Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native language properties on linguistic performance in a foreign language. In this work we provide empirical evidence for this process in the form of a strong correlation between language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native languages. We leverage this finding to recover native language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target languages. Our method achieves 72.2 accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources.
Following the work of , NLI has been gaining increasing interest in NLP, culminating in a recent shared task with 29 participating systems @cite_14 . Much of the NLI efforts thus far have been focused on exploring various feature sets for optimizing classification performance. While many of these features are linguistically motivated, some of the discriminative power of these approaches stems from cultural and domain artifacts. For example, our preliminary experiments with a typical NLI feature set, show that the strongest features for predicting Chinese are strings such as and . Similar features dominate the weights of other languages as well. Such content features boost classification performance, but are hardly relevant for modeling linguistic phenomena, thus weakening the argument that NLI classification performance is indicative of cross-linguistic transfer.
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In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy. Specifically, we first use automatic tools to ascertain frequencies of various stylistic idiosyncrasies in a text. These frequencies then serve as features for support vector machines that learn to classify texts according to author native language.