Huan Chen


2021

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Geo-BERT Pre-training Model for Query Rewriting in POI Search
Xiao Liu | Juan Hu | Qi Shen | Huan Chen
Findings of the Association for Computational Linguistics: EMNLP 2021

Query Rewriting (QR) is proposed to solve the problem of the word mismatch between queries and documents in Web search. Existing approaches usually model QR with an end-to-end sequence-to-sequence (seq2seq) model. The state-of-the-art Transformer-based models can effectively learn textual semantics from user session logs, but they often ignore users’ geographic location information that is crucial for the Point-of-Interest (POI) search of map services. In this paper, we proposed a pre-training model, called Geo-BERT, to integrate semantics and geographic information in the pre-trained representations of POIs. Firstly, we simulate POI distribution in the real world as a graph, in which nodes represent POIs and multiple geographic granularities. Then we use graph representation learning methods to get geographic representations. Finally, we train a BERT-like pre-training model with text and POIs’ graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search. The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.

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An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
Shuangyong Song | Chao Wang | Haiqing Chen | Huan Chen
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention. We presented AliMe Assist, a Chinese intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. According to the survey of user studies and the real online testing, emotional comfort of customers’ negative emotions, which make up more than 5% of whole number of customer visits on AliMe, is a key point for providing considerate service. In this paper, we propose a framework to obtain proper answer to customers’ emotional questions. The framework takes emotion classification model as a core, and final answer selection is based on topic classification and text matching. Our experiments on real online systems show that the framework is very promising.

2013

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Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic
Qi Zhang | Jin Qian | Huan Chen | Jihua Kang | Xuanjing Huang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Chinese Named Entity Abbreviation Generation Using First-Order Logic
Huan Chen | Qi Zhang | Jin Qian | Xuanjing Huang
Proceedings of the Sixth International Joint Conference on Natural Language Processing