Search Results for author: Kshitij Fadnis

Found 6 papers, 1 papers with code

Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents

1 code implementation31 Jan 2020 Mayank Agarwal, Jorge J. Barroso, Tathagata Chakraborti, Eli M. Dow, Kshitij Fadnis, Borja Godoy, Madhavan Pallan, Kartik Talamadupula

This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 Nov 2019 Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue

A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.

Knowledge Graphs Natural Language Inference

Path-Based Contextualization of Knowledge Graphs for Textual Entailment

no code implementations5 Nov 2019 Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos, Achille Fokoue

In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph.

Knowledge Graphs Natural Language Inference

Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems

no code implementations11 Jul 2019 Jatin Ganhotra, Siva Sankalp Patel, Kshitij Fadnis

Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e. g. flight booking, hotel reservation, technical support, student advising etc.

Goal-Oriented Dialog

Local-to-Global Bayesian Network Structure Learning

no code implementations ICML 2017 Tian Gao, Kshitij Fadnis, Murray Campbell

We introduce a new local-to-global structure learning algorithm, called graph growing structure learning (GGSL), to learn Bayesian network (BN) structures.

Cannot find the paper you are looking for? You can Submit a new open access paper.