most citedTowards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning

5 citations · 13 across the 6 of their papers we have counts for

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cs.CL2022

Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension

Jiangnan Li, Mo Yu, Fandong Meng +4

In this work, we focus on dialogue reading comprehension (DRC), a task extracting answer spans for questions from dialogues. Dialogue context modeling in DRC is tricky due to compl…

cs.CL20222 cited

A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models

Yuanxin Liu, Fandong Meng, Zheng Lin +5

Despite the remarkable success of pre-trained language models (PLMs), they still face two challenges: First, large-scale PLMs are inefficient in terms of memory footprint and compu…

cs.CL2022

Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training

Yuanxin Liu, Fandong Meng, Zheng Lin +4

Recent studies on the lottery ticket hypothesis (LTH) show that pre-trained language models (PLMs) like BERT contain matching subnetworks that have similar transfer learning perfor…

cs.CL20224 cited

Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge

Jiangnan Li, Fandong Meng, Zheng Lin +5

Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs t…

cs.CL2020

Learning Class-Transductive Intent Representations for Zero-shot Intent Detection

Qingyi Si, Yuanxin Liu, Peng Fu +3

Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1…