Abstract

Abstract:

Multiword expressions (MWEs) are combinations of two or more words with syntactic and semantic idiosyncratic behaviours. They are prevalent in any language and domain. Different categories of MWEs include idioms, nominal compounds, light verb constructions, verb particle constructions, and each pose particular challenges in processing. While they are known to be processed faster by native speakers, language learners find them difficult to understand and use. Like most machine translation (MT) systems, current Grammatical Error Correction (GEC) systems do not take them into consideration and are not good at correcting them. In this talk, I give a brief presentation of a survey on different approaches to model MWEs in NLP . I summarise on how transformers advanced in dealing with them and what aspects are still lacking proper solutions. And finally, I illustrate our method for processing MWEs in a grammatical error correction system able to capture this type of errors better than baseline GEC systems.

Bio:

Shiva Taslimipoor is a postdoctoral research associate in the NLIP group at the University of Cambridge. She is a member of ALTA Institute where her research focus is on developing tools for automatic language learning and assessment. Before that she was a research associate at RGCL at the University of Wolverhampton where she completed her PhD in the area of Natural Language Processing on the topic of ‘Automatic Identification and Translation of Multiword Expressions’.