activity
20182023
most citedFastBERT: a Self-distilling BERT with Adaptive Inference Time

57 citations · 80 across the 11 of their papers we have counts for

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Showing cs.CLShow all

9 papers · 1 filter

cs.CL2023

Recouple Event Field via Probabilistic Bias for Event Extraction

Xingyu Bai, Taiqiang Wu, Han Guo +7

Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PL…

cs.CL2022

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching

Kunbo Ding, Weijie Liu, Yuejian Fang +3

Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.…

cs.CL20221 cited

Semantic Matching from Different Perspectives

Weijie Liu, Tao Zhu, Weiquan Mao +4

In this paper, we pay attention to the issue which is usually overlooked, i.e., \textit{similarity should be determined from different perspectives}. To explore this issue, we rele…

cs.CL20216 cited

Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders

Chen Xu, Bojie Hu, Yanyang Li +5

Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Autom…

cs.CL2020

Dynamic Curriculum Learning for Low-Resource Neural Machine Translation

Chen Xu, Bojie Hu, Yufan Jiang +6

Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this…

cs.CL20204 cited

Code-switching pre-training for neural machine translation

Zhen Yang, Bojie Hu, Ambyera Han +2

This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP for short) for Neural Machine Translation (NMT). Unlike traditional pre-training method which…