A non-projective greedy dependency parser with bidirectional LSTMs

The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to train a greedy parser with a dynamic oracle to mitigate error propagation. The model participated in the CoNLL 2017 UD Shared Task. In spite of not using any ensemble methods and using the baseline segmentation and PoS tagging, the parser obtained good results on both macro-average LAS and UAS in the big treebanks category (55 languages), ranking 7th out of 33 teams. In the all treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the all and big categories is mainly due to the poor performance on four parallel PUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora) perform poorly on cross-treebank settings, which does not occur with the corresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtain the 11th best LAS among all runs (official and unofficial). The code is made available at https://github.com/CoNLL-UD-2017/LyS-FASTPARSEComment: 12 pages, 2 figures, 5 table

Better, Faster, Stronger Sequence Tagging Constituent Parsers

Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding. To effectively close brackets, we train a model that learns to switch between tagging schemes. To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels. Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient. Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further. On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish.Comment: NAACL 2019 (long papers). Contains corrigendu

Misspelled queries in cross-language IR: analysis and management

Este artículo estudia el impacto de los errores ortográficos en las consultas sobre el rendimiento de los sistemas de recuperación de información multilingüe, proponiendo dos estrategias para su tratamiento: el empleo de técnicas de corrección ortográfica automática y la utilización de n-gramas de caracteres como términos índice y unidad de traducción, para así aprovecharnos de su robustez inherente. Los resultados demuestran la sensibilidad de estos sistemas frente a dichos errores así como la efectividad de las soluciones propuestas. Hasta donde alcanza nuestro conocimiento no existen trabajos similares en el ámbito multilingüe.This paper studies the impact of misspelled queries on the performance of Cross-Language Information Retrieval systems and proposes two strategies for dealing with them: the use of automatic spelling correction techniques and the use of character n-grams both as index terms and translation units, thus allowing to take advantage of their inherent robustness. Our results demonstrate the sensitivity of these systems to such errors and the effectiveness of the proposed solutions. To the best of our knowledge there are no similar jobs in the cross-language field.Trabajo parcialmente subvencionado por el Ministerio de Economía y Competitividad y FEDER (proyectos TIN2010-18552-C03-01 y TIN2010-18552-C03-02) y por la Xunta de Galicia (ayudas CN 2012/008, CN 2012/317 y CN 2012/319)

One model, two languages: training bilingual parsers with harmonized treebanks

We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both. We test the approach on the Universal Dependency Treebanks, training with MaltParser and MaltOptimizer. The results show that these bilingual parsers are more than competitive, as most combinations not only preserve accuracy, but some even achieve significant improvements over the corresponding monolingual parsers. Preliminary experiments also show the approach to be promising on texts with code-switching and when more languages are added.Comment: 7 pages, 4 tables, 1 figur