Detecting Unseen Multiword Expressions in American Sign Language
This work addresses a challenge in American Sign Language translation, but it is incremental as it applies existing methods to a new domain.
The researchers tackled the problem of detecting unseen multiword expressions in American Sign Language translation by testing systems using GloVe word embeddings, finding that these embeddings can detect non-compositionality with decent accuracy.
Multiword expressions present unique challenges in many translation tasks. In an attempt to ultimately apply a multiword expression detection system to the translation of American Sign Language, we built and tested two systems that apply word embeddings from GloVe to determine whether or not the word embeddings of lexemes can be used to predict whether or not those lexemes compose a multiword expression. It became apparent that word embeddings carry data that can detect non-compositionality with decent accuracy.