activity
20162022
most citedA Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

41 citations · 138 across the 20 of their papers we have counts for

collaborators

28 papers

cs.CL2022

DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection

Haoran Meng, Zheng Xin, Tianyu Liu +6

While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUS…

cs.CL20224 cited

Calibrating Factual Knowledge in Pretrained Language Models

Qingxiu Dong, Damai Dai, Yifan Song +3

Previous literature has proved that Pretrained Language Models (PLMs) can store factual knowledge. However, we find that facts stored in the PLMs are not always correct. It motivat…

cs.CL20224 cited

Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation

Peiyi Wang, Yifan Song, Tianyu Liu +4

Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.…

cs.CL20223 cited

Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances

Shoujie Tong, Qingxiu Dong, Damai Dai +4

Fine-tuning pretrained language models (PLMs) on downstream tasks has become common practice in natural language processing. However, most of the PLMs are vulnerable, e.g., they ar…

cs.CL2022

A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction

Runxin Xu, Peiyi Wang, Tianyu Liu +3

Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting eve…

cs.CL2022

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

Liang Chen, Peiyi Wang, Runxin Xu +3

As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enh…