Search Results for author: Gaurav Singh Tomar

Found 9 papers, 2 papers with code

Measuring Attribution in Natural Language Generation Models

no code implementations23 Dec 2021 Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, David Reitter

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world.

Text Generation

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

no code implementations16 Dec 2021 Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.

Conversational Question Answering Passage Retrieval +1

Thieves on Sesame Street! Model Extraction of BERT-based APIs

1 code implementation ICLR 2020 Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer

We study the problem of model extraction in natural language processing, in which an adversary with only query access to a victim model attempts to reconstruct a local copy of that model.

Language Modelling Model extraction +3

Attention Interpretability Across NLP Tasks

1 code implementation24 Sep 2019 Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, Manaal Faruqui

The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque.

End-to-End Retrieval in Continuous Space

no code implementations19 Nov 2018 Daniel Gillick, Alessandro Presta, Gaurav Singh Tomar

Most text-based information retrieval (IR) systems index objects by words or phrases.

Information Retrieval

Coordinating Collaborative Chat in Massive Open Online Courses

no code implementations18 Apr 2017 Gaurav Singh Tomar, Sreecharan Sankaranarayanan, Xu Wang, Carolyn Penstein Rosé

An earlier study of a collaborative chat intervention in a Massive Open Online Course (MOOC) identified negative effects on attrition stemming from a requirement for students to be matched with exactly one partner prior to beginning the activity.

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