Faculty Publications
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Publications by NITK Faculty
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Item Natural Language Inference: Detecting Contradiction and Entailment in Multilingual Text(Springer Science and Business Media Deutschland GmbH, 2021) Sree Harsha, S.; Krishna Swaroop, K.; Chandavarkar, B.R.Natural Language Inference (NLI) is the task of characterising the inferential relationship between a natural language premise and a natural language hypothesis. The premise and the hypothesis could be related in three distinct ways. The hypothesis could be a logical conclusion that follows from the given premise (entailment), the hypothesis could be false (contradiction), or the hypothesis and the premise could be unrelated (neutral). A robust and reliable system for NLI serves as a suitable evaluation measure for true natural language understanding and enables the use of such systems in several modern day application scenarios. We propose a novel technique for the NLI task by leveraging the recently proposed Bidirectional Encoder Representations from Transformers (BERT). We utilize a robustly optimized variant of BERT, integrate a contextualized definition embedding mechanism, and incorporate the use of global average pooling into our proposed NLI system. We use several different benchmark datasets, including a dataset containing premise-hypothesis pairs from 15 different languages to systematically evaluate the performance of our model and show that it yields superior results. © 2021, Springer Nature Switzerland AG.Item Misinformation Detection Through Authentication of Content Creators(Springer, 2023) KSudhama, K.; Siddamsetti, S.G.; G, P.; Chandavarkar, B.R.Recent technological advancements have made content modification and recreation easier and practically undetectable without suitable verification techniques. Users can change data from social media with photo, video, and text editing tools and share the updated content in a different context. As a result, online social media platforms are suitable for distributing fake news and misinformation. Misinformation can take several forms, including one or more types of multimedia, such as text, photos, videos. The modified contents provide fake evidence to the user, leading to various misconceptions. Generally, fake news has eye-catching headlines attracting the readers. These are called click-baits. The content of these click-baits often differs from what the headlines suggest. There are also many fake websites whose IP addresses are slightly modified versions of popular news agencies. Users easily get fooled to open the website as its address seems legitimate. These issues indicate the importance of identifying legitimate content creators from non-legitimate ones. This chapter focuses on authenticating the legitimate content creators verified by a trusted entity using certificates and blockchain technology. Also, check for fakeness in their content using natural language processing and image processing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.