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Found 16 papers, 5 papers with code
Let’s Chat: Understanding User Expectations in Socialbot Interactions
This paper analyzes data from the 2021 Amazon Alexa Prize Socialbot Grand Challenge 4, in order to better understand the differences between human-computer interactions (HCI) in a socialbot setting and conventional human-to-human interactions.
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues
While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues.
EDU-AP: Elementary Discourse Unit based Argument Parser
Neural approaches to end-to-end argument mining (AM) are often formulated as dependency parsing (DP), which relies on token-level sequence labeling and intricate post-processing for extracting argumentative structures from text.
Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines
This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020.
Proto-Gen: An end-to-end neural generator for persona and knowledge grounded response generation
In this paper we detail the implementation of Proto-Gen, an end-to-end neural response generator capable of selecting appropriate persona and fact sentences from available options, and generating persona and fact grounded responses.
Stylistic Response Generation by Controlling Personality Traits and Intent
Personality traits influence human actions and thoughts, which is manifested in day to day conversations.
UB Health Miners@SMM4H’22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.
Here we discuss our implementation of two tasks in the Social Media Mining for Health Applications (SMM4H) 2022 shared tasks – classification, detection, and normalization of Adverse Events (AE) mentioned in English tweets (Task 1) and classification of English tweets self-reporting exact age (Task 4).
Steering Conversational Large Language Models for Long Emotional Support Conversations
In this study, we address the challenge of consistently following emotional support strategies in long conversations by large language models (LLMs).
Consolidating Strategies for Countering Hate Speech Using Persuasive Dialogues
There is now a need to focus on deeper, long-term solutions that involve engaging with the human perpetrator behind the source of the content to change their viewpoint or at least bring down the rhetoric using persuasive means.
Rudolf Christoph Eucken at SemEval-2023 Task 4: An Ensemble Approach for Identifying Human Values from Arguments
Our ensemble comprises three models: (i) An entailment-based model for determining the human values based on their descriptions, (ii) A Roberta-based classifier that predicts the set of human values from an argument.
ArgU: A Controllable Factual Argument Generator
Our results demonstrate that it is possible to automatically generate diverse arguments exhibiting different inference patterns for the same set of facts by using control codes based on argument schemes and stance.
Diving Deep into Modes of Fact Hallucinations in Dialogue Systems
Knowledge Graph(KG) grounded conversations often use large pre-trained models and usually suffer from fact hallucination.
Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection
Ablation studies on the Persona-Chat dataset show that incorporating emotion and entailment improves the accuracy of response selection.
Proto: A Neural Cocktail for Generating Appealing Conversations
In this paper, we present our Alexa Prize Grand Challenge 4 socialbot: Proto.
Similarity Based Label Smoothing For Dialogue Generation
Generative neural conversational systems are generally trained with the objective of minimizing the entropy loss between the training "hard" targets and the predicted logits.
Medical Literature Mining and Retrieval in a Conversational Setting
The Covid-19 pandemic has caused a spur in the medical research literature.
