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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 4 5 6 Hits 1 – 20 of 106 1 English machine reading comprehension: new approaches to answering multiple-choice questions Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021 In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021) Abstract: Reading comprehension is often tested by measuring a person or system’s ability to answer questions about a given text. Machine reading comprehension datasets have proliferated in recent years, particularly for the English language. The aim of this thesis is to investigate and improve data-driven approaches to automatic reading comprehension. Firstly, I provide a full classification of question and answer types for the reading comprehension task. I also present a systematic overview of English reading comprehension datasets (over 50 datasets). I observe that the majority of questions were created using crowdsourcing and the most popular data source is Wikipedia. There is also a lack of why, when, and where questions. Additionally, I address the question “What makes a dataset difficult?” and highlight the difference between datasets created for people and datasets created for machine reading comprehension. Secondly, focusing on multiple-choice question answering, I propose a computationally light method for answer selection based on string similarities and logistic regression. At the time (December 2017), the proposed approach showed the best performance on two datasets (MovieQA and MCQA: IJCNLP 2017 Shared Task 5 Multi-choice Question Answering in Examinations) outperforming some CNN-based methods. Thirdly, I investigate methods for Boolean Reading Comprehension tasks including the use of Knowledge Graph (KG) information for answering questions. I provide an error analysis of a transformer model’s performance on the BoolQ dataset. This reveals several important issues such as unstable model behaviour and some issues with the dataset itself. Experiments with incorporating knowledge graph information into a baseline transformer model do not show a clear improvement due to a combination of the model’s ability to capture new information, inaccuracies in the knowledge graph, and imprecision in entity linking. Finally, I develop a Boolean Reading Comprehension dataset based on spontaneously user-generated questions and reviews which is extremely close to a real-life question-answering scenario. I provide a classification of question difficulty and establish a transformer-based baseline for the new proposed dataset. Keyword: Artificial intelligence; Computational linguistics; Information retrieval; Machine learning; machine reading comprehension; question answering; transformer language models URL: http://doras.dcu.ie/26534/ BASE Hide details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 gaBERT -- an Irish Language Model ... Barry, James; Wagner, Joachim; Cassidy, Lauren. - : arXiv, 2021 BASE Show details 6 Revisiting Tri-training of Dependency Parsers ... Wagner, Joachim; Foster, Jennifer. - : arXiv, 2021 BASE Show details 7 Improving Unsupervised Question Answering via Summarization-Informed Question Generation ... Lyu, Chenyang; Shang, Lifeng; Graham, Yvette. - : arXiv, 2021 BASE Show details 8 The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task ... 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; Barry, James; Foster, Jennifer. - : Underline Science Inc., 2021 BASE Show details 9 Revisiting Tri-training of Dependency Parsers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Foster, Jennifer; Wagner, Joachim. - : Underline Science Inc., 2021 BASE Show details 10 The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task Wagner, Joachim; Foster, Jennifer; Mohammadshahi, Alireza... In: http://infoscience.epfl.ch/record/289182 (2021) BASE Show details 11 English Machine Reading Comprehension Datasets: A Survey ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Vogel, Carl; Foster, Jennifer; Dzendzik, Daria. - : Association for Computational Linguistics, 2021 BASE Show details 12 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 13 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 14 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) BASE Show details 15 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 16 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 17 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 18 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 19 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 20 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 Page: 1 2 3 4 5 6

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