BERT has achieved impressive performance in several NLP tasks. However, there has been
limited investigation on its adaptation guidelines in specialised domains. Here we focus on …

Laws and their interpretations, legal arguments and agreements\ are typically expressed in
writing, leading to the production of vast corpora of legal text. Their analysis, which is at the …

We explore how deep learning methods can be used for contract element extraction. We show
that a BILSTM operating on word, POS tag, and tokenshape embeddings outperforms the …

Legal judgment prediction is the task of automatically predicting the outcome of a court case,
given a text describing the case's facts. Previous work on using neural models for this task …

Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language
Processing (NLP) applications and presents interesting challenges. First, not all labels are …

We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We
release a new dataset of 57k legislative documents from EURLEX, annotated with ~4.3k …

We study how contract element extraction can be automated. We provide a labeled dataset
with gold contract element annotations, along with an unlabeled dataset of contracts that can …

We consider the task of detecting contractual obligations and prohibitions. We show that a
self-attention mechanism improves the performance of a BILSTM classifier, the previous state …

Deep Learning has been widely used for tackling challenging natural language processing
tasks over the recent years. Similarly, the application of Deep Neural Networks in legal …

Transformer-based language models, such as BERT and its variants, have achieved state-of-the-art
performance in several downstream natural language processing (NLP) tasks on …