A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection (D19-1)
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| Challenge: | Dialogue acts are important in conversation modeling, but they are rarely available for new conversations. |
| Approach: | They propose an end-to-end multi-task model that integrates dialogue acts with context and response in a crossway fashion. |
| Outcome: | The proposed model improves the accuracy of the dialogue act prediction task and the MRR for the response selection task. |
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
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Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | Despite their popularity, retrieval-based models have had modest impact on task-oriented dialogue systems . main obstacle to their application is the low-data regime of most task-orientated dialogue tasks . e-commerce, banking, and other domains are applications of retrieval models . |
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| Challenge: | Existing approaches to dialogue state tracking are limited to scenarios with infinite slot values and prediction of unseen slot values. |
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Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
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| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
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Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning (2023.findings-emnlp)
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| Challenge: | Backchanneling is a form of feedback that is produced by listeners in a conversation . since the advent of ChatGPT, modern dialogue systems exhibit answer quality levels on par with humans in various professions. |
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