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1
Incorporating Constituent Syntax for Coreference Resolution ...
Jiang, Fan; Cohn, Trevor. - : arXiv, 2022
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2
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ...
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3
Fairness-aware Class Imbalanced Learning ...
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4
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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5
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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6
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ...
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7
Generating Diverse Descriptions from Semantic Graphs ...
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8
Balancing out Bias: Achieving Fairness Through Training Reweighting ...
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9
ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
In: Front Res Metr Anal (2021)
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10
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ...
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11
Please Mind the Root: Decoding Arborescences for Dependency Parsing
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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12
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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13
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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14
Learning a Cost-Effective Annotation Policy for Question Answering
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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15
Pareto Probing: Trading Off Accuracy for Complexity
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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16
Speakers Fill Lexical Semantic Gaps with Context
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
Abstract: Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient. If ambiguous words cannot be disambiguated from context, however, this gain in efficiency might make language less clear—resulting in frequent miscommunication. For a language to be clear and efficiently encoded, we posit that the lexical ambiguity of a word type should correlate with how much information context provides about it, on average. To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this—one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages. We validate these measures by showing that, on six high-resource languages, there are significant Pearson correlations between our BERT-based estimate of ambiguity and the number of synonyms a word has in WordNet (e.g. 𝜌� = 0.40 in English). We then test our main hypothesis—that a word’s lexical ambiguity should negatively correlate with its contextual uncertainty—and find significant correlations on all 18 typologically diverse languages we analyse. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
URL: https://hdl.handle.net/20.500.11850/462320
https://doi.org/10.3929/ethz-b-000462320
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17
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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18
Intrinsic Probing through Dimension Selection
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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19
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
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20
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
Sutcliffe, Richard; Samothrakis, Spyridon; Quteineh, Husam. - : Association for Computational Linguistics, 2020
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