QASR: question answering using semantic roles for speech interface

In this paper, we evaluate a semantic role labeling approach to the extraction of answers in the open domain question answering task. We show that this technique especially improves the system performance when answers are communicated to the user by voice. Semantic role labeling identifies predicates and semantic argument phrases in a sentence. With this information we are able to analyze and extract structure from both questions and candidate sentences, which helps us identify more relevant and precise answers in a long list of candidate sentences. When searching for an answer to a question, we match the missing argument in the question to the semantic parses of the candidate answers. This technique significantly improves the accuracy of the question answering system and results in more concise and grammatical answers, which is essential for enabling voice interfaces to question answering systems. In this paper we apply our approach to factoid questions containing predicates; however, this technique can be also useful in answering more complex questions.