The ARIEL-CMU situation frame detection pipeline for LoReHLT16: a model translation approach

Abstract

The LoReHLT16 evaluation challenged participants to extract Situation Frames (SFs)—structured descriptions of humanitarian need situations—from monolingual Uyghur text. The ARIEL-CMU SF detector combines two classification paradigms, a manually curated keyword-spotting system and a machine learning classifier. These were applied by translating the models on a per-feature basis, rather than translating the input text. The resulting combined model provides the accuracy of human insight with the generality of machine learning, and is relatively tractable to human analysis and error correction. Other factors contributing to success were automatic dictionary creation, the use of phonetic transcription, detailed, hand-written morphological analysis, and naturalistic glossing for error analysis by humans. The ARIEL-CMU SF pipeline produced the top-scoring LoReHLT16 situation frame detection systems for the metrics SFType, SFType+Place+Need, SFType+Place+Relief, and SFType+Place+Urgency, at each of the three checkpoints.