most citedRLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

2 citations · 5 across the 5 of their papers we have counts for

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5 papers

cs.CL20221 cited

Word-Level Representation From Bytes For Language Modeling

Chu-Tak Lee, Qipeng Guo, Xipeng Qiu

Modern language models mostly take sub-words as input, a design that balances the trade-off between vocabulary size, number of parameters, and performance. However, sub-word tokeni…

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.CL20222 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.CL20222 cited

RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees

Tengxiao Liu, Qipeng Guo, Xiangkun Hu +3

Interpreting the reasoning process from questions to answers poses a challenge in approaching explainable QA. A recently proposed structured reasoning format, entailment tree, mana…

cs.CL2022

Soft-Labeled Contrastive Pre-training for Function-level Code Representation

Xiaonan Li, Daya Guo, Yeyun Gong +6

Code contrastive pre-training has recently achieved significant progress on code-related tasks. In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-t…