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Found 10 papers, 2 papers with code
Annotator-Centric Active Learning for Subjective NLP Tasks
To accurately capture the variability in human judgments for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial.
A Hybrid Intelligence Method for Argument Mining
We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method when compared to a common set of diverse opinions, justifying the need for human insight.
An Empirical Analysis of Diversity in Argument Summarization
Presenting high-level arguments is a crucial task for fostering participation in online societal discussions.
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
To solve this issue we introduce EduGym, a set of educational reinforcement learning environments and associated interactive notebooks tailored for education.
Do Differences in Values Influence Disagreements in Online Discussions?
We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions.
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation
This paper discusses our approaches for task-oriented conversational modelling using subjective knowledge, with a particular emphasis on response generation.
Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction
This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022).
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
Reason Against the Machine: Future Directions for Mass Online Deliberation
Designers of online deliberative platforms aim to counter the degrading quality of online debates.
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning
In this work, we present an alternative approach to making an agent compositional through the use of a diagnostic classifier.
