Changing the Mind of Transformers for Topically-Controllable Language Generation
This addresses the limitation of current interactive writing assistants for authors by allowing topical control, though it is incremental as it builds on existing Transformer models.
The paper tackles the problem of enabling authors to guide text generation in desired topical directions by proposing a framework that displays candidate topics for selection and generates text adhering to chosen topics, with experiments showing improved topic options and fluent, topic-related sentences.
Large Transformer-based language models can aid human authors by suggesting plausible continuations of text written so far. However, current interactive writing assistants do not allow authors to guide text generation in desired topical directions. To address this limitation, we design a framework that displays multiple candidate upcoming topics, of which a user can select a subset to guide the generation. Our framework consists of two components: (1) a method that produces a set of candidate topics by predicting the centers of word clusters in the possible continuations, and (2) a text generation model whose output adheres to the chosen topics. The training of both components is self-supervised, using only unlabeled text. Our experiments demonstrate that our topic options are better than those of standard clustering approaches, and our framework often generates fluent sentences related to the chosen topics, as judged by automated metrics and crowdsourced workers.