CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman,Martin Popel,Milan Straka,Jan Hajič,Joakim Nivre,Filip Ginter,Juhani Luotolahti,Sampo Pyysalo,Slav Petrov,Martin Potthast,Francis M. Tyers,Elena Badmaeva,Memduh Gökırmak,Anna Nedoluzhko,Silvie Cinková,Jaroslava Hlaváčová,Václava Kettnerová,Zdenka Uresova,Jenna Kanerva,Stina Ojala,Anna Missilä,Christopher D. Manning,Sebastian Schuster,Siva Reddy,Dima Taji,Nizar Habash,Herman Leung,Marie-Catherine de Marneffe,Manuela Sanguinetti,Maria Simi,Hiroshi Kanayama,Valeria dePaiva,Kira Droganova,Héctor Martínez Alonso,Ça ugrı Çöltekin,Umut Sulubacak,Hans Uszkoreit,Vivien Macketanz,Aljoscha Burchardt,Kim Harris,Katrin Marheinecke,Georg Rehm,Tolga Kayadelen,Mohammed Attia,Ali Elkahky,Zhuoran Yu,Emily Pitler,Saran Lertpradit,Michael Mandl,Jesse Kirchner,Hector Fernandez Alcalde,Jana Strnadová,Esha Banerjee,Ruli Manurung,Antonio Stella,Atsuko Shimada,Sookyoung Kwak,Gustavo Mendonça,Tatiana Lando,Rattima Nitisaroj,Josie Li +60 more
- Vol. 1, Iss: 1, pp 1-19
TLDR
The task and evaluation methodology is defined, how the data sets were prepared, report and analyze the main results, and a brief categorization of the different approaches of the participating systems are provided.Citations
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Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
TL;DR: This work introduces Stanza, an open-source Python natural language processing toolkit supporting 66 human languages that features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition.
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How multilingual is Multilingual BERT
TL;DR: This article showed that M-BERT is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language.
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Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe
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How multilingual is Multilingual BERT
TL;DR: It is concluded that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs, and that the model can find translation pairs.
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Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task
TL;DR: This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies, which was ranked first according to all five relevant metrics for the system.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
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Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.