Search Results for author: Raghav Gupta

Found 14 papers, 4 papers with code

SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems

no code implementations13 Oct 2021 Harrison Lee, Raghav Gupta, Abhinav Rastogi, Yuan Cao, Bin Zhang, Yonghui Wu

We evaluate two dialogue state tracking models on SGD-X and observe that neither generalizes well across schema variations, measured by joint goal accuracy and a novel metric for measuring schema sensitivity.

Data Augmentation Dialogue State Tracking

Galaxy Morphology Classification using Neural Ordinary Differential Equations

no code implementations14 Dec 2020 Raghav Gupta, P. K. Srijith, Shantanu Desai

We use a continuous depth version of the Residual Network (ResNet) model known as Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification.

Instrumentation and Methods for Astrophysics Astrophysics of Galaxies

Schema-Guided Dialogue State Tracking Task at DSTC8

1 code implementation2 Feb 2020 Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, Pranav Khaitan

The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.

Data Augmentation Dialogue State Tracking

Extremely Small BERT Models from Mixed-Vocabulary Training

no code implementations EACL 2021 Sanqiang Zhao, Raghav Gupta, Yang song, Denny Zhou

Pretrained language models like BERT have achieved good results on NLP tasks, but are impractical on resource-limited devices due to memory footprint.

Knowledge Distillation Language Modelling +2

Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset

no code implementations12 Sep 2019 Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, Pranav Khaitan

In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.

Dialogue State Tracking Slot Filling

Robust Zero-Shot Cross-Domain Slot Filling with Example Values

1 code implementation ACL 2019 Darsh J Shah, Raghav Gupta, Amir A Fayazi, Dilek Hakkani-Tur

Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains.

Zero-shot Slot Filling

Multi-task learning for Joint Language Understanding and Dialogue State Tracking

no code implementations WS 2018 Abhinav Rastogi, Raghav Gupta, Dilek Hakkani-Tur

This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems.

Dialogue State Tracking Multi-Task Learning +1

An Efficient Approach to Encoding Context for Spoken Language Understanding

no code implementations1 Jul 2018 Raghav Gupta, Abhinav Rastogi, Dilek Hakkani-Tur

In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames.

Spoken Language Understanding Task-Oriented Dialogue Systems

Optimal Cost Almost-sure Reachability in POMDPs

no code implementations14 Nov 2014 Krishnendu Chatterjee, Martin Chmelík, Raghav Gupta, Ayush Kanodia

We consider partially observable Markov decision processes (POMDPs) with a set of target states and every transition is associated with an integer cost.

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