THK's Natural Logic-based Compositional Textual Entailment Model at NTCIR-10 RITE-2
نویسندگان
چکیده
This paper describes the THK system that participated in the BC subtask, MC subtask, ExamBC subtask and UnitTest in NTCIR-10 RITE-2. Our system learns plausible transformations of pairs of Text t1 and Hypothesis t2 only from semantic labels of the pairs using a discriminative probabilistic model combined with the framework of Natural Logic. The model is trained so as to prefer alignments and their semantic relations which infer the correct sentence-level semantic relations. In the formal run, we achieved the highest performance of detecting contradictions in the MC subtask (28.57 of F1).
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