Contemporaneous text as side-information in statistical language modeling

We propose new methods to exploit contemporaneous text, such as on-line news articles, to improve language models for automatic speech recognition and other natural language processing applications. In particular, we investigate the use of text from a resource-rich language to sharpen language models for processing a news story or article in a language with scarce linguistic resources. We demonstrate that even with fairly crude cross-language information retrieval and simple machine translation, one can construct story-specific Chinese language models which exploit cues from a side-corpus of English newswire to significantly improve the performance of language models estimated from a static Chinese corpus. Our investigations cover cases when the amount of available Chinese text is small, and a case when a large Chinese text corpus is available. We examine the effectiveness of our techniques both when the side-corpus contains English documents that are near-translations of the Chinese documents being processed, and when the English side-corpus is merely from contemporaneous and independent news sources. We present experimental results for automatic transcription of speech from the Mandarin Broadcast News corpus. (C) 2003 Elsevier Ltd. All rights reserved.