Koichiro Yoshino


2020

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Reflection-based Word Attribute Transfer
Yoichi Ishibashi | Katsuhito Sudoh | Koichiro Yoshino | Satoshi Nakamura
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Word embeddings, which often represent such analogic relations as king - man + woman queen, can be used to change a word’s attribute, including its gender. 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. However, developing such knowledge is very costly for words and attributes. In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation. Experimental results show that our proposed method can transfer the word attributes of the given words without changing the words that do not have the target attributes.

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Emotional Speech Corpus for Persuasive Dialogue System
Sara Asai | Koichiro Yoshino | Seitaro Shinagawa | Sakriani Sakti | Satoshi Nakamura
Proceedings of the 12th Language Resources and Evaluation Conference

Expressing emotion is known as an efficient way to persuade one’s dialogue partner to accept one’s claim or proposal. Emotional expression in speech can express the speaker’s emotion more directly than using only emotion expression in the text, which will lead to a more persuasive dialogue. In this paper, we built a speech dialogue corpus in a persuasive scenario that uses emotional expressions to build a persuasive dialogue system with emotional expressions. We extended an existing text dialogue corpus by adding variations of emotional responses to cover different combinations of broad dialogue context and a variety of emotional states by crowd-sourcing. Then, we recorded emotional speech consisting of of collected emotional expressions spoken by a voice actor. The experimental results indicate that the collected emotional expressions with their speeches have higher emotional expressiveness for expressing the system’s emotion to users.

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Improving Spoken Language Understanding by Wisdom of Crowds
Koichiro Yoshino | Kana Ikeuchi | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the 28th International Conference on Computational Linguistics

Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task. The lack of training data is an important problem, especially for new system tasks, because existing SLU systems are based on statistical approaches. In this paper, we proposed to use two sources of the “wisdom of crowds,” crowdsourcing and knowledge community website, for improving the SLU system. We firstly collected paraphrasing variations for new system tasks through crowdsourcing as seed data, and then augmented them using similar questions from a knowledge community website. We investigated the effects of the proposed data augmentation method in SLU task, even with small seed data. In particular, the proposed architecture augmented more than 120,000 samples to improve SLU accuracies.

2019

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Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
Shohei Tanaka | Koichiro Yoshino | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the First Workshop on NLP for Conversational AI

We propose a novel method for selecting coherent and diverse responses for a given dialogue context. The proposed method re-ranks response candidates generated from conversational models by using event causality relations between events in a dialogue history and response candidates (e.g., “be stressed out” precedes “relieve stress”). We use distributed event representation based on the Role Factored Tensor Model for a robust matching of event causality relations due to limited event causality knowledge of the system. Experimental results showed that the proposed method improved coherency and dialogue continuity of system responses.

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Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Satoshi Nakamura | Milica Gasic | Ingrid Zuckerman | Gabriel Skantze | Mikio Nakano | Alexandros Papangelis | Stefan Ultes | Koichiro Yoshino
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

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Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label-aware Objective
Seiya Kawano | Koichiro Yoshino | Satoshi Nakamura
Proceedings of the 12th International Conference on Natural Language Generation

Building a controllable neural conversation model (NCM) is an important task. In this paper, we focus on controlling the responses of NCMs by using dialogue act labels of responses as conditions. 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. This change strongly encourages the generation of label-conditioned sentences. We compared the proposed method with some existing methods for generating conditional responses. The experimental results show that our proposed method has higher controllability for dialogue acts even though it has higher or comparable naturalness to existing methods.

2018

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Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing
Koichiro Yoshino | Yoko Ishikawa | Masahiro Mizukami | Yu Suzuki | Sakriani Sakti | Satoshi Nakamura
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Japanese Dialogue Corpus of Information Navigation and Attentive Listening Annotated with Extended ISO-24617-2 Dialogue Act Tags
Koichiro Yoshino | Hiroki Tanaka | Kyoshiro Sugiyama | Makoto Kondo | Satoshi Nakamura
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System
Nurul Lubis | Sakriani Sakti | Koichiro Yoshino | Satoshi Nakamura
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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. Standard neural dialogue system approaches still fall short in this situation as they tend to generate only short, generic responses. Learning from expert actions is critical, as these potentially differ from standard dialogue acts. In this paper, we propose using a hierarchical neural network for response generation that is conditioned on 1) expert’s action, 2) dialogue context, and 3) user emotion, encoded from user input. We construct a corpus of interactions between a counselor and 30 participants following a negative emotional exposure to learn expert actions and responses in a positive emotion elicitation scenario. Instead of relying on the expensive, labor intensive, and often ambiguous human annotations, we unsupervisedly cluster the expert’s responses and use the resulting labels to train the network. Our experiments and evaluation show that the proposed approach yields lower perplexity and generates a larger variety of responses.

2017

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Neural Machine Translation via Binary Code Prediction
Yusuke Oda | Philip Arthur | Graham Neubig | Koichiro Yoshino | Satoshi Nakamura
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English-Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10.

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Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems
Louisa Pragst | Koichiro Yoshino | Wolfgang Minker | Satoshi Nakamura | Stefan Ultes
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In a dialogue system, the dialogue manager selects one of several system actions and thereby determines the system’s behaviour. Defining all possible system actions in a dialogue system by hand is a tedious work. While efforts have been made to automatically generate such system actions, those approaches are mostly focused on providing functional system behaviour. Adapting the system behaviour to the user becomes a difficult task due to the limited amount of system actions available. We aim to increase the adaptability of a dialogue system by automatically generating variants of system actions. In this work, we introduce an approach to automatically generate action variants for elaborateness and indirectness. Our proposed algorithm extracts RDF triplets from a knowledge base and rates their relevance to the original system action to find suitable content. We show that the results of our algorithm are mostly perceived similarly to human generated elaborateness and indirectness and can be used to adapt a conversation to the current user and situation. We also discuss where the results of our algorithm are still lacking and how this could be improved: Taking into account the conversation topic as well as the culture of the user is likely to have beneficial effect on the user’s perception.

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An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Makoto Morishita | Yusuke Oda | Graham Neubig | Koichiro Yoshino | Katsuhito Sudoh | Satoshi Nakamura
Proceedings of the First Workshop on Neural Machine Translation

Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sentences in a mini-batch to be equal in length to the longest sentence therein for efficient computation. Previous work has noted that sorting the corpus based on the sentence length before making mini-batches reduces the amount of padding and increases the processing speed. However, despite the fact that mini-batch creation is an essential step in NMT training, widely used NMT toolkits implement disparate strategies for doing so, which have not been empirically validated or compared. This work investigates mini-batch creation strategies with experiments over two different datasets. Our results suggest that the choice of a mini-batch creation strategy has a large effect on NMT training and some length-based sorting strategies do not always work well compared with simple shuffling.

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Information Navigation System with Discovering User Interests
Koichiro Yoshino | Yu Suzuki | Satoshi Nakamura
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

We demonstrate an information navigation system for sightseeing domains that has a dialogue interface for discovering user interests for tourist activities. The system discovers interests of a user with focus detection on user utterances, and proactively presents related information to the discovered user interest. A partially observable Markov decision process (POMDP)-based dialogue manager, which is extended with user focus states, controls the behavior of the system to provide information with several dialogue acts for providing information. We transferred the belief-update function and the policy of the manager from other system trained on a different domain to show the generality of defined dialogue acts for our information navigation system.

2016

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Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition
Nurul Lubis | Randy Gomez | Sakriani Sakti | Keisuke Nakamura | Koichiro Yoshino | Satoshi Nakamura | Kazuhiro Nakadai
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Emotional aspects play a vital role in making human communication a rich and dynamic experience. As we introduce more automated system in our daily lives, it becomes increasingly important to incorporate emotion to provide as natural an interaction as possible. To achieve said incorporation, rich sets of labeled emotional data is prerequisite. However, in Japanese, existing emotion database is still limited to unimodal and bimodal corpora. Since emotion is not only expressed through speech, but also visually at the same time, it is essential to include multiple modalities in an observation. In this paper, we present the first audio-visual emotion corpora in Japanese, collected from 14 native speakers. The corpus contains 100 minutes of annotated and transcribed material. We performed preliminary emotion recognition experiments on the corpus and achieved an accuracy of 61.42% for five classes of emotion.

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Parallel Speech Corpora of Japanese Dialects
Koichiro Yoshino | Naoki Hirayama | Shinsuke Mori | Fumihiko Takahashi | Katsutoshi Itoyama | Hiroshi G. Okuno
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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Cultural Communication Idiosyncrasies in Human-Computer Interaction
Juliana Miehle | Koichiro Yoshino | Louisa Pragst | Stefan Ultes | Satoshi Nakamura | Wolfgang Minker
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Analyzing the Effect of Entrainment on Dialogue Acts
Masahiro Mizukami | Koichiro Yoshino | Graham Neubig | David Traum | Satoshi Nakamura
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2015

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An Investigation of Machine Translation Evaluation Metrics in Cross-lingual Question Answering
Kyoshiro Sugiyama | Masahiro Mizukami | Graham Neubig | Koichiro Yoshino | Sakriani Sakti | Tomoki Toda | Satoshi Nakamura
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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Information Navigation System Based on POMDP that Tracks User Focus
Koichiro Yoshino | Tatsuya Kawahara
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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FlowGraph2Text: Automatic Sentence Skeleton Compilation for Procedural Text Generation
Shinsuke Mori | Hirokuni Maeta | Tetsuro Sasada | Koichiro Yoshino | Atsushi Hashimoto | Takuya Funatomi | Yoko Yamakata
Proceedings of the 8th International Natural Language Generation Conference (INLG)

2013

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Predicate Argument Structure Analysis using Partially Annotated Corpora
Koichiro Yoshino | Shinsuke Mori | Tatsuya Kawahara
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Statistical Dialogue Management using Intention Dependency Graph
Koichiro Yoshino | Shinji Watanabe | Jonathan Le Roux | John R. Hershey
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Language Modeling for Spoken Dialogue System based on Filtering using Predicate-Argument Structures
Koichiro Yoshino | Shinsuke Mori | Tatsuya Kawahara
Proceedings of COLING 2012

2011

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Spoken Dialogue System based on Information Extraction using Similarity of Predicate Argument Structures
Koichiro Yoshino | Shinsuke Mori | Tatsuya Kawahara
Proceedings of the SIGDIAL 2011 Conference