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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 Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Herbig, Nico; Krueger, Antonio. - : Underline Science Inc., 2021 BASE Show details 2 Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ... 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; Liu, Qiuhui; van Genabith, Josef; Xiong, Deyi; Xu, Hongfei; Zhang, Meng. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.23 Abstract: One of the reasons Transformer translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence level. However, the computational complexity of a self-attention network is $O(n^2)$, increasing quadratically with sequence length. By contrast, the complexity of LSTM-based approaches is only O(n). In practice, however, LSTMs are much slower to train than self-attention networks as they cannot be parallelized at sequence level: to model context, the current LSTM state relies on the full LSTM computation of the preceding state. This has to be computed n times for a sequence of length n. The linear transformations involved in the LSTM gate and state computations are the major cost factors in this. To enable sequence-level parallelization of LSTMs, we approximate full LSTM context modelling by computing hidden states and gates with the current input and a simple bag-of-words representation ... 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/fcc7-e373 https://underline.io/lecture/25374-multi-head-highly-parallelized-lstm-decoder-for-neural-machine-translation BASE Hide details 3 Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Amponsah-Kaakyire, Kwabena; Dutta Chowdhury, Koel. - : Underline Science Inc., 2021 BASE Show details 4 Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ... 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; Liu, Qiuhui; van Genabith, Josef. - : Underline Science Inc., 2021 BASE Show details 5 A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ... 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; van Genabith, Josef; Zhang, Jingyi. - : Underline Science Inc., 2021 BASE Show details 6 A Computational Model of the Referential Semantics of Projective Prepositions van Genabith, Josef; Kelleher, John D. In: Conference papers (2006) BASE Show details

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