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pro vyhledávání: '"Loakman, Tyler"'
With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models
Recently, Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated aptitude as potential substitutes for human participants in experiments testing psycholinguistic phenomena. However, an understudied question is to what extent
Externí odkaz:
http://arxiv.org/abs/2409.14917
Machine Translation (MT) has developed rapidly since the release of Large Language Models and current MT evaluation is performed through comparison with reference human translations or by predicting quality scores from human-labeled data. However, th
Externí odkaz:
http://arxiv.org/abs/2406.13698
Autor:
Loakman, Tyler, Lin, Chenghua
This paper presents a partial reproduction of Generating Fact Checking Explanations by Anatanasova et al (2020) as part of the ReproHum element of the ReproNLP shared task to reproduce the findings of NLP research regarding human evaluation. This sha
Externí odkaz:
http://arxiv.org/abs/2404.17481
Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry. In this article, we present new work on the generation of English tongue twisters - a form of language that is requir
Externí odkaz:
http://arxiv.org/abs/2403.13901
Despite recent advancements, existing story generation systems continue to encounter difficulties in effectively incorporating contextual and event features, which greatly influence the quality of generated narratives. To tackle these challenges, we
Externí odkaz:
http://arxiv.org/abs/2311.11271
Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system. However, whilst its importance is accepted by the community at large, the quality of its execution is often brought into question.
Externí odkaz:
http://arxiv.org/abs/2311.05552
Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent from text, resulting in th
Externí odkaz:
http://arxiv.org/abs/2306.16195
Publikováno v:
ACL 2023
Previous work in phonetically-grounded language generation has mainly focused on domains such as lyrics and poetry. In this paper, we present work on the generation of tongue twisters - a form of language that is required to be phonetically condition
Externí odkaz:
http://arxiv.org/abs/2306.03457
Commonsense knowledge is crucial to many natural language processing tasks. Existing works usually incorporate graph knowledge with conventional graph neural networks (GNNs), resulting in a sequential pipeline that compartmentalizes the encoding proc
Externí odkaz:
http://arxiv.org/abs/2305.06294
Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires background knowledge specific to the medical domain. Existing generati
Externí odkaz:
http://arxiv.org/abs/2210.15551