Voici l’annonce du 7e séminaire au DIC pour la session HIVER 2017
Titre : Generalized Natural Language Generation
Jackie CK CHEUNG
Jeudi le 16 mars 2017
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 pattern recognition techniques. However, such training data exists in sufficient quantity and quality for only 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 a recurrent neural network model achieves state-of-the-art performance on this task, learning relevant syntactic and semantic constraints.
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).