Maite Oronoz

Also published as: M. Oronoz


2022

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Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario
Xabier Soto | Olatz Perez-De-Viñaspre | Gorka Labaka | Maite Oronoz
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

Back-translation is a well established approach to improve the performance of Neural Machine Translation (NMT) systems when large monolingual corpora of the target language and domain are available. Recently, diverse approaches have been proposed to get better automatic evaluation results of NMT models using back-translation, including the use of sampling instead of beam search as decoding algorithm for creating the synthetic corpus. Alternatively, it has been proposed to append a tag to the back-translated corpus for helping the NMT system to distinguish the synthetic bilingual corpus from the authentic one. However, not all the combinations of the previous approaches have been tested, and thus it is not clear which is the best approach for developing a given NMT system. In this work, we empirically compare and combine existing techniques for back-translation in a real low resource setting: the translation of clinical notes from Basque into Spanish. Apart from automatically evaluating the MT systems, we ask bilingual healthcare workers to perform a human evaluation, and analyze the different synthetic corpora by measuring their lexical diversity (LD). For reproducibility and generalizability, we repeat our experiments for German to English translation using public data. The results suggest that in lower resource scenarios tagging only helps when using sampling for decoding, in contradiction with the previous literature using bigger corpora from the news domain. When fine-tuning with a few thousand bilingual in-domain sentences, one of our proposed method (tagged restricted sampling) obtains the best results both in terms of automatic and human evaluation. We will publish the code upon acceptance.

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Approximate Nearest Neighbour Extraction Techniques and Neural Networks for Suicide Risk Prediction in the CLPsych 2022 Shared Task
Hermenegildo Fabregat Marcos | Ander Cejudo | Juan Martinez-romo | Alicia Perez | Lourdes Araujo | Nuria Lebea | Maite Oronoz | Arantza Casillas
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

This paper describes the participation of our group on the CLPsych 2022 shared task.For task A, which tries to capture changes in mood over time, we have applied an Approximate Nearest Neighbour (ANN) extraction technique with the aim of relabelling the user messages according to their proximity, based on the representation of these messages in a vector space. Regarding the subtask B, we have used the output of the subtask A to train a Recurrent Neural Network (RNN) to predict the risk of suicide at the user level.The results obtained are very competitive considering that our team was one of the few that made use of the organisers’ proposed virtual environment and also made use of the Task A output to predict the Task B results.

2021

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Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set
Lana Yeganova | Dina Wiemann | Mariana Neves | Federica Vezzani | Amy Siu | Inigo Jauregi Unanue | Maite Oronoz | Nancy Mah | Aurélie Névéol | David Martinez | Rachel Bawden | Giorgio Maria Di Nunzio | Roland Roller | Philippe Thomas | Cristian Grozea | Olatz Perez-de-Viñaspre | Maika Vicente Navarro | Antonio Jimeno Yepes
Proceedings of the Sixth Conference on Machine Translation

In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.

2020

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Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
Rachel Bawden | Giorgio Maria Di Nunzio | Cristian Grozea | Inigo Jauregi Unanue | Antonio Jimeno Yepes | Nancy Mah | David Martinez | Aurélie Névéol | Mariana Neves | Maite Oronoz | Olatz Perez-de-Viñaspre | Massimo Piccardi | Roland Roller | Amy Siu | Philippe Thomas | Federica Vezzani | Maika Vicente Navarro | Dina Wiemann | Lana Yeganova
Proceedings of the Fifth Conference on Machine Translation

Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.

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Ixamed’s submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation
Xabier Soto | Olatz Perez-de-Viñaspre | Gorka Labaka | Maite Oronoz
Proceedings of the Fifth Conference on Machine Translation

In this paper we describe the systems developed at Ixa for our participation in WMT20 Biomedical shared task in three language pairs, en-eu, en-es and es-en. When defining our approach, we have put the focus on making an efficient use of corpora recently compiled for training Machine Translation (MT) systems to translate Covid-19 related text, as well as reusing previously compiled corpora and developed systems for biomedical or clinical domain. Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain. However, after manually inspecting some of the outputs generated by our systems, for most of the submissions we end up using the system trained only with the basic corpus, since the systems including the clinical terminologies generated outputs shorter in length than the corresponding references. Thus, we present simple baselines for translating abstracts between English and Spanish (en/es); while for translating abstracts and terms from English into Basque (en-eu), we concatenate the best en-es system for each kind of text with our es-eu system. We present automatic evaluation results in terms of BLEU scores, and analyse the effect of including clinical terminology on the average sentence length of the generated outputs. Following the recent recommendations for a responsible use of GPUs for NLP research, we include an estimation of the generated CO2 emissions, based on the power consumed for training the MT systems.

2019

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IxaMed at PharmacoNER Challenge 2019
Xabier Lahuerta | Iakes Goenaga | Koldo Gojenola | Aitziber Atutxa Salazar | Maite Oronoz
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

The aim of this paper is to present our approach (IxaMed) in the PharmacoNER 2019 task. The task consists of identifying chemical, drug, and gene/protein mentions from clinical case studies written in Spanish. The evaluation of the task is divided in two scenarios: one corresponding to the detection of named entities and one corresponding to the indexation of named entities that have been previously identified. In order to identify named entities we have made use of a Bi-LSTM with a CRF on top in combination with different types of word embeddings. We have achieved our best result (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records (50M words) with contextual string embeddings of Wikipedia and Electronic Health Records. On the other hand, for the indexation of the named entities we have used the Levenshtein distance obtaining a 85.34 F-Score as our best result.

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Leveraging SNOMED CT terms and relations for machine translation of clinical texts from Basque to Spanish
Xabier Soto | Olatz Perez-De-Viñaspre | Maite Oronoz | Gorka Labaka
Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation

2016

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The impact of simple feature engineering in multilingual medical NER
Rebecka Weegar | Arantza Casillas | Arantza Diaz de Ilarraza | Maite Oronoz | Alicia Pérez | Koldo Gojenola
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.

2014

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Translating SNOMED CT Terminology into a Minor Language
Olatz Perez-de-Viñaspre | Maite Oronoz
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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Adverse Drug Event prediction combining shallow analysis and machine learning
Sara Santiso | Arantza Casillas | Alicia Pérez | Maite Oronoz | Koldo Gojenola
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

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IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical Texts
Koldo Gojenola | Maite Oronoz | Alicia Pérez | Arantza Casillas
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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A Finite-State Approach to Translate SNOMED CT Terms into Basque Using Medical Prefixes and Suffixes
Olatz Perez-de-Viñaspre | Maite Oronoz | Manex Agirrezabal | Mikel Lersundi
Proceedings of the 11th International Conference on Finite State Methods and Natural Language Processing

2012

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First Approaches on Spanish Medical Record Classification Using Diagnostic Term to Class Transduction
A. Casillas | A. Díaz de Ilarraza | K. Gojenola | M. Oronoz | Alicia Pérez
Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing

2011

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Using Kybots for Extracting Events in Biomedical Texts
Arantza Casillas | Arantza Díaz de Ilarraza | Koldo Gojenola | Maite Oronoz | German Rigau
Proceedings of BioNLP Shared Task 2011 Workshop

2009

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Evaluating the Impact of Morphosyntactic Ambiguity in Grammatical Error Detection
Arantza Díaz de Ilarraza | Koldo Gojenola | Maite Oronoz
Proceedings of the International Conference RANLP-2009

2008

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Detecting Erroneous Uses of Complex Postpositions in an Agglutinative Language
Arantza Díaz de Ilarraza | Koldo Gojenola | Maite Oronoz
Coling 2008: Companion volume: Posters

2004

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Abar-Hitz: An Annotation Tool for the Basque Dependency Treebank
Arantza Díaz de Ilarraza | Aitzpea Garmendia | Maite Oronoz
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2000

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Corpus-Based Syntactic Error Detection Using Syntactic Patterns
Koldo Gojenola | Maite Oronoz
Proceedings of the ANLP-NAACL 2000 Student Research Workshop

1997

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From Psycholinguistic Modelling of Interlanguage in Second Language Acquisition to a Computational Model
Montse Maritxalar | Arantza Diaz de Ilarraza | Maite Oronoz
CoNLL97: Computational Natural Language Learning