Cheap IR Evaluation: Fewer Topics, No Relevance Judgements, and Crowdsourced Assessments

To evaluate Information Retrieval (IR) effectiveness, a possible approach is to use test collections, which are composed of a collection of documents, a set of description of information needs (called topics), and a set of relevant documents to each topic. Test collections are modelled in a competition scenario: for example, in the well known TREC initiative, participants run their own retrieval systems over a set of topics and they provide a ranked list of retrieved documents; some of the retrieved documents (usually the first ranked) constitute the so called pool, and their relevance is evaluated by human assessors; the document list is then used to compute effectiveness metrics and rank the participant systems. Private Web Search companies also run their in-house evaluation exercises; although the details are mostly unknown, and the aims are somehow different, the overall approach shares several issues with the test collection approach. The aim of this work is to: (i) develop and improve some state-of-the-art work on the evaluation of IR effectiveness while saving resources, and (ii) propose a novel, more principled and engineered, overall approach to test collection based effectiveness evaluation. [...

A Neural Model to Jointly Predict and Explain Truthfulness of Statements

Automated fact-checking (AFC) systems exist to combat disinformation, however their complexity usually makes them opaque to the end user, making it difficult to foster trust in the system. In this paper, we introduce the E-BART model with the hope of making progress on this front. E-BART is able to provide a veracity prediction for a claim, and jointly generate a human-readable explanation for this decision. We show that E-BART is competitive with the state-of-the-art on the e-FEVER and e-SNLI tasks. In addition, we validate the joint-prediction architecture by showing 1) that generating explanations does not significantly impede the model from performing well in its main task of veracity prediction, and 2) that predicted veracity and explanations are more internally coherent when generated jointly than separately. We also calibrate the E-BART model, allowing the output of the final model be correctly interpreted as the confidence of correctness. Finally, we also conduct and extensive human evaluation on the impact of generated explanations and observe that: explanations increase human ability to spot misinformation and make people more skeptical about claims, and explanations generated by E-BART are competitive with ground truth explanations

Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning

Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin–Eosin slides, which partly mimics the pathologist’s behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.Eduardo Conde-Sousa was supported by the project PPBI-POCI-01-0145-FEDER-022122, in the scope of Fundação para a Ciência e Tecnologia, Portugal (FCT) National Roadmap of Research Infrastructures

Automated ICF Coding of Rehabilitation Notes for Low-Resource Languages via Continual Training of Language Models

: The coding of medical documents and in particular of rehabilitation notes using the International Classification of Functioning, Disability and Health (ICF) is a difficult task showing low agreement among experts. Such difficulty is mainly caused by the specific terminology that needs to be used for the task. In this paper, we address the task developing a model based on a large language model, BERT. By leveraging continual training of such a model using ICF textual descriptions, we are able to effectively encode rehabilitation notes expressed in Italian, an under-resourced language