PLAY PODCASTS
27 - What do Neural Machine Translation Models Learn about Morphology?, with Yonatan Belinkov

27 - What do Neural Machine Translation Models Learn about Morphology?, with Yonatan Belinkov

ACL 2017 paper by Yonatan Belinkov and others at …

NLP Highlights · Allen Institute for Artificial Intelligence

July 5, 201729m 8s

Audio is streamed directly from the publisher (podtrac.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.

Show Notes

ACL 2017 paper by Yonatan Belinkov and others at MIT and QCRI. Yonatan comes on to tell us about their work. They trained a neural MT system, then learned models on top of the NMT representation layers to do morphology tasks, trying to probe how much morphological information is encoded by the MT system. We talk about the specifics of their model and experiments, insights they got from doing these experiments, and how this work relates to other work on representation learning in NLP. https://www.semanticscholar.org/paper/What-do-Neural-Machine-Translation-Models-Learn-ab-Belinkov-Durrani/37ac87ccea1cc9c78a0921693dd3321246e5ef07