Yoav Artzi, together with University of Washington coauthors Kenton Lee and Luke Zettlemoyer, has won one of two Best Paper Awards at the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), one of the premier venues in NLP.

The paper, "Broad-coverage CCG Semantic Parsing with AMR", was selected from among 1300 submissions. It describes a grammar learning approach building Abstract Meaning Representations, a recently proposed, general formalism for representing core aspects of sentence meaning. This is significant step over previous CCG learning algorithms, both in scale and linguistic complexity. The paper will be presented at EMNLP 2015 in Lisbon, Portugal later this month.