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Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems
Mrinmaya Sachan · Kumar Avinava Dubey · Tom Mitchell · Dan Roth · Eric Xing

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #131

As machine learning becomes more widely used in practice, we need new methods to build complex intelligent systems that integrate learning with existing software, and with domain knowledge encoded as rules. As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks. This system, Nuts&Bolts, learns a pipeline process that incorporates existing code, pre-learned machine learning models, and human engineered rules. It jointly trains the entire pipeline to prevent propagation of errors, using a combination of labelled and unlabelled data. Our approach achieves a good performance on the parsing task, outperforming the simple pipeline and its variants. Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems.

Author Information

Mrinmaya Sachan (Carnegie Mellon University)
Kumar Avinava Dubey (Carnegie Mellon University)
Tom Mitchell (Carnegie Mellon University)
Dan Roth (UPenn)
Eric Xing (Petuum Inc. / Carnegie Mellon University)

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