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The Right Model for the Job: An Evaluation of Legal Multi-Label Classification Baselines
Multi-Label Classification (MLC) is a common task in the legal domain, where more than one label may be assigned to a legal document.
A Framework for Monitoring and Retraining Language Models in Real-World Applications
In the Machine Learning (ML) model development lifecycle, training candidate models using an offline holdout dataset and identifying the best model for the given task is only the first step.
Legal Entity Extraction using a Pointer Generator Network
To overcome the challenges of our noisy training data, e. g., text extraction errors and/or typos and unknown label indices, we frame the NER task as a sequence generation task (seq2seq) and train a pointer generator network to generate the entities in the document rather than label them.
Named Entity Recognition in the Legal Domain using a Pointer Generator Network
Named Entity Recognition (NER) is the task of identifying and classifying named entities in unstructured text.
