Erick Fonseca

Also published as: Erick Rocha Fonseca, Erick R. Fonseca


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Revisiting Higher-Order Dependency Parsers
Erick Fonseca | André F. T. Martins
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parsers. This has led to a belief that neural encoders can implicitly encode structural constraints, such as siblings and grandparents in a tree. We tested this hypothesis and found that neural parsers may benefit from higher-order features, even when employing a powerful pre-trained encoder, such as BERT. While the gains of higher-order features are small in the presence of a powerful encoder, they are consistent for long-range dependencies and long sentences. In particular, higher-order models are more accurate on full sentence parses and on the exact match of modifier lists, indicating that they deal better with larger, more complex structures.


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Findings of the WMT 2019 Shared Tasks on Quality Estimation
Erick Fonseca | Lisa Yankovskaya | André F. T. Martins | Mark Fishel | Christian Federmann
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

We report the results of the WMT19 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations. The task includes estimation at three granularity levels: word, sentence and document. A novel addition is evaluating sentence-level QE against human judgments: in other words, designing MT metrics that do not need a reference translation. This year we include three language pairs, produced solely by neural machine translation systems. Participating teams from eleven institutions submitted a variety of systems to different task variants and language pairs.


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Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
Nathan Hartmann | Erick Fonseca | Christopher Shulby | Marcos Treviso | Jéssica Silva | Sandra Aluísio
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology


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A Deep Architecture for Non-Projective Dependency Parsing
Erick Fonseca | Sandra Aluísio
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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Semi-Automatic Construction of a Textual Entailment Dataset: Selecting Candidates with Vector Space Models
Erick R. Fonseca | Sandra Maria Aluísio
Proceedings of the 10th Brazilian Symposium in Information and Human Language Technology


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Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging
Erick Rocha Fonseca | João Luís G. Rosa
Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology