Wen-tau Yih

Wen-tau Yih is a person.

• AKA: Scott Yih.
• See: Dan Roth, Dense Distributional Word Model Training, NER System, Retrieval-Augmented NLG.

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References

• Personal Homepage: http://research.microsoft.com/en-us/people/scottyih/
• DBLP Author Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/y/Yih:Wen=tau.html
• https://scholar.google.com/citations?hl=en&user=8rDNIMsAAAAJ

2020

• (Lewis et al., 2020) ⇒ Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. (2020). “Retrieval-Augmented Generation for Knowledge-intensive NLP Tasks.” In: Advances in Neural Information Processing Systems, 33.
• (Karpukhin et al., 2020) ⇒ Vladimir Karpukhin, Barlas Oguz, Seo Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. (2020). “Dense Passage Retrieval for Open-Domain Question Answering." In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 6769-6781).

2013

• (Mikolov et al., 2013b) ⇒ Tomáš Mikolov, Wen-tau Yih, and Geoffrey Zweig. (2013). “Linguistic Regularities in Continuous Space Word Representations..” In: HLT-NAACL.

2004

• (Roth & Yih, 2004) ⇒ Dan Roth, and Wen-tau Yih. (2004). “A linear programming formulation for global inference in natural language."
• (Punyakanok et al., 2004) ⇒ V Punyakanok, Dan Roth, Wen-tau Yih, and D. Zimak. (2004). “Semantic role labeling via integer linear programming inference.” In: Proceedings of the 20th International Conference on Computational Linguistics.

2002

• (Roth & Yih, 2002) ⇒ Dan Roth, and Wen-tau Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2002).

2001

• (Roth & Yih, 2001) ⇒ Dan Roth, and Wen-tau Yih. (2001). Relational learning via propositional algorithms: An information extraction case study. In: Proceedings of the International Joint Conference on Artificial Intelligence, pages 1257–1263.

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