Enhancing S panish- Q uechua Machine Translation with Pre-Trained Models and Diverse Data Sources: LCT - EHU at A mericas NLP Shared Task (2023)

Abstract: We present the LCT-EHU submission to the AmericasNLP 2023 low-resource machine translation shared task. We focus on the Spanish-Quechua language pair and explore the usage of different approaches: (1) Obtain new parallel corpora from the literature and legal domains, (2) Compare a high-resource Spanish-English pre-trained MT model with a Spanish-Finnish pre-trained model (with Finnish being chosen as a target language due to its morphological similarity to Quechua), and (3) Explore additional techniques such as copied corpus and back-translation. Overall, we show that the Spanish-Finnish pre-trained model outperforms other setups, while low-quality synthetic data reduces the performance.

## introduction the lct-ehu team participated in the americas-nlp 2023 low-resource machine translation shared task. the task involved machine translation from spanish to 11 different indigenous languages. the languages in question are very much low-resource, with the number of speakers spanning from a few tens of thousands to a few million and with limited availability of parallel data. monolingual data is not easily obtained either -wikipedia is available only in a few of these languages, with the number of articles not being very high. our team focused on spanish-quechua language pair with the approach consisting in: the official metric used in the shared task is chrf++ @xcite . in the previous edition of the americasnlp shared task, the chrf score of 34.6 was obtained by the repucs team @xcite for the spanish-quechua language pair. however, this year's shared task takes the second-best result of 34.3 as a baseline. all of the source code and newly collected data are available in the github repository foot_0 . ## related work some previous work and approaches that were important for our experiments are explained in the following sub-sections. ## data in this section, we will describe the data used in the experiments. ## conclusion to summarize our findings, in our submission to the americasnlp 2023 low-resource machine translation shared task for the spanish-quechua language pair, we have explored fine-tuning existing models in different language pairs, combining them with different data setups. we have collected and aligned new parallel data, created synthetic translations, and made use of copied corpus approach. the highest-performing model on the development data achieved 37.70 chrf. this model was obtained by fine-tuning opus mt's spanish-finnish model on the original training data, augmented with additional data presented by previous year's teams, both for ayacucho and cuzco quechua. in the test set, however, the highest performing model was different, obtaining a chrf score of 38.59. this model was the same as the previous one, but the data consisted of the original training data, data from previous year's submissions (excluding cuzco quechua) and the novel alignments introduced in this work.