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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 Page: 1 2 3 Hits 1 – 20 of 46 1 Is Information Density Uniform in Task-Oriented Dialogues? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Fernández, Raquel; Giulianelli, Mario. - : Underline Science Inc., 2021 BASE Show details 2 Analysing Human Strategies of Information Transmission as a Function of Discourse Context ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Fernández, Raquel; Giulianelli, Mario. - : Underline Science Inc., 2021 BASE Show details 3 Semantic shift in social networks ... 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; Fernández, Raquel; Larsson, Staffan. - : Underline Science Inc., 2021 BASE Show details 4 Syntactic Persistence in Language Models: Priming as a Window into Abstract Language Representations ... Sinclair, Arabella; Jumelet, Jaap; Zuidema, Willem. - : arXiv, 2021 BASE Show details 5 Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts ... Takmaz, Ece; Giulianelli, Mario; Pezzelle, Sandro. - : arXiv, 2020 BASE Show details 6 Words are the Window to the Soul: Language-based User Representations for Fake News Detection ... The 28th International Conference on Computational Linguistics 2020; Del Tredici, Marco; Fernández, Raquel. - : Underline Science Inc., 2020 BASE Show details 7 Analysing Lexical Semantic Change with Contextualised Word Representations ... Giulianelli, Mario; Del Tredici, Marco; Fernández, Raquel. - : arXiv, 2020 BASE Show details 8 DUPS: Diachronic Usage Pair Similarity ... Giulianelli, Mario; Del Tredici, Marco; Fernández, Raquel. - : Zenodo, 2020 BASE Show details 9 DUPS: Diachronic Usage Pair Similarity ... Giulianelli, Mario; Del Tredici, Marco; Fernández, Raquel. - : Zenodo, 2020 BASE Show details 10 DUPS: Diachronic Usage Pair Similarity ... Giulianelli, Mario; Del Tredici, Marco; Fernández, Raquel. - : Zenodo, 2020 BASE Show details 11 Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping Cathcart, Chundra; Rama, Taraka In: Cathcart, Chundra; Rama, Taraka (2020). Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping. In: Fernández, Raquel; Linzen, Tal. Proceedings of the 24th Conference on Computational Natural Language Learning. Online: Association for Computational Linguistics, 620-630. (2020) BASE Show details 12 Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network. Dhir, Neil; Bannard, Colin J; Stafford, Tom. - : Association for Computational Linguistics, 2020 BASE Show details 13 Evaluating the Representational Hub of Language and Vision Models ... Shekhar, Ravi; Takmaz, Ece; Fernández, Raquel. - : arXiv, 2019 BASE Show details 14 Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts ... Pezzelle, Sandro; Fernández, Raquel. - : arXiv, 2019 Abstract: This work aims at modeling how the meaning of gradable adjectives of size (`big', `small') can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is `big' or `small' in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as `fixed' attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks. Crucially, models fail in developing abstract representations of gradable adjectives that can be used ... : Accepted at EMNLP-IJCNLP 2019 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences URL: https://arxiv.org/abs/1908.10285 https://dx.doi.org/10.48550/arxiv.1908.10285 BASE Hide details 15 MALeViC: Modeling Adjectives Leveraging Visual Contexts ... Pezzelle, Sandro; Fernández, Raquel. - : Zenodo, 2019 BASE Show details 16 MALeViC: Modeling Adjectives Leveraging Visual Contexts ... Pezzelle, Sandro; Fernández, Raquel. - : Zenodo, 2019 BASE Show details 17 You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP ... Del Tredici, Marco; Marcheggiani, Diego; Walde, Sabine Schulte im. - : arXiv, 2019 BASE Show details 18 Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ... Greco, Claudio; Plank, Barbara; Fernández, Raquel. - : arXiv, 2019 BASE Show details 19 La adquisición del lenguaje de tres a seis años y sus posibles trastornos López Fernández, Raquel. - 2019 BASE Show details 20 Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ... Shekhar, Ravi; Venkatesh, Aashish; Baumgärtner, Tim. - : arXiv, 2018 BASE Show details Page: 1 2 3

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