Benfeng Xu


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EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction
Benfeng Xu | Quan Wang | Yajuan Lyu | Yabing Shi | Yong Zhu | Jie Gao | Zhendong Mao
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations.While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples.We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.


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Curriculum Learning for Natural Language Understanding
Benfeng Xu | Licheng Zhang | Zhendong Mao | Quan Wang | Hongtao Xie | Yongdong Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks. At the fine-tune stage, target task data is usually introduced in a completely random order and treated equally. However, examples in NLU tasks can vary greatly in difficulty, and similar to human learning procedure, language models can benefit from an easy-to-difficult curriculum. Based on this idea, we propose our Curriculum Learning approach. By reviewing the trainset in a crossed way, we are able to distinguish easy examples from difficult ones, and arrange a curriculum for language models. Without any manual model architecture design or use of external data, our Curriculum Learning approach obtains significant and universal performance improvements on a wide range of NLU tasks.