ACL 2018 paper by Omer Goldman, Veronica Latcinnik, Udi Naveh, Amir Globerson, and Jonathan Berant

Omer comes on to tell us about a class project (done mostly by undergraduates!) that made it into ACL.  Omer and colleagues built a semantic parser that gets state-of-the-art results on the Cornell Natural Language Visual Reasoning dataset.  They did this by using "abstract examples" - they replaced the entities in the questions and corresponding logical forms with their types, labeled about a hundred examples in this abstracted formalism, and used those labels to do data augmentation and train their parser.  They also used some interesting caching tricks, and a discriminative reranker.

https://www.semanticscholar.org/paper/Weakly-supervised-Semantic-Parsing-with-Abstract-Goldman-Latcinnik/5aec2ab5bf2979da067e2aa34762b589a0680030

Episode 59_Omer Goldman

59 - Weakly Supervised Semantic Parsing With Abstract Examples, with Omer Goldman

**The podcast is currently on hiatus. For more active NLP content, check out the Holistic Intelligence Podcast linked below.**

Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

59 - Weakly Supervised Semantic Parsing With Abstract Examples, with Omer Goldman

NLP Highlights