John Ortega


Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models
Abteen Ebrahimi | Arya D. McCarthy | Arturo Oncevay | John Ortega | Luis Chiruzzo | Gustavo Giménez-lugo | Rolando Coto-solano | Katharina Kann
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Large multilingual models have inspired a new class of word alignment methods, which work well for the model’s pretraining languages. However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data. In this work, we ask: How do modern aligners perform on unseen languages, and are they better than traditional methods? We contribute gold-standard alignments for Bribri–Spanish, Guarani–Spanish, Quechua–Spanish, and Shipibo-Konibo–Spanish. With these, we evaluate state-of-the-art aligners with and without model adaptation to the target language. Finally, we also evaluate the resulting alignments extrinsically through two downstream tasks: named entity recognition and part-of-speech tagging. We find that although transformer-based methods generally outperform traditional models, the two classes of approach remain competitive with each other.


Findings of the IWSLT 2022 Evaluation Campaign
Antonios Anastasopoulos | Loïc Barrault | Luisa Bentivogli | Marcely Zanon Boito | Ondřej Bojar | Roldano Cattoni | Anna Currey | Georgiana Dinu | Kevin Duh | Maha Elbayad | Clara Emmanuel | Yannick Estève | Marcello Federico | Christian Federmann | Souhir Gahbiche | Hongyu Gong | Roman Grundkiewicz | Barry Haddow | Benjamin Hsu | Dávid Javorský | Vĕra Kloudová | Surafel Lakew | Xutai Ma | Prashant Mathur | Paul McNamee | Kenton Murray | Maria Nǎdejde | Satoshi Nakamura | Matteo Negri | Jan Niehues | Xing Niu | John Ortega | Juan Pino | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Yogesh Virkar | Alexander Waibel | Changhan Wang | Shinji Watanabe
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks
Marcely Zanon Boito | John Ortega | Hugo Riguidel | Antoine Laurent | Loïc Barrault | Fethi Bougares | Firas Chaabani | Ha Nguyen | Florentin Barbier | Souhir Gahbiche | Yannick Estève
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation. For the Tunisian Arabic-English dataset (low-resource and dialect tracks), we build an end-to-end model as our joint primary submission, and compare it against cascaded models that leverage a large fine-tuned wav2vec 2.0 model for ASR. Our results show that in our settings pipeline approaches are still very competitive, and that with the use of transfer learning, they can outperform end-to-end models for speech translation (ST). For the Tamasheq-French dataset (low-resource track) our primary submission leverages intermediate representations from a wav2vec 2.0 model trained on 234 hours of Tamasheq audio, while our contrastive model uses a French phonetic transcription of the Tamasheq audio as input in a Conformer speech translation architecture jointly trained on automatic speech recognition, ST and machine translation losses. Our results highlight that self-supervised models trained on smaller sets of target data are more effective to low-resource end-to-end ST fine-tuning, compared to large off-the-shelf models. Results also illustrate that even approximate phonetic transcriptions can improve ST scores.

AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Abteen Ebrahimi | Manuel Mager | Arturo Oncevay | Vishrav Chaudhary | Luis Chiruzzo | Angela Fan | John Ortega | Ricardo Ramos | Annette Rios | Ivan Vladimir Meza Ruiz | Gustavo Giménez-Lugo | Elisabeth Mager | Graham Neubig | Alexis Palmer | Rolando Coto-Solano | Thang Vu | Katharina Kann
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R’s zero-shot performance is poor for all 10 languages, with an average performance of 38.48%. Continued pretraining offers improvements, with an average accuracy of 43.85%. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12%.

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Introducing QuBERT: A Large Monolingual Corpus and BERT Model for Southern Quechua
Rodolfo Zevallos | John Ortega | William Chen | Richard Castro | Núria Bel | Cesar Toshio | Renzo Venturas | Hilario Aradiel | Nelsi Melgarejo
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

The lack of resources for languages in the Americas has proven to be a problem for the creation of digital systems such as machine translation, search engines, chat bots, and more. The scarceness of digital resources for a language causes a higher impact on populations where the language is spoken by millions of people. We introduce the first official large combined corpus for deep learning of an indigenous South American low-resource language spoken by millions called Quechua. Specifically, our curated corpus is created from text gathered from the southern region of Peru where a dialect of Quechua is spoken that has not traditionally been used for digital systems as a target dialect in the past. In order to make our work repeatable by others, we also offer a public, pre-trained, BERT model called QuBERT which is the largest linguistic model ever trained for any Quechua type, not just the southern region dialect. We furthermore test our corpus and its corresponding BERT model on two major tasks: (1) named-entity recognition (NER) and (2) part-of-speech (POS) tagging by using state-of-the-art techniques where we achieve results comparable to other work on higher-resource languages. In this article, we describe the methodology, challenges, and results from the creation of QuBERT which is on par with other state-of-the-art multilingual models for natural language processing achieving between 71 and 74% F1 score on NER and 84–87% on POS tasks.

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CLPT: A Universal Annotation Scheme and Toolkit for Clinical Language Processing
Saranya Krishnamoorthy | Yanyi Jiang | William Buchanan | Ayush Singh | John Ortega
Proceedings of the 4th Clinical Natural Language Processing Workshop

With the abundance of natural language processing (NLP) frameworks and toolkits being used in the clinical arena, a new challenge has arisen - how do technologists collaborate across several projects in an easy way? Private sector companies are usually not willing to share their work due to intellectual property rights and profit-bearing decisions. Therefore, the annotation schemes and toolkits that they use are rarely shared with the wider community. We present the clinical language pipeline toolkit (CLPT) and its corresponding annotation scheme called the CLAO (Clinical Language Annotation Object) with the aim of creating a way to share research results and other efforts through a software solution. The CLAO is a unified annotation scheme for clinical technology processing (CTP) projects that forms part of the CLPT and is more reliable than previous standards such as UIMA, BioC, and cTakes for annotation searches, insertions, and deletions. Additionally, it offers a standardized object that can be exchanged through an API that the authors release publicly for CTP project inclusion.


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Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)
John Ortega | Atul Kr. Ojha | Katharina Kann | Chao-Hong Liu
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)

Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages
Atul Kr. Ojha | Chao-Hong Liu | Katharina Kann | John Ortega | Sheetal Shatam | Theodorus Fransen
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)

We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the fourth workshop on technologies for machine translation of low resource languages (LoResMT). Parallel corpora is presented and publicly available which includes the following directions: English↔Irish, English↔Marathi, and Taiwanese Sign language↔Traditional Chinese. Training data consists of 8112, 20933 and 128608 segments, respectively. There are additional monolingual data sets for Marathi and English that consist of 21901 segments. The results presented here are based on entries from a total of eight teams. Three teams submitted systems for English↔Irish while five teams submitted systems for English↔Marathi. Unfortunately, there were no systems submissions for the Taiwanese Sign language↔Traditional Chinese task. Maximum system performance was computed using BLEU and follow as 36.0 for English–Irish, 34.6 for Irish–English, 24.2 for English–Marathi, and 31.3 for Marathi–English.

Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
Manuel Mager | Arturo Oncevay | Abteen Ebrahimi | John Ortega | Annette Rios | Angela Fan | Ximena Gutierrez-Vasques | Luis Chiruzzo | Gustavo Giménez-Lugo | Ricardo Ramos | Ivan Vladimir Meza Ruiz | Rolando Coto-Solano | Alexis Palmer | Elisabeth Mager-Hois | Vishrav Chaudhary | Graham Neubig | Ngoc Thang Vu | Katharina Kann
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.


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Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages
Alina Karakanta | Atul Kr. Ojha | Chao-Hong Liu | Jade Abbott | John Ortega | Jonathan Washington | Nathaniel Oco | Surafel Melaku Lakew | Tommi A Pirinen | Valentin Malykh | Varvara Logacheva | Xiaobing Zhao
Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages


Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing
John Ortega | Felipe Sánchez-Martínez | Marco Turchi | Matteo Negri
Proceedings of Machine Translation Summit XVII: Research Track


A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair
Rebecca Knowles | John Ortega | Philipp Koehn
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing

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Using Morphemes from Agglutinative Languages like Quechua and Finnish to Aid in Low-Resource Translation
John Ortega | Krishnan Pillaipakkamnatt
Proceedings of the AMTA 2018 Workshop on Technologies for MT of Low Resource Languages (LoResMT 2018)


Fuzzy-match repair using black-box machine translation systems: what can be expected?
John Ortega | Felipe Sánchez-Martínez | Mikel Forcada
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

Computer-aided translation (CAT) tools often use a translation memory (TM) as the key resource to assist translators. A TM contains translation units (TU) which are made up of source and target language segments; translators use the target segments in the TU suggested by the CAT tool by converting them into the desired translation. Proposals from TMs could be made more useful by using techniques such as fuzzy-match repair (FMR) which modify words in the target segment corresponding to mismatches identified in the source segment. Modifications in the target segment are done by translating the mismatched source sub-segments using an external source of bilingual information (SBI) and applying the translations to the corresponding positions in the target segment. Several combinations of translated sub-segments can be applied to the target segment which can produce multiple repair candidates. We provide a formal algorithmic description of a method that is capable of using any SBI to generate all possible fuzzy-match repairs and perform an oracle evaluation on three different language pairs to ascertain the potential of the method to improve translation productivity. Using DGT-TM translation memories and the machine system Apertium as the single source to build repair operators in three different language pairs, we show that the best repaired fuzzy matches are consistently closer to reference translations than either machine-translated segments or unrepaired fuzzy matches.