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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 – 4 of 4 1 DoT: An efficient Double Transformer for NLP tasks with tables ... 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; Eisenschlos, Julian; Krichene, Syrine. - : Underline Science Inc., 2021 BASE Show details 2 MATE: Multi-view Attention for Table Transformer Efficiency ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cohen, William; Eisenschlos, Julian; Gor, Maharshi; Müller, Thomas. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.600/ Abstract: This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. However, more than 20% of relational tables on the web have 20 or more rows (Cafarella et al., 2008), and these large tables present a challenge for current Transformer models, which are typically limited to 512 tokens. Here we propose MATE, a novel Transformer architecture designed to model the structure of web tables. MATE uses sparse attention in a way that allows heads to efficiently attend to either rows or columns in a table. This architecture scales linearly with respect to speed and memory, and can handle documents containing more than 8000 tokens with current accelerators. MATE also has a more appropriate inductive bias for tabular data, and sets a new state-of-the-art for three table reasoning datasets. For HybridQA (Chen et al., 2020b), a dataset ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis URL: https://underline.io/lecture/37326-mate-multi-view-attention-for-table-transformer-efficiency https://dx.doi.org/10.48448/fney-f862 BASE Hide details 3 DoT: An efficient Double Transformer for NLP tasks with tables ... 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; Eisenschlos, Julian; Krichene, Syrine. - : Underline Science Inc., 2021 BASE Show details 4 MultiFiT: Efficient Multi-lingual Language Model Fine-tuning ... Eisenschlos, Julian Martin; Ruder, Sebastian; Czapla, Piotr. - : arXiv, 2019 BASE Show details

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