Alfonso Mendes

Also published as: Afonso Mendes


pdf bib
Improving abstractive summarization with energy-based re-ranking
Diogo Pernes | Afonso Mendes | André F. T. Martins
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores (Deng et al., 2021) have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.


Priberam Labs at the 3rd Shared Task on SlavNER
Pedro Ferreira | Ruben Cardoso | Afonso Mendes
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

This document describes our participation at the 3rd Shared Task on SlavNER, part of the 8th Balto-Slavic Natural Language Processing Workshop, where we focused exclusively in the Named Entity Recognition (NER) task. We addressed this task by combining multi-lingual contextual embedding models, such as XLM-R (Conneau et al., 2020), with character- level embeddings and a biaffine classifier (Yu et al., 2020). This allowed us to train downstream models for NER using all the available training data. We are able to show that this approach results in good performance when replicating the scenario of the 2nd Shared Task.


Hierarchical Nested Named Entity Recognition
Zita Marinho | Afonso Mendes | Sebastião Miranda | David Nogueira
Proceedings of the 2nd Clinical Natural Language Processing Workshop

In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.

Jointly Extracting and Compressing Documents with Summary State Representations
Afonso Mendes | Shashi Narayan | Sebastião Miranda | Zita Marinho | André F. T. Martins | Shay B. Cohen
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.


The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | Alfonso Mendes | Sebastian Riedel | Mariana S. C. Almeida | Pedro Balage | Shay B. Cohen | Tomasz Dwojak | Philip N. Garner | Andreas Giefer | Marcin Junczys-Dowmunt | Hina Imran | David Nogueira | Ahmed Ali | Sebastião Miranda | Andrei Popescu-Belis | Lesly Miculicich Werlen | Nikos Papasarantopoulos | Abiola Obamuyide | Clive Jones | Fahim Dalvi | Andreas Vlachos | Yang Wang | Sibo Tong | Rico Sennrich | Nikolaos Pappas | Shashi Narayan | Marco Damonte | Nadir Durrani | Sameer Khurana | Ahmed Abdelali | Hassan Sajjad | Stephan Vogel | David Sheppey | Chris Hernon | Jeff Mitchell
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.


Design and Implementation of a Semantic Search Engine for Portuguese
Carlos Amaral | Dominique Laurent | André Martins | Afonso Mendes | Cláudia Pinto
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)