Seiichi Yamamoto


2017

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Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders
Tsuneo Kato | Atsushi Nagai | Naoki Noda | Ryosuke Sumitomo | Jianming Wu | Seiichi Yamamoto
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonlinear operation on the vectors of child nodes, the operation is considered to be different intrinsically depending on types of child nodes. To relax the difference, a data-driven untying of autoencoders (AEs) is proposed. The experimental result of the utterance intent classification showed an improved accuracy with the proposed method compared with the basic tied RAE and untied RAE based on a manual rule.

2016

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Joining-in-type Humanoid Robot Assisted Language Learning System
AlBara Khalifa | Tsuneo Kato | Seiichi Yamamoto
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Dialogue robots are attractive to people, and in language learning systems, they motivate learners and let them practice conversational skills in more realistic environment. However, automatic speech recognition (ASR) of the second language (L2) learners is still a challenge, because their speech contains not just pronouncing, lexical, grammatical errors, but is sometimes totally disordered. Hence, we propose a novel robot assisted language learning (RALL) system using two robots, one as a teacher and the other as an advanced learner. The system is designed to simulate multiparty conversation, expecting implicit learning and enhancement of predictability of learners’ utterance through an alignment similar to “interactive alignment”, which is observed in human-human conversation. We collected a database with the prototypes, and measured how much the alignment phenomenon observed in the database with initial analysis.

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Quantitative Analysis of Gazes and Grounding Acts in L1 and L2 Conversations
Ichiro Umata | Koki Ijuin | Mitsuru Ishida | Moe Takeuchi | Seiichi Yamamoto
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The listener’s gazing activities during utterances were analyzed in a face-to-face three-party conversation setting. The function of each utterance was categorized according to the Grounding Acts defined by Traum (Traum, 1994) so that gazes during utterances could be analyzed from the viewpoint of grounding in communication (Clark, 1996). Quantitative analysis showed that the listeners were gazing at the speakers more in the second language (L2) conversation than in the native language (L1) conversation during the utterances that added new pieces of information, suggesting that they are using visual information to compensate for their lack of linguistic proficiency in L2 conversation.

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What topic do you want to hear about? A bilingual talking robot using English and Japanese Wikipedias
Graham Wilcock | Kristiina Jokinen | Seiichi Yamamoto
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We demonstrate a bilingual robot application, WikiTalk, that can talk fluently in both English and Japanese about almost any topic using information from English and Japanese Wikipedias. The English version of the system has been demonstrated previously, but we now present a live demo with a Nao robot that speaks English and Japanese and switches language on request. The robot supports the verbal interaction with face-tracking, nodding and communicative gesturing. One of the key features of the WikiTalk system is that the robot can switch from the current topic to related topics during the interaction in order to navigate around Wikipedia following the user’s individual interests.

2014

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Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems
Xiaoyun Wang | Jinsong Zhang | Masafumi Nishida | Seiichi Yamamoto
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL)systems. We designed a reduced phoneme set consisting of classified phonemes more aligned with the learners’ speech characteristics than the canonical set of a target language. This reduced phoneme set provides an inherently more appropriate model for dealing with mispronunciation by second language speakers. In this study, we used a phonetic decision tree (PDT)-based top-down sequential splitting method to generate the reduced phoneme set and then applied this method to a translation-game type English CALL system for Japanese to determine its effectiveness. Experimental results showed that the proposed method improves the performance of recognizing non-native speech.

2013

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Inducing Romanization Systems
Keiko Taguchi | Andrew Finch | Seiichi Yamamoto | Eiichiro Sumita
Proceedings of Machine Translation Summit XIV: Papers

2012

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Multimodal Corpus of Multi-party Conversations in Second Language
Shota Yamasaki | Hirohisa Furukawa | Masafumi Nishida | Kristiina Jokinen | Seiichi Yamamoto
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We developed a dialogue-based tutoring system for teaching English to Japanese students and plan to transfer the current software tutoring agent into an embodied robot in the hope that the robot will enrich conversation by allowing more natural interactions in small group learning situations. To enable smooth communication between an intelligent agent and the user, the agent must have realistic models on when to take turns, when to interrupt, and how to catch the partner's attention. For developing the realistic models applicable for computer assisted language learning systems, we also need to consider the differences between the mother tongue and second language that affect communication style. We collected a multimodal corpus of multi-party conversations in English as the second language to investigate the differences in communication styles. We describe our multimodal corpus and explore features of communication style e.g. filled pauses, and non-verbal information, such as eye-gaze, which show different characteristics between the mother tongue and second language.

2011

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Using Features from a Bilingual Alignment Model in Transliteration Mining
Takaaki Fukunishi | Andrew Finch | Seiichi Yamamoto | Eiichiro Sumita
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

2010

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Hierarchical Phrase-based Machine Translation with Word-based Reordering Model
Katsuhiko Hayashi | Hajime Tsukada | Katsuhito Sudoh | Kevin Duh | Seiichi Yamamoto
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2008

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Creation of Learner Corpus and Its Application to Speech Recognition
Hiroki Yamazaki | Keisuke Kitamura | Takashi Harada | Seiichi Yamamoto
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Some big languages like English are spoken by a lot of people whose mother tongues are different from. Their second languages often have not only distinct accent but also different lexical and syntactic characteristics. Speech recognition performance is severely affected when the lexical, syntactic, or semantic characteristics in the training and recognition tasks differ. Language model of a speech recognition system is usually trained with transcribed speech data or text data collected in English native countries, therefore, speech recognition performance is expected to be degraded by mismatch of lexical and syntactic characteristics between native speakers and second language speakers as well as the distinction between their accents. The aim of language model adaptation is to exploit specific, albeit limited, knowledge about the recognition task to compensate for mismatch of the lexical, syntactic, or semantic characteristics. This paper describes whether the language model adaptation is effective for compensating for the mismatch between the lexical, syntactic, or semantic characteristics of native speakers and second language speakers.

2005

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A Machine Learning Approach to Hypotheses Selection of Greedy Decoding for SMT
Michael Paul | Eiichiro Sumita | Seiichi Yamamoto
Workshop on example-based machine translation

This paper proposes a method for integrating example-based and rule-based machine translation systems with statistical methods. It extends a greedy decoder for statistical machine translation (SMT), which searches for an optimal translation by using SMT models starting from a decoder seed, i.e., the source language input paired with an initial translation hypothesis. In order to reduce local optima problems inherent in the search, the outputs generated by multiple translation engines, such as rule-based (RBMT) and example-based (EBMT) systems, are utilized as the initial translation hypotheses. This method outperforms conventional greedy decoding approaches using initial translation hypotheses based on translation examples retrieved from a parallel text corpus. However, the decoding of multiple initial translation hypotheses is computationally expensive. This paper proposes a method to select a single initial translation hypothesis before decoding based on a machine learning approach that judges the appropriateness of multiple initial translation hypotheses and selects the most confident one for decoding. Our approach is evaluated for the translation of dialogues in the travel domain, and the results show that it drastically reduces computational costs without a loss in translation quality.

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Measuring Non-native Speakers’ Proficiency of English by Using a Test with Automatically-Generated Fill-in-the-Blank Questions
Eiichiro Sumita | Fumiaki Sugaya | Seiichi Yamamoto
Proceedings of the Second Workshop on Building Educational Applications Using NLP

2004

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Automatic Measuring of English Language Proficiency using MT Evaluation Technology
Keiji Yasuda | Fumiaki Sugaya | Eiichiro Sumita | Toshiyuki Takezawa | Genichiro Kikui | Seiichi Yamamoto
Proceedings of the Workshop on eLearning for Computational Linguistics and Computational Linguistics for eLearning

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Example-based Rescoring of Statistical Machine Translation Output
Michael Paul | Eiichiro Sumita | Seiichi Yamamoto
Proceedings of HLT-NAACL 2004: Short Papers

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Incremental Methods to Select Test Sentences for Evaluating Translation Ability
Yasuhiro Akiba | Eiichiro Sumita | Hiromi Nakaiwa | Seiichi Yamamoto | Hiroshi G. Okuno
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Using a Mixture of N-Best Lists from Multiple MT Systems in Rank-Sum-Based Confidence Measure for MT Outputs
Yasuhiro Akiba | Eiichiro Sumita | Hiromi Nakaiwa | Seiichi Yamamoto | Hiroshi G. Okuno
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Experimental comparison of MT evaluation methods: RED vs.BLEU
Yasuhiro Akiba | Eiichiro Sumita | Hiromi Nakaiwa | Seiichi Yamamoto | Hiroshi G. Okuno
Proceedings of Machine Translation Summit IX: Papers

This paper experimentally compares two automatic evaluators, RED and BLEU, to determine how close the evaluation results of each automatic evaluator are to average evaluation results by human evaluators, following the ATR standard of MT evaluation. This paper gives several cautionary remarks intended to prevent MT developers from drawing misleading conclusions when using the automatic evaluators. In addition, this paper reports a way of using the automatic evaluators so that their results agree with those of human evaluators.

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Applications of Automatic Evaluation Methods to Measuring a Capability of Speech Translation System
Keiji Yasuda | Fumiaki Sugaya | Toshiyuki Takezawa | Seiichi Yamamoto | Masuzo Yanagida
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Automatic machine translation selection scheme to output the best result
Keiji Yasuda | Fumiaki Sugaya | Toshiyuki Takezawa | Seiichi Yamamoto | Masuzo Yanagida
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Toward a Broad-coverage Bilingual Corpus for Speech Translation of Travel Conversations in the Real World
Toshiyuki Takezawa | Eiichiro Sumita | Fumiaki Sugaya | Hirofumi Yamamoto | Seiichi Yamamoto
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Proposal of a very-large-corpus acquisition method by cell-formed registration
Fumiaki Suyaga | Toshiyuki Takezawa | Genichiro Kikui | Seiichi Yamamoto
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Quality-Sensitive Test Set Selection for a Speech Translation System
Fumiaki Sugaya | Keiji Yasuda | Toshiyuki Takezawa | Seiichi Yamamoto
Proceedings of the ACL-02 Workshop on Speech-to-Speech Translation: Algorithms and Systems

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Corpus-based Generation of Numeral Classifier using Phrase Alignment
Michael Paul | Eiichiro Sumita | Seiichi Yamamoto
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Precise measurement method of a speech translation system’s capability with a paired comparison method between the system and humans
Fumiaki Sugaya | Keiji Yasuda | Toshiyuki Takezawa | Seiichi Yamamoto
Proceedings of Machine Translation Summit VIII

The main goal of the present paper is to propose new schemes for the overall evaluation of a speech translation system. These schemes are expected to support and improve the design of the target application system, and precisely determine its performance. Experiments are conducted on the Japanese-to-English speech translation system ATR-MATRIX, which was developed at ATR Interpreting Telecommunications Research Laboratories. In the proposed schemes, the system’s translations are compared with those of a native Japanese taking the Test of English for International Communication (TOEIC), which is used as a measure of one’s speech translation capability. Subjective and automatic comparisons are made and the results are compared. A regression analysis on the subjective results shows that the speech translation capability of ATR-MATRIX matches a Japanese person scoring around 500 on the TOEIC. The automatic comparisons also show promising results.

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An automatic evaluation method of translation quality using translation answer candidates queried from a parallel corpus
Keiji Yasuda | Fumiaki Sugaya | Toshiyuki Takezawa | Seiichi Yamamoto | Masuzo Yanagida
Proceedings of Machine Translation Summit VIII

An automatic translation quality evaluation method is proposed. In the proposed method, a parallel corpus is used to query translation answer candidates. The translation output is evaluated by measuring the similarity between the translation output and translation answer candidates with DP matching. This method evaluates a language translation subsystem of the Japanese-to-English ATR-MATRIX speech translation system developed at ATR Interpreting Telecommunications Research Laboratories. Discriminant analysis is then carried out to examine the evaluation performance of the proposed method. Experimental results show the effectiveness of the proposed method. The discriminant ratio is 83.5% for 2-class discrimination between absolutely correct and less appropriate translations classified subjectively. Also discussed are issues of the proposed method when it is applied to speech translation systems which inevitably make recognition errors.

1999

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A new evaluation method for speech translation systems and a case study on ATR-MATRIX from Japanese to English
Toshiyuki Takezawa | Fumiaki Sugaya | Akio Yokoo | Seiichi Yamamoto
Proceedings of Machine Translation Summit VII

ATR-MATRIX is a multi-lingual speech-to-speech translation system designed to facilitate communications between two parties of different languages engaged in a spontaneous conversation in a travel arrangement domain. In this paper, we propose a new evaluation method for speech translation systems. Our current focus is on measuring the robustness of a language translation sub-system, with quick calculation and low cost. Therefore, we calculate the difference between the translation output from transcription texts and the translation output from input speech by a dynamic programming method. We present the first trial experiment of this method applied to our Japanese-to-English speech translation system. We also provide related discussions on such points as error analysis and the relationship between the proposed method and translation quality evaluation manually done by humans.