Scalar Adjective Identification and Multilingual Ranking
This work addresses the need for multilingual resources in natural language inference and common-sense reasoning, though it is incremental as it builds on existing English-focused research.
The authors tackled the problem of scalar adjective ranking and identification by introducing a new multilingual dataset and setting performance baselines using contextual language models, achieving baseline results for future comparison on these tasks.
The intensity relationship that holds between scalar adjectives (e.g., nice < great < wonderful) is highly relevant for natural language inference and common-sense reasoning. Previous research on scalar adjective ranking has focused on English, mainly due to the availability of datasets for evaluation. We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. We perform a series of experiments and set performance baselines on this dataset, using monolingual and multilingual contextual language models. Additionally, we introduce a new binary classification task for English scalar adjective identification which examines the models' ability to distinguish scalar from relational adjectives. We probe contextualised representations and report baseline results for future comparison on this task.