Kuniko Saito


2023

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DueT: Image-Text Contrastive Transfer Learning with Dual-adapter Tuning
Taku Hasegawa | Kyosuke Nishida | Koki Maeda | Kuniko Saito
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This paper presents DueT, a novel transfer learning method for vision and language models built by contrastive learning. In DueT, adapters are inserted into the image and text encoders, which have been initialized using models pre-trained on uni-modal corpora and then frozen. By training only these adapters, DueT enables efficient learning with a reduced number of trainable parameters. Moreover, unlike traditional adapters, those in DueT are equipped with a gating mechanism, enabling effective transfer and connection of knowledge acquired from pre-trained uni-modal encoders while preventing catastrophic forgetting. We report that DueT outperformed simple fine-tuning, the conventional method fixing only the image encoder and training only the text encoder, and the LoRA-based adapter method in accuracy and parameter efficiency for 0-shot image and text retrieval in both English and Japanese domains.

2022

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Combining Argumentation Structure and Language Model for Generating Natural Argumentative Dialogue
Koh Mitsuda | Ryuichiro Higashinaka | Kuniko Saito
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Argumentative dialogue is an important process where speakers discuss a specific theme for consensus building or decision making. In previous studies for generating consistent argumentative dialogue, retrieval-based methods with hand-crafted argumentation structures have been used. In this study, we propose a method to generate natural argumentative dialogues by combining an argumentation structure and language model. We trained the language model to rewrite a proposition of an argumentation structure on the basis of its information, such as keywords and stance, into the next utterance while considering its context, and we used the model to rewrite propositions in the argumentation structure. We manually evaluated the generated dialogues and found that the proposed method significantly improved the naturalness of dialogues without losing consistency of argumentation.

2017

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Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Itsumi Saito | Kyosuke Nishida | Kugatsu Sadamitsu | Kuniko Saito | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalization pairs into Japanese morphological analysis. The experimental results show that our method can extract widely covered variants from large Twitter data and improve the recall of normalization without degrading the overall accuracy of Japanese morphological analysis.

2012

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Entity Set Expansion using Interactive Topic Information
Kugatsu Sadamitsu | Kuniko Saito | Kenji Imamura | Yoshihiro Matsuo
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation

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Creating an Extended Named Entity Dictionary from Wikipedia
Ryuichiro Higashinaka | Kugatsu Sadamitsu | Kuniko Saito | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2012

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Constructing a Class-Based Lexical Dictionary using Interactive Topic Models
Kugatsu Sadamitsu | Kuniko Saito | Kenji Imamura | Yoshihiro Matsuo
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper proposes a new method of constructing arbitrary class-based related word dictionaries on interactive topic models; we assume that each class is described by a topic. We propose a new semi-supervised method that uses the simplest topic model yielded by the standard EM algorithm; model calculation is very rapid. Furthermore our approach allows a dictionary to be modified interactively and the final dictionary has a hierarchical structure. This paper makes three contributions. First, it proposes a word-based semi-supervised topic model. Second, we apply the semi-supervised topic model to interactive learning; this approach is called the Interactive Topic Model. Third, we propose a score function; it extracts the related words that occupy the middle layer of the hierarchical structure. Experiments show that our method can appropriately retrieve the words belonging to an arbitrary class.

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Grammar Error Correction Using Pseudo-Error Sentences and Domain Adaptation
Kenji Imamura | Kuniko Saito | Kugatsu Sadamitsu | Hitoshi Nishikawa
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Entity Set Expansion using Topic information
Kugatsu Sadamitsu | Kuniko Saito | Kenji Imamura | Genichiro Kikui
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2009

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Tag Confidence Measure for Semi-Automatically Updating Named Entity Recognition
Kuniko Saito | Kenji Imamura
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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Discriminative Approach to Predicate-Argument Structure Analysis with Zero-Anaphora Resolution
Kenji Imamura | Kuniko Saito | Tomoko Izumi
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2006

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A Clustered Global Phrase Reordering Model for Statistical Machine Translation
Masaaki Nagata | Kuniko Saito | Kazuhide Yamamoto | Kazuteru Ohashi
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2005

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Portable Translator Capable of Recognizing Characters on Signboard and Menu Captured by its Built-in Camera
Hideharu Nakajima | Yoshihiro Matsuo | Masaaki Nagata | Kuniko Saito
Proceedings of the ACL Interactive Poster and Demonstration Sessions

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NUT-NTT Statistical Machine Translation System for IWSLT 2005
Kazuteru Ohashi | Kazuhide Yamamoto | Kuniko Saito | Masaaki Nagata
Proceedings of the Second International Workshop on Spoken Language Translation

2003

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Multi-Language Named-Entity Recognition System based on HMM
Kuniko Saito | Masaaki Nagata
Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition