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20162022
most citedModeling Text-visual Mutual Dependency for Multi-modal Dialog Generation

12 citations · 16 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CL20221 cited

There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning

Xueliang Zhao, Tingchen Fu, Chongyang Tao +1

Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowled…

cs.CL202112 cited

Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation

Shuhe Wang, Yuxian Meng, Xiaofei Sun +5

Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal d…

cs.CL2020

OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts

Yuxian Meng, Shuhe Wang, Qinghong Han +4

When humans converse, what a speaker will say next significantly depends on what he sees. Unfortunately, existing dialogue models generate dialogue utterances only based on precedi…

cs.CL2019

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

Yiping Song, Zequn Liu, Wei Bi +2

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning frame…

cs.CL2016

StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation

Xiang Li, Lili Mou, Rui Yan +1

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presum…