Context is Everything: From language modeling to language generation

Friday, October 13, 2017, 3:00 pm - 4:00 pm PDTiCal
11th Flr Conf Room-CR #1135
This event is open to the public.
NL Seminar
Yangfeng Ji (University of Washington)

Abstract: Contextual information is critical for language processing and generation. Particularly for large texts consisting of multiple sentences or paragraphs, how to capture the contextual information beyond sentence boundaries is important for building better language processing systems. This talk will discuss our recent effort on incorporating contextual information to language modeling and generation. It presents three models with each of them corresponds a specific linguistic phenomenon of context shared in written texts: (i) local context from preceding sentences; (ii) semantic and pragmatic relations between adjacent sentences; and (iii) evolving of entities (e.g., characters in novels) through coreference links in texts. The starting point of our model design is sentence-level recurrent neural network language models (RNNLMs). To capture these aspects of contextual information, we extend RNNLMs by either adding extra connections among existing network components, or adding dedicated components particularly to encode specific linguistic information. Evaluation results show that these models outperforms strong baselines and prior work language modeling tasks. Their ability of capturing contextual information is also verified by the quantitative evaluation on each corresponding task, such as identifying the relation between sentences, and resolving coreference ambiguity. Qualitative analysis is also included to demonstrate the ability of these models for text generation.

Bio: Yangfeng Ji is a postdoc researcher at University of Washington working with Noah Smith. His research interests lie in the interaction of natural language processing and machine learning. He is interested in designing machine learning models and algorithms for language processing, and also fascinated by how linguistic knowledge helps build better learning models. He completed his Ph.D. in Computer Science at Georgia Institute of Technology in 2016, advised by Jacob Eisenstein. He was one of the area co-chairs on Discourse and Pragmatics in ACL 2017.

« Return to Upcoming Events