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Language Modelling as a Multi-Task Problem ...
Abstract: In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains. ... : Accepted for publication at EACL 2021 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://arxiv.org/abs/2101.11287
https://dx.doi.org/10.48550/arxiv.2101.11287
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2
Assessing incrementality in sequence-to-sequence models ...
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3
Compositionality decomposed: how do neural networks generalise? ...
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Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ...
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Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text
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