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of 13
pro vyhledávání: '"Filighera, Anna"'
Enhancing Multi-Domain Automatic Short Answer Grading through an Explainable Neuro-Symbolic Pipeline
Grading short answer questions automatically with interpretable reasoning behind the grading decision is a challenging goal for current transformer approaches. Justification cue detection, in combination with logical reasoners, has shown a promising
Externí odkaz:
http://arxiv.org/abs/2403.01811
Autor:
Leong, Colin, Nemecek, Joshua, Mansdorfer, Jacob, Filighera, Anna, Owodunni, Abraham, Whitenack, Daniel
Publikováno v:
EMNLP 2022
We present Bloom Library, a linguistically diverse set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition. These datasets represent either the most, or among the most
Externí odkaz:
http://arxiv.org/abs/2210.14712
Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic g
Externí odkaz:
http://arxiv.org/abs/2201.08318
Most learners fail to develop deep text comprehension when reading textbooks passively. Posing questions about what learners have read is a well-established way of fostering their text comprehension. However, many textbooks lack self-assessment quest
Externí odkaz:
http://arxiv.org/abs/2110.04123
Publikováno v:
International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.); Jun2024, Vol. 34 Issue 2, p616-646, 31p
Publikováno v:
Artificial Intelligence in Education
With the rising success of adversarial attacks on many NLP tasks, systems which actually operate in an adversarial scenario need to be reevaluated. For this purpose, we pose the following research question: How difficult is it to fool automatic short
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmc_________::7c0a0781da2dc78b8d6b7ae84f1319d3
http://europepmc.org/articles/PMC7334174
http://europepmc.org/articles/PMC7334174
Autor:
Camus, Leon, Filighera, Anna
Publikováno v:
Artificial Intelligence in Education
Recent advancements in the field of deep learning for natural language processing made it possible to use novel deep learning architectures, such as the Transformer, for increasingly complex natural language processing tasks. Combined with novel unsu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmc_________::714dd981e91d158a30654d17a7731ee8
http://europepmc.org/articles/PMC7334688
http://europepmc.org/articles/PMC7334688
Publikováno v:
Artificial Intelligence in Education
Reading is a crucial skill in the 21st century. Thus, scaffolding text comprehension by automatically generated questions may greatly profit learners. Yet, the state-of-the-art methods for automatic question generation, answer-aware neural question g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmc_________::43647c02bd76f5ff9a9e6683f77a33e7
http://europepmc.org/articles/PMC7334179
http://europepmc.org/articles/PMC7334179
Background: Asking learners manually authored questions about their readings improves their text comprehension. Yet, not all reading materials comprise sufficiently many questions and many informal reading materials do not contain any. Therefore, aut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f2880d9c6663f240d99f3836d5247a0
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