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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 – 7 of 7 1 ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Garg, Shweta; Han, Jiawei. - : Underline Science Inc., 2021 BASE Show details 2 MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ma, Xinyin. - : Underline Science Inc., 2021 BASE Show details 3 Few-Shot Named Entity Recognition: An Empirical Baseline Study ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Balakrishnan, Shobana; Chen, Weizhu. - : Underline Science Inc., 2021 BASE Show details 4 Data Augmentation for Cross-Domain Named Entity Recognition ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Gustavo; Chen, Shuguang. - : Underline Science Inc., 2021 BASE Show details 5 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 6 A Partition Filter Network for Joint Entity and Relation Extraction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Fu, Jinlan; Wei, Zhongyu. - : Underline Science Inc., 2021 BASE Show details 7 Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gallinari, Patrick; Guigue, Vincent. - : Underline Science Inc., 2021 BASE Show details

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