H Chen, G Zheng, Y Ji - arXiv preprint arXiv:2004.02015, 2020 - arxiv.org
Generating explanations for neural networks has become crucial for their applications in real- world with respect to reliability and trustworthiness. In natural language processing, existing …
The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for …
Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token …
Natural language processing (NLP) has been one of the subfields of artificial intelligence much affected by the recent neural revolution. Architectures such as recurrent neural …
The state-of-the-art systems for most natural language engineering tasks employ machine learning methods. Despite the improved performances of these systems, there is a lack of …
Named entity recognition (NER) is an important task in natural language processing (NLP). Until the revival of neural network based models for NLP, NER taggers employed traditional …
Explainability in machine learning remains a critical unsolved challenge that slows the adoption of machine learning systems in real-world applications. Machine learning …