activity
20222024
most citedCharacter, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

2 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt

Jian Yang, Hongcheng Guo, Yuwei Yin +7

Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between…

cs.CL20241 cited

Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework

Peng Xing, Yinghui Li, Shirong Ma +6

Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers bec…

cs.CL20231 cited

Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization

Weixiao Zhou, Gengyao Li, Xianfu Cheng +4

Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a…

cs.CL2023

Retrieval-Augmented Classification with Decoupled Representation

Xinnian Liang, Shuangzhi Wu, Hui Huang +3

Retrieval augmented methods have shown promising results in various classification tasks. However, existing methods focus on retrieving extra context to enrich the input, which is…

cs.CL20232 cited

Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

Xinnian Liang, Zefan Zhou, Hui Huang +5

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and complete…

cs.CL2023

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Xinnian Liang, Shuangzhi Wu, Chenhao Cui +3

Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles' viewpoints and conversational topics change in the dialo…