no code implementations • 27 Oct 2020 • Shubhangi Tandon, Saratchandra Indrakanti, Amit Jaiswal, Svetlana Strunjas, Manojkumar Rangasamy Kannadasan
It is critical for eCommerce search engines to showcase in the top results the variety and selection of inventory available, specifically in the context of the various buying intents that may be associated with a search query.
no code implementations • 10 Aug 2019 • Saratchandra Indrakanti, Svetlana Strunjas, Shubhangi Tandon, Manojkumar Rangasamy Kannadasan
Surfacing a ranked list of items for a search query to help buyers discover inventory and make purchase decisions is a critical problem in eCommerce search.
no code implementations • 5 Sep 2018 • Shereen Oraby, Lena Reed, Sharath TS, Shubhangi Tandon, Marilyn Walker
Natural language generators for task-oriented dialog should be able to vary the style of the output utterance while still effectively realizing the system dialog actions and their associated semantics.
no code implementations • WS 2018 • Shereen Oraby, Lena Reed, Shubhangi Tandon, T. S. Sharath, Stephanie Lukin, Marilyn Walker
We show that our most explicit model can simultaneously achieve high fidelity to both semantic and stylistic goals: this model adds a context vector of 36 stylistic parameters as input to the hidden state of the encoder at each time step, showing the benefits of explicit stylistic supervision, even when the amount of training data is large.
no code implementations • E2E NLG Challenge System Descriptions 2018 • Shereen Oraby, Lena Reed, Shubhangi Tandon, Stephanie Lukin, Marilyn A. Walker
In the area of natural language generation (NLG), there has been a great deal of interest in end-to-end (E2E) neural models that learn and generate natural language sentence realizations in one step.
Ranked #6 on Data-to-Text Generation on E2E NLG Challenge (using extra training data)
no code implementations • 31 Oct 2017 • Amita Misra, Shereen Oraby, Shubhangi Tandon, Sharath TS, Pranav Anand, Marilyn Walker
We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0. 74 for gun control, 0. 71 for gay marriage, and 0. 67 for abortion.
1 code implementation • 28 Oct 2017 • Sharath T. S., Shubhangi Tandon, Ryan Bauer
Ever since the successful application of sequence to sequence learning for neural machine translation systems, interest has surged in its applicability towards language generation in other problem domains.
no code implementations • 28 Oct 2017 • Sharath T. S., Shubhangi Tandon
In this paper , we tackle Sentiment Analysis conditioned on a Topic in Twitter data using Deep Learning .