Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Goyal, Vikram"'
Employing language models to generate explanations for an incoming implicit hate post is an active area of research. The explanation is intended to make explicit the underlying stereotype and aid content moderators. The training often combines top-k
Externí odkaz:
http://arxiv.org/abs/2406.03953
Citing pertinent literature is pivotal to writing and reviewing a scientific document. Existing techniques mainly focus on the local context or the global context for recommending citations but fail to consider the actual human citation behaviour. We
Externí odkaz:
http://arxiv.org/abs/2406.01606
Despite the widespread adoption, there is a lack of research into how various critical aspects of pretrained language models (PLMs) affect their performance in hate speech detection. Through five research questions, our findings and recommendations l
Externí odkaz:
http://arxiv.org/abs/2402.02144
Abstractive text summarization is surging with the number of training samples to cater to the needs of the deep learning models. These models tend to exploit the training data representations to attain superior performance by improving the quantitati
Externí odkaz:
http://arxiv.org/abs/2312.06022
Machine Learning (ML) models become vulnerable to Model Stealing Attacks (MSA) when they are deployed as a service. In such attacks, the deployed model is queried repeatedly to build a labelled dataset. This dataset allows the attacker to train a thi
Externí odkaz:
http://arxiv.org/abs/2311.04588
Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences by interpreting the linguistic information in them. To test the mathematical reasoning capabilities of the learners, sometimes the proble
Externí odkaz:
http://arxiv.org/abs/2307.01240
The realm of scientific text summarization has experienced remarkable progress due to the availability of annotated brief summaries and ample data. However, the utilization of multiple input modalities, such as videos and audio, has yet to be thoroug
Externí odkaz:
http://arxiv.org/abs/2306.13968
Social media is awash with hateful content, much of which is often veiled with linguistic and topical diversity. The benchmark datasets used for hate speech detection do not account for such divagation as they are predominantly compiled using hate le
Externí odkaz:
http://arxiv.org/abs/2306.01105
In this paper, we propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems having practical applications in online pedagogy as a means to reduce pla
Externí odkaz:
http://arxiv.org/abs/2302.02780
Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. Howeve
Externí odkaz:
http://arxiv.org/abs/2301.05150