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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 – 7 of 7 1 Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Abdou, Mostafa; Frank, Stella. - : Underline Science Inc., 2021 BASE Show details 2 Positional Artefacts Propagate Through Masked Language Model Embeddings ... 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; Kulmizev, Artur; Luo, Ziyang. - : Underline Science Inc., 2021 BASE Show details 3 Attention Can Reflect Syntactic Structure (If You Let It) ... Ravishankar, Vinit; Kulmizev, Artur; Abdou, Mostafa; Søgaard, Anders; Nivre, Joakim. - : arXiv, 2021 Abstract: Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head attention mechanism. However, much of such work focused almost exclusively on English -- a language with rigid word order and a lack of inflectional morphology. In this study, we present decoding experiments for multilingual BERT across 18 languages in order to test the generalizability of the claim that dependency syntax is reflected in attention patterns. We show that full trees can be decoded above baseline accuracy from single attention heads, and that individual relations are often tracked by the same heads across languages. Furthermore, in an attempt to address recent debates about the status of attention as an explanatory mechanism, we experiment with fine-tuning mBERT on a supervised parsing objective while freezing different series of parameters. Interestingly, in steering the objective to learn explicit linguistic structure, ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2101.10927 https://arxiv.org/abs/2101.10927 BASE Hide details 4 Schrödinger's Tree -- On Syntax and Neural Language Models ... Kulmizev, Artur; Nivre, Joakim. - : arXiv, 2021 BASE Show details 5 Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding ... Hershcovich, Daniel; de Lhoneux, Miryam; Kulmizev, Artur. - : arXiv, 2020 BASE Show details 6 Do Neural Language Models Show Preferences for Syntactic Formalisms? ... Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa. - : arXiv, 2020 BASE Show details 7 Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited ... Kulmizev, Artur; de Lhoneux, Miryam; Gontrum, Johannes. - : arXiv, 2019 BASE Show details

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