Constructing a Pluralist Moral Sentence Embedding Space using Contrastive Learning

Moral values influence humans in decision-making. Pluralist moral philosophers argue that human morality can be represented by a finite number of moral values, respecting the differences in moral views. Recent advancements in NLP show that language models retain a discernible level of knowledge in deontological ethics and moral norms of society....

Extractive Explanations for Interpretable Text Ranking

Neural document ranking models perform impressively well due to superior language understanding gained from pre-Training tasks. However, due to their complexity and large number of parameters these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the...

Investigating sentence severity with judicial open data: A case study on sentencing high-tech crime in the Dutch criminal justice system

Open data promotes transparency and accountability as everyone can analyse it. Law enforcement and the judiciary are increasingly making data available, to increase trust and confidence in the criminal justice system. Due to privacy legislation, judicial open data — like court judgements — in Europe is usually anonymized. And even if the...

Why does the machine punish?: The effects of the use of machine learning in criminal sentencing on the application of the theories of punishment

In recent years, the practice of risk-assessment has started to utilize machine learning to take in larger data sets and improve prediction accuracy. Simultaneously, it has expanded into the sentencing stage of the criminal justice system. This paper analyzes the effect of these developments on the consideration of the theories of punishment...

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...

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...

Content-based recommender support system for counselors in a suicide prevention chat helpline: Design and evaluation study

Background: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block....

Who said that? Comparing performance of TF-IDF and fastText to identify authorship of short sentences

Authorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature...