Kazunori Komatani


2020

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User Impressions of Questions to Acquire Lexical Knowledge
Kazunori Komatani | Mikio Nakano
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

For the acquisition of knowledge through dialogues, it is crucial for systems to ask questions that do not diminish the user’s willingness to talk, i.e., that do not degrade the user’s impression. This paper reports the results of our analysis on how user impression changes depending on the types of questions to acquire lexical knowledge, that is, explicit and implicit questions, and the correctness of the content of the questions. We also analyzed how sequences of the same type of questions affect user impression. User impression scores were collected from 104 participants recruited via crowdsourcing and then regression analysis was conducted. The results demonstrate that implicit questions give a good impression when their content is correct, but a bad impression otherwise. We also found that consecutive explicit questions are more annoying than implicit ones when the content of the questions is correct. Our findings reveal helpful insights for creating a strategy to avoid user impression deterioration during knowledge acquisition.

2018

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Collection of Multimodal Dialog Data and Analysis of the Result of Annotation of Users’ Interest Level
Masahiro Araki | Sayaka Tomimasu | Mikio Nakano | Kazunori Komatani | Shogo Okada | Shinya Fujie | Hiroaki Sugiyama
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Kazunori Komatani | Diane Litman | Kai Yu | Alex Papangelis | Lawrence Cavedon | Mikio Nakano
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

2017

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Unsupervised Segmentation of Phoneme Sequences based on Pitman-Yor Semi-Markov Model using Phoneme Length Context
Ryu Takeda | Kazunori Komatani
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Unsupervised segmentation of phoneme sequences is an essential process to obtain unknown words during spoken dialogues. In this segmentation, an input phoneme sequence without delimiters is converted into segmented sub-sequences corresponding to words. The Pitman-Yor semi-Markov model (PYSMM) is promising for this problem, but its performance degrades when it is applied to phoneme-level word segmentation. This is because of insufficient cues for the segmentation, e.g., homophones are improperly treated as single entries and their different contexts are also confused. We propose a phoneme-length context model for PYSMM to give a helpful cue at the phoneme-level and to predict succeeding segments more accurately. Our experiments showed that the peak performance with our context model outperformed those without such a context model by 0.045 at most in terms of F-measures of estimated segmentation.

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Lexical Acquisition through Implicit Confirmations over Multiple Dialogues
Kohei Ono | Ryu Takeda | Eric Nichols | Mikio Nakano | Kazunori Komatani
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

We address the problem of acquiring the ontological categories of unknown terms through implicit confirmation in dialogues. We develop an approach that makes implicit confirmation requests with an unknown term’s predicted category. Our approach does not degrade user experience with repetitive explicit confirmations, but the system has difficulty determining if information in the confirmation request can be correctly acquired. To overcome this challenge, we propose a method for determining whether or not the predicted category is correct, which is included in an implicit confirmation request. Our method exploits multiple user responses to implicit confirmation requests containing the same ontological category. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly predicted categories than when only single user responses were considered.

2016

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Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words
Ryu Takeda | Kazunori Komatani
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper describes a Bayesian language model for predicting spontaneous utterances. People sometimes say unexpected words, such as fillers or hesitations, that cause the miss-prediction of words in normal N-gram models. Our proposed model considers mixtures of possible segmental contexts, that is, a kind of context-word selection. It can reduce negative effects caused by unexpected words because it represents conditional occurrence probabilities of a word as weighted mixtures of possible segmental contexts. The tuning of mixture weights is the key issue in this approach as the segment patterns becomes numerous, thus we resolve it by using Bayesian model. The generative process is achieved by combining the stick-breaking process and the process used in the variable order Pitman-Yor language model. Experimental evaluations revealed that our model outperformed contiguous N-gram models in terms of perplexity for noisy text including hesitations.

2015

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User Adaptive Restoration for Incorrectly-Segmented Utterances in Spoken Dialogue Systems
Kazunori Komatani | Naoki Hotta | Satoshi Sato | Mikio Nakano
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2013

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Generating More Specific Questions for Acquiring Attributes of Unknown Concepts from Users
Tsugumi Otsuka | Kazunori Komatani | Satoshi Sato | Mikio Nakano
Proceedings of the SIGDIAL 2013 Conference

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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Online Error Detection of Barge-In Utterances by Using Individual Users’ Utterance Histories in Spoken Dialogue System
Kazunori Komatani | Hiroshi G. Okuno
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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Ranking Help Message Candidates Based on Robust Grammar Verification Results and Utterance History in Spoken Dialogue Systems
Kazunori Komatani | Satoshi Ikeda | Yuichiro Fukubayashi | Tetsuya Ogata | Hiroshi Okuno
Proceedings of the SIGDIAL 2009 Conference

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Predicting Barge-in Utterance Errors by using Implicitly-Supervised ASR Accuracy and Barge-in Rate per User
Kazunori Komatani | Alexander I. Rudnicky
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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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

2008

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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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Introducing Utterance Verification in Spoken Dialogue System to Improve Dynamic Help Generation for Novice Users
Kazunori Komatani | Yuichiro Fukubayashi | Tetsuya Ogata | Hiroshi G. Okuno
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

2006

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Multi-Domain Spoken Dialogue System with Extensibility and Robustness against Speech Recognition Errors
Kazunori Komatani | Naoyuki Kanda | Mikio Nakano | Kazuhiro Nakadai | Hiroshi Tsujino | Tetsuya Ogata | Hiroshi G. Okuno
Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue

2005

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Empirical Verification of Meaning-Game-based Generalization of Centering Theory with Large Japanese Corpus
Shun Shiramatsu | Kazunori Komatani | Takashi Miyata | Koichi Hashida | Hiroshi Okuno
Proceedings of the 19th Pacific Asia Conference on Language, Information and Computation

2004

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Efficient Confirmation Strategy for Large-scale Text Retrieval Systems with Spoken Dialogue Interface
Kazunori Komatani | Teruhisa Misu | Tatsuya Kawahara | Hiroshi G. Okuno
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Flexible Spoken Dialogue System based on User Models and Dynamic Generation of VoiceXML Scripts
Kazunori Komatani | Fumihiro Adachi | Shinichi Ueno | Tatsuya Kawahara | Hiroshi G. Okuno
Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue

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Flexible Guidance Generation Using User Model in Spoken Dialogue Systems
Kazunori Komatani | Shinichi Ueno | Tatsuya Kawahara | Hiroshi G. Okuno
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Dialog Navigator : A Spoken Dialog Q-A System based on Large Text Knowledge Base
Yoji Kiyota | Sadao Kurohashi | Teruhisa Misu | Kazunori Komatani | Tatsuya Kawahara | Fuyuko Kido
The Companion Volume to the Proceedings of 41st Annual Meeting of the Association for Computational Linguistics

2002

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Efficient Dialogue Strategy to Find Users’ Intended Items from Information Query Results
Kazunori Komatani | Tatsuya Kawahara | Ryosuke Ito | Hiroshi G. Okuno
COLING 2002: The 19th International Conference on Computational Linguistics

2000

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Flexible Mixed-Initiative Dialogue Management using Concept-Level Confidence Measures of Speech Recognizer Output
Kazunori Komatani | Tatsuya Kawahara
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics