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A Neighbourhood Framework for Resource-Lean Content Flagging ...
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A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives ...
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QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension ...
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Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ...
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Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models? ...
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CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding ...
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How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? ...
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Quantifying Gender Biases Towards Politicians on Reddit ...
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Semi-Supervised Exaggeration Detection of Health Science Press Releases ...
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Inducing Language-Agnostic Multilingual Representations ...
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SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension ...
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20 |
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP ...
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Abstract:
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle of 'visualizing preferred inputs of neurons' to make it usable transfer analysis and NLP. This allows one to analyze, track and quantify how self- or supervised NLP models first build knowledge abstractions in pretraining (1), and then transfer these abstractions to a new domain (2), or adapt them during supervised fine-tuning (3). TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. We find that, similar to Lottery Ticket based pruning, TX-Ray based pruning can improve test set generalization and that it can reveal how early stages of self-supervision automatically learn linguistic abstractions like ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML
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URL: https://arxiv.org/abs/1912.00982 https://dx.doi.org/10.48550/arxiv.1912.00982
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