![]() Pretrained transformer models have achieved state-of-the-art results in many tasks and bench-marks recently. “BudgetLongformer: Can we Cheaply Pretrain a SOTA Legal Language Model From Scratch?” In Proceedings of the 2nd workshop on Efficient Natural Language and Speech Processing (ENLSP). BudgetLongformer: Can we Cheaply Pretrain a SOTA Legal Language Model From Scratch? Implications for downstream systems that use legal opinion word embeddings and suggestions for potential mitigation strategies based on our observations are also discussed. These biases are not mitigated by exclusion of historical data, and appear across multiple large topical areas of the law. Our analyses using these methods suggest that racial and gender biases are encoded into word embeddings trained on legal opinions. We then propose a domain adapted method for identifying gender and racial biases in the legal domain. ![]() ![]() We first explain how previously proposed methods for identifying these biases are not well suited for use with word embeddings trained on legal opinion text. Embeddings containing stereotype information may cause harm when used by downstream systems for classification, information extraction, question answering, or other machine learning systems used to build legal research tools. In this article, we propose an approach for identifying gender and racial stereotypes in word embeddings trained on judicial opinions from U.S. Studies have shown that some Natural Language Processing (NLP) systems encode and replicate harmful biases with potential adverse ethical effects in our society. “Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings.” In Proceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). Matthews, Sean, John Hudzina, and Dawn Sepehr. Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings We conclude that new corpora accounting for negation are needed to solve natural language understanding tasks when negation is present. Additionally, experimental results show that state-of-the-art transformers trained with these corpora obtain substantially worse results with instances that contain negation, especially if the negations are important. Indeed, one can often ignore negations and still make the right predictions. We show that these corpora have few negations compared to general-purpose English, and that the few negations in them are often unimportant. This paper analyzes negation in eight popular corpora spanning six natural language understanding tasks. Association for Computational Linguistics. ![]() “An Analysis of Negation in Natural Language Understanding Corpora.” In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL). ![]() Hossain, Md Mosharaf, Dhivya Chinnappa, and Eduardo Blanco. An Analysis of Negation in Natural Language Understanding Corpora ![]()
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