We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service.
Machine learning has always played an important role in bioinformatics and recent applications of deep learning have allowed solving a new spectrum of biologically relevant tasks.
By imposing a series of regularization constraints to the learning problem, we exploit a pooling mechanism that incorporates such notion of fragments within the node soft assignment function that produces the embeddings.
We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble.
Ranked #1 on Link Prediction on AbstRCT - Neoplasm
We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another.
Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents.
As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly available to a wide public, allowing end-users to submit queries with their own data, and to efficiently retrieve results.
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.
We explore the use of residual networks for argumentation mining, with an emphasis on link prediction.
Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer.