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A Convolutional Neural Network Based Approach to Recognize Bangla Spoken Digits from Speech Signal ...
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Thought Flow Nets: From Single Predictions to Trains of Model Thought ...
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Comparing Approaches to Dravidian Language Identification ...
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Challenges in Detoxifying Language Models ...
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Welbl, Johannes; Glaese, Amelia; Uesato, Jonathan; Dathathri, Sumanth; Mellor, John; Hendricks, Lisa Anne; Anderson, Kirsty; Kohli, Pushmeet; Coppin, Ben; Huang, Po-Sen. - : arXiv, 2021
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Abstract:
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of safety is imperative for deploying LMs in the real world; to this end, prior work often relies on automatic evaluation of LM toxicity. We critically discuss this approach, evaluate several toxicity mitigation strategies with respect to both automatic and human evaluation, and analyze consequences of toxicity mitigation in terms of model bias and LM quality. We demonstrate that while basic intervention strategies can effectively optimize previously established automatic metrics on the RealToxicityPrompts dataset, this comes at the cost of reduced LM coverage for both texts about, and dialects of, marginalized groups. Additionally, we find that human raters often disagree with high automatic toxicity scores after strong toxicity reduction interventions -- highlighting further the nuances involved in careful evaluation of LM toxicity. ... : 23 pages, 6 figures, published in Findings of EMNLP 2021 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; Computers and Society cs.CY; FOS Computer and information sciences; I.2.6; I.2.7; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2109.07445 https://arxiv.org/abs/2109.07445
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Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 14 ; 1868-6354 (2021)
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Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
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In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
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Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings ...
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MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System ...
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Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ...
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Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports ...
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Tripartitions of the first person space (Tamil speakers, Condition 1) ...
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Compositional Processing Emerges in Neural Networks Solving Math Problems ...
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Measuring and Improving BERT's Mathematical Abilities by Predicting the Order of Reasoning ...
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An empirical analysis of phrase-based and neural machine translation ...
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ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning ...
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Updater-Extractor Architecture for Inductive World State Representations ...
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Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? ...
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multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning ...
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Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines ...
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Exceeding the Limits of Visual-Linguistic Multi-Task Learning ...
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