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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya... In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021) BASE Show details 2 Learning Compact Metrics for MT ... Pu, Amy; Chung, Hyung Won; Parikh, Ankur P.. - : arXiv, 2021 BASE Show details 3 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ... Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya. - : arXiv, 2021 BASE Show details 4 Learning Compact Metrics for MT ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chung, Hyung Won; Gehrmann, Sebastian. - : Underline Science Inc., 2021 BASE Show details 5 Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Belinkov, Yonatan; Finlayson, Matthew; Gehrmann, Sebastian; Linzen, Tal; Mueller, Aaron; Shieber, Stuart. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.144 Abstract: Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models. We investigate the magnitude of models' preferences for grammatical inflections, as well as whether neurons process subject-verb agreement similarly across sentences with different syntactic structures. We uncover similarities and differences across architectures and model sizes—notably, that larger models do not necessarily learn stronger preferences. We also observe two distinct mechanisms for producing subject-verb agreement depending on the syntactic structure of the input sentence. Finally, we find that language models rely on similar sets of neurons when given sentences with similar syntactic structure. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/76fq-n338 https://underline.io/lecture/25457-causal-analysis-of-syntactic-agreement-mechanisms-in-neural-language-models BASE Hide details 6 LSTM Networks Can Perform Dynamic Counting Suzgun, Mirac; Gehrmann, Sebastian; Belinkov, Yonatan. - 2019 BASE Show details

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