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QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension ...
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus ...
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COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images ...
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Enforcing Consistency in Weakly Supervised Semantic Parsing ...
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Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution ...
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Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization ...
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Tailor: Generating and Perturbing Text with Semantic Controls ...
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Competency Problems: On Finding and Removing Artifacts in Language Data ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.135/ Abstract: Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have "spurious" instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word "amazing" on its own should not give information about a sentiment label independent of the context in which it appears, which could include negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account, showing that realistic datasets will increasingly deviate from competency problems as dataset size increases. This analysis gives us a simple statistical test for dataset ...
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Keyword:
Language Models; Natural Language Processing; Semantic Evaluation; Sociolinguistics
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URL: https://underline.io/lecture/37929-competency-problems-on-finding-and-removing-artifacts-in-language-data https://dx.doi.org/10.48448/xnpn-5692
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Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization ...
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Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering ...
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Evaluating Models' Local Decision Boundaries via Contrast Sets ...
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IIRC: A Dataset of Incomplete Information Reading Comprehension Questions ...
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Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning ...
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The Phonology of the Canadian Shift Revisited: Thunder Bay & Cape Breton
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In: University of Pennsylvania Working Papers in Linguistics (2013)
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