Zhang, Wei

Wei Zhang is a Senior Staff Scientist at Wayfair. His goal is to create the world's best product search experience using ML and NLP. Before Wayfair, he was a research scientist at IBM Thomas J. Watson Research Center until 2021. He joined IBM right after his graduation from CMU LTI in 2014. He is broadly interested in artificial neural networks and natural language processing. Recently he worked on explainable NLP, human-AI and AI-AI communication in dialogues and language games, and NLP applications to high-stake domains such as finance.

News

May 2021: Our ACL 2021 paper identifies issues when applying sample-based explanation methods in NLP, and proposed a solution to enhance the interpretability of explanations that are friendly to long documents.

Feb 2021: We recently proved for the first time that BERT can encode lexical semantics better than GloVe on a intrinsic evaluation using psycholinguistic data, help explaining why pre-trained Transformers are better encoders.

Jan 2020: Our CHI 2020 paper studying human behavior in human-AI communication has been nominated the best paper award!

He recently serves as a PC member for: ARR May 2021, ACL 2021, EMNLP 2021, ACL 2020, EMNLP 2020, NAACL 2021, EACL 2021, SLT 2021, NeurIPS 2020.


Semantic Scholar
Google Scholar

Selected Publications

[ACL 2021] Wei Zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang. On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation.

[AAAI 2021] Wei Zhang, Murray Campbell, Yang Yu, Sadhana Kumaravel. Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Word Embeddings and the Implications to Representation Learning. [paper]

[NeurIPS 2020] Zahra Ashktorab, James Johnson, Qian Pan, Wei Zhang, Casey Dugan. The Design and Development of Games with a Purpose for AI System. Workshop of Human And Machine in-the-Loop Evaluation and Learning Strategies.

[CHI 2020] Katy Ilonka Gero, Zahra Ashktorab, Casey Dugan, Qian Pan, James Johnson, Werner Geyer, Maria Ruiz, Sarah Miller, David R Millen, Murray Campbell, Sadhana Kumaravel, Wei Zhang. Mental Models of AI Agents in a Cooperative Game Setting. (Best Paper Award)

[AAAI 2018] Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, Jing Jiang. R $^ 3$: Reinforced Reader-Ranker for Open-Domain Question Answering.

[ICLR 2018] Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell. Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering.

[arxiv 2017] Wei Zhang, Bowen Zhou. Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization. arxiv. preprint arXiv:1709.06493 (2017). [paper]

[arxiv 2016] Wei Zhang*, Yang Yu*, Kazi Hasan, Mo Yu, Bing Xiang, Bowen Zhou. Dynamic Chunk Reader for Machine Reading Comprehension arxiv. preprint: arXiv:1610.09996 (2016) (* equal contribution)[paper]

[NIPS 2015] Wei Zhang, Yang Yu, Bowen Zhou. Structured Memory for Neural Turing Machines Reasoning, Memory and Attention Workshop [slides][paper]

[arxiv 2015] Yang Yu, Wei Zhang, Chung-Wei Hang, and Bowen Zhou. Empirical Study on Deep Learning Models for Question Answering. arXiv preprint arXiv:1510.07526 (2015).

[Journal 2014] Wei Zhang, and Judith Gelernter. Geocoding location expressions in Twitter messages: A preference learning method. Journal of Spatial Information Science 2014, no. 9 (2014): 37-70.

[ACM GIR 2013] Judith Gelernter, and Wei Zhang. Cross-lingual geo-parsing for non-structured data. In Proceedings of the 7th Workshop on Geographic Information Retrieval, pp. 64-71. ACM, 2013. [paper]

[SemEval 2007] Yuhang Guo, Wanxiang Che, Yuxuan Hu, Wei Zhang, and Ting Liu. HIT-IR-WSD: A wsd system for english lexical sample task. In Proceedings of the ACL SemEval. (2007). (System won 1st place on SemEval 2007 Task 11)[paper]

Talks

"On Machine Reading Comprehension and Question Answering" [slides] at Harvard NLP Reading Group

"Structured Memory for Neural Turing Machines" [slides] on NIPS 2015 RAM workshop