Papers with neural conversation models
Jointly Optimizing Diversity and Relevance in Neural Response Generation (N19-1)
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| Challenge: | Recent neural conversation models often generate bland and generic responses . however, the improvement often comes at the cost of decreased relevance . |
| Approach: | They propose a spacefusion model to jointly optimize diversity and relevance that fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. |
| Outcome: | The proposed model improves diversity and relevance compared to baselines in both diversity and diversity. |
DIRECT: Direct and Indirect Responses in Conversational Text Corpus (2021.findings-emnlp)
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| Challenge: | Neural conversation models have been able to generate fluent responses through training on a dialogue corpus, but they lack the ability to reveal the implied intentions of users. |
| Approach: | They propose to train neural conversation models on a dialogue corpus that provides pragmatic paraphrases to advance techniques for natural language understanding in dialogue systems. |
| Outcome: | The proposed corpus provides 71,498 pairs of indirect–direct utterance pairs accompanied by a multi-turn dialogue history extracted from the MultiWoZ dataset. |
CausalDialogue: Modeling Utterance-level Causality in Conversations (2023.findings-acl)
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| Challenge: | Despite widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans . despite their widespread adoption in society, chatbots have yet not shown natural chat capability . |
| Approach: | They propose a causality-enhanced method to enhance the impact of causality at the utterance level in training neural conversation models. |
| Outcome: | The proposed method improves diversity and agility of loss functions and still needs improvement . the proposed method is based on a CausalDialogue dataset . |