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Found 18 papers, 2 papers with code
CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems
Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI.
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation
A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training.
Aff2Vec: Affect--Enriched Distributional Word Representations
Human communication includes information, opinions, and reactions.
Investigating how well contextual features are captured by bi-directional recurrent neural network models
Our experiments focus on important contextual words as features, which can easily be extended to analyze various other feature types.
Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email
We present a novel approach to model human frustration in text.
Towards Building Affect sensitive Word Distributions
Our work addresses the question: can affect lexica improve the word representations learnt from a corpus?
Forecasting Granular Audience Size for Online Advertising
Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e. g. {US, Chrome, Mobile}).
Improving generation quality of pointer networks via guided attention
The first shortcoming is the extractive nature of the generated summaries, since the network eventually learns to copy from the input article most of the times, affecting the abstractive nature of the generated summaries.
Generating Formality-Tuned Summaries Using Input-Dependent Rewards
Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content.
Generating summaries tailored to target characteristics
Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries.
Exploring Early Prediction of Buyer-Seller Negotiation Outcomes
Agents that negotiate with humans find broad applications in pedagogy and conversational AI.
LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets
We describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets.
Pilot: Winner of the Human-Agent Negotiation Challenge at IJCAI 2020
This document describes our agent Pilot, winner of the Human-Agent Negotiation Challenge at ANAC, IJCAI 2020.
Towards Emotion-Aware Agents For Negotiation Dialogues
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making.
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks
Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios.
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data.
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue
Knowledge-grounded dialogue generation is a challenging task because it requires satisfying two fundamental yet often competing constraints: being responsive in a manner that is specific to what the conversation partner has said while also being attributable to an underlying source document.
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues
A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems.
