Benfeng Xu


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.

UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Wei Tang | Benfeng Xu | Yuyue Zhao | Zhendong Mao | Yifeng Liu | Yong Liao | Haiyong Xie
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions. Therefore, the rich correlations are not fully exploited by existing works. In this paper, we propose UniRel to address these challenges. Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block. With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient. The source code is available at


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.