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The Many Benefits of Annotator Rationales for Relevance Judgments., , , и . IJCAI, стр. 4909-4913. ijcai.org, (2017)Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement?, , , , и . SIGIR, стр. 805-814. ACM, (2018)Constructing Test Collections using Multi-armed Bandits and Active Learning., , и . WWW, стр. 3158-3164. ACM, (2019)Interpretable by Design: Wrapper Boxes Combine Neural Performance with Faithful Explanations., , и . CoRR, (2023)Learning Complementary Policies for Human-AI Teams., , , , , , и . CoRR, (2023)An improved markov random field model for supporting verbose queries.. SIGIR, стр. 476-483. ACM, (2009)Linguistic Resources for Speech Parsing., , , , , , , и . LREC, стр. 629-634. European Language Resources Association (ELRA), (2006)SQUARE: Benchmarking Crowd Consensus at MediaEval., и . MediaEval, том 1043 из CEUR Workshop Proceedings, CEUR-WS.org, (2013)Improving Quality of Crowdsourced Labels via Probabilistic Matrix Factorization., и . HCOMP@AAAI, том WS-12-08 из AAAI Technical Report, AAAI Press, (2012)Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection., , , и . WWW, стр. 3689-3700. ACM, (2023)