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EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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Delving Deeper into Cross-lingual Visual Question Answering ...
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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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Improving Word Translation via Two-Stage Contrastive Learning ...
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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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Modelling Latent Translations for Cross-Lingual Transfer ...
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Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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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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Parameter space factorization for zero-shot learning across tasks and languages ...
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