Noun compounds are abundant in various languages and their interpretations have been applied in a wide range of NLP tasks. However, most existing work only uses relation classification- or paraphrasing-based methods to model this problem, failing in coverage or accuracy. We argue that the above two approaches are complementary to each other for the noun compound interpretation. In this paper, we propose a two-phase strategy to solve this task. The first phase is to perform the relation classification sub-task with a novel multi-view representation learning model. When noun compounds are predicted as the non-semantic relation, i.e., NA, or the confidence scores are below the threshold, the second phase, namely paraphrasing, will be triggered to interpret noun compounds with a contrastive slot filling method. To evaluate the effectiveness of our methods, we construct the largest Chinese dataset for noun compound interpretation in the life service domain. The experimental results on our constructed and public datasets prove the effectiveness of our solution. Furthermore, the online A/B testing on Meituan APP suggests that the Query View Click-Through Rate increases by 0.91% when noun compounds are used to enrich semantic information of items with the help of their interpretations on the platform.

Di Liang, Fubao Zhang, Qi Zhang, Xuanjing Huang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Community-based question answering (CQA), which provides a platform for people with diverse backgrounds to share information and knowledge, has become increasingly popular. With the accumulation of site data, methods to detect duplicate questions in CQA sites have attracted considerable attention. Existing methods typically use only questions to complete the task. However, the paired answers may also provide valuable information. In this paper, we propose an answer information- enhanced adaptive multi-attention network (AMAN) to perform this task. AMAN takes full advantage of the semantic information in the paired answers while alleviating the noise problem caused by adding the answers. To evaluate the proposed method, we use a CQADupStack set and the Quora question-pair dataset expanded with paired answers. Experimental results demonstrate that the proposed model can achieve state-of-the-art performance on the above two data sets.