![]() ![]() ![]() ![]() However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. Publisher = "Association for Computational Linguistics ",Ībstract = "Explaining neural network models is important for increasing their trustworthiness in real-world applications. Title = "Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks ",Īuthor = "Chen, Hanjie and Feng, Song and Ganhotra, Jatin and Wan, Hui and Gunasekara, Chulaka and Joshi, Sachindra and Ji, Yangfeng ",īooktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ", ![]()
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