This paper investigates explainability in Natural Legal Language Processing (NLLP). We study the task of legal outcome prediction of the European Court of Human Rights cases in a ternary classification setup, where a language model is fine-tuned to predict whether an article has been claimed and violated (positive outcome), claimed but not violated (negative outcome) or not claimed at all (null outcome). Specifically, we experiment with three popular NLP explainability methods. Correlating the attribution scores of input-level methods (Integrated Gradients and Contrastive Explanations) with rationales from court rulings, we show that the correlations are very weak, with absolute values of Spearman and Kendall correlation coefficients ranging between 0.003 and 0.094. Furthermore, we use a concept-level interpretability method (Concept Erasure) with human expert annotations of legal reasoning, to show that obscuring legal concepts from the model representation has an insignificant effect on model performance (at most a decline of 0.26 F1). Therefore, our results indicate that automated legal outcome prediction models are not reliably grounded in legal reasoning.

In recent years, COVID-19 has impacted all aspects of human life. As a result, numerous publications relating to this disease have been issued. Due to the massive volume of publications, some retrieval systems have been developed to provide researchers with useful information. In these systems, lexical searching methods are widely used, which raises many issues related to acronyms, synonyms, and rare keywords. In this paper, we present a hybrid relation retrieval system, CovRelex-SE, based on embeddings to provide high-quality search results. Our system can be accessed through the following URL: https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/

This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. This work aims at building a system supporting users efficiently in acquiring knowledge across a huge number of COVID-19 scientific papers published rapidly. Our system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/.