@Book{K18-2:2018, editor = {Daniel Zeman and Jan Haji{\v{c}}}, title = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, url = {http://www.aclweb.org/anthology/K18-2} } @InProceedings{zeman-EtAl:2018:K18-2, author = {Zeman, Daniel and Haji{\v{c}}, Jan and Popel, Martin and Potthast, Martin and Straka, Milan and Ginter, Filip and Nivre, Joakim and Petrov, Slav}, title = {{CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {1--21}, abstract = {Every year, 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 2018, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on test input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. This shared task constitutes a 2nd edition—the first one took place in 2017 (Zeman et al., 2017); the main metric from 2017 has been kept, allowing for easy comparison, also in 2018, and two new main metrics have been used. New datasets added to the Universal Dependencies collection between mid-2017 and the spring of 2018 have contributed to increased difficulty of the task this year. In this overview paper, we define the task and the updated evaluation methodology, describe data preparation, 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/K18-2001} } @InProceedings{fares-EtAl:2018:K18-2, author = {Fares, Murhaf and Oepen, Stephan and Øvrelid, Lilja and Bj{\"{o}}rne, Jari and Johansson, Richard}, title = {The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of {English} Universal Dependency Parsers}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {22--33}, abstract = {We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018). We review the basic task setup, downstream applications involved, and end-to-end results for seventeen participating teams. Based on in-depth quantitative and qualitative analysis, we correlate intrinsic evaluation results at different layers of morph-syntactic analysis with observed downstream behavior.}, url = {http://www.aclweb.org/anthology/K18-2002} } @InProceedings{duthoo-mesnard:2018:K18-2, author = {Duthoo, Elie and Mesnard, Olivier}, title = {{CEA} {LIST}: Processing Low-Resource Languages for {CoNLL} 2018}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {34--44}, abstract = {In this paper, we describe the system used for our first participation at the CoNLL 2018 shared task. The submitted system largely reused the state of the art parser from CoNLL 2017 (https://github.com/tdozat/Parser-v2). We enhanced this system for morphological features predictions, and we used all available resources to provide accurate models for low-resource languages. We ranked 5th of 27 participants in MLAS for building morphology aware dependency trees, 2nd for morphological features only, and 3rd for tagging (UPOS) and parsing (LAS) low-resource languages.}, url = {http://www.aclweb.org/anthology/K18-2003} } @InProceedings{rybak-wrblewska:2018:K18-2, author = {Rybak, Piotr and Wr{\'{o}}blewska, Alina}, title = {Semi-Supervised Neural System for Tagging, Parsing and Lematization}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {45--54}, abstract = {This paper describes the ICS PAS system which took part in CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. The system consists of jointly trained tagger, lemmatizer, and dependency parser which are based on features extracted by a biLSTM network. The system uses both fully connected and dilated convolutional neural architectures. The novelty of our approach is the use of an additional loss function, which reduces the number of cycles in the predicted dependency graphs, and the use of self-training to increase the system performance. The proposed system, i.e. ICS PAS (Warszawa), ranked 3th/4th in the official evaluation obtaining the following overall results: 73.02 (LAS), 60.25 (MLAS) and 64.44 (BLEX).}, url = {http://www.aclweb.org/anthology/K18-2004} } @InProceedings{che-EtAl:2018:K18-2, author = {Che, Wanxiang and Liu, Yijia and Wang, Yuxuan and Zheng, Bo and Liu, Ting}, title = {Towards Better {UD} Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {55--64}, abstract = {This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84%) and outperformed the other systems by a large margin.}, url = {http://www.aclweb.org/anthology/K18-2005} } @InProceedings{li-EtAl:2018:K18-2, author = {Li, Zuchao and He, Shexia and Zhang, Zhuosheng and Zhao, Hai}, title = {Joint Learning of {POS} and Dependencies for Multilingual Universal Dependency Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {65--73}, abstract = {This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31% LAS F1 score, with an improvement of 2.51% compared with the UDPipe.}, url = {http://www.aclweb.org/anthology/K18-2006} } @InProceedings{wu-zhao-tong:2018:K18-2, author = {Wu, Yingting and Zhao, Hai and Tong, Jia-Jun}, title = {Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {74--80}, abstract = {This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61%, 52.26%, 55.71% for LAS, MLAS and BLEX respectively.}, url = {http://www.aclweb.org/anthology/K18-2007} } @InProceedings{nguyen-verspoor:2018:K18-2, author = {Nguyen, Dat Quoc and Verspoor, Karin}, title = {An Improved Neural Network Model for Joint {POS} Tagging and Dependency Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {81--91}, abstract = {We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP}, url = {http://www.aclweb.org/anthology/K18-2008} } @InProceedings{wan-EtAl:2018:K18-2, author = {Wan, Hui and Naseem, Tahira and Lee, Young-Suk and Castelli, Vittorio and Ballesteros, Miguel}, title = {{IBM} Research at the {CoNLL} 2018 Shared Task on Multilingual Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {92--102}, abstract = {This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.}, url = {http://www.aclweb.org/anthology/K18-2009} } @InProceedings{hershcovich-abend-rappoport:2018:K18-2, author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari}, title = {Universal Dependency Parsing with a General Transition-Based {DAG} Parser}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {103--112}, abstract = {This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.}, url = {http://www.aclweb.org/anthology/K18-2010} } @InProceedings{smith-EtAl:2018:K18-2, author = {Smith, Aaron and Bohnet, Bernd and de Lhoneux, Miryam and Nivre, Joakim and Shao, Yan and Stymne, Sara}, title = {82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {113--123}, abstract = {We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of-speech tags and morphological features; the third predicts dependency trees from words and tags. Instead of training a single parsing model for each treebank, we trained models with multiple treebanks for one language or closely related languages, greatly reducing the number of models. On the official test run, we ranked 7th of 27 teams for the LAS and MLAS metrics. Our system obtained the best scores overall for word segmentation, universal POS tagging, and morphological features.}, url = {http://www.aclweb.org/anthology/K18-2011} } @InProceedings{krnap-dayank-yuret:2018:K18-2, author = {Kırnap, {\"{O}}mer and Dayanık, Erenay and Yuret, Deniz}, title = {Tree-Stack {LSTM} in Transition Based Dependency Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {124--132}, abstract = {We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack’s σ-LSTM, buffer’s β-LSTM, actions’ LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the ”KParse” team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.}, url = {http://www.aclweb.org/anthology/K18-2012} } @InProceedings{kanerva-EtAl:2018:K18-2, author = {Kanerva, Jenna and Ginter, Filip and Miekka, Niko and Leino, Akseli and Salakoski, Tapio}, title = {Turku Neural Parser Pipeline: An End-to-End System for the {CoNLL} 2018 Shared Task}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {133--142}, abstract = {In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. Compared to the last year, this year the shared task includes two new main metrics to measure the morphological tagging and lemmatization accuracies in addition to syntactic trees. Basing our motivation into these new metrics, we developed an end-to-end parsing pipeline especially focusing on developing a novel and state-of-the-art component for lemmatization. Our system reached the highest aggregate ranking on three main metrics out of 26 teams by achieving 1st place on metric involving lemmatization, and 2nd on both morphological tagging and parsing.}, url = {http://www.aclweb.org/anthology/K18-2013} } @InProceedings{lim-EtAl:2018:K18-2, author = {Lim, KyungTae and Park, Cheoneum and Lee, Changki and Poibeau, Thierry}, title = {{SEx} {BiST}: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {143--152}, abstract = {We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies). The main characteristic of our work is the encoding of three different modes of contextual information for parsing: (i) Treebank feature representations, (ii) Multilingual word representations, (iii) ELMo representations obtained via unsupervised learning from external resources. Our parser performed well in the official end-to-end evaluation (73.02 LAS -- 4th/26 teams, and 78.72 UAS -- 2nd/26); remarkably, we achieved the best UAS scores on all the English corpora by applying the three suggested feature representations. Finally, we were also ranked 1st at the optional event extraction task, part of the 2018 Extrinsic Parser Evaluation campaign.}, url = {http://www.aclweb.org/anthology/K18-2014} } @InProceedings{bhat-bhat-bangalore:2018:K18-2, author = {Bhat, Riyaz A. and Bhat, Irshad and Bangalore, Srinivas}, title = {The {SLT}-Interactions Parsing System at the {CoNLL} 2018 Shared Task}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {153--159}, abstract = {This paper describes our system (SLT-Interactions) for the CoNLL 2018 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system performs three main tasks: word segmentation (only for few treebanks), POS tagging and parsing. While segmentation is learned separately, we use neural stacking for joint learning of POS tagging and parsing tasks. For all the tasks, we employ simple neural network architectures that rely on long short-term memory (LSTM) networks for learning task-dependent features. At the basis of our parser, we use an arc-standard algorithm with Swap action for general non-projective parsing. Additionally, we use neural stacking as a knowledge transfer mechanism for cross-domain parsing of low resource domains. Our system shows substantial gains against the UDPipe baseline, with an average improvement of 4.18% in LAS across all languages. Overall, we are placed at the 12th position on the official test sets.}, url = {http://www.aclweb.org/anthology/K18-2015} } @InProceedings{qi-EtAl:2018:K18-2, author = {Qi, Peng and Dozat, Timothy and Zhang, Yuhao and Manning, Christopher D.}, title = {Universal Dependency Parsing from Scratch}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {160--170}, abstract = {This paper describes Stanford’s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS, MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies.}, url = {http://www.aclweb.org/anthology/K18-2016} } @InProceedings{boro-dumitrescu-burtica:2018:K18-2, author = {Boroș, Tiberiu and Dumitrescu, Stefan Daniel and Burtica, Ruxandra}, title = {{NLP}-Cube: End-to-End Raw Text Processing With Neural Networks}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {171--179}, abstract = {We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL's "Multilingual Parsing from Raw Text to Universal Dependencies 2018" Shared Task. It performs sentence splitting, tokenization, compound word expansion, lemmatization, tagging and parsing. Based entirely on recurrent neural networks, written in Python, this ready-to-use open source system is freely available on GitHub. For each task we describe and discuss its specific network architecture, closing with an overview on the results obtained in the competition.}, url = {http://www.aclweb.org/anthology/K18-2017} } @InProceedings{arakelyan-hambardzumyan-khachatrian:2018:K18-2, author = {Arakelyan, Gor and Hambardzumyan, Karen and Khachatrian, Hrant}, title = {Towards {JointUD}: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {180--186}, abstract = {This paper describes our submission to CoNLL UD Shared Task 2018. We have extended an LSTM-based neural network designed for sequence tagging to additionally generate character-level sequences. The network was jointly trained to produce lemmas, part-of-speech tags and morphological features. Sentence segmentation, tokenization and dependency parsing were handled by UDPipe 1.2 baseline. The results demonstrate the viability of the proposed multitask architecture, although its performance still remains far from state-of-the-art.}, url = {http://www.aclweb.org/anthology/K18-2018} } @InProceedings{rosa-mareek:2018:K18-2, author = {Rosa, Rudolf and Mare{\v{c}}ek, David}, title = {{CUNI} x-ling: Parsing Under-Resourced Languages in {CoNLL} 2018 {UD} Shared Task}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {187--196}, abstract = {This is a system description paper for the CUNI x-ling submission to the CoNLL 2018 UD Shared Task. We focused on parsing under-resourced languages, with no or little training data available. We employed a wide range of approaches, including simple word-based treebank translation, combination of delexicalized parsers, and exploitation of available morphological dictionaries, with a dedicated setup tailored to each of the languages. In the official evaluation, our submission was identified as the clear winner of the Low-resource languages category.}, url = {http://www.aclweb.org/anthology/K18-2019} } @InProceedings{straka:2018:K18-2, author = {Straka, Milan}, title = {{UDPipe} 2.0 Prototype at {CoNLL} 2018 {UD} Shared Task}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {197--207}, abstract = {UDPipe is a trainable pipeline which performs sentence segmentation, tokenization, POS tagging, lemmatization and dependency parsing. We present a prototype for UDPipe 2.0 and evaluate it in the CoNLL 2018 UD Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, which employs three metrics for submission ranking. Out of 26 participants, the prototype placed first in the MLAS ranking, third in the LAS ranking and third in the BLEX ranking. In extrinsic parser evaluation EPE 2018, the system ranked first in the overall score.}, url = {http://www.aclweb.org/anthology/K18-2020} } @InProceedings{seker-more-tsarfaty:2018:K18-2, author = {Seker, Amit and More, Amir and Tsarfaty, Reut}, title = {Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the {ONLP} Lab Submission to the {CoNLL} 2018 Shared Task}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {208--215}, abstract = {We present the contribution of the ONLP lab at the Open University of Israel to the UD shared task on multilingual parsing from raw text to Universal Dependencies. Our contribution is based on a transition-based parser called 'yap -- yet another parser', which includes a standalone morphological model, a standalone dependency model, and a joint morphosyntactic model. In the task we used {\em yap}'s standalone dependency parser to parse input morphologically disambiguated by UDPipe, and obtained the official score of 58.35 LAS. In our follow up investigation we use yap to show how the incorporation of morphological and lexical resources may improve the performance of end-to-end raw-to-dependencies parsing in the case of a {\em morphologically-rich} and {\em low-resource} language, Modern Hebrew. Our results on Hebrew underscore the importance of CoNLL-UL, a UD-compatible standard for accessing external lexical resources, for enhancing end-to-end UD parsing, in particular for morphologically rich and low-resource languages. We thus encourage the community to create, convert, or make available more such lexica in future tasks.}, url = {http://www.aclweb.org/anthology/K18-2021} } @InProceedings{nder-gmeli-yuret:2018:K18-2, author = {{\"{O}}nder, Berkay and G{\"{u}}meli, Can and Yuret, Deniz}, title = {{SParse}: Ko{\c{c}} University Graph-Based Parsing System for the CoNLL 2018 Shared Task}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {216--222}, abstract = {We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48% LAS, 78.63% MLAS, 78.69% BLEX and 81.76% CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78% LAS, 59.10% MLAS, 61.38% BLEX and 61.72% CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.}, url = {http://www.aclweb.org/anthology/K18-2022} } @InProceedings{jawahar-EtAl:2018:K18-2, author = {Jawahar, Ganesh and Muller, Benjamin and Fethi, Amal and Martin, Louis and Villemonte de la Clergerie, Eric and Sagot, Beno{\^{i}}t and Seddah, Djam{\'{e}}}, title = {{ELMoLex}: Connecting {ELMo} and Lexicon Features for Dependency Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {223--237}, abstract = {In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team 'ParisNLP' to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system 'ELMoLex'. In addition to incorporating character embeddings, ELMoLex benefits from pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex ranked 11th by Labeled Attachment Score metric (70.64%), Morphology-aware LAS metric (55.74%) and ranked 9th by Bilexical dependency metric (60.70%).}, url = {http://www.aclweb.org/anthology/K18-2023} } @InProceedings{zate-EtAl:2018:K18-2, author = {{\"{O}}zate{\c{s}}, {\c{S}}aziye Bet{\"{u}}l and {\"{O}}zg{\"{u}}r, Arzucan and Gungor, Tunga and {\"{O}}zt{\"{u}}rk, Balkız}, title = {A Morphology-Based Representation Model for {LSTM}-Based Dependency Parsing of Agglutinative Languages}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {238--247}, abstract = {We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.}, url = {http://www.aclweb.org/anthology/K18-2024} } @InProceedings{ji-EtAl:2018:K18-2, author = {Ji, Tao and Liu, Yufang and Wang, Yijun and Wu, Yuanbin and Lan, Man}, title = {{AntNLP} at {CoNLL} 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {248--255}, abstract = {We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).}, url = {http://www.aclweb.org/anthology/K18-2025} } @InProceedings{chen-EtAl:2018:K18-2, author = {Chen, Danlu and Lin, Mengxiao and Hu, Zhifeng and Qiu, Xipeng}, title = {A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing}, booktitle = {Proceedings of the {CoNLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, month = {October}, year = {2018}, address = {Brussels, Belgium}, publisher = {Association for Computational Linguistics}, pages = {256--263}, abstract = {This paper describes Fudan’s submission to CoNLL 2018’s shared task Universal Dependency Parsing. We jointly train models when two languages are similar according to linguistic typology and then ensemble the models using a simple re-parse algorithm. We outperform the baseline method by 4.4% (2.1%) on average on development (test) set in CoNLL 2018 UD Shared Task.}, url = {http://www.aclweb.org/anthology/K18-2026} }