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Found 8 papers, 3 papers with code
GupShup: Summarizing Open-Domain Code-Switched Conversations
Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation.
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters.
First 100 days of pandemic; an interplay of pharmaceutical, behavioral and digital interventions -- A study using agent based modeling
Pandemics, notably the recent COVID-19 outbreak, have impacted both public health and the global economy.
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering.
Domain Generalization In Robust Invariant Representation
Unsupervised approaches for learning representations invariant to common transformations are used quite often for object recognition.
Curb Your Carbon Emissions: Benchmarking Carbon Emissions in Machine Translation
In recent times, there has been definitive progress in the field of NLP, with its applications growing as the utility of our language models increases with advances in their performance.
Evaluating Gender Bias in Hindi-English Machine Translation
The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject.
GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations
Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6, 831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English.
