Papers by Ryuichiro Higashinaka
Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs (2020.lrec-1)
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Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata
| Challenge: | Existing approaches to realize consistent personalities require expensive data collection. |
| Approach: | They propose to collect question-answer pairs for particular characters from online users . meta information such as emotion and intimacy was also collected . |
| Outcome: | The proposed method can be used to train neural conversational models with high quality questions and meta information. |
Optimal Summaries for Enabling a Smooth Handover in Chat-Oriented Dialogue (2022.aacl-srw)
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| Challenge: | In dialogue systems, it is difficult to provide fully autonomous dialogue . to ensure a good dialogue experience, human operators sometimes need to intervene . |
| Approach: | They conducted large-scale experiments on chat dialogues to determine which type of summary is most useful for handover . abstractive summary plus one utterance immediately before handover and extractive summary consisting of five utterrances immediately before the handover were found to be the most useful . |
| Outcome: | The best summaries were abstractive summary plus one utterance before handover and extractive summary consisting of five utterrances before hand over. |
Dialogue Collection for Recording the Process of Building Common Ground in a Collaborative Task (2022.lrec-1)
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| Challenge: | Existing studies on the process of building common ground have not been well conducted. |
| Approach: | They propose a method for recording the process of building common ground through a dialogue by using the intermediate result of a task. |
| Outcome: | The proposed method can record the building common ground process by using the intermediate result of a task and can be estimated quite accurately. |
Creating Large-Scale Argumentation Structures for Dialogue Systems (L18-1)
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Kazuki Sakai, Akari Inago, Ryuichiro Higashinaka, Yuichiro Yoshikawa, Hiroshi Ishiguro, Junji Tomita
| Challenge: | Argumentation is a process of reaching consensus through premises and rebuttals and is important for making decisions and exchanging views. |
| Approach: | They propose to create argumentation structures in ten languages using argumentation databases . they also examine differences between the two languages to determine their effectiveness . |
| Outcome: | The proposed arguments can be applied to argumentative dialogue systems and can be used as training data. |
Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue (2022.lrec-1)
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| Challenge: | Despite advances in deep learning, dialogue systems struggle to achieve fully autonomous transactions with users. |
| Approach: | They conducted an experiment in which two operators switched periodically while performing chat, consultation, and sales tasks in dialogue. |
| Outcome: | The results show that adjacency pairs are useful for recording conversation history . key-value pairs are also useful when there are underlying tasks, such as consultation and sales . |
Analysis of Dialogue in Human-Human Collaboration in Minecraft (2022.lrec-1)
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| Challenge: | Recent studies have focused on developing dialogue systems that enable collaborative work, but few studies have centered on creative collaborative work. |
| Approach: | They collected 500 dialogues of human-human collaboration in Minecraft as a basis for developing a dialogue system that enables creative collaborative work. |
| Outcome: | The proposed system can be used to create a collaborative garden in Minecraft and collect text chats, action logs, and subjective evaluations. |
I Remember You!: SUI Corpus for Remembering and Utilizing Users’ Information in Chat-oriented Dialogue Systems (2024.lrec-main)
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| Challenge: | Existing methods for remembering and utilizing information on users in system utterances do not always fit the context of the dialogue. |
| Approach: | They propose to use user information to fill in utterance templates but the utterrances do not always fit the context. |
| Outcome: | The proposed system can remember and utilize user information on users in dialogues while keeping appropriateness for the context. |
Investigating person-specific errors in chat-oriented dialogue systems (2022.acl-short)
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| Challenge: | Errors in general chatbots and chatbot that follow a rough persona have been studied . but those in chatbot based on real people have not been thoroughly investigated . |
| Approach: | They analyze dialogue data of a generation-based chatbot trained from dialogue data . they find errors in attributes and relations can be divided into two levels: self and other . |
| Outcome: | The results show that errors in chatbots can be divided into two types . the correspondence with an existing taxonomy of errors was also investigated . |
Predicting Nods by using Dialogue Acts in Dialogue (L18-1)
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| Challenge: | Existing studies have generated nods from the final morphemes at the end of an utterance. |
| Approach: | They propose to generate head nods from Japanese dialogues using morphemes . they compile a corpus of 24 dialogues including utterance and nod information . |
| Outcome: | The proposed model outperforms a model using morpheme information in the Japanese language and shows that dialog acts can predict nods. |
Combining Argumentation Structure and Language Model for Generating Natural Argumentative Dialogue (2022.aacl-short)
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| Challenge: | Argumentative dialogue is important process where speakers discuss a specific theme for consensus building or decision making. |
| Approach: | They propose a method to generate argumentative dialogues by combining argumentation structure and language model. |
| Outcome: | The proposed method significantly improves the naturalness of arguments without losing consistency. |
Collection and Analysis of Travel Agency Task Dialogues with Age-Diverse Speakers (2022.lrec-1)
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| Challenge: | Using deep neural networks, task-oriented dialogue systems can be used to generate an appropriate response to users' inputs. |
| Approach: | They collected a multimodal dialogue corpus with a wide range of speaker ages and set up a dialogue task based on travel . results suggest adult speakers have more independent opinions, older speakers express opinions more frequently compared with other age groups, and operators expressed a smile more frequently to minor speakers. |
| Outcome: | The results show that adult speakers have more independent opinions, the older speakers express their opinions more frequently compared with other age groups, and the operators expressed a smile more frequently to the minor speakers. |
Enhancing Task-oriented Dialogue Systems with Generative Post-processing Networks (2023.emnlp-main)
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| Challenge: | Recent work proposes a method to optimize pipelined dialogue systems by fine-tuning modules directly. |
| Approach: | They propose a new post-processing component for natural language generation (NLG) they use dialogue act contribution to evaluate contribution of GenPPN-generated utterances . |
| Outcome: | The proposed method improves the performance of task-oriented dialogue systems by modifying arbitrary modules including non-differentiable ones. |
Investigating the Impact of Incremental Processing and Voice Activity Projection on Spoken Dialogue Systems (2025.coling-main)
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| Challenge: | a large language model (LLM) is gaining attention for its ability to model human-like turn-taking in human conversations. |
| Approach: | They developed a turn-taking model that can be trained in unsupervised manner using spoken dialogue data between two speakers. |
| Outcome: | The proposed model can be trained in unsupervised manner using spoken dialogue data between two speakers. |
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset (2024.lrec-main)
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| Challenge: | Existing datasets for task-oriented dialogue systems in English are limited compared to Japanese. |
| Approach: | They evaluated the dialogue state tracking and response generation capabilities of Japanese language datasets using multi-domain task-oriented dialogues. |
| Outcome: | The proposed dataset provides a benchmark that is on par with MultiWOZ2.2 and the latest large language model (LLM)-based methods. |
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning (2022.coling-1)
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| Challenge: | In task-oriented dialogue systems, the role of the natural language generation component is to convert a system's intentions, called dialogue acts (DAs), into natural language utterances and to convey DAs accurately to users. |
| Approach: | They propose a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning that incorporates a natural language understanding module into the objective function of RL. |
| Outcome: | The proposed method generates adaptive utterances against speech recognition errors and the different vocabulary levels of users in a multi-world task-oriented dialogue system. |
Dialogue Corpus Construction Considering Modality and Social Relationships in Building Common Ground (2022.lrec-1)
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| Challenge: | Several studies have examined the process of building common ground in text chat, but none have investigated the process in depth. |
| Approach: | They constructed a dialogue corpus to investigate the process of building common ground with a particular focus on the modality of dialogue and the social relationship between workers. |
| Outcome: | The results suggest that adding the modality or developing the relationship between workers speeds up the building of common ground. |
A Speculative and Tentative Common Ground Handling for Efficient Composition of Uncertain Dialogue (2022.lrec-1)
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| Challenge: | a study explores how the grounding process is composed and adapts to human cognitive processes . common ground is a set of information shared among participants that serves as a precondition for understanding individual utterances . |
| Approach: | a study investigates how the grounding process is composed by participants . it suggests that common ground may not necessarily be formed bottom-up through analytic expressions . |
| Outcome: | a new approach to human-like dialogue may be more suitable for natural human communication, the authors say . they show that common ground is mutually accepted among participants through holistic expressions . |
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling (C18-1)
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| Challenge: | Existing studies on multitask and multilingual joint learning focus on cross-task or cross-lingual knowledge transfer. |
| Approach: | They propose to divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and different languages. |
| Outcome: | The proposed method is able to support multi-task and multi-lingual learning using Japanese and English data sets with three different lexical utterance classification tasks. |
Collecting and Analyzing Dialogues in a Tagline Co-Writing Task (2024.lrec-main)
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| Challenge: | Currently, most studies on dialogue systems focus on problemsolving dialogues and relatively little research has been done on systems that can engage in creative collaboration with users. |
| Approach: | They designed a tagline co-writing task in which two people collaborate to create taglines via text chat and collected dialogue logs, editing logs and questionnaire results. |
| Outcome: | The proposed task involved a tagline co-writing task in which two people collaborate to create taglines via text chat, and collected dialogue logs, editing logs and questionnaire results. |
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (D18-1)
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| Challenge: | In multi-task and multi-lingual joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. |
| Approach: | They propose to introduce language-specific adversarial networks and task-specific language adversarials to purge the task or language dependencies of shared networks. |
| Outcome: | The proposed method is demonstrated using Japanese and English data sets for three different utterance intent classification tasks. |