6 accepted papers
Despite progress in comment-aware multimodal and multilingual summarization for English and Chinese, research in Indian languages remains limited. This study addresses this gap by introducing COSMMIC, a pioneering comment-sensitive multimodal, multilingual dataset featuring nine major Indian languag…
Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce CultSportQA , a benchmark designed to assess LMs’ understanding of traditional sports across 60 countries and 6 continents, encom
Neural text generation is receiving broad attention with the publication of new tools such as ChatGPT. The main reason for that is that the achieved quality of the generated text may be attributed to a human writer by the naked eye of a human evaluator. In this paper, we propose a new corpus in Fren…
Data annotation is crucial for machine learning, notably in technical domains, where the quality and quantity of annotated data, significantly affect effectiveness of trained models. Employing humans is costly, especially when annotating for multi-label classification, as instances may bear multiple…
Despite the success of state-of-the-art pre-trained language models (PLMs) on a series of multi-hop reasoning tasks, they still suffer from their limited abilities to transfer learning from simple to complex tasks and vice-versa. We argue that one step forward to overcome this limitation is to bette…
Knowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of knowledge distillatio…