Improving Language Generation with Sentence Coherence Objective
This addresses coherence issues in conditional story generation and text continuation for NLP applications, but it is incremental as it builds on existing GPT-2 models.
The paper tackled the problem of generated text diverging from prompts in language generation by introducing a sentence coherence objective, resulting in a model that generates lengthy paragraphs without significant divergence.
Conditional story generation and contextual text continuation have become increasingly popular topics in NLP community. Existing models are often prone to output paragraphs of texts that gradually diverge from the given prompt. Although the generated text may have a reasonable perplexity and diversity, it could easily be identified by human as gibberish. The goal of our project is to improve the coherence and consistency across sentences in a language-generation model. We aim to solve this issue by first training a sentence pair coherence classifier with GPT-2 pretrained model, and then co-train the GPT-2 language model with this new coherence objective using a method analogous to the REINFORCE algorithm. This fine-tuned language model is able to generate lengthy paragraph conditioned on a given topic without diverging too much. The simplicity of this model allows it to be applicable to a variety of underlying language model architecture since it only modifies the final layer of the pre-trained model.