Friendly Conditional Text Generator

Abstract

Our goal is to control text generation with more fine-grained conditions at lower computational cost than is possible with current alternatives; these conditions are attributes (i.e., multiple codes and free-text). As large-scale pre-trained language models (PLMs) offer excellent performance in free-form text generation, we explore efficient architectures and training schemes that can best leverage PLMs. Our framework, Friendly Conditional Text Generator (FCTG), introduces a multi-view attention (MVA) mechanism and two training tasks, Masked Attribute Modeling (MAM) and Attribute Linguistic Matching (ALM), to direct various PLMs via modalities between the text and its attributes. The motivation of FCTG is to map texts and attributes into a shared space, and bridge their modality gaps, as the texts and attributes reside in different regions of semantic space. To avoid catastrophic forgetting, modality-free embedded representations are learnt, and used to direct PLMs in this space, FCTG applies MAM to learn attribute representations, maps them in the same space as text through MVA, and optimizes their alignment in this space via ALM. Experiments on publicly available datasets show that FCTG outperforms baselines over higher level conditions at lower computation cost.