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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 – 3 of 3 1 Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction ... Yarmohammadi, Mahsa; Wu, Shijie; Marone, Marc. - : arXiv, 2021 BASE Show details 2 Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Yarmohammadi, Mahsa. - : Underline Science Inc., 2021 BASE Show details 3 CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models ... Singh, Abhinav; Xia, Patrick; Qin, Guanghui; Yarmohammadi, Mahsa; Van Durme, Benjamin. - : arXiv, 2020 Abstract: Copy mechanisms are employed in sequence to sequence models (seq2seq) to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed. ... : 4th Workshop on Structured Prediction for NLP (EMNLP 2020) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2010.15266 https://dx.doi.org/10.48550/arxiv.2010.15266 BASE Hide details

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