Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Jangra, Anubhav"'
Digital education has gained popularity in the last decade, especially after the COVID-19 pandemic. With the improving capabilities of large language models to reason and communicate with users, envisioning intelligent tutoring systems (ITSs) that ca
Externí odkaz:
http://arxiv.org/abs/2404.04728
Publikováno v:
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024)
Nowadays, individuals tend to engage in dialogues with Large Language Models, seeking answers to their questions. In times when such answers are readily accessible to anyone, the stimulation and preservation of human's cognitive abilities, as well as
Externí odkaz:
http://arxiv.org/abs/2403.18426
Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities. This information can further enhance many downstream tasks in the field of information retrieval and natura
Externí odkaz:
http://arxiv.org/abs/2302.06560
The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for
Externí odkaz:
http://arxiv.org/abs/2212.01669
Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle
Externí odkaz:
http://arxiv.org/abs/2212.01667
The task of Question Answering (QA) has attracted significant research interest for long. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting makes the task of QA crucial for strong AI systems. Recent
Externí odkaz:
http://arxiv.org/abs/2204.09140
The task of automatic text summarization has gained a lot of traction due to the recent advancements in machine learning techniques. However, evaluating the quality of a generated summary remains to be an open problem. The literature has widely adopt
Externí odkaz:
http://arxiv.org/abs/2201.09282
The new era of technology has brought us to the point where it is convenient for people to share their opinions over an abundance of platforms. These platforms have a provision for the users to express themselves in multiple forms of representations,
Externí odkaz:
http://arxiv.org/abs/2109.05199
The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploi
Externí odkaz:
http://arxiv.org/abs/2105.01296
Significant development of communication technology over the past few years has motivated research in multi-modal summarization techniques. A majority of the previous works on multi-modal summarization focus on text and images. In this paper, we prop
Externí odkaz:
http://arxiv.org/abs/2005.09252