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Jointly Learning Similarity Transformations for Textual Entailment
Transactions of the Japanese society for artificial intelligence
Predicting entailment between two given texts is an important task on which the performance of numerous NLP tasks such as question answering, text summarization, and information extraction depend.The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose adoi:10.1527/tjsai.28.220 fatcat:v7sryhqnbrhgfhyy7fi2vh5xzu