tailieunhanh - Báo cáo khoa học: "Learning Strategies for Open-Domain Natural Language Question Answering"

This work presents a model for learning inference procedures for story comprehension through inductive generalization and reinforcement learning, based on classified examples. The learned inference procedures (or strategies) are represented as of sequences of transformation rules. The approach is compared to three prior systems, and experimental results are presented demonstrating the efficacy of the model. | Learning Strategies for Open-Domain Natural Language Question Answering Eugene Grois Department of Computer Science University of Illinois Urbana-Champaign Urbana Illinois e-grois@ Abstract This work presents a model for learning inference procedures for story comprehension through inductive generalization and reinforcement learning based on classified examples. The learned inference procedures or strategies are represented as of sequences of transformation rules. The approach is compared to three prior systems and experimental results are presented demonstrating the efficacy of the model. 1 Introduction This paper presents an approach to automatically learning strategies for natural language question answering from examples composed of textual sources questions and answers. Our approach is focused on one specific type of text-based question answering known as story comprehension. Most TREC-style QA systems are designed to extract an answer from a document contained in a fairly large general collection Voorhees 2003 . They tend to follow a generic architecture such as the one suggested by Hirschman and Gaizauskas 2001 that includes components for document preprocessing and analysis candidate passage selection answer extraction and response generation. Story comprehension requires a similar approach but involves answering questions from a single narrative document. An important challenge in text-based question answering in general is posed by the syntactic and semantic variability of question and answer forms which makes it difficult to establish a match between the question and answer candidate. This problem is particularly acute in the case of story comprehension due to the rarity of information restatement in the single document. Several recent systems have specifically addressed the task of story comprehension. The Deep Read reading comprehension system Hirschman et al. 1999 uses a statistical bag-of-words approach matching the question with the .