%0 Conference Proceedings %T Very Low Resource Sentence Alignment: Luhya and Swahili %A Chimoto, Everlyn %A Bassett, Bruce %Y Ojha, Atul Kr. %Y Liu, Chao-Hong %Y Vylomova, Ekaterina %Y Abbott, Jade %Y Washington, Jonathan %Y Oco, Nathaniel %Y Pirinen, Tommi A. %Y Malykh, Valentin %Y Logacheva, Varvara %Y Zhao, Xiaobing %S Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022) %D 2022 %8 October %I Association for Computational Linguistics %C Gyeongju, Republic of Korea %F chimoto-bassett-2022-low %X Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASER and LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili. For this work, we created a new parallel set of nearly 8000 Luhya-English sentences which allows a new zero-shot test of LASER and LaBSE. We find that LaBSE significantly outperforms LASER on both languages. Both LASER and LaBSE however perform poorly at zero-shot alignment on Luhya, achieving just 1.5% and 22.0% successful alignments respectively (P@1 score). We fine-tune the embeddings on a small set of parallel Luhya sentences and show significant gains, improving the LaBSE alignment accuracy to 53.3%. Further, restricting the dataset to sentence embedding pairs with cosine similarity above 0.7 yielded alignments with over 85% accuracy. %U https://aclanthology.org/2022.loresmt-1.1 %P 1-8