LANGUAGE MAINTENANCE AND LANGUAGE SHIFT

In language shifts, ancestral tongues are abandoned by their speakers and replaced, in one way or another, by dominant languages. Such changes in language use will ultimately lead to the irreversible suppression of the world's language diversity. Language maintenance attempts to counter these processes. Linguists may assist ethno linguistic minorities in safeguarding their threatened languages in many different ways, including establishing orthography when necessary, but speakers decide to abandon their heritage languages within a broad socio-political and economic context. Communities uphold or give up languages, so only the speakers of endangered languages themselves can opt for and execute language maintenance activities. Linguists might have to accept that some communities may no longer care for their heritage languages

e-SNLI: Natural Language Inference with Natural Language Explanations

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.Comment: NeurIPS 201