Search Results for author:
Found 16 papers, 4 papers with code
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.
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.
Stylistic Response Generation by Controlling Personality Traits and Intent
Personality traits influence human actions and thoughts, which is manifested in day to day conversations.
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.
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.
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.
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.
Self-Supervised Claim Identification for Automated Fact Checking
We propose a novel, attention-based self-supervised approach to identify "claim-worthy" sentences in a fake news article, an important first step in automated fact-checking.
Ranked #1 on
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BREAKING! Presenting Fake News Corpus for Automated Fact Checking
Popular fake news articles spread faster than mainstream articles on the same topic which renders manual fact checking inefficient.
Summarizing World Speak : A Preliminary Graph Based Approach
Social media platforms play a crucial role in piecing together global news stories via their corresponding online discussions.
Time-Independent and Language-Independent Extraction of Multiword Expressions From Twitter
Multiword Expressions (MWEs) are crucial lexico-semantic units in any language.
