We’ve just submitted a perspective paper on second (and third and …) language learning as hierarchical inference that I hope might be of interest to some of you (feedback welcome).
- Pajak, B., Fine, A.B., Kleinschmidt, D., and Jaeger, T.F. submitted. Learning additional languages as hierarchical probabilistic inference: insights from L1 processing. submitted for review to Language Learning.
We’re building on Bozena’s thesis work on L2 acquisition with Eric Baković and Roger Levy and motivating it through work on L1 language processing (focusing on speech perception and syntactic processing). Specifically, we review evidence that L1 language processing can be construed as hierarchical inference over generative models –roughly speaking, language models (or ‘mini grammars’, as some people called it during a workshop at the 2013 LSA Summer Institute) for specific context, speakers, etc. that are hierarchically organized, thereby allowing to capture generalizations across speakers and groups of speaker (a paper that further details this view for L1 speech perception is under revision; Kleinschmidt and Jaeger, draft available upon request). While tentative, we think that this view provides a unifying framework for a variety of otherwise unrelated phenomena in L1, L2, etc. language learning and processing in terms of inference over uncertainty at multiples level. For me, well, read the paper. We provide an informal and, hopefully, somewhat intuitive introduction into this computational framework that draws on and incorporates earlier work in statistical / distributional learning in second language acquisition and adaptation during speech perception and sentence processing in L1.
Here are some figures (some are also in the paper) that illustrate the idea for speech perception, courtesy of Dave Kleinschmidt.