Tianyang Zhao


Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction
Tianyang Zhao | Zhao Yan | Yunbo Cao | Zhoujun Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


Natural Language Response Generation from SQL with Generalization and Back-translation
Saptarashmi Bandyopadhyay | Tianyang Zhao
Proceedings of the First Workshop on Interactive and Executable Semantic Parsing

Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.

Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction
Tianyang Zhao | Zhao Yan | Yunbo Cao | Zhoujun Li
Proceedings of the 19th Chinese National Conference on Computational Linguistics

Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, the Multi-Head Selection (MHS) framework is efficient in extracting entities and relations simultaneously. However, the method is weak for its limited performance. In this paper, we propose several effective insights to address this problem. First, we propose an entity-specific Relative Position Representation (eRPR) to allow the model to fully leverage the distance information between entities and context tokens. Second, we introduce an auxiliary Global Relation Classification (GRC) to enhance the learning of local contextual features. Moreover, we improve the semantic representation by adopting a pre-trained language model BERT as the feature encoder. Finally, these new keypoints are closely integrated with the multi-head selection framework and optimized jointly. Extensive experiments on two benchmark datasets demonstrate that our approach overwhelmingly outperforms previous works in terms of all evaluation metrics, achieving significant improvements for relation F1 by +2.40% on CoNLL04 and +1.90% on ACE05, respectively.