Duygu Ataman


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Proceedings of the 1st Workshop on Multilingual Representation Learning
Duygu Ataman | Alexandra Birch | Alexis Conneau | Orhan Firat | Sebastian Ruder | Gozde Gul Sahin
Proceedings of the 1st Workshop on Multilingual Representation Learning

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SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
Tiago Pimentel | Maria Ryskina | Sabrina J. Mielke | Shijie Wu | Eleanor Chodroff | Brian Leonard | Garrett Nicolai | Yustinus Ghanggo Ate | Salam Khalifa | Nizar Habash | Charbel El-Khaissi | Omer Goldman | Michael Gasser | William Lane | Matt Coler | Arturo Oncevay | Jaime Rafael Montoya Samame | Gema Celeste Silva Villegas | Adam Ek | Jean-Philippe Bernardy | Andrey Shcherbakov | Aziyana Bayyr-ool | Karina Sheifer | Sofya Ganieva | Matvey Plugaryov | Elena Klyachko | Ali Salehi | Andrew Krizhanovsky | Natalia Krizhanovsky | Clara Vania | Sardana Ivanova | Aelita Salchak | Christopher Straughn | Zoey Liu | Jonathan North Washington | Duygu Ataman | Witold Kieraś | Marcin Woliński | Totok Suhardijanto | Niklas Stoehr | Zahroh Nuriah | Shyam Ratan | Francis M. Tyers | Edoardo M. Ponti | Grant Aiton | Richard J. Hatcher | Emily Prud'hommeaux | Ritesh Kumar | Mans Hulden | Botond Barta | Dorina Lakatos | Gábor Szolnok | Judit Ács | Mohit Raj | David Yarowsky | Ryan Cotterell | Ben Ambridge | Ekaterina Vylomova
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, Võro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Asháninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems' predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving >90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems' performance on previously unseen lemmas.

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Evaluating Multiway Multilingual NMT in the Turkic Languages
Jamshidbek Mirzakhalov | Anoop Babu | Aigiz Kunafin | Ahsan Wahab | Bekhzodbek Moydinboyev | Sardana Ivanova | Mokhiyakhon Uzokova | Shaxnoza Pulatova | Duygu Ataman | Julia Kreutzer | Francis Tyers | Orhan Firat | John Licato | Sriram Chellappan
Proceedings of the Sixth Conference on Machine Translation

Despite the increasing number of large and comprehensive machine translation (MT) systems, evaluation of these methods in various languages has been restrained by the lack of high-quality parallel corpora as well as engagement with the people that speak these languages. In this study, we present an evaluation of state-of-the-art approaches to training and evaluating MT systems in 22 languages from the Turkic language family, most of which being extremely under-explored. First, we adopt the TIL Corpus with a few key improvements to the training and the evaluation sets. Then, we train 26 bilingual baselines as well as a multi-way neural MT (MNMT) model using the corpus and perform an extensive analysis using automatic metrics as well as human evaluations. We find that the MNMT model outperforms almost all bilingual baselines in the out-of-domain test sets and finetuning the model on a downstream task of a single pair also results in a huge performance boost in both low- and high-resource scenarios. Our attentive analysis of evaluation criteria for MT models in Turkic languages also points to the necessity for further research in this direction. We release the corpus splits, test sets as well as models to the public.

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A Large-Scale Study of Machine Translation in Turkic Languages
Jamshidbek Mirzakhalov | Anoop Babu | Duygu Ataman | Sherzod Kariev | Francis Tyers | Otabek Abduraufov | Mammad Hajili | Sardana Ivanova | Abror Khaytbaev | Antonio Laverghetta Jr. | Bekhzodbek Moydinboyev | Esra Onal | Shaxnoza Pulatova | Ahsan Wahab | Orhan Firat | Sriram Chellappan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.

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Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
Jiaoda Li | Duygu Ataman | Rico Sennrich
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all. We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as Multi30K, where the translations of image captions were prepared without actually showing the images to human translators. In this paper, we present a qualitative study that examines the role of datasets in stimulating the leverage of visual modality and we propose methods to highlight the importance of visual signals in the datasets which demonstrate improvements in reliance of models on the source images. Our findings suggest the research on effective MMT architectures is currently impaired by the lack of suitable datasets and careful consideration must be taken in creation of future MMT datasets, for which we also provide useful insights.


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On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman | Orhan Firat | Mattia A. Di Gangi | Marcello Federico | Alexandra Birch
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model.


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Compositional Representation of Morphologically-Rich Input for Neural Machine Translation
Duygu Ataman | Marcello Federico
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Neural machine translation (NMT) models are typically trained with fixed-size input and output vocabularies, which creates an important bottleneck on their accuracy and generalization capability. As a solution, various studies proposed segmenting words into sub-word units and performing translation at the sub-lexical level. However, statistical word segmentation methods have recently shown to be prone to morphological errors, which can lead to inaccurate translations. In this paper, we propose to overcome this problem by replacing the source-language embedding layer of NMT with a bi-directional recurrent neural network that generates compositional representations of the input at any desired level of granularity. We test our approach in a low-resource setting with five languages from different morphological typologies, and under different composition assumptions. By training NMT to compose word representations from character n-grams, our approach consistently outperforms (from 1.71 to 2.48 BLEU points) NMT learning embeddings of statistically generated sub-word units.

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An Evaluation of Two Vocabulary Reduction Methods for Neural Machine Translation
Duygu Ataman | Marcello Federico
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)


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FBK’s Neural Machine Translation Systems for IWSLT 2016
M. Amin Farajian | Rajen Chatterjee | Costanza Conforti | Shahab Jalalvand | Vevake Balaraman | Mattia A. Di Gangi | Duygu Ataman | Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the 13th International Conference on Spoken Language Translation

In this paper, we describe FBK’s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-{de, fr} and {de, fr}-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.

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FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings
Duygu Ataman | José G. C. de Souza | Marco Turchi | Matteo Negri
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)