PRE-TRAINING TEXT-TO-TEXT TRANSFORMERS FOR CONCEPT-CENTRIC COMMON SENSE

Abstract

Pre-trained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the relational commonsense knowledge about everyday concepts, which is crucial to many downstream tasks that need common sense to understand or generate. To augment PTLMs with concept-centric commonsense knowledge, in this paper, we propose both generative and contrastive objectives for learning common sense from the text, and use them as intermediate self-supervised learning tasks for incrementally pre-training PTLMs (before task-specific fine-tuning on downstream datasets). Furthermore, we develop a joint pre-training framework to unify generative and contrastive objectives so that they can mutually reinforce each other. Extensive experimental results show that our method, concept-aware language model (CALM) 1 , can pack more commonsense knowledge into the parameters of a pre-trained text-to-text transformer without relying on external knowledge graphs, yielding better performance on both NLU and NLG tasks. We show that while only incrementally pre-trained on a relatively small corpus for a few steps, CALM outperforms baseline methods by a consistent margin and even comparable with some larger PTLMs, which suggests that CALM can serve as a general, "plug-and-play" method for improving the commonsense reasoning ability of a PTLM.

1. INTRODUCTION

Pre-trained language models (PLTMs) such as BERT (Devlin et al., 2018) and T5 (Raffel et al., 2019) have revolutionized the field of NLP, yielding impressive performance on various conventional natural language understanding (NLU) and generation (NLG) tasks. BERT and its novel variants such as RoBERTa (Liu et al., 2019) and ALBERT (Lan et al., 2019) capture syntactical and semantic knowledge mainly from the pre-training task of masked language modeling, while T5-style models such as BART (Lewis et al., 2019) instead focus on masked span infilling tasks. Though yielding better performance on many downstream tasks, these pre-training objectives, however, do not explicitly guide the models to reason with concept-centric commonsense knowledge from language, including the relation and composition of daily concepts in our lives. This leaves room for equipping current PTLMs with richer commonsense reasoning ability. For example, consider a multi-choice question "What do you fill with ink to write notes on a piece of copy paper? (A) fountain pen (B) pencil case (C) printer (D) notepad". The current state-of-the-art question answering model, UnifiedQA (Khashabi et al., 2020) , which was fine-tuned on T5-large with multiple datasets, still predicts '(C) printer' as its answer. The model may be overly sensitive to the co-occurrence between phrases in question sentence like 'ink' and 'copy paper' and the answer choice 'printer', but fails to reason with the concept-centric knowledge that 'fountain pen' is a writing instrument that needs to be filled with 'ink'. Such mistake in commonsense reasoning becomes a bottleneck for current PTLMs (Davis & Marcus, 2015) . Towards augmenting PTLMs with more

funding

work was done when Wangchunshu was visiting USC.

