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Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan
Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …
Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven
Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …