Sentence Level Curriculum Learning for Improved Neural Conversational Models
This work addresses the challenge of enhancing conversational AI for natural language processing by introducing an incremental training technique that could improve model performance in dialogue systems.
The paper tackled the problem of improving neural conversational models by mimicking the gradual buildup in sentence complexity experienced by humans, using a sentence-level curriculum learning approach that segments training into increasingly longer sentence pairs, resulting in lower validation loss compared to standard training methods.
Designing machine intelligence to converse with a human user necessarily requires an understanding of how humans participate in conversation, and thus conversation modeling is an important task in natural language processing. New breakthroughs in architecture and data gathering continue to push the performance of such conversational AI models. However, designs neglect the gradual buildup in sentence structure and complexity experienced by humans as we learn to communicate. During training, our model accepts one or more sentences as input and attempts to predict the next sentence in the conversation one word at a time, so our goal is to separate training into segments, with each segment's corpus comprised of longer sentence pairs than the previous one. This will mimic the desired "buildup" component of human learning. We begin with only "short" length sentence pairs, then only "medium" length pairs, and so on. A majority of our experiments were toward optimizing this technique, ensuring a proper representation of the technique's potential, since many of the details were new questions. Our segment-trained models were then able to achieve lower validation loss at the end of training than models trained with standard text preparation. This segmented training is straightforward to implement and our results provide a general direction for future research to implement and improve it.