Hiroaki Saito


2024

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Deep Reinforcement Learning with Hierarchical Action Exploration for Dialogue Generation
Itsugun Cho | Ryota Takahashi | Yusaku Yanase | Hiroaki Saito
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Traditionally, approximate dynamic programming is employed in dialogue generation with greedy policy improvement through action sampling, as the natural language action space is vast. However, this practice is inefficient for reinforcement learning (RL) due to the sparsity of eligible responses with high action values, which leads to weak improvement sustained by random sampling. This paper presents theoretical analysis and experiments that reveal the performance of the dialogue policy is positively correlated with the sampling size. To overcome this limitation, we introduce a novel dual-granularity Q-function that explores the most promising response category to intervene in the sampling process. Our approach extracts actions based on a grained hierarchy, thereby achieving the optimum with fewer policy iterations. Additionally, we use offline RL and learn from multiple reward functions designed to capture emotional nuances in human interactions. Empirical studies demonstrate that our algorithm outperforms baselines across automatic metrics and human evaluations. Further testing reveals that our algorithm exhibits both explainability and controllability, as well as generates responses with higher expected rewards.

2022

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A Personalized Dialogue Generator with Implicit User Persona Detection
Itsugun Cho | Dongyang Wang | Ryota Takahashi | Hiroaki Saito
Proceedings of the 29th International Conference on Computational Linguistics

Current works in the generation of personalized dialogue primarily contribute to the agent presenting a consistent personality and driving a more informative response. However, we found that the generated responses from most previous models tend to be self-centered, with little care for the user in the dialogue. Moreover, we consider that human-like conversation is essentially built based on inferring information about the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting an implicit user persona. Because it is hard to collect a large number of detailed personas for each user, we attempted to model the user’s potential persona and its representation from dialogue history, with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people being aware of each other’s persona and producing a corresponding expression in conversation. Finally, posterior-discriminated regularization was presented to enhance the training procedure. Empirical studies demonstrate that, compared to state-of-the-art methods, our approach is more concerned with the user’s persona and achieves a considerable boost across both automatic metrics and human evaluations.

2010

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A Hybrid Approach for Functional Expression Identification in a Japanese Reading Assistant
Gregory Hazelbeck | Hiroaki Saito
Proceedings of the 2010 Workshop on Multiword Expressions: from Theory to Applications

2008

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The Japanese FrameNet Software Tools
Hiroaki Saito | Shunta Kuboya | Takaaki Sone | Hayato Tagami | Kyoko Ohara
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes an ongoing project “Japanese FrameNet (JFN)”, a corpus-based lexicon of Japanese in the FrameNet style. This paper focuses on the set of software tools tailored for the JFN annotation process. As the first step in the annotation, annotators select target sentences from the JFN corpus using the JFN kwic search tool, where they can specify cooccurring words and/or the part of speech of collocates. Our search tool is capable of displaying the parsed tree of a target sentence and its neigbouring sentences. The JFN corpus mainly consists of balanced and copyright-free “Japanese Corpus” which is being built as a national project. After the sentence to be annotated is chosen, the annotator labels syntactic and semantic tags to the appropriate phrases in the sentence. This work is performed on an annotation platform called JFNDesktop, in which the functions of labeling assist and consistency checking of annotations are available. Preliminary evaluation of our platform shows such functions accelerate the annotation process.

2004

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A New E-Learning Paradigm through Annotating Operations
Hiroaki Saito | Kyoko Ohara | Kengo Sato | Kazunari Ito | Hiroyuki Okamoto
Proceedings of the Workshop on eLearning for Computational Linguistics and Computational Linguistics for eLearning

2003

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Preferential Presentation of Japanese Near-synonyms using Definition Statements
Hiroyuki Okamoto | Kengo Sato | Hiroaki Saito
Proceedings of the Second International Workshop on Paraphrasing

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An Annotation Tool for Multimodal Dialogue Corpora using Global Document Annotation
Kazunari Ito | Hiroaki Saito
Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue

2002

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Extracting Word Sequence Correspondences with Support Vector Machines
Kengo Sato | Hiroaki Saito
COLING 2002: The 19th International Conference on Computational Linguistics

2000

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Automatic Semantic Sequence Extraction from Unrestricted Non-Tagged Texts
Shiho Nobesawa | Hiroaki Saito | Masakazu Nakanishi
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

1994

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An Efficient Parser Generator for Natural Language
Masayuki Ishii | Kazuhisa Ohta | Hiroaki Saito
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics

1992

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Interactive Speech Understanding
Hiroaki Saito
COLING 1992 Volume 3: The 14th International Conference on Computational Linguistics

1990

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Bi-directional LR Parsing from an Anchor Word for Speech Recognition
Hiroaki Saito
COLING 1990 Volume 3: Papers presented to the 13th International Conference on Computational Linguistics

1989

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Parsing Continuous Speech by HMM-LR Method
Kenji Kita | Takeshi Kawabata | Hiroaki Saito
Proceedings of the First International Workshop on Parsing Technologies

This paper describes a speech parsing method called HMM-LR. In HMM-LR, an LR parsing table is used to predict phones in speech input, and the system drives an HMM-based speech recognizer directly without any intervening structures such as a phone lattice. Very accurate, efficient speech parsing is achieved through the integrated processes of speech recognition and language analysis. The HMM-LR m ethod is applied to large-vocabulary speaker-dependent Japanese phrase recognition. The recognition rate is 87.1% for the top candidates and 97.7% for the five best candidates.

1988

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The Universal Parser Compiler and its application to a speech translation system
Masaru Tomita | Marion Kee | Hiroaki Saito | Teruko Mitamura | Hideto Tomabechi
Proceedings of the Second Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

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Parsing Noisy Sentences
Hiroaki Saito | Masaru Tomita
Coling Budapest 1988 Volume 2: International Conference on Computational Linguistics