Active Learning with Multi-annotator Disagreement

Moral values are often used as guidelines for human behaviour. The ability to identify moral values is important for social and ethical artificial intelligence. We address the difficulties of using contemporary natural language processing (NLP) techniques to classify moral values in texts. As the classification criterion of moral values is...

Training a Negotiating Agent through Self-Play

Recent developments in applying reinforcement learning to cooperative environments, like negotiation, have brought forward an important question: how well can a negotiating agent be trained through self-play? Previous research has seen successful application of self-play to other settings, like the games of chess and Go. This paper explores the...

Learning the Problem Representation for Improving Negotiation Strategies

The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a...

Opponent Modeling in Automated Bilateral Negotiation: Can Machine Learning Techniques Outperform State-of-the-Art Heuristic Techniques?

Automated negotiation agents can highly benefit from learning their opponent’s preferences. Multiple algorithms have been developed with the two main categories being: heuristic techniques and machine learning techniques. Historically, heuristic techniques have dominated the field, but with the recent development in the field of machine learning...

Learning a Latent Representation of the Opponent in Automated Negotiation

This paper introduces a strategy for learning opponent parameters in automated negotiation and using them for future negotiation sessions. The goal is to maximize the agent’s utility while being consistent in its performance over various negotiation scenarios. While a number of reinforcement learning approaches in the field have used Q-learning,...

Improving a Reinforcement Learning Negotiating Agent’s Performance by Extracting Information from the Opponent’s Sequence of Offers

With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.<br/><br/>Recent literature has shown great...

Unravelling Twitter chaos during a policy crisis: Applying Sentiment Analysis and Topic Modelling to Tweets about the Dutch Nitrogen Crisis

In May 2019, the Dutch Council of State rejected the national approach for reducing nitrogen emissions in Dutch nature. Farmers were targeted by the policy change: all licenses for agricultural expansion were revoked, affecting the financial livelihoods of farmers. Farmers did not take this well and, using social media platforms, started...

Moral Embeddings: A closer look at their Performance, Generalizability and Transferability

Moral values are abstract ideas that ground our judgements towards what is right or wrong. However, with the rapid unfold of moral rhetoric on social media, it becomes increasingly important to place these ideas in a moral frame, contain their harmful effects, and recognise their positive ones. So far, estimating values from opinionated text has...

Estimating the transferability of state-of-the-art models in predicting moral values

Moral values play a crucial role in our decision-making process by defining what is right and wrong. With the emergence of political activism and moral discourse on social media, and the latest developments in Natural Language Processing, we are looking at an opportunity to analyze moral values to observe trends as they form. Recent studies have...

Evaluating catastrophic forgetting of state-of-the-art NLP models for predicting moral values

Personal moral values represent the motivation behind individuals' actions and opinions. Understanding these values is helpful both in predicting individuals' actions, such as violent protests, and building AI that can better collaborate with humans. Predicting moral values is a challenging problem due to the abstract and subjective essence of...

Evaluating interpretability of state-of-the-art NLP models for predicting moral values

Understanding personal values is a crucial aspect that can facilitate the collaboration between AI and humans. Nonetheless, the implementation of collaborative agents in real life greatly depends on the amount of trust that is built in their relationship with people. In order to bridge this gap, more extensive analysis of the explainability of...

Performance analysis of the state-of-the-art NLP models for predicting moral values

Moral values are instrumental in understanding people's beliefs and behaviors. Estimating such values from text would facilitate the interaction between humans and computers. To date, no comparison between NLP models for predicting moral values from text exists. This paper addresses this by comparing LSTM and more novel models such as BERT and...

Deep Learning for Stance Detection: A Review and Comparison of the State-of-the-Art Approaches

Stance detection is a Natural Language Processing task that can detect if the input text is in favour, against or neutral towards a target. Research on stance detection has been growing and evolving over the last decade. In this paper, the current approaches for stance detection are discussed with a focus on the deep learning approaches. The...

Political Stance Detection using Knowledge Graphs and Sentiment Analysis

Sentiment analysis techniques estimate the opinion of the au- thor of a text towards an entity from that text. Current sen- timent analysis techniques are based on language features or deep learning methods. However, they do not make use of the extensive background knowledge that human readers can have. This makes it difficult for these models...

A Hybrid Approach for Sentence Similarity: Combining Semantic and Structural Similarity Metrics

Predicting similarity between sentence pairs is essential for applications such as recommender systems and plagiarism detection. There have been several categories of approaches for predicting sentence similarity. This paper combines approaches from two categories, semantic and structural similarity, to find a hybrid approach that aligns more...

Preparing Stance Detection: Feature Extraction Methods and Their Performance Used for Feature-Based Machine Learning Algorithms

Stance detection is a Natural Language Processing task that aims to detect the stance (support, agreement, or disagreement) of a piece of text towards some target. In this paper, we aim to find the best performing pair of feature extraction method and feature-based machine learning algorithm. By doing so, an explainable method can be found to...

Tackling the Headline Incongruity Problem using Stance Detection

The increasing amount of untrusted content on the internet is a worrisome trend. The headline of an article can be adjusted to influence a potential readers attention and click-through rate. This clickbait or sensationalism can mislead the reader as the headline does not accurately represent the information in the corresponding article body....