23 citations · 24 across the 3 of their papers we have counts for
7 papers
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning
Swarnadeep Saha, Prateek Yadav, Mohit Bansal
Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks. However, there has been relatively less work on analyzing…
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
Swarnadeep Saha, Prateek Yadav, Mohit Bansal
We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent…
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Swarnadeep Saha, Prateek Yadav, Lisa Bauer +1
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting…
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Shikhar Vashishth, Prateek Yadav, Manik Bhandari +1
Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi-Supervised Learning (SSL) methods aim to address this…
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Shikhar Vashishth, Manik Bhandari, Prateek Yadav +3
Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While th…
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav +3
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible…