@Book{K17-3:2017, editor = {Jan Hajič and Dan Zeman}, title = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, url = {http://www.aclweb.org/anthology/K17-3} } @InProceedings{zeman-EtAl:2017:K17-3, author = {Zeman, Daniel and Popel, Martin and Straka, Milan and Hajic, Jan and Nivre, Joakim and Ginter, Filip and Luotolahti, Juhani and Pyysalo, Sampo and Petrov, Slav and Potthast, Martin and Tyers, Francis and Badmaeva, Elena and Gokirmak, Memduh and Nedoluzhko, Anna and Cinkova, Silvie and Hajic jr., Jan and Hlavacova, Jaroslava and Kettnerov\'{a}, V\'{a}clava and Uresova, Zdenka and Kanerva, Jenna and Ojala, Stina and Missil\"{a}, Anna and Manning, Christopher D. and Schuster, Sebastian and Reddy, Siva and Taji, Dima and Habash, Nizar and Leung, Herman and de Marneffe, Marie-Catherine and Sanguinetti, Manuela and Simi, Maria and Kanayama, Hiroshi and dePaiva, Valeria and Droganova, Kira and Mart\'{i}nez Alonso, H\'{e}ctor and \c{C}\"{o}ltekin, \c{C}a\u{g}rı and Sulubacak, Umut and Uszkoreit, Hans and Macketanz, Vivien and Burchardt, Aljoscha and Harris, Kim and Marheinecke, Katrin and Rehm, Georg and Kayadelen, Tolga and Attia, Mohammed and Elkahky, Ali and Yu, Zhuoran and Pitler, Emily and Lertpradit, Saran and Mandl, Michael and Kirchner, Jesse and Alcalde, Hector Fernandez and Strnadov\'{a}, Jana and Banerjee, Esha and Manurung, Ruli and Stella, Antonio and Shimada, Atsuko and Kwak, Sookyoung and Mendonca, Gustavo and Lando, Tatiana and Nitisaroj, Rattima and Li, Josie}, title = {CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {1--19}, abstract = {The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.}, url = {http://www.aclweb.org/anthology/K17-3001} } @InProceedings{dozat-qi-manning:2017:K17-3, author = {Dozat, Timothy and Qi, Peng and Manning, Christopher D.}, title = {Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {20--30}, abstract = {This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).}, url = {http://www.aclweb.org/anthology/K17-3002} } @InProceedings{shi-EtAl:2017:K17-3, author = {Shi, Tianze and Wu, Felix G. and Chen, Xilun and Cheng, Yao}, title = {Combining Global Models for Parsing Universal Dependencies}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {31--39}, abstract = {We describe our entry, C2L2, to the CoNLL 2017 shared task on parsing Universal Dependencies from raw text. Our system features an ensemble of three global parsing paradigms, one graph-based and two transition-based. Each model leverages character-level bi-directional LSTMs as lexical feature extractors to encode morphological information. Though relying on baseline tokenizers and focusing only on parsing, our system ranked second in the official end-to-end evaluation with a macro-average of 75.00 LAS F1 score over 81 test treebanks. In addition, we had the top average performance on the four surprise languages and on the small treebank subset.}, url = {http://www.aclweb.org/anthology/K17-3003} } @InProceedings{bjorkelund-EtAl:2017:K17-3, author = {Bj\"{o}rkelund, Anders and Falenska, Agnieszka and Yu, Xiang and Kuhn, Jonas}, title = {IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {40--51}, abstract = {This paper presents the IMS contribution to the CoNLL 2017 Shared Task. In the preprocessing step we employed a CRF POS/morphological tagger and a neural tagger predicting supertags. On some languages, we also applied word segmentation with the CRF tagger and sentence segmentation with a perceptron-based parser. For parsing we took an ensemble approach by blending multiple instances of three parsers with very different architectures. Our system achieved the third place overall and the second place for the surprise languages.}, url = {http://www.aclweb.org/anthology/K17-3004} } @InProceedings{che-EtAl:2017:K17-3, author = {Che, Wanxiang and Guo, Jiang and Wang, Yuxuan and Zheng, Bo and Zhao, Huaipeng and Liu, Yang and Teng, Dechuan and Liu, Ting}, title = {The HIT-SCIR System for End-to-End Parsing of Universal Dependencies}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {52--62}, abstract = {This paper describes our system (HIT-SCIR) for the CoNLL 2017 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system includes three pipelined components: \textit{tokenization}, \textit{Part-of-Speech} (POS) \textit{tagging} and \textit{dependency parsing}. We use character-based bidirectional long short-term memory (LSTM) networks for both tokenization and POS tagging. Afterwards, we employ a list-based transition-based algorithm for general non-projective parsing and present an improved Stack-LSTM-based architecture for representing each transition state and making predictions. Furthermore, to parse low/zero-resource languages and cross-domain data, we use a model transfer approach to make effective use of existing resources. We demonstrate substantial gains against the UDPipe baseline, with an average improvement of 3.76% in LAS of all languages. And finally, we rank the 4th place on the official test sets.}, url = {http://www.aclweb.org/anthology/K17-3005} } @InProceedings{lim-poibeau:2017:K17-3, author = {Lim, KyungTae and Poibeau, Thierry}, title = {A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {63--70}, abstract = {In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with “Multilingual Parsing from Raw Text to Universal Dependencies”. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).}, url = {http://www.aclweb.org/anthology/K17-3006} } @InProceedings{sato-EtAl:2017:K17-3, author = {Sato, Motoki and Manabe, Hitoshi and Noji, Hiroshi and Matsumoto, Yuji}, title = {Adversarial Training for Cross-Domain Universal Dependency Parsing}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {71--79}, abstract = {We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique. Our approach is an extension to the recently proposed adversarial training technique for domain adaptation, which we apply on top of a graph-based neural dependency parsing model on bidirectional LSTMs. In our experiments, we find our baseline graph-based parser already outperforms the official baseline model (UDPipe) by a large margin. Further, by applying our technique to the treebanks of the same language with different domains, we observe an additional gain in the performance, in particular for the domains with less training data.}, url = {http://www.aclweb.org/anthology/K17-3007} } @InProceedings{krnap-onder-yuret:2017:K17-3, author = {Kırnap, \"{O}mer and \"{O}nder, Berkay Furkan and Yuret, Deniz}, title = {Parsing with Context Embeddings}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {80--87}, abstract = {We introduce context embeddings, dense vectors derived from a language model that represent the left/right context of a word instance, and demonstrate that context embeddings significantly improve the accuracy of our transition based parser. Our model consists of a bidirectional LSTM (BiLSTM) based language model that is pre-trained to predict words in plain text, and a multi-layer perceptron (MLP) decision model that uses features from the language model to predict the correct actions for an ArcHybrid transition based parser. We participated in the CoNLL 2017 UD Shared Task as the ``Ko\c{c} University'' team and our system was ranked 7th out of 33 systems that parsed 81 treebanks in 49 languages.}, url = {http://www.aclweb.org/anthology/K17-3008} } @InProceedings{straka-strakova:2017:K17-3, author = {Straka, Milan and Strakov\'{a}, Jana}, title = {Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {88--99}, abstract = {Many natural language processing tasks, including the most advanced ones, routinely start by several basic processing steps -- tokenization and segmentation, most likely also POS tagging and lemmatization, and commonly parsing as well. A multilingual pipeline performing these steps can be trained using the Universal Dependencies project, which contains annotations of the described tasks for 50 languages in the latest release UD 2.0. We present an update to UDPipe, a simple-to-use pipeline processing CoNLL-U version 2.0 files, which performs these tasks for multiple languages without requiring additional external data. We provide models for all 50 languages of UD 2.0, and furthermore, the pipeline can be trained easily using data in CoNLL-U format. UDPipe is a standalone application in C++, with bindings available for Python, Java, C\# and Perl. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, UDPipe was the eight best system, while achieving low running times and moderately sized models.}, url = {http://www.aclweb.org/anthology/K17-3009} } @InProceedings{vania-zhang-lopez:2017:K17-3, author = {Vania, Clara and Zhang, Xingxing and Lopez, Adam}, title = {UParse: the Edinburgh system for the CoNLL 2017 UD shared task}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {100--110}, abstract = {This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).}, url = {http://www.aclweb.org/anthology/K17-3010} } @InProceedings{heinecke-asadullah:2017:K17-3, author = {Heinecke, Johannes and Asadullah, Munshi}, title = {Multi-Model and Crosslingual Dependency Analysis}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {111--118}, abstract = {This paper describes the system of the Team Orange-Deski\~{n}, used for the CoNLL 2017 UD Shared Task in Multilingual Dependency Parsing. We based our approach on an existing open source tool (BistParser), which we modified in order to produce the required output. Additionally we added a kind of pseudo-projectivisation. This was needed since some of the task’s languages have a high percentage of non-projective dependency trees. In most cases we also employed word embeddings. For the 4 surprise languages, the data provided seemed too little to train on. Thus we decided to use the training data of typologically close languages instead. Our system achieved a macro-averaged LAS of 68.61% (10th in the overall ranking) which improved to 69.38% after bug fixes.}, url = {http://www.aclweb.org/anthology/K17-3011} } @InProceedings{kanerva-luotolahti-ginter:2017:K17-3, author = {Kanerva, Jenna and Luotolahti, Juhani and Ginter, Filip}, title = {TurkuNLP: Delexicalized Pre-training of Word Embeddings for Dependency Parsing}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {119--125}, abstract = {We present the TurkuNLP entry in the CoNLL 2017 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. The system is based on the UDPipe parser with our focus being in exploring various techniques to pre-train the word embeddings used by the parser in order to improve its performance especially on languages with small training sets. The system ranked 11th among the 33 participants overall, being 8th on the small treebanks, 10th on the large treebanks, 12th on the parallel test sets, and 26th on the surprise languages.}, url = {http://www.aclweb.org/anthology/K17-3012} } @InProceedings{yu-EtAl:2017:K17-3, author = {Yu, Kuan and Sofroniev, Pavel and Schill, Erik and Hinrichs, Erhard}, title = {The parse is darc and full of errors: Universal dependency parsing with transition-based and graph-based algorithms}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {126--133}, abstract = {We developed two simple systems for dependency parsing: darc, a transition-based parser, and mstnn, a graph-based parser. We tested our systems in the CoNLL 2017 UD Shared Task, with darc being the official system. Darc ranked 12th among 33 systems, just above the baseline. Mstnn had no official ranking, but its main score was above the 27th. In this paper, we describe our two systems, examine their strengths and weaknesses, and discuss the lessons we learned.}, url = {http://www.aclweb.org/anthology/K17-3013} } @InProceedings{nguyen-dras-johnson:2017:K17-3, author = {Nguyen, Dat Quoc and Dras, Mark and Johnson, Mark}, title = {A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {134--142}, abstract = {We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/ datquocnguyen/jPTDP}, url = {http://www.aclweb.org/anthology/K17-3014} } @InProceedings{qian-liu:2017:K17-3, author = {Qian, Xian and Liu, Yang}, title = {A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {143--151}, abstract = {For this year’s multilingual dependency parsing shared task, we developed a pipeline system, which uses a variety of features for each of its components. Unlike the recent popular deep learning approaches that learn low dimensional dense features using non-linear classifier, our system uses structured linear classifiers to learn millions of sparse features. Specifically, we trained a linear classifier for sentence boundary prediction, linear chain conditional random fields (CRFs) for tokenization, part-of-speech tagging and morph analysis. A second order graph based parser learns the tree structure (without relations), and fa linear tree CRF then assigns relations to the dependencies in the tree. Our system achieves reason- able performance -- 67.87% official aver- aged macro F1 score}, url = {http://www.aclweb.org/anthology/K17-3015} } @InProceedings{vilares-gomezrodriguez:2017:K17-3, author = {Vilares, David and G\'{o}mez-Rodr\'{i}guez, Carlos}, title = {A non-projective greedy dependency parser with bidirectional LSTMs}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {152--162}, abstract = {The LyS-FASTPARSE team present BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kiperwasser 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-FASTPARSE}, url = {http://www.aclweb.org/anthology/K17-3016} } @InProceedings{aufrant-wisniewski-yvon:2017:K17-3, author = {Aufrant, Lauriane and Wisniewski, Guillaume and Yvon, Fran\c{c}ois}, title = {LIMSI$@$CoNLL'17: UD Shared Task}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {163--173}, abstract = {This paper describes LIMSI's submission to the CoNLL 2017 UD Shared Task, which is focused on small treebanks, and how to improve low-resourced parsing only by ad hoc combination of multiple views and resources. We present our approach for low-resourced parsing, together with a detailed analysis of the results for each test treebank. We also report extensive analysis experiments on model selection for the PUD treebanks, and on annotation consistency among UD treebanks.}, url = {http://www.aclweb.org/anthology/K17-3017} } @InProceedings{dumitrescu-borocs-tufics:2017:K17-3, author = {Dumitrescu, Stefan Daniel and Boro\c{s}, Tiberiu and Tufi\c{s}, Dan}, title = {RACAI's Natural Language Processing pipeline for Universal Dependencies}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {174--181}, abstract = {This paper presents RACAI's approach, experiments and results at CONLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. We handle raw text and we cover tokenization, sentence splitting, word segmentation, tagging, lemmatization and parsing. All results are reported under strict training, development and testing conditions, in which the corpora provided for the shared tasks is used "as is", without any modifications to the composition of the train and development sets.}, url = {http://www.aclweb.org/anthology/K17-3018} } @InProceedings{das-zaffar-sarkar:2017:K17-3, author = {Das, Ayan and Zaffar, Affan and Sarkar, Sudeshna}, title = {Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {182--190}, abstract = {This paper describes our dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We primarily focus on the low-resource languages (surprise languages). We have developed a framework to combine multiple treebanks to train parsers for low resource languages by delexicalization method. We have applied transformation on source language treebanks based on syntactic features of the low-resource language to improve performance of the parser. In the official evaluation, our system achieves an macro-averaged LAS score of 67.61 and 37.16 on the entire blind test data and the surprise language test data respectively.}, url = {http://www.aclweb.org/anthology/K17-3019} } @InProceedings{wang-zhao-zhang:2017:K17-3, author = {Wang, Hao and Zhao, Hai and Zhang, Zhisong}, title = {A Transition-based System for Universal Dependency Parsing}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {191--197}, abstract = {This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this work, we design a system based on UDPipe1 for universal dependency parsing, where multilingual transition-based models are trained for different treebanks. Our system directly takes raw texts as input, performing several intermediate steps like tokenizing and tagging, and finally generates the corresponding dependency trees. For the special surprise languages for this task, we adopt a delexicalized strategy and predict basing on transfer learning from other related languages. In the final evaluation of the shared task, our system achieves a result of 66.53% in macro-averaged LAS F1-score.}, url = {http://www.aclweb.org/anthology/K17-3020} } @InProceedings{hornby-taylor-park:2017:K17-3, author = {Hornby, Ryan and Taylor, Clark and Park, Jungyeul}, title = {Corpus Selection Approaches for Multilingual Parsing from Raw Text to Universal Dependencies}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {198--206}, abstract = {This paper describes UALing's approach to the udst using corpus selection techniques to reduce training data size. The methodology is simple: we use similarity measures to select a corpus from available training data (even from multiple corpora for surprise languages) and use the resulting corpus to complete the parsing task. The training and parsing is done with the baseline UDPipe system. While our approach reduces the size of training data significantly, it retains performance within 0.5% of the baseline system. Due to the reduction in training data size, our system performs faster than the naive, complete corpus method. Specifically, our system runs in less than 10 minutes, ranking it among the fastest entries for this task. Our system is available at https://github.com/CoNLL-UD-2017/UALING.}, url = {http://www.aclweb.org/anthology/K17-3021} } @InProceedings{delhoneux-EtAl:2017:K17-3, author = {de Lhoneux, Miryam and Shao, Yan and Basirat, Ali and Kiperwasser, Eliyahu and Stymne, Sara and Goldberg, Yoav and Nivre, Joakim}, title = {From Raw Text to Universal Dependencies - Look, No Tags!}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {207--217}, abstract = {We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies. Our system is a simple pipeline consisting of two components. The first performs joint word and sentence segmentation on raw text; the second predicts dependency trees from raw words. The parser bypasses the need for part-of-speech tagging, but uses word embeddings based on universal tag distributions. We achieved a macro-averaged LAS F1 of 65.11 in the official test run, which improved to 70.49 after bug fixes. We obtained the 2nd best result for sentence segmentation with a score of 89.03.}, url = {http://www.aclweb.org/anthology/K17-3022} } @InProceedings{akku-azizoglu-cakici:2017:K17-3, author = {Akkuş, Burak Kerim and Azizoglu, Heval and Cakici, Ruket}, title = {Initial Explorations of CCG Supertagging for Universal Dependency Parsing}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {218--227}, abstract = {In this paper we describe the system by METU team for universal dependency parsing of multilingual text. We use a neural network-based dependency parser that has a greedy transition approach to dependency parsing. CCG supertags contain rich structural information that proves useful in certain NLP tasks. We experiment with CCG supertags as additional features in our experiments. The neural network parser is trained together with dependencies and simplified CCG tags as well as other features provided.}, url = {http://www.aclweb.org/anthology/K17-3023} } @InProceedings{moor-EtAl:2017:K17-3, author = {Moor, Christophe and Merlo, Paola and Henderson, James and Wang, Haozhou}, title = {CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {228--236}, abstract = {This paper describes the University of Geneva's submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry). Our submitted parsing system is the grandchild of the first transition-based neural network dependency parser, which was the University of Geneva's entry in the CoNLL 2007 multilingual dependency parsing shared task, with some improvements to speed and portability. These results provide a baseline for investigating how far we have come in the past ten years of work on neural network dependency parsing.}, url = {http://www.aclweb.org/anthology/K17-3024} } @InProceedings{ji-wu-lan:2017:K17-3, author = {Ji, Tao and Wu, Yuanbin and Lan, Man}, title = {A Fast and Lightweight System for Multilingual Dependency Parsing}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {237--242}, abstract = {We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.}, url = {http://www.aclweb.org/anthology/K17-3025} } @InProceedings{delaclergerie-sagot-seddah:2017:K17-3, author = {De La Clergerie, Eric and Sagot, Beno\^{i}t and Seddah, Djam\'{e}}, title = {The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a \#ParsingTragedy}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {243--252}, abstract = {We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task’s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this \#ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.}, url = {http://www.aclweb.org/anthology/K17-3026} } @InProceedings{more-tsarfaty:2017:K17-3, author = {More, Amir and Tsarfaty, Reut}, title = {Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {253--264}, abstract = {We present the Open University's submission to the CoNLL 2017 Shared Task on multilingual parsing from raw text to Universal Dependencies. The core of our system is a joint morphological disambiguator and syntactic parser which accepts morphologically analyzed surface tokens as input and returns morphologically disambiguated dependency trees as output. Our parser requires a lattice as input, so we generate morphological analyses of surface tokens using a data-driven morphological analyzer that derives its lexicon from the UD training corpora, and we rely on UDPipe for sentence segmentation and surface-level tokenization. We report our official macro-average LAS is 56.56. Although our model is not as performant as many others, it does not make use of neural networks, therefore we do not rely on word embeddings or any other data source other than the corpora themselves. In addition, we show the utility of a lexicon-backed morphological analyzer for the MRL Modern Hebrew. We use our results on Modern Hebrew to argue that the UD community should define a UD-compatible standard for access to lexical resources, which we argue is crucial for MRLs and low resource languages in particular.}, url = {http://www.aclweb.org/anthology/K17-3027} } @InProceedings{kanayama-muraoka-yoshikawa:2017:K17-3, author = {Kanayama, Hiroshi and Muraoka, Masayasu and Yoshikawa, Katsumasa}, title = {A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {265--273}, abstract = {This paper presents our system submitted for the CoNLL 2017 Shared Task, ``Multilingual Parsing from Raw Text to Universal Dependencies.'' We ran the system for all languages with our own fully pipelined components without relying on re-trained baseline systems. To train the dependency parser, we used only the universal part-of-speech tags and distance between words, and applied deterministic rules to assign dependency labels. The simple and delexicalized models are suitable for cross-lingual transfer approaches and a universal language model. Experimental results show that our model performed well in some metrics and leads discussion on topics such as contribution of each component and on syntactic similarities among languages.}, url = {http://www.aclweb.org/anthology/K17-3028} } @InProceedings{garcia-gamallo:2017:K17-3, author = {Garcia, Marcos and Gamallo, Pablo}, title = {A rule-based system for cross-lingual parsing of Romance languages with Universal Dependencies}, booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {August}, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, pages = {274--282}, abstract = {This article describes MetaRomance, a rule-based cross-lingual parser for Romance languages submitted to CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. The system is an almost delexicalized parser which does not need training data to analyze Romance languages. It contains linguistically motivated rules based on PoS-tag patterns. The rules included in MetaRomance were developed in about 12 hours by one expert with no prior knowledge in Universal Dependencies, and can be easily extended using a transparent formalism. In this paper we compare the performance of MetaRomance with other supervised systems participating in the competition, paying special attention to the parsing of different treebanks of the same language. We also compare our system with a delexicalized parser for Romance languages, and take advantage of the harmonized annotation of Universal Dependencies to propose a language ranking based on the syntactic distance each variety has from Romance languages.}, url = {http://www.aclweb.org/anthology/K17-3029} }