Context-Driven Construction Learning

Context-Driven Construction Learning Nancy Chang (nchang@icsi.berkeley.edu) UC Berkeley, Department of Computer Science and International Computer Science Institute 1947 Center St., Suite 600, Berkeley, CA 94704 Olya Gurevich (olya@socrates.berkeley.edu) UC Berkeley, Department of Linguistics 1203 Dwinelle Hall, University of California at Berkeley Berkeley, CA 94720-2650 communicated in context. We assume along with many the- ories of language that the basic unit of linguistic knowledge, for both lexical items and larger phrasal and clausal units, is a symbolic pairing of form and meaning, or construction (Langacker, 1987; Goldberg, 1995; Fillmore and Kay, 1999). Since the target of learning is rooted in both form and mean- ing domains, the learner should exploit information from both domains during learning. Most importantly, we view linguistic constructions as in- herently dependent on and supportive of dynamic processes of language use, anchored in a communicative context. A crucial but often neglected source of bias in learning con- structions must therefore be how much they help the child meet her communicative goals. This paper presents a computational model of construction learning consistent with these principles, focusing on how language understanding drives language learning. We de- scribe a statistically driven machine learning framework that takes as input a sequence of child-directed utterances paired with their associated situational context, along with the cur- rent grammar, or set of constructions; this grammar is ini- tially restricted to lexical items. The utterances are passed to a language understanding system (Bryant, 2003) that pro- duces a partial interpretation, which provides the basis for the learning model to form new constructions. We present re- sults showing how the model acquires simple English “verb island” constructions (Tomasello, 1992), and discuss how the same mechanisms handle the more complex constructions in- volved in Russian nominal case marking. These studies lend support for the larger program of integrating cognitive and constructional approaches to linguistics, crosslinguistic de- velopmental evidence, and machine learning techniques to address the puzzles of language acquisition. Abstract We present a computational model of how partial comprehen- sion of utterances in context may drive the acquisition of chil- dren’s earliest grammatical constructions. The model aims to satisfy convergent constraints from cognitive linguistics and crosslinguistic developmental evidence within a statistically driven computational framework. We examine how the tight coupling between contextually grounded language comprehen- sion and learning processes can be exploited to improve the model’s ability to search the space of possible constructions. In particular, previously learned constructions may not fully ac- count for all contextually perceived mappings between forms and meanings. In the model, these incomplete analyses di- rectly prompt the formation of new relational mappings that bridge the gap. We describe an experiiment applying the model to the acquisition of English verb island constructions and dis- cuss how the model handles more complex examples involving Russian morphological constructions. Together these demon- strate the viability of the overall approach and representational potential of the model. Beyond single words How do children make the leap from single words to complex combinations? The simple act of putting one word in front of another to indicate some relation between their meanings is widely considered the defining characteristic of linguistic competence and the key to unlocking the combinatorial and expressive power of language. A viable account of the acqui- sition of these combinatorial patterns, or grammatical con- structions, would thus have significant implications for any theory of language that aspires to cognitive plausibility. As with most issues impinging on the nature of gram- mar, linguistic and developmental inquiries into the source of combinatorial constructions have bifurcated along theoret- ical lines. These reflect divergent assumptions about, among other things, what kind of learning bias children bring to the task, how the target linguistic knowledge should be repre- sented, what kind of data should be considered part of the training input, and how (if at all) language learning interacts with other linguistic and cognitive processes. Theoreticians within the formalist “learnability” paradigm, for example, have generally restricted their attention to the form domain, taking the input for learning to be a set of surface strings (each a sequence of surface forms) and positing relatively abstract structures that govern the combination of linguistic units. This paper takes as starting point the hypothesis that the learning problem at hand may encompass a broader subset of the child’s experience, centrally including meaning as it is The Construction Learning model We briefly describe the construction learning model in terms of (1) the target representation of learning, (2) assumptions about the child language learning scenario, and (3) the com- putational learning framework; see (Chang, 2004; Chang and Maia, 2001) for more details. Target representation: embodied constructions Embodied Construction Grammar (Bergen and Chang, in press; Chang et al., 2002) is a computationally explicit for- malism for capturing insights from the construction gram- mar and cognitive linguistics literature. ECG supports an approach to language understanding based on two linked