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Language and vision in conceptual processing: Multilevel analysis and statistical power ...
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Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics.
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Performance measurement of construction suppliers under localization, agility, and digitalization criteria: Fuzzy Ordinal Priority Approach
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In: Environ Dev Sustain (2022)
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Consensus-Based Decision Support Model and Fusion Architecture for Dynamic Decision Making
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In: Electrical and Computer Engineering Publications (2022)
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Two-Dimensional Physical Modeling of the Human Vocal Tract using Computer-Aided Design
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"Obama never said that": Evaluating fact-checks for topical consistency and quality
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Modeling human-like morphological prediction
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In: Proceedings of the Society for Computation in Linguistics (2022)
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The Electrophysiological Correlates of Text Integration and Direct vs. Indirect Articles: A Centralized and Lateralized Examination
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In: Theses and Dissertations (Comprehensive) (2022)
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Ranking Semantics for Argumentation Systems With Necessities
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In: IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03002056 ; IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, Jan 2021, Yokohama / Virtual, Japan. pp.1912-1918, ⟨10.24963/ijcai.2020/265⟩ (2021)
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A novel source-filter stochastic model for voice production
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In: ISSN: 0892-1997 ; Journal of Voice ; https://hal-upec-upem.archives-ouvertes.fr/hal-03179837 ; Journal of Voice, Elsevier, 2021, In Press, pp.1-11. ⟨10.1016/j.jvoice.2020.11.015⟩ (2021)
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Investigating alignment interpretability for low-resource NMT
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In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
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A stochastic model of voice generation and the corresponding solution for the inverse problem using Artificial Neural Network for case with pathology in the vocal folds
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In: ISSN: 1746-8094 ; Biomedical Signal Processing and Control ; https://hal-upec-upem.archives-ouvertes.fr/hal-03193501 ; Biomedical Signal Processing and Control, Elsevier, 2021, 68, pp.102623 (2021)
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Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation
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In: INTERSPEECH 2021 ; https://hal.archives-ouvertes.fr/hal-03372487 ; INTERSPEECH 2021, Aug 2021, Brno, Czech Republic (2021)
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Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
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In: Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03372802 ; Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.3865-3869, ⟨10.21437/Interspeech.2021-275⟩ (2021)
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Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms
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In: NL4IA Workshop Proceedings ; https://hal.archives-ouvertes.fr/hal-03474696 ; NL4IA Workshop Proceedings, Nov 2021, Milan, Italy (2021)
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Language, Internet and Platform Competition
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In: ISSN: 0022-1996 ; Journal of International Economics ; https://hal.archives-ouvertes.fr/hal-03081660 ; Journal of International Economics, Elsevier, 2021 (2021)
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Constrained control of gene-flow models
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In: https://hal.archives-ouvertes.fr/hal-02373668 ; 2021 (2021)
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English machine reading comprehension: new approaches to answering multiple-choice questions
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Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
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In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021)
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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.
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Keyword:
Artificial intelligence; Computational linguistics; Information retrieval; Machine learning; machine reading comprehension; question answering; transformer language models
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URL: http://doras.dcu.ie/26534/
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cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations
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In: Erofeev, Gleb, Sorokina, Irina, Han, Lifeng orcid:0000-0002-3221-2185 and Gladkoff, Serge (2021) cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations. In: Machine Translation Summit 2021, 16-20 Aug 2021, USA (online). (In Press) (2021)
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Meta-evaluation of machine translation evaluation methods
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In: Han, Lifeng orcid:0000-0002-3221-2185 (2021) Meta-evaluation of machine translation evaluation methods. In: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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