Chao Liu


2022

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LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis
Tianhao Gao | Jun Fang | Hanyu Liu | Zhiyuan Liu | Chao Liu | Pengzhang Liu | Yongjun Bao | Weipeng Yan
Proceedings of the 29th International Conference on Computational Linguistics

Aspect-based sentiment analysis (ABSA) has received increasing attention recently. ABSA can be divided into multiple tasks according to the different extracted elements. Existing generative methods usually treat the output as a whole string rather than the combination of different elements and only focus on a single task at once. This paper proposes a unified generative multi-task framework that can solve multiple ABSA tasks by controlling the type of task prompts consisting of multiple element prompts. Further, the proposed approach can train on simple tasks and transfer to difficult tasks by assembling task prompts, like assembling Lego bricks. We conduct experiments on six ABSA tasks across multiple benchmarks. Our proposed multi-task approach achieves new state-of-the-art results in almost all tasks and competitive results in task transfer scenarios.

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TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
Yizhi Li | Wei Fan | Chao Liu | Chenghua Lin | Jiang Qian
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Knowledge graph embedding methods are important for the knowledge graph completion (or link prediction) task. One state-of-the-art method, PairRE, leverages two separate vectors to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion. To address this issue, we propose a novel score function TranSHER, which leverages relation-specific translations between head and tail entities to relax the constraint of hyper-ellipsoid restrictions. By introducing an intuitive and simple relation-specific translation, TranSHER can provide more direct guidance on optimization and capture more semantic characteristics of entities with complex relations. Experimental results show that TranSHER achieves state-of-the-art performance on link prediction and generalizes well to datasets in different domains and scales. Our codes are public available at https://github.com/yizhilll/TranSHER.

2019

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A Multi-Task Learning Framework for Extracting Bacteria Biotope Information
Qi Zhang | Chao Liu | Ying Chi | Xuansong Xie | Xiansheng Hua
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019. To alleviate the data deficiency problem in domain-specific information extraction, we use BERT(Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction on both general corpus and medical corpus like PubMed. In fine-tuning stage, we fine-tune the relation extraction layer and mention recognition layer designed by us on the top of BERT to extract mentions and relations simultaneously. The evaluation results show that our method achieves the best performance on all metrics (including slot error rate, precision and recall) in the Bacteria Biotope rel+ner subtask.

2015

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Radical Embedding: Delving Deeper to Chinese Radicals
Xinlei Shi | Junjie Zhai | Xudong Yang | Zehua Xie | Chao Liu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation
Chao Xing | Dong Wang | Chao Liu | Yiye Lin
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies