147 citations · 366 across the 10 of their papers we have counts for
10 papers
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…
BertGCN: Transductive Text Classification by Combining GCN and BERT
Yuxiao Lin, Yuxian Meng, Xiaofei Sun +4
In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification. BertGCN constructs a heterogeneous graph over the…
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…
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…
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…
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…
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…
A Unified MRC Framework for Named Entity Recognition
Xiaoya Li, Jingrong Feng, Yuxian Meng +4
The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately dev…
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Xiaoya Li, Yuxian Meng, Xiaofei Sun +3
Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental questi…
Glyce: Glyph-vectors for Chinese Character Representations
Yuxian Meng, Wei Wu, Fei Wang +8
It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages. However, due to the lack of rich pict…