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1
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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3
Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness ...
Abstract: Open-domain dialogue agents have vastly improved, but still confidently hallucinate knowledge or express doubt when asked straightforward questions. In this work, we analyze whether state-of-the-art chit-chat models can express metacognition capabilities through their responses: does a verbalized expression of doubt (or confidence) match the likelihood that the model's answer is incorrect (or correct)? We find that these models are poorly calibrated in this sense, yet we show that the representations within the models can be used to accurately predict likelihood of correctness. By incorporating these correctness predictions into the training of a controllable generation model, we obtain a dialogue agent with greatly improved linguistic calibration. ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://arxiv.org/abs/2012.14983
https://dx.doi.org/10.48550/arxiv.2012.14983
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4
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset ...
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5
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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6
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model ...
Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018
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7
Are All Languages Equally Hard to Language-Model? ...
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8
Unsupervised Disambiguation of Syncretism in Inflected Lexicons ...
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