The robustness of an almost-parsing language model given errorful training data

An almost-parsing language model has been developed (Wang and Harper 2002) that provides a framework for tightly integrating multiple knowledge sources. Lexical features and syntactic constraints are integrated into a uniform linguistic structure (called a SuperARV) that is associated with words in the lexicon. The SuperARV language model has been found able to reduce perplexity and word error rate (WER) compared to trigram, part-of-speech-based, and parser-based language models on the DARPA Wall Street Journal (WSJ) CSR task. In this paper we further investigate the robustness of the language model to possibly inconsistent and flawed training data, as well as its ability to scale up to sophisticated LVCSR tasks by comparing performance on the DARPA WSJ and Hub4 (Broadcast News) CSR tasks.