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Found 15 papers, 0 papers with code
Athena 2.0: Discourse and User Modeling in Open Domain Dialogue
Conversational agents are consistently growing in popularity and many people interact with them every day.
Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBot
Athena 2. 0 is an Alexa Prize SocialBot that has been a finalist in the last two Alexa Prize Grand Challenges.
Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue
We experiment with few-shot prompt-based learning, comparing GPT-Neo to Jurassic-1, for the movies, music, TV, sports, and video game domains, both within and cross-domain, with different prompt set sizes (2, 3, 10), formats, and meaning representations consisting of either sets of WikiData KG triples, or dialogue acts.
Athena: Constructing Dialogues Dynamically with Discourse Constraints
This report describes Athena, a dialogue system for spoken conversation on popular topics and current events.
Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation
Here we explore, for the first time, whether it is possible to train an NLG for a new larger ontology using existing training sets for the restaurant domain, where each set is based on a different ontology.
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast
Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant.
Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?
While neural generation methods integrate sentence planning and surface realization in one end-to-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training.
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.
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.
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)
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
The use of irony and sarcasm in social media allows us to study them at scale for the first time.
And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue
We investigate the characteristics of factual and emotional argumentation styles observed in online debates.
Debbie, the Debate Bot of the Future
Chatbots are a rapidly expanding application of dialogue systems with companies switching to bot services for customer support, and new applications for users interested in casual conversation.
Learning Lexico-Functional Patterns for First-Person Affect
Informal first-person narratives are a unique resource for computational models of everyday events and people's affective reactions to them.
