Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model

arXiv (Cornell University)

Abstract

This paper summarizes our submission to Task 2 of the second track of the 10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations". Similar to the previous year's iteration, the task consists of three subtasks: detecting whether a turn is knowledge seeking, selecting the relevant knowledge document and finally generating a grounded response. This year, the focus lies on adapting the system to noisy ASR transcripts. We explore different approaches to make the models more robust to this type of input and to adapt the generated responses to the style of spoken conversations. For the latter, we get the best results with a noisy channel model that additionally reduces the number of short and generic responses. Our best system achieved the 1st rank in the automatic and the 3rd rank in the human evaluation of the challenge.

Authors 4

1. David Thulke Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   AppTek GmbH , Aachen , Germany

   Human Language Technology and Pattern Recognition Group , RWTH Aachen University , Germany

2. Nico Daheim Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition Group , RWTH Aachen University , Germany

3. Christian Dugast
   Affiliation as printed

   AppTek GmbH , Aachen , Germany

4. Hermann Ney Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   AppTek GmbH , Aachen , Germany

   Human Language Technology and Pattern Recognition Group , RWTH Aachen University , Germany