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Incorporating Constituent Syntax for Coreference Resolution ...
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PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ...
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As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ...
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Balancing out Bias: Achieving Fairness Through Training Reweighting ...
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Abstract:
Bias in natural language processing arises primarily from models learning characteristics of the author such as gender and race when modelling tasks such as sentiment and syntactic parsing. This problem manifests as disparities in error rates across author demographics, typically disadvantaging minority groups. Existing methods for mitigating and measuring bias do not directly account for correlations between author demographics and linguistic variables. Moreover, evaluation of bias has been inconsistent in previous work, in terms of dataset balance and evaluation methods. This paper introduces a very simple but highly effective method for countering bias using instance reweighting, based on the frequency of both task labels and author demographics. We extend the method in the form of a gated model which incorporates the author demographic as an input, and show that while it is highly vulnerable to input data bias, it provides debiased predictions through demographic input perturbation, and outperforms all ... : 7 pages ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2109.08253 https://dx.doi.org/10.48550/arxiv.2109.08253
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ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
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In: Front Res Metr Anal (2021)
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Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ...
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Learning a Cost-Effective Annotation Policy for Question Answering
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Speakers Fill Lexical Semantic Gaps with Context
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Intrinsic Probing through Dimension Selection
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
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In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
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Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
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