Abstract Shalom Lappin March 26 2018

From IMC wiki
Jump to: navigation, search

Modelling the Influence of Document Context on Human Acceptability Judgements

Joint work with Jean Philippe Bernardy, University of Gothenburg and Jey Han Lau, IBM Research Melbourne

We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments. The first compares ratings for sentences presented on their own with ratings for the same set of sentences given in their document contexts. The second assesses the accuracy with which two types of neural network models – one that incorporates context during training and one that does not – predict these judgements. Our results indicate that: (1) context improves acceptability ratings for ill-formed sentences, but also reduces them for well-formed sentences; and (2) context helps unsupervised systems to model acceptability.