Linguistics in the age of deep learning

Speaker: Tal Linzen , Johns Hopkins University

Date: Thursday, April 18, 2019

Time: 5:00 PM to 6:00 PM

Public: Yes

Location: 32-141

Event Type:

Room Description:

Host:

Contact: Yen-Ling Kuo, ylkuo@csail.mit.edu

Relevant URL:

Speaker URL: http://tallinzen.net/

Speaker Photo:
None

Reminders to: seminars@lists.csail.mit.edu, comp-lang@mit.edu

Reminder Subject: TALK: [CompLang] Linguistics in the age of deep learning

Deep learning systems with minimal or no explicit linguistic structure have recently proved to be surprisingly successful in language technologies. What, then, is the role of linguistics in language technologies in the deep learning age? I will argue that the widespread use of these "black box" models provides an opportunity for a new type of contribution: characterizing the desired behavior of the system along interpretable axes of generalization from the training set, and identifying the areas in which the system falls short of that standard.

I will illustrate this approach in word prediction (language models) and natural language inference. I will show that recurrent neural network language models are able to process many syntactic dependencies in typical sentences with considerable success, but when evaluated on carefully controlled materials, their error rate increases sharply. Perhaps more strikingly, neural inference systems (including ones based on the widely popular BERT model), which appear to be quite accurate according to the standard evaluation criteria used in the NLP community, perform very poorly in controlled experiments; for example, they universally infer from "the judge chastised the lawyer” that "the lawyer chastised the judge”. Finally, if time permits, I will show how neural network models can be used to address classic questions in linguistics, in particular by providing a platform for testing for the necessity and sufficiency of explicit structural biases in the acquisition of syntactic transformations.

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About CompLang:
CompLang is a student-run discussion group on language and computation. The aim of the group is to bring together the language community at MIT and nearby, learn about each other's research, and foster cross-laboratory collaborations. The broad topic of the meetings is using computational models to study scientific questions about language. We will discuss work from computational linguistics, psycholinguistics, cognitive science, natural language processing and formal linguistics. Please visit http://complang.mit.edu for future events.

Research Areas:
AI & Machine Learning

Impact Areas:

This event is not part of a series.

Created by Yen-Ling Kuo Email at Monday, April 15, 2019 at 10:43 PM.