838 citations · 968 across the 27 of their papers we have counts for
29 papers
From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification
Manish Gupta, Chaitanya Giri, Jayasimha Talur
Large language models (LLMs) struggle to classify text into taxonomies with many semantically similar labels, as the distinctions are domain-specific and not captured by pre-traini…
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering
Abhirama Subramanyam Penamakuri, Manish Gupta, Mithun Das Gupta +1
We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must…
Neural models for Factual Inconsistency Classification with Explanations
Tathagata Raha, Mukund Choudhary, Abhinav Menon +4
Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarizatio…
Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents
Sayar Ghosh Roy, Anshul Padhi, Risubh Jain +2
Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We i…
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages
Shivprasad Sagare, Tushar Abhishek, Bhavyajeet Singh +3
Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been develope…
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models
Bishal Santra, Ravi Ghadia, Manish Gupta +1
In the field of Natural Language Processing, there are many tasks that can be tackled effectively using the cross-entropy (CE) loss function. However, the task of dialog generation…