Rodolfo Zevallos


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Findings of the CoCo4MT 2023 Shared Task on Corpus Construction for Machine Translation
Ananya Ganesh | Marine Carpuat | William Chen | Katharina Kann | Constantine Lignos | John E. Ortega | Jonne Saleva | Shabnam Tafreshi | Rodolfo Zevallos
Proceedings of the Second Workshop on Corpus Generation and Corpus Augmentation for Machine Translation

This paper provides an overview of the first shared task on choosing beneficial instances for machine translation, conducted as part of the CoCo4MT 2023 Workshop at MTSummit. This shared task was motivated by the need to make the data annotation process for machine translation more efficient, particularly for low-resource languages for which collecting human translations may be difficult or expensive. The task involved developing methods for selecting the most beneficial instances for training a machine translation system without access to an existing parallel dataset in the target language, such that the best selected instances can then be manually translated. Two teams participated in the shared task, namely the Williams team and the AST team. Submissions were evaluated by training a machine translation model on each submission’s chosen instances, and comparing their performance with the chRF++ score. The system that ranked first is by the Williams team, that finds representative instances by clustering the training data.

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Milind Agarwal | Sweta Agrawal | Antonios Anastasopoulos | Luisa Bentivogli | Ondřej Bojar | Claudia Borg | Marine Carpuat | Roldano Cattoni | Mauro Cettolo | Mingda Chen | William Chen | Khalid Choukri | Alexandra Chronopoulou | Anna Currey | Thierry Declerck | Qianqian Dong | Kevin Duh | Yannick Estève | Marcello Federico | Souhir Gahbiche | Barry Haddow | Benjamin Hsu | Phu Mon Htut | Hirofumi Inaguma | Dávid Javorský | John Judge | Yasumasa Kano | Tom Ko | Rishu Kumar | Pengwei Li | Xutai Ma | Prashant Mathur | Evgeny Matusov | Paul McNamee | John P. McCrae | Kenton Murray | Maria Nadejde | Satoshi Nakamura | Matteo Negri | Ha Nguyen | Jan Niehues | Xing Niu | Atul Kr. Ojha | John E. Ortega | Proyag Pal | Juan Pino | Lonneke van der Plas | Peter Polák | Elijah Rippeth | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Yun Tang | Brian Thompson | Kevin Tran | Marco Turchi | Alex Waibel | Mingxuan Wang | Shinji Watanabe | Rodolfo Zevallos
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper reports on the shared tasks organized by the 20th IWSLT Conference. The shared tasks address 9 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, multilingual, dialect and low-resource speech translation, and formality control. The shared tasks attracted a total of 38 submissions by 31 teams. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.

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QUESPA Submission for the IWSLT 2023 Dialect and Low-resource Speech Translation Tasks
John E. Ortega | Rodolfo Zevallos | William Chen
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE–SPA) track featured in the Evaluation Campaign of IWSLT 2023: low-resource and dialect speech translation. Two main submission types were supported in the campaign: constrained and unconstrained. We submitted six total systems of which our best (primary) constrained system consisted of an ST model based on the Fairseq S2T framework where the audio representations were created using log mel-scale filter banks as features and the translations were performed using a transformer. The best (primary) unconstrained system used a pipeline approach which combined automatic speech recognition (ASR) with machine translation (MT). The ASR transcriptions for the best unconstrained system were computed using a pre-trained XLS-R-based model along with a fine-tuned language model. Transcriptions were translated using a MT system based on a fine-tuned, pre-trained language model (PLM). The four other submissions are presented in this article (2 constrained and 2 unconstrained) for comparison because they consist of various architectures. Our results show that direct ST (ASR and MT combined together) can be more effective than a PLM in a low-resource (constrained) setting for Quechua to Spanish. On the other hand, we show that fine-tuning of any type on both the ASR and MT system is worthwhile, resulting in nearly 16 BLEU for the unconstrained task.

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Hints on the data for language modeling of synthetic languages with transformers
Rodolfo Zevallos | Nuria Bel
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language Models (LM) are becoming more and more useful for providing representations upon which to train Natural Language Processing applications. However, there is now clear evidence that attention-based transformers require a critical amount of language data to produce good enough LMs. The question we have addressed in this paper is to what extent the critical amount of data varies for languages of different morphological typology, in particular those that have a rich inflectional morphology, and whether the tokenization method to preprocess the data can make a difference. These details can be important for low-resourced languages that need to plan the production of datasets. We evaluated intrinsically and extrinsically the differences of five different languages with different pretraining dataset sizes and three different tokenization methods for each. The results confirm that the size of the vocabulary due to morphological characteristics is directly correlated with both the LM perplexity and the performance of two typical downstream tasks such as NER identification and POS labeling. The experiments also provide new evidence that a canonical tokenizer can reduce perplexity by more than a half for a polysynthetic language like Quechua as well as raising F1 from 0.8 to more than 0.9 in both downstream tasks with a LM trained with only 6M tokens.


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Preparing an endangered language for the digital age: The Case of Judeo-Spanish
Alp Öktem | Rodolfo Zevallos | Yasmin Moslem | Özgür Güneş Öztürk | Karen Gerson Şarhon
Proceedings of the Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference

We develop machine translation and speech synthesis systems to complement the efforts of revitalizing Judeo-Spanish, the exiled language of Sephardic Jews, which survived for centuries, but now faces the threat of extinction in the digital age. Building on resources created by the Sephardic community of Turkey and elsewhere, we create corpora and tools that would help preserve this language for future generations. For machine translation, we first develop a Spanish to Judeo-Spanish rule-based machine translation system, in order to generate large volumes of synthetic parallel data in the relevant language pairs: Turkish, English and Spanish. Then, we train baseline neural machine translation engines using this synthetic data and authentic parallel data created from translations by the Sephardic community. For text-to-speech synthesis, we present a 3.5-hour single speaker speech corpus for building a neural speech synthesis engine. Resources, model weights and online inference engines are shared publicly.

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Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition
Rodolfo Zevallos | Luis Camacho | Nelsi Melgarejo
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The Huqariq corpus is a multilingual collection of speech from native Peruvian languages. The transcribed corpus is intended for the research and development of speech technologies to preserve endangered languages in Peru. Huqariq is primarily designed for the development of automatic speech recognition, language identification and text-to-speech tools. In order to achieve corpus collection sustainably, we employs the crowdsourcing methodology. Huqariq includes four native languages of Peru, and it is expected that by the year 2022, it can reach up to 20 native languages out of the 48 native languages in Peru. The corpus has 220 hours of transcribed audio recorded by more than 500 volunteers, making it the largest speech corpus for native languages in Peru. In order to verify the quality of the corpus, we present speech recognition experiments using 220 hours of fully transcribed audio.

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WordNet-QU: Development of a Lexical Database for Quechua Varieties
Nelsi Melgarejo | Rodolfo Zevallos | Hector Gomez | John E. Ortega
Proceedings of the 29th International Conference on Computational Linguistics

In the effort to minimize the risk of extinction of a language, linguistic resources are fundamental. Quechua, a low-resource language from South America, is a language spoken by millions but, despite several efforts in the past, still lacks the resources necessary to build high-performance computational systems. In this article, we present WordNet-QU which signifies the inclusion of Quechua in a well-known lexical database called wordnet. We propose WordNet-QU to be included as an extension to wordnet after demonstrating a manually-curated collection of multiple digital resources for lexical use in Quechua. Our work uses the synset alignment algorithm to compare Quechua to its geographically nearest high-resource language, Spanish. Altogether, we propose a total of 28,582 unique synset IDs divided according to region like so: 20510 for Southern Quechua, 5993 for Central Quechua, 1121 for Northern Quechua, and 958 for Amazonian Quechua.

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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.