Modelling Latent Skills for Multitask Language Generation

• URL: http://arxiv.org/abs/2002.09543v1
• Date: Fri, 21 Feb 2020 20:39:09 GMT
• Title: Modelling Latent Skills for Multitask Language Generation
• Authors: Kris Cao, Dani Yogatama
• Abstract summary: We present a generative model for multitask conditional language generation. Our guiding hypothesis is that a shared set of latent skills underlies many disparate language generation tasks. We instantiate this task embedding space as a latent variable in a latent variable sequence-to-sequence model.
• Score: 15.126163032403811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a generative model for multitask conditional language generation. Our guiding hypothesis is that a shared set of latent skills underlies many disparate language generation tasks, and that explicitly modelling these skills in a task embedding space can help with both positive transfer across tasks and with efficient adaptation to new tasks. We instantiate this task embedding space as a latent variable in a latent variable sequence-to-sequence model. We evaluate this hypothesis by curating a series of monolingual text-to-text language generation datasets - covering a broad range of tasks and domains - and comparing the performance of models both in the multitask and few-shot regimes. We show that our latent task variable model outperforms other sequence-to-sequence baselines on average across tasks in the multitask setting. In the few-shot learning setting on an unseen test dataset (i.e., a new task), we demonstrate that model adaptation based on inference in the latent task space is more robust than standard fine-tuning based parameter adaptation and performs comparably in terms of overall performance. Finally, we examine the latent task representations learnt by our model and show that they cluster tasks in a natural way.

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