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1
Pushing the right buttons: adversarial evaluation of quality estimation
In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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2
Translation Error Detection as Rationale Extraction ...
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3
Knowledge Distillation for Quality Estimation ...
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4
BERTGEN: Multi-task Generation through BERT ...
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5
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ...
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6
A Generative Framework for Simultaneous Machine Translation ...
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Continual Quality Estimation with Online Bayesian Meta-Learning ...
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8
SentSim: Crosslingual Semantic Evaluation of Machine Translation ...
NAACL 2021 2021; Song, Yurun; Specia, Lucia. - : Underline Science Inc., 2021
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9
Knowledge Distillation for Quality Estimation ...
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10
BERTGen: Multi-task Generation through BERT ...
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11
What Makes a Scientific Paper be Accepted for Publication? ...
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12
MultiSubs: A Large-scale Multimodal and Multilingual Dataset ...
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13
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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14
Classifying Dyads for Militarized Conflict Analysis
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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15
Efficient Sampling of Dependency Structure
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
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16
Searching for More Efficient Dynamic Programs
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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17
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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18
A Bayesian Framework for Information-Theoretic Probing
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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19
Findings of the WMT 2021 Shared Task on Quality Estimation ...
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20
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
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