Search Results for author: Arindam Mandal

Found 8 papers, 0 papers with code

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

no code implementations27 Dec 2018 Chandra Khatri, Behnam Hedayatnia, Anu Venkatesh, Jeff Nunn, Yi Pan, Qing Liu, Han Song, Anna Gottardi, Sanjeev Kwatra, Sanju Pancholi, Ming Cheng, Qinglang Chen, Lauren Stubel, Karthik Gopalakrishnan, Kate Bland, Raefer Gabriel, Arindam Mandal, Dilek Hakkani-Tur, Gene Hwang, Nate Michel, Eric King, Rohit Prasad

In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.

Knowledge Graphs Natural Language Understanding +2

Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems

no code implementations30 Nov 2018 Rahul Goel, Shachi Paul, Tagyoung Chung, Jeremie Lecomte, Arindam Mandal, Dilek Hakkani-Tur

This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained.

Dialogue State Tracking Goal-Oriented Dialogue Systems +1

Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision

no code implementations30 Nov 2018 Chandra Khatri, Behnam Hedayatnia, Rahul Goel, Anushree Venkatesh, Raefer Gabriel, Arindam Mandal

We train models using publicly available annotated datasets as well as using the proposed large-scale semi-supervised datasets.

Chatbot

Parsing Coordination for Spoken Language Understanding

no code implementations26 Oct 2018 Sanchit Agarwal, Rahul Goel, Tagyoung Chung, Abhishek Sethi, Arindam Mandal, Spyros Matsoukas

Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology.

Spoken Language Understanding

Contextual Topic Modeling For Dialog Systems

no code implementations18 Oct 2018 Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Angeliki Metanillou, Anushree Venkatesh, Raefer Gabriel, Arindam Mandal

On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances.

Chatbot Classification +3

Data Augmentation for Robust Keyword Spotting under Playback Interference

no code implementations1 Aug 2018 Anirudh Raju, Sankaran Panchapagesan, Xing Liu, Arindam Mandal, Nikko Strom

Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.

Acoustic echo cancellation Data Augmentation +1

Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting

no code implementations5 May 2017 Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Geng-Shen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni

Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.

Small-Footprint Keyword Spotting

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