Learning Semantic Correspondences with Less Supervision

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 91–99, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Learning Semantic Correspondences with Less Supervision Percy Liang UC Berkeley pliang@cs.berkeley.edu Michael I. Jordan UC Berkeley jordan@cs.berkeley.edu Dan Klein UC Berkeley klein@cs.berkeley.edu Abstract A central problem in grounded language acqui- sition is learning the correspondences between a rich world state and a stream of text which refer- ences that world state. To deal with the high de- gree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty—Robocup sportscasting, weather forecasts (a new domain), and NFL recaps. 1 Introduction Recent work in learning semantics has focused on mapping sentences to meaning representa- tions (e.g., some logical form) given aligned sen- tence/meaning pairs as training data (Ge and Mooney, 2005; Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Lu et al., 2008). However, this degree of supervision is unrealistic for modeling human language acquisition and can be costly to obtain for building large-scale, broad- coverage language understanding systems. A more flexible direction is grounded language acquisition: learning the meaning of sentences in the context of an observed world state. The grounded approach has gained interest in various disciplines (Siskind, 1996; Yu and Ballard, 2004; Feldman and Narayanan, 2004; Gorniak and Roy, 2007). Some recent work in the NLP commu- nity has also moved in this direction by relaxing the amount of supervision to the setting where each sentence is paired with a small set of can- didate meanings (Kate and Mooney, 2007; Chen and Mooney, 2008). The goal of this paper is to reduce the amount of supervision even further. We assume that we are given a world state represented by a set of records along with a text, an unsegmented sequence of words. For example, in the weather forecast do- main (Section 2.2), the text is the weather report, and the records provide a structured representation of the temperature, sky conditions, etc. In this less restricted data setting, we must re- solve multiple ambiguities: (1) the segmentation of the text into utterances; (2) the identification of relevant facts, i.e., the choice of records and as- pects of those records; and (3) the alignment of ut- terances to facts (facts are the meaning represen- tations of the utterances). Furthermore, in some of our examples, much of the world state is not referenced at all in the text, and, conversely, the text references things which are not represented in our world state. This increased amount of ambigu- ity and noise presents serious challenges for learn- ing. To cope with these challenges, we propose a probabilistic generative model that treats text seg- mentation, fact identification, and alignment in a single unified framework. The parameters of this hierarchical hidden semi-Markov model can be es- …

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