Papers by Xiaowu Hu

2 papers
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

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Challenge: Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach: They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome: The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)

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Challenge: Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP).
Approach: They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses.
Outcome: The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets.

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