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AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
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Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
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VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
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Attention-based Contextual Language Model Adaptation for Speech Recognition ...
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Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
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LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
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Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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A DQN-based Approach to Finding Precise Evidences for Fact Verification ...
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Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ...
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A Span-based Dynamic Local Attention Model for Sequential Sentence Classification ...
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Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
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2A: Sentiment Analysis, Stylistic Analysis, and Argument Mining #1 ...
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On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.419 Abstract: In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness. In this work, for the first time, we can improve the interpretability of explanations by allowing arbitrary text sequences as the explanation unit. On top of this, we implement a hessian-free method with a model faithfulness guarantee. Finally, to compare our method with the others, we propose a semantic-based evaluation metric that can better align with humans' judgment of explanations than the widely adopted diagnostic or re-training measures. The empirical results on multiple real data sets demonstrate the proposed method's superior performance to popular explanation techniques such as Influence Function or TracIn on semantic evaluation. ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://underline.io/lecture/25843-on-sample-based-explanation-methods-for-nlp-faithfulness,-efficiency-and-semantic-evaluation https://dx.doi.org/10.48448/zffx-yb03
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