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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 3 4 Hits 1 – 20 of 63 1 Incorporating Constituent Syntax for Coreference Resolution ... Jiang, Fan; Cohn, Trevor. - : arXiv, 2022 BASE Show details 2 PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ... Kurniawan, Kemal; Frermann, Lea; Schulz, Philip. - : arXiv, 2021 BASE Show details 3 Fairness-aware Class Imbalanced Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Baldwin, Timothy; Cohn, Trevor; Frermann, Lea; Rahimi, Afshin; Subramanian, Shivashankar. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.155/ Abstract: Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases. ... Keyword: Computational Linguistics; Ethics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37636-fairness-aware-class-imbalanced-learning https://dx.doi.org/10.48448/te9s-pa19 BASE Hide details 4 As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical 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; Cohn, Trevor; El-Kishky, Ahmed. - : Underline Science Inc., 2021 BASE Show details 5 Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ... 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; Cohn, Trevor; El-Kishky, Ahmed. - : Underline Science Inc., 2021 BASE Show details 6 It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Gholamreza; Arthur, Philip. - : Underline Science Inc., 2021 BASE Show details 7 Generating Diverse Descriptions from Semantic Graphs ... Han, Jiuzhou; Beck, Daniel; Cohn, Trevor. - : arXiv, 2021 BASE Show details 8 Balancing out Bias: Achieving Fairness Through Training Reweighting ... Han, Xudong; Baldwin, Timothy; Cohn, Trevor. - : arXiv, 2021 BASE Show details 9 ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents He, Jiayuan; Nguyen, Dat Quoc; Akhondi, Saber A.... In: Front Res Metr Anal (2021) BASE Show details 10 Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ... Arthur, Philip; Cohn, Trevor; Haffari, Gholamreza. - : arXiv, 2020 BASE Show details 11 Please Mind the Root: Decoding Arborescences for Dependency Parsing Vieira, Tim; Cotterell, Ryan; Zmigrod, Ran In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 12 Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions McCarthy, Arya D.; Cotterell, Ryan; Williams, Adina... In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 13 Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model Emerson, Guy; Cotterell, Ryan; Leung, Jun Yen In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 14 Learning a Cost-Effective Annotation Policy for Question Answering Kratzwald, Bernhard; Huan, Sun; Feuerriegel, Stefan In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 15 Pareto Probing: Trading Off Accuracy for Complexity Saphra, Naomi; Cotterell, Ryan; Pimentel, Tiago... In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 16 Speakers Fill Lexical Semantic Gaps with Context Blasi, Damián; Hall Maudslay, Rowan; Cotterell, Ryan... In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 17 Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation Cotterell, Ryan; Vargas, Francisco In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 18 Intrinsic Probing through Dimension Selection Cotterell, Ryan; Williams, Adina; Torroba Hennigen, Lucas In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020) BASE Show details 19 Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation Hollenstein, Nora; Zhang, Ce; Russo, Giuseppe... In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020) BASE Show details 20 Textual Data Augmentation for Efficient Active Learning on Tiny Datasets Sutcliffe, Richard; Samothrakis, Spyridon; Quteineh, Husam. - : Association for Computational Linguistics, 2020 BASE Show details Page: 1 2 3 4

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