A Modality Lexicon and its use in Automatic Tagging
This work addresses modality tagging for machine translation, particularly in low-resource settings like English-Urdu, but it is incremental as it builds on existing annotation frameworks.
The paper tackled the problem of automatically tagging modality in text by constructing a modality lexicon and annotation scheme, achieving 86% precision in tagging and improving Urdu machine translation quality by 0.3 BLEU points.
This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger---a structure-based tagger---results in precision around 86% (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.