Stig-Arne Grönroos


2023

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OdiaGenAI’s Participation at WAT2023
Sk Shahid | Guneet Singh Kohli | Sambit Sekhar | Debasish Dhal | Adit Sharma | Shubhendra Kushwaha | Shantipriya Parida | Stig-Arne Grönroos | Satya Ranjan Dash
Proceedings of the 10th Workshop on Asian Translation

This paper offers an in-depth overview of the team “ODIAGEN’s” translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.

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Dozens of Translation Directions or Millions of Shared Parameters? Comparing Two Types of Multilinguality in Modular Machine Translation
Michele Boggia | Stig-Arne Grönroos | Niki Loppi | Timothee Mickus | Alessandro Raganato | Jörg Tiedemann | Raúl Vázquez
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

There are several ways of implementing multilingual NLP systems but little consensus as to whether different approaches exhibit similar effects. Are the trends that we observe when adding more languages the same as those we observe when sharing more parameters? We focus on encoder representations drawn from modular multilingual machine translation systems in an English-centric scenario, and study their quality from multiple aspects: how adequate they are for machine translation, how independent of the source language they are, and what semantic information they convey. Adding translation directions in English-centric scenarios does not conclusively lead to an increase in translation quality. Shared layers increase performance on zero-shot translation pairs and lead to more language-independent representations, but these improvements do not systematically align with more semantically accurate representations, from a monolingual standpoint.

2022

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Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components
Raúl Vázquez | Michele Boggia | Alessandro Raganato | Niki A. Loppi | Stig-Arne Grönroos | Jörg Tiedemann
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

We describe the enhancement of a multilingual NMT toolkit developed as part of the FoTran project. We devise our modular attention-bridge model, which connects language-specific components through a shared network layer. The system now supports distributed training over many nodes and GPUs in order to substantially scale up the number of languages that can be included in a modern neural translation architecture. The model enables the study of emerging language-agnostic representations and also provides a modular toolkit for efficient machine translation.

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Morfessor-enriched features and multilingual training for canonical morphological segmentation
Aku Rouhe | Stig-Arne Grönroos | Sami Virpioja | Mathias Creutz | Mikko Kurimo
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

In our submission to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation, we study whether an unsupervised morphological segmentation method, Morfessor, can help in a supervised setting. Previous research has shown the effectiveness of the approach in semisupervised settings with small amounts of labeled data. The current tasks vary in data size: the amount of word-level annotated training data is much larger, but the amount of sentencelevel annotated training data remains small. Our approach is to pre-segment the input data for a neural sequence-to-sequence model with the unsupervised method. As the unsupervised method can be trained with raw text data, we use Wikipedia to increase the amount of training data. In addition, we train multilingual models for the sentence-level task. The results for the Morfessor-enriched features are mixed, showing benefit for all three sentencelevel tasks but only some of the word-level tasks. The multilingual training yields considerable improvements over the monolingual sentence-level models, but it negates the effect of the enriched features.

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Silo NLP’s Participation at WAT2022
Shantipriya Parida | Subhadarshi Panda | Stig-Arne Grönroos | Mark Granroth-Wilding | Mika Koistinen
Proceedings of the 9th Workshop on Asian Translation

This paper provides the system description of “Silo NLP’s” submission to the Workshop on Asian Translation (WAT2022). We have participated in the Indic Multimodal tasks (English->Hindi, English->Malayalam, and English->Bengali, Multimodal Translation). For text-only translation, we used the Transformer and fine-tuned the mBART. For multimodal translation, we used the same architecture and extracted object tags from the images to use as visual features concatenated with the text sequence for input. Our submission tops many tasks including English->Hindi multimodal translation (evaluation test), English->Malayalam text-only and multimodal translation (evaluation test), English->Bengali multimodal translation (challenge test), and English->Bengali text-only translation (evaluation test).

2020

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Morfessor EM+Prune: Improved Subword Segmentation with Expectation Maximization and Pruning
Stig-Arne Grönroos | Sami Virpioja | Mikko Kurimo
Proceedings of the Twelfth Language Resources and Evaluation Conference

Data-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently it has became more widely adopted, as models based on deep neural networks often benefit from subword units even for morphologically simpler languages. In this paper, we discuss and compare training algorithms for a unigram subword model, based on the Expectation Maximization algorithm and lexicon pruning. Using English, Finnish, North Sami, and Turkish data sets, we show that this approach is able to find better solutions to the optimization problem defined by the Morfessor Baseline model than its original recursive training algorithm. The improved optimization also leads to higher morphological segmentation accuracy when compared to a linguistic gold standard. We publish implementations of the new algorithms in the widely-used Morfessor software package.

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The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks
Yves Scherrer | Stig-Arne Grönroos | Sami Virpioja
Proceedings of the Fifth Conference on Machine Translation

This paper describes the joint participation of University of Helsinki and Aalto University to two shared tasks of WMT 2020: the news translation between Inuktitut and English and the low-resource translation between German and Upper Sorbian. For both tasks, our efforts concentrate on efficient use of monolingual and related bilingual corpora with scheduled multi-task learning as well as an optimized subword segmentation with sampling. Our submission obtained the highest score for Upper Sorbian -> German and was ranked second for German -> Upper Sorbian according to BLEU scores. For English–Inuktitut, we reached ranks 8 and 10 out of 11 according to BLEU scores.

2019

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North Sámi morphological segmentation with low-resource semi-supervised sequence labeling
Stig-Arne Grönroos | Sami Virpioja | Mikko Kurimo
Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages

2018

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Cognate-aware morphological segmentation for multilingual neural translation
Stig-Arne Grönroos | Sami Virpioja | Mikko Kurimo
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This article describes the Aalto University entry to the WMT18 News Translation Shared Task. We participate in the multilingual subtrack with a system trained under the constrained condition to translate from English to both Finnish and Estonian. The system is based on the Transformer model. We focus on improving the consistency of morphological segmentation for words that are similar orthographically, semantically, and distributionally; such words include etymological cognates, loan words, and proper names. For this, we introduce Cognate Morfessor, a multilingual variant of the Morfessor method. We show that our approach improves the translation quality particularly for Estonian, which has less resources for training the translation model.

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The WMT’18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English
Franck Burlot | Yves Scherrer | Vinit Ravishankar | Ondřej Bojar | Stig-Arne Grönroos | Maarit Koponen | Tommi Nieminen | François Yvon
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

Progress in the quality of machine translation output calls for new automatic evaluation procedures and metrics. In this paper, we extend the Morpheval protocol introduced by Burlot and Yvon (2017) for the English-to-Czech and English-to-Latvian translation directions to three additional language pairs, and report its use to analyze the results of WMT 2018’s participants for these language pairs. Considering additional, typologically varied source and target languages also enables us to draw some generalizations regarding this morphology-oriented evaluation procedure.

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The MeMAD Submission to the WMT18 Multimodal Translation Task
Stig-Arne Grönroos | Benoit Huet | Mikko Kurimo | Jorma Laaksonen | Bernard Merialdo | Phu Pham | Mats Sjöberg | Umut Sulubacak | Jörg Tiedemann | Raphael Troncy | Raúl Vázquez
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the MeMAD project entry to the WMT Multimodal Machine Translation Shared Task. We propose adapting the Transformer neural machine translation (NMT) architecture to a multi-modal setting. In this paper, we also describe the preliminary experiments with text-only translation systems leading us up to this choice. We have the top scoring system for both English-to-German and English-to-French, according to the automatic metrics for flickr18. Our experiments show that the effect of the visual features in our system is small. Our largest gains come from the quality of the underlying text-only NMT system. We find that appropriate use of additional data is effective.

2017

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Extending hybrid word-character neural machine translation with multi-task learning of morphological analysis
Stig-Arne Grönroos | Sami Virpioja | Mikko Kurimo
Proceedings of the Second Conference on Machine Translation

2016

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A Comparative Study of Minimally Supervised Morphological Segmentation
Teemu Ruokolainen | Oskar Kohonen | Kairit Sirts | Stig-Arne Grönroos | Mikko Kurimo | Sami Virpioja
Computational Linguistics, Volume 42, Issue 1 - March 2016

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Hybrid Morphological Segmentation for Phrase-Based Machine Translation
Stig-Arne Grönroos | Sami Virpioja | Mikko Kurimo
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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Tuning Phrase-Based Segmented Translation for a Morphologically Complex Target Language
Stig-Arne Grönroos | Sami Virpioja | Mikko Kurimo
Proceedings of the Tenth Workshop on Statistical Machine Translation

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LeBLEU: N-gram-based Translation Evaluation Score for Morphologically Complex Languages
Sami Virpioja | Stig-Arne Grönroos
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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Morfessor 2.0: Toolkit for statistical morphological segmentation
Peter Smit | Sami Virpioja | Stig-Arne Grönroos | Mikko Kurimo
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of Morphology
Stig-Arne Grönroos | Sami Virpioja | Peter Smit | Mikko Kurimo
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers