Search Results for author: Anjishnu Kumar

Found 6 papers, 1 papers with code

Learning to Retrieve Engaging Follow-Up Queries

1 code implementation21 Feb 2023 Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Omar Zia Khan, Zeynab Raeesy, Abhinav Sethy

The retrieval system is trained on a dataset which contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates.

Retrieval valid

Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning

no code implementations WS 2019 Daniele Bonadiman, Anjishnu Kumar, Arpit Mittal

The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question.

Community Question Answering Information Retrieval +3

Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering

no code implementations NAACL 2019 Fréderic Godin, Anjishnu Kumar, Arpit Mittal

In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications.

Knowledge Graphs Question Answering +2

Efficient Large-Scale Neural Domain Classification with Personalized Attention

no code implementations ACL 2018 Young-Bum Kim, Dongchan Kim, Anjishnu Kumar, Ruhi Sarikaya

In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs).

Classification domain classification +3

Efficient Large-Scale Domain Classification with Personalized Attention

no code implementations22 Apr 2018 Young-Bum Kim, Dongchan Kim, Anjishnu Kumar, Ruhi Sarikaya

In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs).

Classification domain classification +2

Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

no code implementations1 Nov 2017 Anjishnu Kumar, Arpit Gupta, Julian Chan, Sam Tucker, Bjorn Hoffmeister, Markus Dreyer, Stanislav Peshterliev, Ankur Gandhe, Denis Filiminov, Ariya Rastrow, Christian Monson, Agnika Kumar

This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa.

Spoken Language Understanding

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