Yifeng Liu


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 https://github.com/wtangdev/UniRel.

The VolcTrans System for WMT22 Multilingual Machine Translation Task
Xian Qian | Kai Hu | Jiaqiang Wang | Yifeng Liu | Xingyuan Pan | Jun Cao | Mingxuan Wang
Proceedings of the Seventh Conference on Machine Translation (WMT)

This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation. We participated in the unconstrained track which allows the use of external resources. Our system is a transformer-based multilingual model trained on data from multiple sources including the public training set from the data track, NLLB data provided by Meta AI, self-collected parallel corpora, and pseudo bitext from back-translation. Both bilingual and monolingual texts are cleaned by a series of heuristic rules. On the official test set, our system achieves $17.3$ BLEU, $21.9$ spBLEU, and $41.9$ chrF2++ on average over all language pairs. Averaged inference speed is $11.5$ sentences per second using a single Nvidia Tesla V100 GPU.