One Model, Multiple Tasks: Pathways for Natural Language Understanding

• URL: http://arxiv.org/abs/2203.03312v1
• Date: Mon, 7 Mar 2022 11:48:09 GMT
• Title: One Model, Multiple Tasks: Pathways for Natural Language Understanding
• Authors: Duyu Tang, Fan Zhang, Yong Dai, Cong Zhou, Shuangzhi Wu and Shuming Shi
• Abstract summary: This paper presents a Pathways approach to handle many tasks at once. Unlike prevailing single-purpose models that overspecialize at individual tasks and learn from scratch when being extended to new tasks, our approach is general-purpose with the ability of stitching together existing skills to learn new tasks more effectively.
• Score: 34.58880663537492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a Pathways approach to handle many tasks at once. Our approach is general-purpose and sparse. Unlike prevailing single-purpose models that overspecialize at individual tasks and learn from scratch when being extended to new tasks, our approach is general-purpose with the ability of stitching together existing skills to learn new tasks more effectively. Different from traditional dense models that always activate all the model parameters, our approach is sparsely activated: only relevant parts of the model (like pathways through the network) are activated. We take natural language understanding as a case study and define a set of skills like \textit{the skill of understanding the sentiment of text} and \textit{the skill of understanding natural language questions}. These skills can be reused and combined to support many different tasks and situations. We develop our system using Transformer as the backbone. For each skill, we implement skill-specific feed-forward networks, which are activated only if the skill is relevant to the task. An appealing feature of our model is that it not only supports sparsely activated fine-tuning, but also allows us to pretrain skills in the same sparse way with masked language modeling and next sentence prediction. We call this model \textbf{SkillNet}. We have three major findings. First, with only one model checkpoint, SkillNet performs better than task-specific fine-tuning and two multi-task learning baselines (i.e., dense model and Mixture-of-Experts model) on six tasks. Second, sparsely activated pre-training further improves the overall performance. Third, SkillNet significantly outperforms baseline systems when being extended to new tasks.

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