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A Non-Linear Structural Probe
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Understanding Learning Dynamics Of Language Models with SVCCA ...
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Abstract:
Research has shown that neural models implicitly encode linguistic features, but there has been no research showing \emph{how} these encodings arise as the models are trained. We present the first study on the learning dynamics of neural language models, using a simple and flexible analysis method called Singular Vector Canonical Correlation Analysis (SVCCA), which enables us to compare learned representations across time and across models, without the need to evaluate directly on annotated data. We probe the evolution of syntactic, semantic, and topic representations and find that part-of-speech is learned earlier than topic; that recurrent layers become more similar to those of a tagger during training; and embedding layers less similar. Our results and methods could inform better learning algorithms for NLP models, possibly to incorporate linguistic information more effectively. ... : Accepted for publication in NAACL 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Neural and Evolutionary Computing cs.NE
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URL: https://dx.doi.org/10.48550/arxiv.1811.00225
https://arxiv.org/abs/1811.00225
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A framework for (under)specifying dependency syntax without overloading annotators ...
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