Language Modelling as a Multi-Task Problem

Conference of the European Chapter of the Association for Computational Linguistics (EACL)

Abstract

In this paper, we propose to study language modeling as a multi-task problem, bringing together three strands of research: multitask learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. We showcase the idea by analysing the generalization behavior of language models during learning of the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modeling. We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.

Latest Publications

Boosted Dense Retriever

Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel

NAACL - 2022