In modern natural language processing pipelines, it is common practice to “pretrain” a generative language model on a large corpus of text, and then to “finetune” the created representations by continuing to train them on a discriminative textual inference task.
A current open question in natural language processing is to what extent language models, which are trained with access only to the form of language, are able to capture the meaning of language.
In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs).
Since it can be expensive to obtain training data to learn to extract implications for each new domain of reviews, we propose an unsupervised KBC system, Sampo, Specifically, Sampo is tailored to build KBs for domains where many reviews on the same domain are available.
We evaluate STANCE's ability to detect whether two strings can refer to the same entity--a task we term alias detection.