In our lab meeting this week, Nadia will talk about making use of deep neural networks for encoding semantic information in word representations. Here is title and abstract of her talk:
Title: Deep contextualized word representations
Abstract: We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bi-directional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment, and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.
Link to paper: https://arxiv.org/abs/1802.05365
Wednesday, July 3rd, 10:00 a.m. TASC1 9408.