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1
On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation ...
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The Grammar-Learning Trajectories of Neural Language Models ...
Abstract: The learning trajectories of linguistic phenomena in humans provide insight into linguistic representation, beyond what can be gleaned from inspecting the behavior of an adult speaker. To apply a similar approach to analyze neural language models (NLM), it is first necessary to establish that different models are similar enough in the generalizations they make. In this paper, we show that NLMs with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena. Taking inspiration from psycholinguistics, we argue that studying this inductive bias is an opportunity to study the linguistic representation implicit in NLMs. Leveraging these findings, we compare the relative performance on different phenomena at varying learning stages with simpler reference models. Results suggest that NLMs exhibit ... : ACL camera-ready ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2109.06096
https://arxiv.org/abs/2109.06096
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3
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA ...
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SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation ...
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