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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 Page: 1 2 Hits 1 – 20 of 31 1 Translation Error Detection as Rationale Extraction ... Fomicheva, Marina; Specia, Lucia; Aletras, Nikolaos. - : arXiv, 2021 BASE Show details 2 Knowledge Distillation for Quality Estimation ... Gajbhiye, Amit; Fomicheva, Marina; Alva-Manchego, Fernando. - : arXiv, 2021 BASE Show details 3 BERTGEN: Multi-task Generation through BERT ... Mitzalis, Faidon; Caglayan, Ozan; Madhyastha, Pranava. - : arXiv, 2021 BASE Show details 4 Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ... Sun, Shuo; El-Kishky, Ahmed; Chaudhary, Vishrav. - : arXiv, 2021 BASE Show details 5 A Generative Framework for Simultaneous Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Blunsom, Phil; Miao, Yishu. - : Underline Science Inc., 2021 BASE Show details 6 Continual Quality Estimation with Online Bayesian Meta-Learning ... 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; Fomicheva, Marina; Obamuyide, Abiola. - : Underline Science Inc., 2021 BASE Show details 7 SentSim: Crosslingual Semantic Evaluation of Machine Translation ... NAACL 2021 2021; Song, Yurun; Specia, Lucia. - : Underline Science Inc., 2021 BASE Show details 8 Knowledge Distillation for Quality Estimation ... 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; Aletras, Nikolaos; Alva-Manchego, Fernando. - : Underline Science Inc., 2021 BASE Show details 9 BERTGen: Multi-task Generation through BERT ... 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; Caglayan, Ozan; Madhyastha, Pranava. - : Underline Science Inc., 2021 BASE Show details 10 What Makes a Scientific Paper be Accepted for Publication? ... Fytas, Panagiotis; Rizos, Georgios; Specia, Lucia. - : arXiv, 2021 BASE Show details 11 MultiSubs: A Large-scale Multimodal and Multilingual Dataset ... Wang, Josiah; Madhyastha, Pranava; Figueiredo, Josiel. - : arXiv, 2021 BASE Show details 12 Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP Vaidhya, Tejas; Sachan, Mrinmaya; von Kügelgen, Julius... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 13 Classifying Dyads for Militarized Conflict Analysis Torroba Hennigen, Lucas; Stoehr, Niklas; West, Robert... In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 14 Efficient Sampling of Dependency Structure Vieira, Tim; Zmigrod, Ran; Cotterell, Ryan In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) Abstract: Probabilistic distributions over spanning trees in directed graphs are a fundamental model of dependency structure in natural language processing, syntactic dependency trees. In NLP, dependency trees often have an additional root constraint: only one edge may emanate from the root. However, no sampling algorithm has been presented in the literature to account for this additional constraint. In this paper, we adapt two spanning tree sampling algorithms to faithfully sample dependency trees from a graph subject to the root constraint. Wilson (1996(’s sampling algorithm has a running time of O(H) where H is the mean hitting time of the graph. Colbourn (1996)’s sampling algorithm has a running time of O(N3), which is often greater than the mean hitting time of a directed graph. Additionally, we build upon Colbourn’s algorithm and present a novel extension that can sample K trees without replacement in O(K N3 + K2 N) time. To the best of our knowledge, no algorithm has been given for sampling spanning trees without replacement from a directed graph. URL: https://hdl.handle.net/20.500.11850/518990 https://doi.org/10.3929/ethz-b-000518990 BASE Hide details 15 Searching for More Efficient Dynamic Programs Cotterell, Ryan; Eisner, Jason; Vieira, Tim In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021) BASE Show details 16 “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding Zhao, Chao; Sachan, Mrinmaya; Tafjord, Oyvind... In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021) BASE Show details 17 A Bayesian Framework for Information-Theoretic Probing Cotterell, Ryan; Pimentel, Tiago In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021) BASE Show details 18 Findings of the WMT 2021 Shared Task on Quality Estimation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Blain, Frédéric; Chaudhary, Vishrav. - : Underline Science Inc., 2021 BASE Show details 19 Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Tharindu; Blain, Frédéric. - : Underline Science Inc., 2021 BASE Show details 20 The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Carolina; Alva-Manchego, Fernando. - : Underline Science Inc., 2021 BASE Show details Page: 1 2

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