[PDF] Top 20 UDPipe 2 0 Prototype at CoNLL 2018 UD Shared Task
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UDPipe 2 0 Prototype at CoNLL 2018 UD Shared Task
... 36.42 2 4.14 20 13.03 8 Basque-BDT 99.97 3 99.97 3 99.83 3 96.08 2 — ...83.17 2 Buryat-BDT ...96.69 2 90.33 7 85.93 6 72.85 2 79.84 2 Czech-CAC ...93.64 2 93.55 2 ... See full document
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AX Semantics’ Submission to the CoNLL–SIGMORPHON 2018 Shared Task
... The hyperparameters of the model are a dropout of 0.2, the number of features is 370, lemma in- put and output lengths are 100, and length of fea- ture sequence is set to 20. The 2 bidirectional GRUs (Chung et ... See full document
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The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy
... the UD CoNLL 2017 parsing shared ...the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon- based morphological ...the shared ... See full document
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A Semi universal Pipelined Approach to the CoNLL 2017 UD Shared Task
... Componentized pipeline. Components in the pipeline can be divided and optimized inde- pendently so that they are interchangeable with other corresponding components such as the UDPipe tokenizer. Our dependency ... See full document
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Experiments on Morphological Reinflection: CoNLL 2018 Shared Task
... The embedding size for each language is differ- ent depending upon the alphabet set of that lan- guage available in the given dataset and similarly for morphological tags which are split into indi- vidual components. We ... See full document
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IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks
... training the parser on a single document took con- siderable time and also did not perform very well. Instead, we created artificial documents for train- ing by taking chunks of 10 sentences from the training set and ... See full document
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IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
... Treebank Baseline Ours Treebank Baseline Ours af_afribooms 77.88% 80.53% (13) ar_padt 66.41% 69.13% (15) bg_btb 84.91% 86.83% (15) br_keb 10.25% 9.45% (18) bxr_bdt 12.61% 19.22% (2) ca_ancora 85.61% 87.60% (16) ... See full document
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The SLT Interactions Parsing System at the CoNLL 2018 Shared Task
... the CoNLL 2018 shared task: Multilingual Parsing from Raw Text to Universal ...learning task-dependent fea- ...the UDPipe baseline, with an aver- age improvement of ... See full document
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UZH at CoNLL–SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection
... a task-specific ...both CoNLL–SIGMORPHON 2018 tasks. On Task I, our five-strong ensemble uzh-02 outper- forms the nearest competitor by over 1% absolute accuracy in the high setting ...over ... See full document
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KU CST at CoNLL–SIGMORPHON 2018 Shared Task: a Tridirectional Model
... Christo Kirov, Ryan Cotterell, John Sylak-Glassman, Graldine Walther, Ekaterina Vylomova, Patrick Xia, Manaal Faruqui, Sebastian Mielke, Arya D. Mc- Carthy, Sandra Kbler, David Yarowsky, Jason Eis- ner, and Mans Hulden. ... See full document
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Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2 0 with UDPipe
... the shared task eval- uation, the performance on the parallel treebanks was shown to be significantly worse if different treebanks than the “main” were used (even if they were larger or provided higher LAS ... See full document
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The NYU System for the CoNLL–SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection
... the shared task development sets, we de- cide on the following hyperparameters: We em- ploy 100-dimensional BPE and character embed- dings, and the encoder and decoder hidden states are ... See full document
6
A Fast and Lightweight System for Multilingual Dependency Parsing
... In this paper, we present a fast and lightweight multilingual dependency parsing system for the CoNLL 2017 UD Shared Task, which composed of a BiLSTMs feature extractor and a MLP classi- fier. ... See full document
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CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
... Table 14 gives the average LAS on the 5 ex- tra test sets (no own training data, but other tree- banks of the same language exist). Four of them come from the Parallel UD (PUD) collection in- troduced in the 2017 ... See full document
21
Turku Neural Parser Pipeline: An End to End System for the CoNLL 2018 Shared Task
... the UD- Pipe baseline runs provided by the shared task or- ...the UD- Pipe baseline, trained without ever seeing a sin- gle character of the Thai alphabet, would perform ...the UDPipe ... See full document
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SParse: Koç University Graph Based Parsing System for the CoNLL 2018 Shared Task
... Milan Straka, Jan Hajiˇc, and Jana Strakov´a. 2016. UD- Pipe: trainable pipeline for processing CoNLL-U files performing tokenization, morphological anal- ysis, POS tagging and parsing. In Proceedings of ... See full document
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AntNLP at CoNLL 2018 Shared Task: A Graph Based Parser for Universal Dependency Parsing
... the CoNLL 2018 UD Shared Task, including 61 big treebanks, 5 PUD treebanks (additional parallel test sets), 7 small treebanks and 9 low-resource language ... See full document
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UParse: the Edinburgh system for the CoNLL 2017 UD shared task
... Next, we describe UParse, the extended version of DENSE which we use for the UD shared task. As mentioned in Section 1, UParse is a combination of monolingual, multilingual, UDPipe baseline, ... See full document
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An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing
... al., 2018) show that our model outperforms the baseline UDPipe (Straka and Strakov´a, 2017) with ...downstream task applications (Fares et al., 2018) show that our joint model helps produce ... See full document
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CUNI x ling: Parsing Under Resourced Languages in CoNLL 2018 UD Shared Task
... The UDPipe parser uses vector representations of input word forms, which it by default trains jointly with training the parser, i.e. using only the words that appear in the training treebank. However, the parsing ... See full document
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