Search Results for author:
Found 9 papers, 4 papers with code
KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
Our research contributes to advancing the field of domain-specific language understanding and showcases the potential of knowledge infusion techniques in improving the performance of language models on question-answering.
Let the CAT out of the bag: Contrastive Attributed explanations for Text
Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse.
Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services
We propose a combination of unsupervised and supervised model with minimum human intervention that leverages domain knowledge to predict artefacts for a small amount of conversation data and use that for fine-tuning an already pretrained language model for artefact prediction on a large amount of conversation data.
No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection
Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English.
Meta-Context Transformers for Domain-Specific Response Generation
Though these models have exhibited excellent language coherence, they often lack relevance and terms when used for domain-specific response generation.
Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers' Conversations in Hybrid Cloud Operations
Identifying segmented conversations and extracting key insights or artefacts from them can help engineers to improve the efficiency of the incident remediation process by using information retrieval mechanisms for similar incidents.
Collective Learning From Diverse Datasets for Entity Typing in the Wild
The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets.
Deep Domain Adaptation under Deep Label Scarcity
The goal behind Domain Adaptation (DA) is to leverage the labeled examples from a source domain so as to infer an accurate model in a target domain where labels are not available or in scarce at the best.
Unsupervised Controllable Text Formalization
We propose a novel framework for controllable natural language transformation.
