As Large Language Models (LLMs) become increasingly integrated into high-stakes domains, there have been several approaches proposed toward generating natural language explanations. These explanations are crucial for …
Recent advancements in language technology and Artificial Intelligence have resulted in numerous Language Models being proposed to perform various tasks in the legal domain ranging from predicting judgments to generating …
In the recent years, it has been seen that deep neural networks are lacking robustness and are vulnerable in case of adversarial perturbations in input data. Strong adversarial attacks are proposed by various authors for …
Recent methods in speech and language technology pretrain very LARGE models which are fine-tuned for specific tasks. However, the benefits of such LARGE models are often limited to a few resource rich languages of the …
Abstract We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from …
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing …
BERT has been a top contender in the space of NLP models. With its sucess, a parallel stream of research, named BERTology, has emerged, that tries to understand how does BERT work so well. With the similar objective, …
We are working towards building a better ecosystem for Indian languages while also keeping up with the recent advancements in NLP. To this end, we are releasing IndicNLPSuite, which is a collection of various resources …
There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all …
The success of Deep Learning has created a surge in interest in a wide range of Natural Language Generation (NLG) tasks. Deep Learning has not only pushed the state of the art in several existing NLG tasks but has also …
The self-attention module is a key component of Transformer-based models, wherein each token pays attention to every other token. Recent studies have shown that these heads exhibit syntactic, semantic, or local …
Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most …
We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task is to generate the next response based …
Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model’s predictions. Attention distributions can be considered a faithful explanation …
BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being captured in BERT. However, the current works …
Given the success of Transformer-based models, two directions of study have emerged: interpreting role of individual attention heads and down-sizing the models for efficiency. Our work straddles these two streams: We …
In this paper, we introduce NLP resources for 11 major Indian languages from two major language families. These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) …
Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint …
I am a PhD scholar working with Dr Nirav P Bhatt. My areas of interest span many topics on artificial intelligence applications in biology. I am currently working to develop algorithms by leveraging both artificial …
I am currently pursuing PhD at Department of Computer Science and Engineering under the guidance of Harish G. Ramaswamy. My topic of research is theoretical deep learning.
“I am currently pursuing PhD under the guidance of Dr. Manikandan Narayanan. I want to solve biology related problems using ML/DL. "
I have completed my Masters at IIT Madras. Currently working as a Project officer on dialogue evaluation systems and metrics at RBC-DSAI
My name is Adhil Ahmed P M Shums. I have completed my B.E. in Automobile Engineering from MIT, Anna University. I am also doing an online B.S in Data Science and Applications from IIT Madras, in parallel. Apart from …
