This paper studies Negation Scope Resolution (NSR) for Chinese as a Second Language (CSL), which shows many unique characteristics that distinguish itself from “standard” Chinese. We annotate a new moderate-sized corpus that covers two background L1 languages, viz. English and Japanese. We build a neural NSR system, which achieves a new state-of-the-art accuracy on English benchmark data. We leverage this system to gauge how successful NSR for CSL can be. Different native language backgrounds of language learners result in unequal cross-lingual transfer, which has a significant impact on processing second language data. In particular, manual annotation, empirical evaluation and error analysis indicate two non-obvious facts: 1) L2-Chinese, L1-Japanese data are more difficult to analyze and thus annotate than L2-Chinese, L1-English data; 2) computational models trained on L2-Chinese, L1-Japanese data perform better than models trained on L2-Chinese, L1-English data.