activity
20182024
most citedTENER: Adapting Transformer Encoder for Named Entity Recognition

247 citations · 400 across the 46 of their papers we have counts for

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Showing 2022Show all

9 papers · 1 filter

cs.CL2022

Rethinking Label Smoothing on Multi-hop Question Answering

Zhangyue Yin, Yuxin Wang, Xiannian Hu +6

Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence predicti…

cs.CL2022

Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next

Xiaotian Zhang, Yanjun Zheng, Hang Yan +1

While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge. Previous…

cs.CL2022

DORE: Document Ordered Relation Extraction based on Generative Framework

Qipeng Guo, Yuqing Yang, Hang Yan +2

In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dep…

cs.CL2022★ 2 cited

SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking

Xiaotian Zhang, Hang Yan, Yu Sun +1

Due to the ambiguity of homophones, Chinese Spell Checking (CSC) has widespread applications. Existing systems typically utilize BERT for text encoding. However, CSC requires the m…

cs.CL2022★ 6 cited

An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition

Hang Yan, Yu Sun, Xiaonan Li +1

Named entity recognition (NER) is the task to detect and classify the entity spans in the text. When entity spans overlap between each other, this problem is named as nested NER. S…

cs.CL2022

Dialogue Meaning Representation for Task-Oriented Dialogue Systems

Xiangkun Hu, Junqi Dai, Hang Yan +4

Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically fo…