Search Results for author: Koichiro Yoshino

Found 33 papers, 5 papers with code

Whats New? Identifying the Unfolding of New Events in Narratives

no code implementations15 Feb 2023 Seyed Mahed Mousavi, Shohei Tanaka, Gabriel Roccabruna, Koichiro Yoshino, Satoshi Nakamura, Giuseppe Riccardi

We publish the annotated dataset, annotation materials, and machine learning baseline models for the task of new event extraction for narrative understanding.

Event Extraction

ARTA: Collection and Classification of Ambiguous Requests and Thoughtful Actions

1 code implementation SIGDIAL (ACL) 2021 Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, Satoshi Nakamura

In order to train the classification model on such training data, we applied the positive/unlabeled (PU) learning method, which assumes that only a part of the data is labeled with positive examples.


Improving Spoken Language Understanding by Wisdom of Crowds

no code implementations COLING 2020 Koichiro Yoshino, Kana Ikeuchi, Katsuhito Sudoh, Satoshi Nakamura

Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task.

Data Augmentation Spoken Language Understanding

Reflection-based Word Attribute Transfer

2 code implementations ACL 2020 Yoichi Ishibashi, Katsuhito Sudoh, Koichiro Yoshino, Satoshi Nakamura

For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male.

Word Attribute Transfer Word Embeddings

Emotional Speech Corpus for Persuasive Dialogue System

no code implementations LREC 2020 Sara Asai, Koichiro Yoshino, Seitaro Shinagawa, Sakriani Sakti, Satoshi Nakamura

Expressing emotion is known as an efficient way to persuade one{'}s dialogue partner to accept one{'}s claim or proposal.

Caption Generation of Robot Behaviors based on Unsupervised Learning of Action Segments

no code implementations23 Mar 2020 Koichiro Yoshino, Kohei Wakimoto, Yuta Nishimura, Satoshi Nakamura

Two reasons make it challenging to apply existing sequence-to-sequence models to this mapping: 1) it is hard to prepare a large-scale dataset for any kind of robots and their environment, and 2) there is a gap between the number of samples obtained from robot action observations and generated word sequences of captions.


Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label-aware Objective

no code implementations WS 2019 Seiya Kawano, Koichiro Yoshino, Satoshi Nakamura

We introduce an adversarial learning framework for the task of generating conditional responses with a new objective to a discriminator, which explicitly distinguishes sentences by using labels.

An Incremental Turn-Taking Model For Task-Oriented Dialog Systems

2 code implementations28 May 2019 Andrei C. Coman, Koichiro Yoshino, Yukitoshi Murase, Satoshi Nakamura, Giuseppe Riccardi

To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario.

dialog state tracking

Dialog System Technology Challenge 7

no code implementations11 Jan 2019 Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros Polymenakos, Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan, Xiang Gao, Huda Alamari, Tim K. Marks, Devi Parikh, Dhruv Batra

This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.

Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System

no code implementations26 Nov 2018 Hisao Katsumi, Takuya Hiraoka, Koichiro Yoshino, Kazeto Yamamoto, Shota Motoura, Kunihiko Sadamasa, Satoshi Nakamura

It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems often do not have enough of such information in realistic situations.

Another Diversity-Promoting Objective Function for Neural Dialogue Generation

1 code implementation20 Nov 2018 Ryo Nakamura, Katsuhito Sudoh, Koichiro Yoshino, Satoshi Nakamura

Although generation-based dialogue systems have been widely researched, the response generations by most existing systems have very low diversities.

Dialogue Generation

Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System

no code implementations WS 2018 Nurul Lubis, Sakriani Sakti, Koichiro Yoshino, Satoshi Nakamura

Positive emotion elicitation seeks to improve user{'}s emotional state through dialogue system interaction, where a chat-based scenario is layered with an implicit goal to address user{'}s emotional needs.

Emotion Recognition Goal-Oriented Dialogue Systems +1

Interactive Image Manipulation with Natural Language Instruction Commands

no code implementations23 Feb 2018 Seitaro Shinagawa, Koichiro Yoshino, Sakriani Sakti, Yu Suzuki, Satoshi Nakamura

We propose an interactive image-manipulation system with natural language instruction, which can generate a target image from a source image and an instruction that describes the difference between the source and the target image.

Image Generation Image Manipulation

Information Navigation System with Discovering User Interests

no code implementations WS 2017 Koichiro Yoshino, Yu Suzuki, Satoshi Nakamura

We demonstrate an information navigation system for sightseeing domains that has a dialogue interface for discovering user interests for tourist activities.

Semantic Textual Similarity Speech Recognition

Analysis of the Effect of Dependency Information on Predicate-Argument Structure Analysis and Zero Anaphora Resolution

no code implementations31 May 2017 Koichiro Yoshino, Shinsuke Mori, Satoshi Nakamura

This paper investigates and analyzes the effect of dependency information on predicate-argument structure analysis (PASA) and zero anaphora resolution (ZAR) for Japanese, and shows that a straightforward approach of PASA and ZAR works effectively even if dependency information was not available.

Dependency Parsing POS

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