Seminar by Dr. Jackie C. K. Cheung (McGill University)

Speaker: Dr. Jackie C. K. Cheung
                McGill University

Title: Generalized Natural Language Generation

Date: Monday, February 13th, 2017

Time: 10:30 a.m

Place: EV3.309

ABSTRACT

In popular language generation tasks such as machine translation, automatic systems are typically given pairs of expected input and output (e.g., a sentence in some source language and its translation in the target language). A single task-specific model is then learned from these samples using statistical techniques. However, such training data only exists in sufficient quantity and quality for a small number of high-profile, standardized generation tasks. In this talk, I argue for the need for generic tools in natural language generation, and discuss my lab's work on developing generic generation tasks and methods to solve them. First, I discuss progress on defining a task in sentence aggregation, which involves predicting whether units of semantic content can be meaningfully expressed in the same sentence. Then, I present a system for predicting noun phrase definiteness, and show that an artificial neural network model achieves state-of-the-art performance on this task, learning relevant syntactic and semantic constraints.

BIO

Jackie CK Cheung is an assistant professor in the School of Computer Science at McGill University. He received his Ph.D. at the University of Toronto, and was awarded a Facebook Fellowship for his doctoral research. He conducts research in computational semantics and natural language generation, with a focus on topics such as inducing event structure from distributional semantics, and automatic summarization. His work is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Fonds de recherche du Québec - Nature et technologies (FRQNT).