most citedRetrofitting Structure-aware Transformer Language Model for End Tasks

22 citations · 29 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2020

Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning

Shengqiong Wu, Hao Fei, Donghong Ji

Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community. Most existing works treat ALD as re…

cs.CL2020

Nominal Compound Chain Extraction: A New Task for Semantic-enriched Lexical Chain

Bobo Li, Hao Fei, Yafeng Ren +1

Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work foc…

cs.CL20203 cited

Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP

Hao Fei, Yafeng Ren, Donghong Ji

Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network. In this paper, we investigate…

cs.CL202022 cited

Retrofitting Structure-aware Transformer Language Model for End Tasks

Hao Fei, Yafeng Ren, Donghong Ji

We consider retrofitting structure-aware Transformer-based language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constitue…

cs.CL20204 cited

High-order Refining for End-to-end Chinese Semantic Role Labeling

Hao Fei, Yafeng Ren, Donghong Ji

Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any pre…

cs.CL2020

Cross-lingual Semantic Role Labeling with Model Transfer

Hao Fei, Meishan Zhang, Fei Li +1

Prior studies show that cross-lingual semantic role labeling (SRL) can be achieved by model transfer under the help of universal features. In this paper, we fill the gap of cross-l…