Language-models for questions

Natural-language question-answering is a promising interface for retrieving information in mobile contexts because it by-passes the problem of presenting documents and interim search results on a small screen. This paper considers language-models suitable for rapid predictive text-input and spoken input of natural-language questions. It describes a varied corpus of fact-seeking questions posed by users online and analyzes its structure. We find it to be highly constrained lexically despite its wide spectrum of topics, with a per-word perplexity less than 47 with around 2.6% of words in the test set out-of-vocabulary. One implication is that predictive interfaces can greatly speed up the input of natural-language questions with a keypad or stylus. Another is that automatic speech-recognition of such questions can be quite accurate.