Pretraining Methods for Dialog Context Representation Learning
This work addresses the challenge of efficient dialog modeling for natural language processing applications, representing an incremental advancement in pretraining techniques.
The paper tackles the problem of learning dialog context representations by proposing two novel unsupervised pretraining methods and evaluating four methods in total, resulting in strong performance improvements on downstream dialog tasks using the MultiWoz dataset, with benefits including better convergence, reduced data hunger, and improved domain generalizability.
This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using the MultiWoz dataset and strong performance improvement is observed. Further evaluation shows that our pretraining objectives result in not only better performance, but also better convergence, models that are less data hungry and have better domain generalizability.