activity
20192021
most citedSelf-Explaining Structures Improve NLP Models

25 citations · 36 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL2021

ConRPG: Paraphrase Generation using Contexts as Regularizer

Yuxian Meng, Xiang Ao, Qing He +5

A long-standing issue with paraphrase generation is how to obtain reliable supervision signals. In this paper, we propose an unsupervised paradigm for paraphrase generation based o…

cs.CL202025 cited

Self-Explaining Structures Improve NLP Models

Zijun Sun, Chun Fan, Qinghong Han +4

Existing approaches to explaining deep learning models in NLP usually suffer from two major drawbacks: (1) the main model and the explaining model are decoupled: an additional prob…

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.CL2020

SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection

Xiaoya Li, Yuxian Meng, Mingxin Zhou +3

While the self-attention mechanism has been widely used in a wide variety of tasks, it has the unfortunate property of a quadratic cost with respect to the input length, which make…

cs.CL202011 cited

Non-Autoregressive Neural Dialogue Generation

Qinghong Han, Yuxian Meng, Fei Wu +1

Maximum Mutual information (MMI), which models the bidirectional dependency between responses () and contexts (), i.e., the forward probability and the backward…

cs.CL2020

Description Based Text Classification with Reinforcement Learning

Duo Chai, Wei Wu, Qinghong Han +2

The task of text classification is usually divided into two stages: {\it text feature extraction} and {\it classification}. In this standard formalization categories are merely rep…