Tunga Güngör

Also published as: Tunga Gungor


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Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks
Şaziye Betül Özateş | Arzucan Özgür | Tunga Gungor | Özlem Çetinoğlu
Findings of the Association for Computational Linguistics: NAACL 2022

Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.


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Overcoming the challenges in morphological annotation of Turkish in universal dependencies framework
Talha Bedir | Karahan Şahin | Onur Gungor | Suzan Uskudarli | Arzucan Özgür | Tunga Güngör | Balkiz Ozturk Basaran
Proceedings of The Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

This paper presents several challenges faced when annotating Turkish treebanks in accordance with the Universal Dependencies (UD) guidelines and proposes solutions to address them. Most of these challenges stem from the lack of adequate support in the UD framework to accurately represent null morphemes and complex derivations, which results in a significant loss of information for Turkish. This loss negatively impacts the tools that are developed based on these treebanks. We raised and discussed these issues within the community on the official UD portal. This paper presents these issues and our proposals to more accurately represent morphosyntactic information for Turkish while adhering to guidelines of UD. This work aims to contribute to the representation of Turkish and other agglutinative languages in UD-based treebanks, which in turn aids to develop more accurately annotated datasets for such languages.


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Data and Representation for Turkish Natural Language Inference
Emrah Budur | Rıza Özçelik | Tunga Gungor | Christopher Potts
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.

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Edition 1.2 of the PARSEME Shared Task on Semi-supervised Identification of Verbal Multiword Expressions
Carlos Ramisch | Agata Savary | Bruno Guillaume | Jakub Waszczuk | Marie Candito | Ashwini Vaidya | Verginica Barbu Mititelu | Archna Bhatia | Uxoa Iñurrieta | Voula Giouli | Tunga Güngör | Menghan Jiang | Timm Lichte | Chaya Liebeskind | Johanna Monti | Renata Ramisch | Sara Stymne | Abigail Walsh | Hongzhi Xu
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.

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ERMI at PARSEME Shared Task 2020: Embedding-Rich Multiword Expression Identification
Zeynep Yirmibeşoğlu | Tunga Güngör
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

This paper describes the ERMI system submitted to the closed track of the PARSEME shared task 2020 on automatic identification of verbal multiword expressions (VMWEs). ERMI is an embedding-rich bidirectional LSTM-CRF model, which takes into account the embeddings of the word, its POS tag, dependency relation, and its head word. The results are reported for 14 languages, where the system is ranked 1st in the general cross-lingual ranking of the closed track systems, according to the Unseen MWE-based F1.


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Turkish Treebanking: Unifying and Constructing Efforts
Utku Türk | Furkan Atmaca | Şaziye Betül Özateş | Abdullatif Köksal | Balkiz Ozturk Basaran | Tunga Gungor | Arzucan Özgür
Proceedings of the 13th Linguistic Annotation Workshop

In this paper, we present the current version of two different treebanks, the re-annotation of the Turkish PUD Treebank and the first annotation of the Turkish National Corpus Universal Dependency (henceforth TNC-UD). The annotation of both treebanks, the Turkish PUD Treebank and TNC-UD, was carried out based on the decisions concerning linguistic adequacy of re-annotation of the Turkish IMST-UD Treebank (Türk et. al., forthcoming). Both of the treebanks were annotated with the same annotation process and morphological and syntactic analyses. The TNC-UD is planned to have 10,000 sentences. In this paper, we will present the first 500 sentences along with the annotation PUD Treebank. Moreover, this paper also offers the parsing results of a graph-based neural parser on the previous and re-annotated PUD, as well as the TNC-UD. In light of the comparisons, even though we observe a slight decrease in the attachment scores of the Turkish PUD treebank, we demonstrate that the annotation of the TNC-UD improves the parsing accuracy of Turkish. In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options. Both the treebanks, including a full edit-history and the annotation guidelines, and the custom software are publicly available under an open license online.

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Improving the Annotations in the Turkish Universal Dependency Treebank
Utku Türk | Furkan Atmaca | Şaziye Betül Özateş | Balkız Öztürk Başaran | Tunga Güngör | Arzucan Özgür
Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)


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A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative Languages
Şaziye Betül Özateş | Arzucan Özgür | Tunga Güngör | Balkız Öztürk
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

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.

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Edition 1.1 of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions
Carlos Ramisch | Silvio Ricardo Cordeiro | Agata Savary | Veronika Vincze | Verginica Barbu Mititelu | Archna Bhatia | Maja Buljan | Marie Candito | Polona Gantar | Voula Giouli | Tunga Güngör | Abdelati Hawwari | Uxoa Iñurrieta | Jolanta Kovalevskaitė | Simon Krek | Timm Lichte | Chaya Liebeskind | Johanna Monti | Carla Parra Escartín | Behrang QasemiZadeh | Renata Ramisch | Nathan Schneider | Ivelina Stoyanova | Ashwini Vaidya | Abigail Walsh
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year’s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed.

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Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification
Gözde Berk | Berna Erden | Tunga Güngör
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

This paper describes the Deep-BGT system that participated to the PARSEME shared task 2018 on automatic identification of verbal multiword expressions (VMWEs). Our system is language-independent and uses the bidirectional Long Short-Term Memory model with a Conditional Random Field layer on top (bidirectional LSTM-CRF). To the best of our knowledge, this paper is the first one that employs the bidirectional LSTM-CRF model for VMWE identification. Furthermore, the gappy 1-level tagging scheme is used for discontiguity and overlaps. Our system was evaluated on 10 languages in the open track and it was ranked the second in terms of the general ranking metric.

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Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
Onur Güngör | Suzan Uskudarli | Tunga Güngör
Proceedings of the 27th International Conference on Computational Linguistics

Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator’s performance is shown to be competitive.


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A Stochastic Finite-State Morphological Parser for Turkish
Haşim Sak | Tunga Güngör | Murat Saraçlar
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers