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Integrating Vectorized Lexical Constraints for Neural Machine Translation ...
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Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction
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In: Mathematics; Volume 10; Issue 8; Pages: 1344 (2022)
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Virtual Reality-Integrated Immersion-Based Teaching to English Language Learning Outcome
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In: Front Psychol (2022)
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Alternated Training with Synthetic and Authentic Data for Neural Machine Translation ...
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CPM-2: Large-scale Cost-effective Pre-trained Language Models ...
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VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator ...
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Assessing Multilingual Fairness in Pre-trained Multimodal Representations ...
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Dialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset ...
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Transfer Learning for Sequence Generation: from Single-source to Multi-source ...
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Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision ...
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Learning to Selectively Learn for Weakly-supervised Paraphrase Generation ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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Analyzing the Limits of Self-Supervision in Handling Bias in Language ...
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Abstract:
Prompting inputs with natural language task descriptions has emerged as a popular mechanism to elicit reasonably accurate outputs from large-scale generative language models with little to no in-context supervision. This also helps gain insight into how well language models capture the semantics of a wide range of downstream tasks purely from self-supervised pre-training on massive corpora of unlabeled text. Such models have naturally also been exposed to a lot of undesirable content like racist and sexist language and there is limited work on awareness of models along these dimensions. In this paper, we define and comprehensively evaluate how well such language models capture the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. We define three broad classes of task descriptions for these tasks: statement, question, and completion, with numerous lexical variants within each class. We study the efficacy of prompting for each task using these classes and the null task ... : 16 pages, 1 figure ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2112.08637 https://arxiv.org/abs/2112.08637
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Statistically significant detection of semantic shifts using contextual word embeddings ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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Statistically Significant Detection of Semantic Shifts using Contextual Word Embeddings ...
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Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation ...
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SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection ...
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