Kyong-Ho Lee


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Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation
Donghyun Kim | Youbin Ahn | Wongyu Kim | Chanhee Lee | Kyungchan Lee | Kyong-Ho Lee | Jeonguk Kim | Donghoon Shin | Yeonsoo Lee
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Generating diverse and consistent responses is the ultimate goal of a persona-based dialogue. Although many studies have been conducted, the generated responses tend to be generic and bland due to the personas’ limited descriptiveness. Therefore, it is necessary to expand the given personas for more attractive responses. However, indiscriminate expansion of personas threaten the consistency of responses and therefore reduce the interlocutor’s interest in conversation. To alleviate this issue, we propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. To do so, we define consistency criteria to avoid possible contradictions among personas as follows: 1) Intra-Consistency and 2) Inter-Consistency. Then, we construct a silver profile dataset to deliver the ability to conform with the consistency criteria to the expansion model. Finally, we propose a persona expansion model with an encoder-decoder structure, which considers the relatedness and consistency among personas. Our experiments on the Persona-Chat dataset demonstrate the superiority of the proposed framework.

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CLICK: Contrastive Learning for Injecting Contextual Knowledge to Conversational Recommender System
Hyeongjun Yang | Heesoo Won | Youbin Ahn | Kyong-Ho Lee
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Conversational recommender systems (CRSs) capture a user preference through a conversation. However, the existing CRSs lack capturing comprehensive user preferences. This is because the items mentioned in a conversation are mainly regarded as a user preference. Thus, they have limitations in identifying a user preference from a dialogue context expressed without preferred items. Inspired by the characteristic of an online recommendation community where participants identify a context of a recommendation request and then comment with appropriate items, we exploit the Reddit data. Specifically, we propose a Contrastive Learning approach for Injecting Contextual Knowledge (CLICK) from the Reddit data to the CRS task, which facilitates the capture of a context-level user preference from a dialogue context, regardless of the existence of preferred item-entities. Moreover, we devise a relevance-enhanced contrastive learning loss to consider the fine-grained reflection of multiple recommendable items. We further develop a response generation module to generate a persuasive rationale for a recommendation. Extensive experiments on the benchmark CRS dataset show the effectiveness of CLICK, achieving significant improvements over state-of-the-art methods.

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Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue
Donghyun Kim | Youbin Ahn | Chanhee Lee | Wongyu Kim | Kyong-Ho Lee | Donghoon Shin | Yeonsoo Lee
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

A persona-grounded dialogue model aims to improve the quality of responses to promote user engagement. However, because the given personas are mostly short and limited to only a few informative words, it is challenging to utilize them to generate diverse responses. To tackle this problem, we propose a novel persona expansion framework, Concept-based Persona eXpansion (CPX). CPX takes the original persona as input and generates expanded personas that contain conceptually rich content. We constitute CPX with two task modules: 1) Concept Extractor and 2) Sentence Generator. To train these modules, we exploit the duality of two tasks with a commonsense dataset consisting of a concept set and the corresponding sentences which contain the given concepts. Extensive experiments on persona expansion and response generation show that our work sufficiently contributes to improving the quality of responses in diversity and richness.


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Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances
Wongyu Kim | Youbin Ahn | Donghyun Kim | Kyong-Ho Lee
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail to perceive the transitions because they extract features for the context at the coarse-grained level. To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention. Also, we introduce a response generation strategy to help focus on emotion and keywords related to appropriate features when generating responses. Experimental results show that our approach outperforms baselines and especially, achieves significant improvements on multi-turn dialogues.


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Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information
Byungkook Oh | Seungmin Seo | Cheolheon Shin | Eunju Jo | Kyong-Ho Lee
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering. Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation. Thus, TGCM can improve global dependencies among sentences and preserve relatively informative paragraph-level semantics. Moreover, to predict the next sentence, we capture topic-enhanced sentence-pair interactions between the current predicted sentence and each next-sentence candidate. With the coherent topical context matching, we promote local dependencies that help identify the tight semantic connections for sentence ordering. The experimental results show that TGCM outperforms state-of-the-art models from various perspectives.