Luis Marujo

Also published as: Luís Marujo


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Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering
Lahari Poddar | Leonardo Neves | William Brendel | Luis Marujo | Sergey Tulyakov | Pradeep Karuturi
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

Tracking user reported bugs requires considerable engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural architecture that can jointly (1) detect if two bug reports are duplicates, and (2) aggregate them into latent topics. Leveraging the assumption that learning the topic of a bug is a sub-task for detecting duplicates, we design a loss function that can jointly perform both tasks but needs supervision for only duplicate classification, achieving topic clustering in an unsupervised fashion. We use a two-step attention module that uses self-attention for topic clustering and conditional attention for duplicate detection. We study the characteristics of two types of real world datasets that have been marked for duplicate bugs by engineers and by non-technical annotators. The results demonstrate that our model not only can outperform state-of-the-art methods for duplicate classification on both cases, but can also learn meaningful latent clusters without additional supervision.


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Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
Jianfei Yu | Luís Marujo | Jing Jiang | Pradeep Karuturi | William Brendel
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.


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Mining Parallel Corpora from Sina Weibo and Twitter
Wang Ling | Luís Marujo | Chris Dyer | Alan W. Black | Isabel Trancoso
Computational Linguistics, Volume 42, Issue 2 - June 2016


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Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach
Luís Marujo | Ricardo Ribeiro | David Martins de Matos | João Neto | Anatole Gershman | Jaime Carbonell
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Wang Ling | Chris Dyer | Alan W Black | Isabel Trancoso | Ramón Fermandez | Silvio Amir | Luís Marujo | Tiago Luís
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Automatic Keyword Extraction on Twitter
Luís Marujo | Wang Ling | Isabel Trancoso | Chris Dyer | Alan W. Black | Anatole Gershman | David Martins de Matos | João Neto | Jaime Carbonell
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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Crowdsourcing High-Quality Parallel Data Extraction from Twitter
Wang Ling | Luís Marujo | Chris Dyer | Alan W. Black | Isabel Trancoso
Proceedings of the Ninth Workshop on Statistical Machine Translation


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Recognition of Named-Event Passages in News Articles
Luis Marujo | Wang Ling | Anatole Gershman | Jaime Carbonell | João P. Neto | David Matos
Proceedings of COLING 2012: Demonstration Papers

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Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization
Luís Marujo | Anatole Gershman | Jaime Carbonell | Robert Frederking | João P. Neto
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Fast and effective automated indexing is critical for search and personalized services. Key phrases that consist of one or more words and represent the main concepts of the document are often used for the purpose of indexing. In this paper, we investigate the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction. These features include the use of signal words and freebase categories. Some of these features lead to significant improvements in the accuracy of the results. We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization. Light filtering removes sentences from the document, which are judged peripheral to its main content. Co-reference normalization unifies several written forms of the same named entity into a unique form. We also needed a “Gold Standard” ― a set of labeled documents for training and evaluation. While the subjective nature of key phrase selection precludes a true “Gold Standard”, we used Amazon's Mechanical Turk service to obtain a useful approximation. Our data indicates that the biggest improvements in performance were due to shallow semantic features, news categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of deeper semantic features such as Freebase sub-categories was not beneficial by itself, but in combination with pre-processing, did cause slight improvements in the nDCG scores.


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BP2EP - Adaptation of Brazilian Portuguese texts to European Portuguese
Luis Marujo | Nuno Grazina | Tiago Luis | Wang Ling | Luisa Coheur | Isabel Trancoso
Proceedings of the 15th Annual Conference of the European Association for Machine Translation