated Incremental Understanding of Navigational Utterances
Takashi Yamauchi | Mikio Nakano | Kotaro Funakoshi
Proceedings of the SIGDIAL 2013 Conference

2012

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A Unified Probabilistic Approach to Referring Expressions
Kotaro Funakoshi | Mikio Nakano | Takenobu Tokunaga | Ryu Iida
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2011

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A Two-Stage Domain Selection Framework for Extensible Multi-Domain Spoken Dialogue Systems
Mikio Nakano | Shun Sato | Kazunori Komatani | Kyoko Matsuyama | Kotaro Funakoshi | Hiroshi G. Okuno
Proceedings of the SIGDIAL 2011 Conference

2010

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Non-humanlike Spoken Dialogue: A Design Perspective
Kotaro Funakoshi | Mikio Nakano | Kazuki Kobayashi | Takanori Komatsu | Seiji Yamada
Proceedings of the SIGDIAL 2010 Conference

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Automatic Allocation of Training Data for Rapid Prototyping of Speech Understanding based on Multiple Model Combination
Kazunori Komatani | Masaki Katsumaru | Mikio Nakano | Kotaro Funakoshi | Tetsuya Ogata | Hiroshi G. Okuno
Coling 2010: Posters

2009

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A Speech Understanding Framework that Uses Multiple Language Models and Multiple Understanding Models
Masaki Katsumaru | Mikio Nakano | Kazunori Komatani | Kotaro Funakoshi | Tetsuya Ogata | Hiroshi G. Okuno
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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A Probabilistic Model of Referring Expressions for Complex Objects
Kotaro Funakoshi | Philipp Spanger | Mikio Nakano | Takenobu Tokunaga
Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009)

2008

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A Framework for Building Conversational Agents Based on a Multi-Expert Model
Mikio Nakano | Kotaro Funakoshi | Yuji Hasegawa | Hiroshi Tsujino
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue

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Rapid Prototyping of Robust Language Understanding Modules for Spoken Dialogue Systems
Yuichiro Fukubayashi | Kazunori Komatani | Mikio Nakano | Kotaro Funakoshi | Hiroshi Tsujino | Tetsuya Ogata | Hiroshi G. Okuno
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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Analysis of User Reactions to Turn-Taking Failures in Spoken Dialogue Systems
Mikio Nakano | Yuka Nagano | Kotaro Funakoshi | Toshihiko Ito | Kenji Araki | Yuji Hasegawa | Hiroshi Tsujino
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

2006

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Group-Based Generation of Referring Expressions
Kotaro Funakoshi | Satoru Watanabe | Takenobu Tokunaga
Proceedings of the Fourth International Natural Language Generation Conference

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Identifying Repair Targets in Action Control Dialogue
Kotaro Funakoshi | Takenobu Tokunaga
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Controlling Animated Agents in Natural Language
Kotaro Funakoshi | Takenobu Tokugana
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

2004

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Generation of Relative Referring Expressions based on Perceptual Grouping
Kotaro Funakoshi | Satoru Watanabe | Naoko Kuriyama | Takenobu Tokunaga
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2002

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Processing Japanese Self-correction in Speech Dialog Systems
Kotaro Funakoshi | Takenobu Tokunaga | Hozumi Tanaka
COLING 2002: The 19th International Conference on Computational Linguistics