Speaker: Jackie Cheung (McGill University)
Date & Time: December 2nd at 3:30 pm
Place: Education Bldg. rm. 624
Title: Generalized Natural Language Generation
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 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 an artificial neural network model achieves state-of-the-art performance on this task, learning relevant syntactic and semantic constraints.