Ondřej Dušek: Better Supervision for End-to-end Neural Dialogue Systems

DusekOndřej Dušek is an assistant professor at the Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University. His research is in the areas of dialogue systems and natural language generation; he specifically focuses on neural-networks-based approaches to these problems and their evaluation. He is also involved in the THEaiTRE project on automatic theatre play generation. Ondřej got his PhD in 2017 at Charles University. Between 2016 and 2018, he worked at the Interaction Lab at Heriot Watt University in Edinburgh, one of the leading groups in dialogue systems and natural-language interaction with computers and robots. There he co-organized the E2E NLG text generation challenge and co-led a team of PhD students in the Amazon Alexa Prize dialogue system competition, which came third in two consecutive years.

Better Supervision for End-to-end Neural Dialogue Systems

While end-to-end neural models have been the research trend in task-oriented dialogue systems in the past years, they still suffer from significant problems: The neural models often produce replies inconsistent with past dialogue context or database results, their replies may be dull and formulaic, and they require large amounts of annotated data to train. In this talk, I will present two of our recent experiments that aim at solving these problems.

First, our end-to-end neural system AuGPT based on the GPT-2 pretrained language model aims at consistency and variability in dialogue responses by using massive data augmentation and filtering as well as specific auxiliary training objectives which check for dialogue consistency. It reached favorable results in terms of both automatic metrics and human judgments (in the DSTC9 competition).

Second, we designed a system that is able to discover relevant dialogue slots (domain attributes) without any human annotation. It uses weak supervision from generic linguistic annotation models (semantic parser, named entities), which is further filtered and clustered. We train a neural slot tagger on the discovered slots, which then reaches state-of-the-art results in dialogue slot tagging without labeled training data. We further show that the discovered slots are helpful for training an end-to-end neural dialogue system.

His talk takes place on Wednesday, December 1, 2021 at 15:00 in “little theater” R211 (next to Kachnicka student club in “Stary Pivovar”). The talk will be streamed live and recorded at https://www.youtube.com/watch?v=JzBy-QuLxiE.