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CLT seminar: Shalom Lappin - Grammaticality and Gradience


The question of whether grammaticality is a binary categorical or a gradient property has been the subject of ongoing debate in linguistics and psychology for many years. Linguists have tended to use constructed examples to test speakers’ judgements on specific sorts of constraint violation.

We applied machine translation to randomly selected subsets of the British National Corpus (BNC) to generate a large test set which contains well-formed English source sentences, and sentences that exhibit a wide variety of grammatical infelicities. We tested a large number of speakers through (filtered) crowd sourcing, with three distinct modes of classification, one binary and two ordered scales. We found a high degree of correlation in mean judgements for sentences across the three classification tasks. We also did two visual image classification tasks to obtain benchmarks for binary and gradient judgement patterns, respectively.

The sentence judgement distributions for individual speakers strongly resemble the gradience benchmark pattern. This evidence suggests that speakers represent grammatical well-formedness as a gradient property. In current work we are studying the extent to which enriched lexical n-gram models and other probabilistic models track the AMT judgements in our experiment. We are also employing machine learning techniques to identify the most significant features of these models. We briefly describe this modeling work.

Shalom Lappin, King's College London

Joint work with Jey Han Lau and Alexander Clark, King's College London

Date: 2014-02-20 10:30 - 11:30

Location: L308, Lennart Torstenssonsgatan 8


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