Edoardo Maria Ponti

TL;DR: A systematic empirical analysis across six typologically diverse languages and five different lexical tasks indicates patterns and best practices that hold universally, but also point to prominent variations across languages and tasks.

TL;DR: It is shown that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance, due to both intrinsic limitations of databases and under-employment of the typological features included in them.

TL;DR: Fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams.

TL;DR: This work introduces Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, revealing that current methods based on multilingual pretraining and zero-shot fine-tuning transfer suffer from the curse of multilinguality and fall short of performance in monolingual settings by a large margin.

TL;DR: The authors introduce a cross-lingual choice of plausible alternatives (XCOPA) dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apurimac Quechua and Haitian Creole.