The five idioms (i.e., varieties) of the Romansh language are largely standardized and are taught in the schools of the respective communities in Switzerland. In this paper, we present the first parallel corpus of Romansh idioms. The corpus is based on 291 schoolbook volumes, which are comparable in content for the five idioms. We use automatic alignment methods to extract 207k multi-parallel segments from the books, with more than 2M tokens in total. A small-scale human evaluation confirms that the segments are highly parallel, making the dataset suitable for NLP applications such as machine translation between Romansh idioms. We release the parallel and unaligned versions of the dataset under a CC-BY-NC-SA license and demonstrate its utility for machine translation by training and evaluating an LLM and a supervised multilingual MT model on the dataset.

The challenges of building speech-to-text translation (ST) systems (e.g., a relative lack of parallel speech–text data and robustness to noise in audio) are exacerbated for low-resource language pairs. In this work, we seek to improve low-resource ST by building on previous studies that regularize ST training with the connectionist temporal classification (CTC) loss. By systematically evaluating a diverse range of linguistic annotations as CTC labels across multiple auxiliary loss configurations, we improve speech translation systems for both low- and high-resource settings. These improvements over both a standard end-to-end ST system and a speech LLM indicate a need for continued research on regularizing speech representations in ST.

The distinction between function and content units of the lexicon has been somewhat neglected in recent NLP work, but it could still be useful when working with low-resource languages, and, in particular, to improve cross-lingual transfer. In this paper, we investigate to what extent BPE subword tokenization can be used to identify units of the functional lexicon in a language without any annotated data. We analyze subword tokens in terms of their productivity and attempt to find thresholds that best distinguish function from content tokens. On a sample of seven diverse languages, we find that the best results are obtained with 50 BPE merges. We also show that this subword tokenization setting can be beneficial for the interlinear glossing task.