Miquel Sbert Garau versus Miquel Sbert Garau

Abstract not availabl

An Integral geometry based method for fast form-factor computation

Monte Carlo techniques have been widely used in rendering algorithms for local integration. For example, to compute the contribution of a patch to the luminance of another. In the present paper we propose an algorithm based on Integral geometry where Monte Carlo is applied globally. We give some results of the implementation to validate the proposition and we study the error of the technique, as well as its complexity.Postprint (published version

Leveraging Context Patterns for Medical Entity Classification

The ability of patients to understand health-related text is important for optimal health outcomes. A system that can automatically annotate medical entities could help patients better understand health-related text. Such a system would also accelerate manual data annotation for this low-resource domain as well as assist in down- stream medical NLP tasks such as finding textual similarity, identifying conflicting medical advice, and aspect-based sentiment analysis. In this work, we investigate a state-of-the-art entity set expansion model, BootstrapNet, for the task of medical entity classification on a new dataset of medical advice text. We also propose EP SBERT, a simple model that utilizes Sentence-BERT embeddings of entities and context patterns to more effectively capture the semantics of the entities. Our experiments show that EP SBERT significantly outperforms a random classifier baseline, outperforms the more complex BootstrapNet by 5.2 F1 points, and achieves a 5-fold cross validated weighted F1 score of 0.835. Further experiments show that EP SBERT achieves a weighted F1 score of 0.870 when we remove a peripheral class whose inclusion is nonessential to the problem formulation, and a weighted F1 score of 0.949 when using top-2 evaluation. This makes us confident that EP SBERT can be useful when building human-in-the-loop data annotation tools. Finally, we perform an extensive error analysis of EP SBERT, identifying two core challenges and future work. Our code will be made available at https://github.com/garrettjohnston99/EP-SBERT

SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization

We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18-39%. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.Comment: ACL 202

Learning to Understand Child-directed and Adult-directed Speech

Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language learners. This study explores the effect of child-directed speech when learning to extract semantic information from speech directly. We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS). We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better. The results suggest that this is at least partially due to linguistic rather than acoustic properties of the two registers, as we see the same pattern when looking at models trained on acoustically comparable synthetic speech.Comment: Authors found an error in preprocessing of transcriptions before they were fed to SBERT. After correction, the experiments were rerun. The updated results can be found in this version. Importantly, - Most scores were affected to a small degree (performance was slightly worse). - The effect was consistent across conditions. Therefore, the general patterns remain the sam