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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 – 9 of 9 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models Wilcox, Ethan; Futrell, Richard; Qian, Peng... In: Association for Computational Linguistics (2021) BASE Show details 5 Structural Supervision Improves Learning of Non-Local Grammatical Dependencies Wilcox, Ethan; Qian, Peng; Futrell, Richard... In: Association for Computational Linguistics (2021) BASE Show details 6 Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings ... Saravanakumar, Kailash Karthik; Ballesteros, Miguel; Chandrasekaran, Muthu Kumar. - : arXiv, 2021 BASE Show details 7 A Bag of Tricks for Dialogue Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ballesteros, Miguel; Khalifa, Muhammad; McKeown, Kathleen. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.631/ Abstract: Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines. ... Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Named Entity Recognition; Natural Language Processing URL: https://underline.io/lecture/37447-a-bag-of-tricks-for-dialogue-summarization https://dx.doi.org/10.48448/fx6j-wp43 BASE Hide details 8 On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations ... Pérez-Mayos, Laura; Carlini, Roberto; Ballesteros, Miguel. - : arXiv, 2021 BASE Show details 9 How much pretraining data do language models need to learn syntax? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ballesteros, Miguel; Pérez-Mayos, Laura. - : Underline Science Inc., 2021 BASE Show details

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