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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 Annotating verbal MWEs in Irish for the PARSEME Shared Task 1.2 Lynn, Teresa; Foster, Jennifer; Walsh, Abigail In: Walsh, Abigail, Lynn, Teresa and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Annotating verbal MWEs in Irish for the PARSEME Shared Task 1.2. In: Joint Workshop on Multiword Expressions and Electronic Lexicons, 13 Dec 2020, Barcelona, Spain (Online). (2020) BASE Show details 2 Improving document-level sentiment analysis with user and product context Foster, Jennifer; Lyu, Chenyang; Graham, Yvette In: Lyu, Chenyang, Foster, Jennifer orcid:0000-0002-7789-4853 and Graham, Yvette (2020) Improving document-level sentiment analysis with user and product context. In: Proceedings of the 28th International Conference on Computational Linguistics, 8-13 Dec 20, Barcelona, Spain (Online). (2020) BASE Show details 3 How to make neural natural language generation as reliable as templates in task-oriented dialogue O'Connor, Alexander; Elder, Henry; Foster, Jennifer In: Elder, Henry, O'Connor, Alexander orcid:0000-0003-0301-999X and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) How to make neural natural language generation as reliable as templates in task-oriented dialogue. In: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 16-20 Nov 2020, Online. (2020) Abstract: Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task. The system trained using this approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system. Keyword: Computational linguistics URL: http://doras.dcu.ie/25957/ BASE Hide details 4 Treebank embedding vectors for out-of-domain dependency parsing Wagner, Joachim; Barry, James; Foster, Jennifer In: Wagner, Joachim orcid:0000-0002-8290-3849 , Barry, James orcid:0000-0003-3051-585X and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Treebank embedding vectors for out-of-domain dependency parsing. In: 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 05-10 Jul 2020, Online (virtual conference). (2020) BASE Show details 5 Q. Can knowledge graphs be used to answer Boolean questions? A. It’s complicated! Vogel, Carl; Dzendzik, Daria; Foster, Jennifer In: Dzendzik, Daria, Vogel, Carl orcid:0000-0001-8928-8546 and Foster, Jennifer orcid:0000-0002-7789-4853 (2020) Q. Can knowledge graphs be used to answer Boolean questions? A. It’s complicated! In: First Workshop on Insights from Negative Results in NLP, 10 Nov 2020, Online. (2020) BASE Show details 6 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 7 Annotated corpora and tools of the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2) Ramisch, Carlos; Guillaume, Bruno; Savary, Agata. - : PARSEME, 2020 BASE Show details 8 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 9 Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2 ... The 28th International Conference on Computational Linguistics 2020; Foster, Jennifer; Lynn, Teresa. - : Underline Science Inc., 2020 BASE Show details

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