This paper introduces a pose-based pipeline designed to support scalable annotation of headshakes in sign language corpora. Motivated by the scarcity of annotated datasets and the need for quantitative typological research, the study evaluates whether automated detection can reduce human annotation effort. The system operates on yaw trajectories extracted with MediaPipe Holistic and uses sliding-window segmentation with neural sequence models (LSTM/CNN) to surface candidate segments for review. Training and evaluation are conducted on a subset of the German Sign Language (DGS) Corpus annotated to target grammatical headshakes functioning as negation rather than for every instance of headshakes. On the DGS dataset the best performing LSTM model achieves an F2-score of 0.45, recall of 0.63. Despite the narrow annotation scope, the pipeline reduces search space: annotators need review only 13% of frames to recover 87% of labeled instances. Error analysis indicates that many false positives correspond to plausible head movements excluded by the annotation criteria. A pilot transfer to Swedish Sign Language shows reduced effectiveness without adaptation, underscoring the need for alignment in cross-lingual transfer scenarios.

Training a language model for low-resource languages is challenging due to data scarcity and computational cost. Tokenizer transfer offers a way to adapt a pre-trained model to a new tokenizer without full retraining, improving efficiency and cross-lingual applicability. To the best our of knowledge, we present the first controlled evaluation of tokenizer transfer on monolingually pretrained base models trained on language-specific corpora, Orthogonal Mapping Pursuit (OMP) and Fast Vocabulary Transfer (FVT), across six languages and multiple finetuning regimes. Using the Goldfish model family, we evaluate using byte-normalized log-perplexity and MultiBlimp accuracy for target-language adaptability, source-language retention, and the interaction between transfer and monolingual or mixed finetuning. OMP with monolingual target finetuning yields the best target-language scores (lower log-perplexity and higher MultiBlimp) among our evaluated conditions, compared with (i) a model trained only on the source language, (ii) a model trained on a smaller amount of target-language data, and (iii) the source language model adapted via standard finetuning on the target data. The results suggest tokenizer transfer is a compute-efficient alternative for low-resource LM training: train a monolingual tokenizer for the target language, transfer it to a larger pre-trained model, and fine-tune using the target data.