Find related and similar companies as well as employees by title and. Social media infogmask.io supportgmask. Preview: This item is a commodity, where all the individual items are effectively identical. View G Mask (location in Kazakhstan, revenue, industry and description. Experiments show the effectiveness of GMASK in providing faithful explanations to these models. Gmask is bringing crypto payments to mainstream transactions to create a more just and equitable financial world. Thank you to all of those who replied to the gmask query. GMask was written by Tsuyoshi Furumizo and can currently be downloaded (as well as with.
The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks.
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In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. michael cruz Director of Operations at Gmask Experience Looking for career advice Others named michael cruz View michaels full profile Sign in to view. 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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