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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 – 5 of 5 1 Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks ... Cui, Ruixiang; Hershcovich, Daniel; Søgaard, Anders. - : arXiv, 2022 BASE Show details 2 Challenges and Strategies in Cross-Cultural NLP ... Hershcovich, Daniel; Frank, Stella; Lent, Heather. - : arXiv, 2022 BASE Show details 3 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 4 A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; de Lhoneux, Miryam; Hartmann, Mareike. - : Underline Science Inc., 2021 BASE Show details 5 Does injecting linguistic structure into language models lead to better alignment with brain recordings? ... Abdou, Mostafa; Gonzalez, Ana Valeria; Toneva, Mariya; Hershcovich, Daniel; Søgaard, Anders. - : arXiv, 2021 Abstract: Neuroscientists evaluate deep neural networks for natural language processing as possible candidate models for how language is processed in the brain. These models are often trained without explicit linguistic supervision, but have been shown to learn some linguistic structure in the absence of such supervision (Manning et al., 2020), potentially questioning the relevance of symbolic linguistic theories in modeling such cognitive processes (Warstadt and Bowman, 2020). We evaluate across two fMRI datasets whether language models align better with brain recordings, if their attention is biased by annotations from syntactic or semantic formalisms. Using structure from dependency or minimal recursion semantic annotations, we find alignments improve significantly for one of the datasets. For another dataset, we see more mixed results. We present an extensive analysis of these results. Our proposed approach enables the evaluation of more targeted hypotheses about the composition of meaning in the brain, expanding ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2101.12608 https://arxiv.org/abs/2101.12608 BASE Hide details

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