Atom Responding Machine for Dialog Generation
This addresses the issue of safe and trivial responses in dialogue systems for applications like chatbots, though it appears incremental as it builds on existing encoder-decoder models.
The paper tackled the problem of generating diverse and relevant responses in dialogue systems by proposing the Atom Responding Machine (ARM), which uses an encoder-composer-decoder network with atom-mechanisms to create various responding styles, resulting in improved diversity and quality of responses.
Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world corpus, while the conventional encoder-decoder model tends to output the high-frequency (safe but trivial) responses and thus is difficult to handle the large number of responding styles. To address these issues, we propose the Atom Responding Machine (ARM), which is based on a proposed encoder-composer-decoder network trained by a teacher-student framework. To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms. In other words, even a little of atom-mechanisms can make a mickle of molecule-mechanisms. The experiments demonstrate diversity and quality of the responses generated by ARM. We also present generating process to show underlying interpretability for the result.