Toward Better Storylines with Sentence-Level Language Models

Abstract: We propose a sentence-level LLM which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level LLM can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.