Dialogue Generation

Dialog generation is a task in natural language processing aimed at understanding natural language input to generate corresponding output. The core objective of this task is to build systems, such as chatbots, that can engage in smooth two-way conversations with humans. Through benchmarking on datasets like FusedChat and the Ubuntu Dialogue Corpus, dialog generation models can be evaluated using metrics such as BLEU, ROUGE, and METEOR. Although these metrics have weak correlations with human judgment, new evaluation methods like Unsupervised and Reference-Free (USR) and Automatic Unreferenced Dialogue Evaluation (MaUde) are addressing this issue. The application value of dialog generation lies in enhancing the naturalness and effectiveness of human-computer interaction, thereby improving user experience.