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Jayant Krishnamurthy

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3 papers

1 author row

AAAI Conference 2017 Conference Paper

Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

Matt Gardner
Jayant Krishnamurthy

Traditional semantic parsers map language onto compositional, executable queries in a ﬁxed schema. This mapping allows them to effectively leverage the information contained in large, formal knowledge bases (KBs, e. g. , Freebase) to answer questions, but it is also fundamentally limiting— these semantic parsers can only assign meaning to language that falls within the KB’s manually-produced schema. Recently proposed methods for open vocabulary semantic parsing overcome this limitation by learning execution models for arbitrary language, essentially using a text corpus as a kind of knowledge base. However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models. We show how to combine the disparate representations used by these two approaches, presenting for the ﬁrst time a semantic parser that (1) produces compositional, executable representations of language, (2) can successfully leverage the information contained in both a formal KB and a large corpus, and (3) is not limited to the schema of the underlying KB. We demonstrate signiﬁcantly improved performance over state-of-the-art baselines on an open-domain natural language question answering task.

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AAAI Conference 2016 Conference Paper

Instructable Intelligent Personal Agent

Amos Azaria
Jayant Krishnamurthy
Tom Mitchell

Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is likely to grow. We introduce our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. LIA uses a CCG semantic parser to ground the semantics of each command in terms of primitive executable procedures deﬁning sensors and effectors of the agent. Given a natural language command that LIA does not understand, it prompts the user to explain how to achieve the command through a sequence of steps, also speciﬁed in natural language. A novel lexicon induction algorithm enables LIA to generalize across taught commands, e. g. , having been taught how to “forward an email to Alice, ” LIA can correctly interpret the command “forward this email to Bob. ” A user study involving email tasks demonstrates that users voluntarily teach LIA new commands, and that these taught commands signiﬁcantly reduce task completion time. These results demonstrate the potential of natural language instruction as a signiﬁcant, under-explored paradigm for machine learning.

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AAAI Conference 2015 Conference Paper

Never-Ending Learning

Tom Mitchell
William Cohen
Estevam Hruschka
Partha Talukdar
Justin Betteridge
Andrew Carlson
Bhavana Dalvi Mishra
Matthew Gardner

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reﬂect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never- Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million conﬁdence-weighted beliefs (e. g. , servedWith(tea, biscuits)), while learning continually to improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs from old ones, and is now beginning to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http: //rtw. ml. cmu. edu, and followed on Twitter at @CMUNELL.

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