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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.151 Abstract: Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrained language models. Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational language models is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final perfor mance in dialog tasks, e.g., conversational response generation. In this work, we present REDDITBIAS, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender,race,religion, and ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://underline.io/lecture/25465-redditbias-a-real-world-resource-for-bias-evaluation-and-debiasing-of-conversational-language-models https://dx.doi.org/10.48448/b20r-x634
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23 |
Parameter space factorization for zero-shot learning across tasks and languages ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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27 |
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
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32 |
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Parameter space factorization for zero-shot learning across tasks and languages
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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Is supervised syntactic parsing beneficial for language understanding tasks? An empirical investigation
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