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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 – 5 of 5 1 The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues ... Testoni, Alberto; Bernardi, Raffaella. - : arXiv, 2021 BASE Show details 2 Overprotective Training Environments Fall Short at Testing Time: Let Models Contribute to Their Own Training ... Testoni, Alberto; Bernardi, Raffaella. - : arXiv, 2021 BASE Show details 3 Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bernardi, Raffaella; Testoni, Alberto. - : Underline Science Inc., 2021 BASE Show details 4 “I’ve Seen Things You People Wouldn’t Believe”: Hallucinating Entities in GuessWhat?! ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bernardi, Raffaella; Testoni, Alberto. - : Underline Science Inc., 2021 Abstract: Link to full paper: https://aclanthology.org/2021.acl-srw.11/ Natural language generation systems have witnessed important progress in the last years, but they are shown to generate tokens that are unrelated to the source input. This problem affects computational models in many NLP tasks, and it is particularly unpleasant in multimodal systems. In this work, we assess the rate of object hallucination in multimodal conversational agents playing the GuessWhat?! referential game. Better visual processing has been shown to mitigate this issue in image captioning; hence, we adapt to the GuessWhat?! task the best visual processing models at disposal, and propose two new models to play the Questioner agent. We show that the new models generate few hallucinations compared to other renowned models available in the literature. Moreover, their hallucinations are less severe (affect task-accuracy less) and are more human-like. We also analyse where hallucinations tend to occur more often through the dialogue: ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/30045-ve-seen-things-you-people-wouldnt-believe-hallucinating-entities-in-guesswhatquestion https://dx.doi.org/10.48448/vvce-9x16 BASE Hide details 5 Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Benotti, Luciana; Bernardi, Raffaella. - : Underline Science Inc., 2021 BASE Show details

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