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Found 60 papers, 4 papers with code
OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue
These results also demonstrate the remaining performance gap between the baselines and human performance, highlighting the challenges of entity linking in open-domain dialogue, and suggesting many avenues for future research using OpenEL.
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.
Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize
There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community.
Dependency Dialogue Acts -- Annotation Scheme and Case Study
DDA combines and adapts features from existing dialogue annotation frameworks, and emphasizes the multi-relational response structure of dialogues in addition to the dialogue acts and rhetorical relations.
A Transformer-based Response Evaluator for Open-Domain Spoken Conversation
First, we collect a corpus of Athena conversations with live human traffic, where potential responses from all enabled response generators are logged and subsequently annotated for response quality.
Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning
We also test whether NLG personality demonstrations from the restaurant domain can be used with meaning representations for the video game domain to generate personality stylized utterances about video games.
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model
We project these features to the dialogue level and train a dialogue-level MLP regression model, a dialogue-level LSTM, and a novel causal inference model called counterfactual-LSTM (CF-LSTM) to predict ratings.
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.
Modeling Performance in Open-Domain Dialogue with PARADISE
Here we develop a PARADISE model for predicting the performance of Athena, a dialogue system that has participated in thousands of conversations with real users, while competing as a finalist in the Alexa Prize.
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.
Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG
Ever since neural models were adopted in data-to-text language generation, they have invariably been reliant on extrinsic components to improve their semantic accuracy, because the models normally do not exhibit the ability to generate text that reliably mentions all of the information provided in the input.
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.
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
Generating sequential natural language descriptions from graph-structured data (e. g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text.
ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation
The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models.
Ranked #2 on
Data-to-Text Generation
on ViGGO
Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System
In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven.
SlugBot: Developing a Computational Model andFramework of a Novel Dialogue Genre
One of the most interesting aspects of the Amazon Alexa Prize competition is that the framing of the competition requires the development of new computational models of dialogue and its structure.
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.
Implicit Discourse Relation Identification for Open-domain Dialogues
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system.
Implicit Discourse Relation Classification
Implicit Relations
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Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.
CruzAffect at AffCon 2019 Shared Task: A feature-rich approach to characterize happiness
Our results indicate that generic characteristics are shared between the classes of agency, social and concepts, suggesting it should be possible to build general models for affective classification tasks.
Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs
One of the biggest challenges of end-to-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied.
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 Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features
Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels.
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.
Modeling Linguistic and Personality Adaptation for Natural Language Generation
Previous work has shown that conversants adapt to many aspects of their partners{'} language.
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.
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems
In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference.
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework
In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics.
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.
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.
Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
The greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog
We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0. 76 F1 for "sarcastic" and 0. 77 F1 for "other" in forums, and 0. 83 F1 for both "sarcastic" and "other" in tweets.
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.
Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews
We learn hyperbolic adjective patterns that are representative of the strongly-valenced expressive language commonly used in either positive or negative reviews.
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.
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available.
Using Summarization to Discover Argument Facets in Online Ideological Dialog
What are the CENTRAL PROPOSITIONS associated with different stances on an issue, what are the abstract objects under discussion that are central to a speaker's argument?
A Semi-Supervised Approach to Detecting Stance in Tweets
Stance classification aims to identify, for a particular issue under discussion, whether the speaker or author of a conversational turn has Pro (Favor) or Con (Against) stance on the issue.
Stylistic Variation in Television Dialogue for Natural Language Generation
Conversation is a critical component of storytelling, where key information is often revealed by what/how a character says it.
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.
Linguistic Reflexes of Well-Being and Happiness in Echo
Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then examine the extent to which different theoretical accounts can explain the variance in the happiness scores.
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Americans spend about a third of their time online, with many participating in online conversations on social and political issues.
Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue
Our first phase, using crowdsourced nasty indicators, achieves 58% precision and 49% recall, which increases to 75% precision and 62% recall when we bootstrap over the first level with generalized syntactic patterns.
A Verbal and Gestural Corpus of Story Retellings to an Expressive Embodied Virtual Character
We used a novel data collection method where an agent presented story components in installments, which the human would then retell to the agent.
A Multimodal Motion-Captured Corpus of Matched and Mismatched Extravert-Introvert Conversational Pairs
This paper presents a new corpus, the Personality Dyads Corpus, consisting of multimodal data for three conversations between three personality-matched, two-person dyads (a total of 9 separate dialogues).
A Corpus of Gesture-Annotated Dialogues for Monologue-to-Dialogue Generation from Personal Narratives
This paper presents a new corpus, the Story Dialogue with Gestures (SDG) corpus, consisting of 50 personal narratives regenerated as dialogues, complete with annotations of gesture placement and accompanying gesture forms.
Coordinating Communication in the Wild: The Artwalk Dialogue Corpus of Pedestrian Navigation and Mobile Referential Communication
The task provides a setting for real-world situated dialogic language and is designed to: (1) elicit entrainment and coordination of referring expressions between the dialogue participants, (2) examine the effect of friendship on dialogue strategies, and (3) examine how the need to complete the task while negotiating myriad, unanticipated events in the real world ― such as avoiding cars and other pedestrians ― affects linguistic coordination and other dialogue behaviors.
A Corpus for Research on Deliberation and Debate
As an application of this resource, the paper closes with a discussion of the relationship between discourse marker pragmatics, agreement, emotionality, and sarcasm in the IAC corpus.
An Annotated Corpus of Film Dialogue for Learning and Characterizing Character Style
We briefly show how film characters can be represented by models learned from the corpus, how the models can be distinguished based on different categories such as gender and film genre, and how they can be applied to a language generator to generate utterances that can be perceived as being similar to the intended character model.
