Sheng Zhang – Transductive Semantic Parsing

January 27, 2020 @ 12:00 pm – 1:00 pm
Hackerman B17
While semantic parsing receives a long-standing interest from the community, developing robust semantic parsing algorithms remains a challenging problem. In this talk, we will consider the following challenges in semantic parsing: 1) representing the semantics of multiple natural languages in a single semantic analysis; 2) developing parsing systems for broad-coverage semantics; 3) designing unifying parsing paradigms to support distinct meaning representation frameworks; and 4) training systems with limited amounts of labeled data. We approach semantic parsing as sequence-to-graph transduction problems, and introduce novel algorithms/components into transductive settings that extend beyond what a typical neural machine translation system would do on this problem. Our approach achieves the state-of-the-art performance on a number of tasks, including cross-lingual open information extraction, cross-lingual decompositional semantic parsing, and broad-coverage semantic parsing for Abstract Meaning Representation (AMR), Semantic Dependencies (SDP) and Universal Conceptual Cognitive Annotation (UCCA).
Lunch will be served.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2680

Center for Language and Speech Processing