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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. - : Underline Science Inc., 2021 BASE Show 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 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.24 Abstract: Word alignment and machine translation are two closely related tasks. Neural translation models, such as RNN-based and Transformer models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help neural machine translation in many different ways, such as missing word detection, annotation transfer and lexicon injection. Existing methods for learning word alignment include statistical word aligners (e.g. GIZA++) and recently neural word alignment models. This paper presents a bidirectional Transformer based alignment (BTBA) model for unsupervised learning of the word alignment task. Our BTBA model predicts the current target word by attending the source context and both left-side and right-side target context to produce accurate target-to-source attention (alignment). We further fine-tune the target-to-source attention in the BTBA model to ... 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/pmd0-7v13 https://underline.io/lecture/25375-a-bidirectional-transformer-based-alignment-model-for-unsupervised-word-alignment BASE Hide 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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