FEATURE-BASED LANGUAGE UNDERSTANDING

Abstract

We consider translating natural language sentences into a formal language using a system that is data-driven and built automatically from training data. We use features that capture correlations between automatically determined key phrases in both languages. The features and their associated weights are selected using a training corpus of matched pairs of source and target language sentences to maximize the entropy of the resulting conditional probability model. Given a source-language sentence, we select as the translation a target-language candidate to which the model assigns maximum probability. We report results in Air Travel Information System (ATIS) domain.