12 March 2019, Computational Linguistics Seminar, Victoria Yaneva

p> When processing a text, both humans and machines must cope with ambiguity and non-compositionality. These phenomena represent a considerable challenge for NLP systems, while at the same time there is limited evidence from online measures on how humans solve them during natural reading. We approach these two problems as one and hypothesize that obtaining information on how humans process ambiguous and non-compositional phrases can improve the computational treatment of such instances. I will present experiments on using eye-tracking data to improve NLP models for two tasks: classifying the different roles of the pronoun It (nominal anaphoric, clause anaphoric and non-referential), as well as the identification of multi-word expressions. The experiments test whether gaze-based features improve the performance of state-of-the-art NLP models and the extent to which gaze features can be used to partially or entirely substitute the crafting of linguistic ones. The best-performing models are then analysed to better understand the cognitive processing of these linguistic phenomena and findings are discussed with respect to the E-Z model of reading and the processing stages during which disambiguation occurs.