Manabu Sassano


2017

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Predicting Causes of Reformulation in Intelligent Assistants
Shumpei Sano | Nobuhiro Kaji | Manabu Sassano
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.

2016

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Prediction of Prospective User Engagement with Intelligent Assistants
Shumpei Sano | Nobuhiro Kaji | Manabu Sassano
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Design of Word Association Games using Dialog Systems for Acquisition of Word Association Knowledge
Yuichiro Machida | Daisuke Kawahara | Sadao Kurohashi | Manabu Sassano
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

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Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System
Naoki Otani | Daisuke Kawahara | Sadao Kurohashi | Nobuhiro Kaji | Manabu Sassano
Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)

Commonsense knowledge is essential for fully understanding language in many situations. We acquire large-scale commonsense knowledge from humans using a game with a purpose (GWAP) developed on a smartphone spoken dialogue system. We transform the manual knowledge acquisition process into an enjoyable quiz game and have collected over 150,000 unique commonsense facts by gathering the data of more than 70,000 players over eight months. In this paper, we present a simple method for maintaining the quality of acquired knowledge and an empirical analysis of the knowledge acquisition process. To the best of our knowledge, this is the first work to collect large-scale knowledge via a GWAP on a widely-used spoken dialogue system.

2015

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Effects of Game on User Engagement with Spoken Dialogue System
Hayato Kobayashi | Kaori Tanio | Manabu Sassano
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

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Deterministic Word Segmentation Using Maximum Matching with Fully Lexicalized Rules
Manabu Sassano
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Rapid Development of a Corpus with Discourse Annotations using Two-stage Crowdsourcing
Daisuke Kawahara | Yuichiro Machida | Tomohide Shibata | Sadao Kurohashi | Hayato Kobayashi | Manabu Sassano
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2010

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Using Smaller Constituents Rather Than Sentences in Active Learning for Japanese Dependency Parsing
Manabu Sassano | Sadao Kurohashi
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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A Unified Single Scan Algorithm for Japanese Base Phrase Chunking and Dependency Parsing
Manabu Sassano | Sadao Kurohashi
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Learning Semantic Categories from Clickthrough Logs
Mamoru Komachi | Shimpei Makimoto | Kei Uchiumi | Manabu Sassano
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2008

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An Experimental Comparison of the Voted Perceptron and Support Vector Machines in Japanese Analysis Tasks
Manabu Sassano
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

2005

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Using a Partially Annotated Corpus to Build a Dependency Parser for Japanese
Manabu Sassano
Second International Joint Conference on Natural Language Processing: Full Papers

2004

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Linear-Time Dependency Analysis for Japanese
Manabu Sassano
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Virtual Examples for Text Classification with Support Vector Machines
Manabu Sassano
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

2002

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Combining Outputs of Multiple Japanese Named Entity Chunkers by Stacking
Takehito Utsuro | Manabu Sassano | Kiyotaka Uchimoto
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

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Learning with Multiple Stacking for Named Entity Recognition
Koji Tsukamoto | Yutaka Mitsuishi | Manabu Sassano
COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002)

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An Empirical Study of Active Learning with Support Vector Machines forJapanese Word Segmentation
Manabu Sassano
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

2000

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Minimally Supervised Japanese Named Entity Recognition: Resources and Evaluation
Takehito Utsuro | Manabu Sassano
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Named Entity Chunking Techniques in Supervised Learning for Japanese Named Entity Recognition
Manabu Sassano | Takehito Utsuro
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics