Variation Sets Facilitate Artificial Language Learning

Variation Sets Facilitate Artificial Language Learning Heidi Waterfall (he32@cornell.edu) 1 Luca Onnis (lo35@cornell.edu) Shimon Edelman (se37@cornell.edu) Department of Psychology, Cornell University Ithaca, NY 14853 USA Abstract others change. Specifically, local alignment of (1b) and (2b) suggests that (1b) is composed of at least three el- ements: Variation set structure — partial alignment of successive utterances in child-directed speech — has been shown to correlate with progress in the acquisition of syntax by children. The present study demonstrates that ar- ranging a certain proportion of utterances in a train- ing corpus in variation sets facilitates word segmenta- tion and phrase structure learning in miniature artifi- cial languages by adults. Our findings have implications for understanding the mechanisms of L1 acquisition by children, and for the development of more efficient al- gorithms for automatic language acquisition, as well as better methods for L2 instruction. Keywords: language acquisition, grammar inference, artificial language learning, implicit learning, variation sets, child-directed speech. (1c) kedmalbu rafuloro pesai (2c) -------- rafuloro ----- Aligning the next pair yields new elements: (2c) rafu loro ----- (3c) mana loro pesai Further, (3c) -------- mana loro pesai (4c) kedmalbu mana loro pesai Variation sets in language learning Imagine entering a room and overhearing the following round of conversation in an unfamiliar language: (1a) (2a) (3a) (4a) kedmalburafuloropesai gianaber manadukbiunel kiciorudanamjeisulcaz Not surprisingly, you cannot even make out the individ- ual words in any of those utterances (represented here in print by unbroken sequences of letters). Now, suppose the utterances you overheard were these: (1b) (2b) (3b) (4b) kedmalburafuloropesai rafuloro manaloropesai kedmalbumanaloropesai Because these four utterances appear related to each other, this sample (unlike the previous one) affords a glimpse of some of the structures behind the unfamiliar language. These structures are readily revealed by the two computational operations proposed by Zellig Harris (1946) for language discovery: alignment of utterances and their subsequent comparison (when applied recur- sively along with statistical control over structure infer- ence, this approach proved effective in unsupervised lan- guage acquisition from raw corpora; Solan, Horn, Rup- pin, and Edelman, 2005). In the present example, sim- ple, “local” alignment of successive utterances immedi- ately reveals that some sequences of letters repeat, while Also with the Department of Psychology, University of Chicago, Chicago, IL 60637 USA. The property of sample (1b-4b) that afford this discov- ery, which is absent from sample (1a-4a), is that its suc- cessive utterances form partial self-repetitions, or varia- tion sets. Variation sets are a prominent feature of child-directed speech: 2 about 20% of utterances in child-directed speech appear within variation sets, whose prevalence and composition has been shown to facilitate lexical and syntactic development (Hoff-Ginsberg, 1986; K¨ untay and Slobin, 1996; for recent reviews and results, see Water- fall, 2007a,b). Indeed, our second example (1b-4b) comes from a snippet of a real corpus — a variation set ad- dressed to a 14 month old child studied by Waterfall, in which we replaced the English words with nonce strings: You got to push them to school. Push them. Push them to school. Take them to school. You got to take them to school. From a cognitive computational standpoint, the key characteristic of variation sets is that the structure they contain can be revealed by a local mechanism that aligns and compares adjacent utterances. This characteristic allows even memory-limited learners to discover struc- ture that they would miss, if the relatable utterances were scattered over a longer exchange. In addition to facilitating the segmentation of ut- terances into lexical elements, variation sets can yield higher-order structural properties of the language. For There are indications that variation sets are also preva- lent in adult conversations (Pickering and Garrod, 2004; Szm- recsanyi, 2005).