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Found 8 papers, 1 papers with code
Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators
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.
Summarizing Dialogic Arguments from Social Media
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.
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue Modeling
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.
Topic Based Sentiment Analysis Using Deep Learning
In this paper , we tackle Sentiment Analysis conditioned on a Topic in Twitter data using Deep Learning .
Neural MultiVoice Models for Expressing Novel Personalities in Dialog
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.
TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation
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 #7 on
Data-to-Text Generation
on E2E NLG Challenge
(using extra training data)
Influence of Neighborhood on the Preference of an Item in eCommerce Search
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.
Addressing Purchase-Impression Gap through a Sequential Re-ranker
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.
