Real world deployments of word alignment are almost certain to cover both high and low resource languages.However, the state-ofthe-art for this task recommends a different model class depending on the availability of gold alignment training data for a particular language pair.We propose BinaryAlign, a novel word alignment technique based on binary sequence labeling that outperforms existing approaches in both scenarios, offering a unifying approach to the task.Additionally, we vary the specific choice of multilingual foundation model, perform stratified error analysis over alignment error type, and explore the performance of BinaryAlign on non-English language pairs.We make our source code publicly available.1

Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora.However, these annotations are unavailable in many low-resource languages.In this paper, we investigate GED in this context.Leveraging the zero-shot cross-lingual transfer capabilities of multilingual pre-trained language models, we train a model using data from a diverse set of languages to generate synthetic errors in other languages.These synthetic error corpora are then used to train a GED model.Specifically we propose a two-stage fine-tuning pipeline where the GED model is first fine-tuned on multilingual synthetic data from target languages followed by fine-tuning on human-annotated GED corpora from source languages.This approach outperforms current state-of-the-art annotation-free GED methods.We also analyse the errors produced by our method and other strong baselines, finding that our approach produces errors that are more diverse and more similar to human errors.

Language transfer, the preferential second language behavior caused by similarities to the speaker's native language, requires considerable expertise to be detected by humans alone.Our goal in this work is to replace expert intervention by data-driven methods wherever possible.We define a computational methodology that produces a concise list of lexicalized syntactic patterns that are controlled for redundancy and ranked by relevancy to language transfer.We demonstrate the ability of our methodology to detect hundreds of such candidate patterns from currently available data sources, and validate the quality of the proposed patterns through classification experiments.