A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 234–244, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings Tom Kwiatkowski* † tomk@cs.washington.edu Sharon Goldwater∗ sgwater@inf.ed.ac.uk Luke Zettlemoyer† lsz@cs.washington.edu Mark Steedman∗ steedman@inf.ed.ac.uk ∗ ILCC, School of Informatics University of Edinburgh Edinburgh, EH8 9AB, UK †Computer Science & Engineering University of Washington Seattle, WA, 98195, USA Abstract This paper presents an incremental prob- abilistic learner that models the acquis- tion of syntax and semantics from a cor- pus of child-directed utterances paired with possible representations of their meanings. These meaning representations approxi- mate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model. We use the CCG grammatical framework and train a non-parametric Bayesian model of parse structure with online variational Bayesian expectation maximization. When tested on utterances from the CHILDES corpus, our learner outperforms a state-of-the-art se- mantic parser. In addition, it models such aspects of child acquisition as “fast map- ping,” while also countering previous crit- icisms of statistical syntactic learners. 1 Introduction Children learn language by mapping the utter- ances they hear onto what they believe those ut- terances mean. The precise nature of the child’s prelinguistic representation of meaning is not known. We assume for present purposes that it can be approximated by compositional logical representations such as (1), where the meaning is a logical expression that describes a relationship have between the person you refers to and the object another(x, cookie(x)): Utterance : you have another cookie (1) Meaning : have(you, another(x, cookie(x))) Most situations will support a number of plausi- ble meanings, so the child has to learn in the face of propositional uncertainty1, from a set of con- textually afforded meaning candidates, as here: Utterance : you have another cookie Candidate Meanings    have(you, another(x, cookie(x))) eat(you, your(x, cake(x))) want(i, another(x, cookie(x))) The task is then to learn, from a sequence of such (utterance, meaning-candidates) pairs, the correct lexicon and parsing model. Here we present a probabilistic account of this task with an empha- sis on cognitive plausibility. Our criteria for plausibility are that the learner must not require any language-specific informa- tion prior to learning and that the learning algo- rithm must be strictly incremental: it sees each training instance sequentially and exactly once. We define a Bayesian model of parse structure with Dirichlet process priors and train this on a set of (utterance, meaning-candidates) pairs de- rived from the CHILDES corpus (MacWhinney, 2000) using online variational Bayesian EM. We evaluate the learnt grammar in three ways. First, we test the accuracy of the trained model in parsing unseen utterances onto gold standard annotations of their meaning. We show that it outperforms a state-of-the-art semantic parser …

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