Mathias Creutz


2022

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Modeling Noise in Paraphrase Detection
Teemu Vahtola | Eetu Sjöblom | Jörg Tiedemann | Mathias Creutz
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Noisy labels in training data present a challenging issue in classification tasks, misleading a model towards incorrect decisions during training. In this paper, we propose the use of a linear noise model to augment pre-trained language models to account for label noise in fine-tuning. We test our approach in a paraphrase detection task with various levels of noise and five different languages. Our experiments demonstrate the effectiveness of the additional noise model in making the training procedures more robust and stable. Furthermore, we show that this model can be applied without further knowledge about annotation confidence and reliability of individual training examples and we analyse our results in light of data selection and sampling strategies.

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A Closer Look at Parameter Contributions When Training Neural Language and Translation Models
Raúl Vázquez | Hande Celikkanat | Vinit Ravishankar | Mathias Creutz | Jörg Tiedemann
Proceedings of the 29th International Conference on Computational Linguistics

We analyze the learning dynamics of neural language and translation models using Loss Change Allocation (LCA), an indicator that enables a fine-grained analysis of parameter updates when optimizing for the loss function. In other words, we can observe the contributions of different network components at training time. In this article, we systematically study masked language modeling, causal language modeling, and machine translation. We show that the choice of training objective leads to distinctive optimization procedures, even when performed on comparable Transformer architectures. We demonstrate how the various Transformer parameters are used during training, supporting that the feed-forward components of each layer are the main contributors to the optimization procedure. Finally, we find that the learning dynamics are not affected by data size and distribution but rather determined by the learning objective.

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Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection
Sami Itkonen | Jörg Tiedemann | Mathias Creutz
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes the University of Helsinki submission to the SemEval 2022 task on multilingual idiomaticity detection. Our system utilizes several models made available by HuggingFace, along with the baseline BERT model for the task. We focus on feature engineering based on properties that typically characterize idiomatic expressions. The additional features lead to improvements over the baseline and the final submission achieves 15th place out of 20 submissions. The paper provides error analysis of our model including visualisations of the contributions of individual features.

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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.

2021

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An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation
Alessandro Raganato | Raúl Vázquez | Mathias Creutz | Jörg Tiedemann
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Zero-shot translations is a fascinating feature of Multilingual Neural Machine Translation (MNMT) systems. These MNMT models are usually trained on English-centric data, i.e. English either as the source or target language, and with a language label prepended to the input indicating the target language. However, recent work has highlighted several flaws of these models in zero-shot scenarios where language labels are ignored and the wrong language is generated or different runs show highly unstable results. In this paper, we investigate the benefits of an explicit alignment to language labels in Transformer-based MNMT models in the zero-shot context, by jointly training one cross attention head with word alignment supervision to stress the focus on the target language label. We compare and evaluate several MNMT systems on three multilingual MT benchmarks of different sizes, showing that simply supervising one cross attention head to focus both on word alignments and language labels reduces the bias towards translating into the wrong language, improving the zero-shot performance overall. Moreover, as an additional advantage, we find that our alignment supervision leads to more stable results across different training runs.

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Grammatical Error Generation Based on Translated Fragments
Eetu Sjöblom | Mathias Creutz | Teemu Vahtola
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction. Our method aims at simulating mistakes made by second language learners, and produces a wider range of non-native style language in comparison to a state-of-the-art baseline model. We carry out quantitative and qualitative evaluation. Our method is shown to outperform the baseline on data with a high proportion of errors.

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Coping with Noisy Training Data Labels in Paraphrase Detection
Teemu Vahtola | Mathias Creutz | Eetu Sjöblom | Sami Itkonen
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

We present new state-of-the-art benchmarks for paraphrase detection on all six languages in the Opusparcus sentential paraphrase corpus: English, Finnish, French, German, Russian, and Swedish. We reach these baselines by fine-tuning BERT. The best results are achieved on smaller and cleaner subsets of the training sets than was observed in previous research. Additionally, we study a translation-based approach that is competitive for the languages with more limited and noisier training data.

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On the differences between BERT and MT encoder spaces and how to address them in translation tasks
Raúl Vázquez | Hande Celikkanat | Mathias Creutz | Jörg Tiedemann
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks. This is even more astonishing considering the similarities between the architectures. This paper sheds some light on the embedding spaces they create, using average cosine similarity, contextuality metrics and measures for representational similarity for comparison, revealing that BERT and NMT encoder representations look significantly different from one another. In order to address this issue, we propose a supervised transformation from one into the other using explicit alignment and fine-tuning. Our results demonstrate the need for such a transformation to improve the applicability of BERT in MT.

2020

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A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation
Raúl Vázquez | Alessandro Raganato | Mathias Creutz | Jörg Tiedemann
Computational Linguistics, Volume 46, Issue 2 - June 2020

Neural machine translation has considerably improved the quality of automatic translations by learning good representations of input sentences. In this article, we explore a multilingual translation model capable of producing fixed-size sentence representations by incorporating an intermediate crosslingual shared layer, which we refer to as attention bridge. This layer exploits the semantics from each language and develops into a language-agnostic meaning representation that can be efficiently used for transfer learning. We systematically study the impact of the size of the attention bridge and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that there is no conflict between translation performance and the use of sentence representations in downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. Nevertheless, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. Similarly, we show that trainable downstream tasks benefit from multilingual models, whereas additional language signals do not improve performance in non-trainable benchmarks. This is an important insight that helps to properly design models for specific applications. Finally, we also include an in-depth analysis of the proposed attention bridge and its ability to encode linguistic properties. We carefully analyze the information that is captured by individual attention heads and identify interesting patterns that explain the performance of specific settings in linguistic probing tasks.

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Paraphrase Generation and Evaluation on Colloquial-Style Sentences
Eetu Sjöblom | Mathias Creutz | Yves Scherrer
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper, we investigate paraphrase generation in the colloquial domain. We use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages: German, English, Finnish, French, Russian, and Swedish. We perform experiments to understand how data selection and filtering for diverse paraphrase pairs affects the generated paraphrases. We compare two different model architectures, an RNN and a Transformer model, and find that the Transformer does not generally outperform the RNN. We also conduct human evaluation on five of the six languages and compare the results to the automatic evaluation metrics BLEU and the recently proposed BERTScore. The results advance our understanding of the trade-offs between the quality and novelty of generated paraphrases, affected by the data selection method. In addition, our comparison of the evaluation methods shows that while BLEU correlates well with human judgments at the corpus level, BERTScore outperforms BLEU in both corpus and sentence-level evaluation.

2019

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An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation
Alessandro Raganato | Raúl Vázquez | Mathias Creutz | Jörg Tiedemann
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

In this paper, we explore a multilingual translation model with a cross-lingually shared layer that can be used as fixed-size sentence representation in different downstream tasks. We systematically study the impact of the size of the shared layer and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that the performance in translation does correlate with trainable downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. On the other hand, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. We hypothesize that the training procedure on the downstream task enables the model to identify the encoded information that is useful for the specific task whereas non-trainable benchmarks can be confused by other types of information also encoded in the representation of a sentence.

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Multilingual NMT with a Language-Independent Attention Bridge
Raúl Vázquez | Alessandro Raganato | Jörg Tiedemann | Mathias Creutz
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

In this paper, we propose an architecture for machine translation (MT) capable of obtaining multilingual sentence representations by incorporating an intermediate attention bridge that is shared across all languages. We train the model with language-specific encoders and decoders that are connected through an inner-attention layer on the encoder side. The attention bridge exploits the semantics from each language for translation and develops into a language-agnostic meaning representation that can efficiently be used for transfer learning. We present a new framework for the efficient development of multilingual neural machine translation (NMT) using this model and scheduled training. We have tested the approach in a systematic way with a multi-parallel data set. The model achieves substantial improvements over strong bilingual models and performs well for zero-shot translation, which demonstrates its ability of abstraction and transfer learning.

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Toward automatic improvement of language produced by non-native language learners
Mathias Creutz | Eetu Sjöblom
Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning

2018

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Paraphrase Detection on Noisy Subtitles in Six Languages
Eetu Sjöblom | Mathias Creutz | Mikko Aulamo
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text

We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish. We train two types of supervised sentence embedding models: a word-averaging (WA) model and a gated recurrent averaging network (GRAN) model. We find out that GRAN outperforms WA and is more robust to noisy training data. Better results are obtained with more and noisier data than less and cleaner data. Additionally, we experiment on other datasets, without reaching the same level of performance, because of domain mismatch between training and test data.

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Open Subtitles Paraphrase Corpus for Six Languages
Mathias Creutz
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2009

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Web Augmentation of Language Models for Continuous Speech Recognition of SMS Text Messages
Mathias Creutz | Sami Virpioja | Anna Kovaleva
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

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Speech to speech machine translation: Biblical chatter from Finnish to English
David Ellis | Mathias Creutz | Timo Honkela | Mikko Kurimo
Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages

2007

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Analysis of Morph-Based Speech Recognition and the Modeling of Out-of-Vocabulary Words Across Languages
Mathias Creutz | Teemu Hirsimäki | Mikko Kurimo | Antti Puurula | Janne Pylkkönen | Vesa Siivola | Matti Varjokallio | Ebru Arisoy | Murat Saraçlar | Andreas Stolcke
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Morphology-aware statistical machine translation based on morphs induced in an unsupervised manner
Sami Virpioja | Jaako J. Väyrynen | Mathias Creutz | Markus Sadeniemi
Proceedings of Machine Translation Summit XI: Papers

2004

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Induction of a Simple Morphology for Highly-Inflecting Languages
Mathias Creutz | Krista Lagus
Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology: Current Themes in Computational Phonology and Morphology

2003

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Unsupervised Segmentation of Words Using Prior Distributions of Morph Length and Frequency
Mathias Creutz
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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Unsupervised Discovery of Morphemes
Mathias Creutz | Krista Lagus
Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning