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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya... In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021) BASE Show details 2 The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ... Gehrmann, Sebastian; Adewumi, Tosin; Aggarwal, Karmanya. - : arXiv, 2021 BASE Show details 3 Towards Syntax-Aware DialogueSummarization using Multi-task Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lee, Seolhwa; Lim, Heuiseok. - : Underline Science Inc., 2021 BASE Show details 4 Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning ... Lee, Seolhwa; Yang, Kisu; Park, Chanjun. - : arXiv, 2021 BASE Show details 5 Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders Tang, Sunny X.; Kriz, Reno; Cho, Sunghye... In: NPJ Schizophr (2021) BASE Show details 6 Measuring the `I don't know' Problem through the Lens of Gricean Quantity ... Khayrallah, Huda; Sedoc, João. - : arXiv, 2020 Abstract: We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the `I don't know' problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, `I don't know' is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning. RUQ allows for the direct analysis of the `I don't know' problem, which has been addressed but not analyzed by prior work. ... : to appear at NAACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.12786 https://dx.doi.org/10.48550/arxiv.2010.12786 BASE Hide details 7 SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training ... Khayrallah, Huda; Sedoc, João. - : arXiv, 2020 BASE Show details 8 Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification ... Kriz, Reno; Sedoc, João; Apidianaki, Marianna. - : arXiv, 2019 BASE Show details 9 Comparison of Diverse Decoding Methods from Conditional Language Models ... Ippolito, Daphne; Kriz, Reno; Kustikova, Maria. - : arXiv, 2019 BASE Show details 10 Learning Word Ratings for Empathy and Distress from Document-Level User Responses ... Sedoc, João; Buechel, Sven; Nachmany, Yehonathan. - : arXiv, 2019 BASE Show details 11 Unsupervised Post-processing of Word Vectors via Conceptor Negation ... Liu, Tianlin; Ungar, Lyle; Sedoc, João. - : arXiv, 2018 BASE Show details

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