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Wen-tau Yih
Researcher at Facebook
Publications - 171
Citations - 21562
Wen-tau Yih is an academic researcher from Facebook. The author has contributed to research in topics: Question answering & Computer science. The author has an hindex of 56, co-authored 150 publications receiving 16303 citations. Previous affiliations of Wen-tau Yih include National Taiwan University & Microsoft.
Papers
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Proceedings Article
Linguistic Regularities in Continuous Space Word Representations
TL;DR: The vector-space word representations that are implicitly learned by the input-layer weights are found to be surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset.
Proceedings Article
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TL;DR: It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication.
Posted Content
Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin,Barlas Oguz,Sewon Min,Patrick S. H. Lewis,Ledell Wu,Sergey Edunov,Danqi Chen,Wen-tau Yih +7 more
TL;DR: This work shows that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework.
Proceedings ArticleDOI
WikiQA: A Challenge Dataset for Open-Domain Question Answering
TL;DR: The WIKIQA dataset is described, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering, which is more than an order of magnitude larger than the previous dataset.
Proceedings ArticleDOI
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
TL;DR: This work proposes a novel semantic parsing framework for question answering using a knowledge base that leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem.