Automatic Generation of Large-scale Multi-turn Dialogues from Reddit

Daniil Huryn, William M. Hutsell, Jinho D. Choi


This paper presents novel methods to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. Our methods are generalizable to any forums; thus, they allow us to generate a massive amount of dialogues for diverse topics that can be used to pretrain language models. Four methods are introduced, Greedy Baseline, Greedy Advanced, Beam Search, and Threading, which are applied to posts from 10 subreddits and assessed. Each method makes a noticeable improvement over its predecessor such that the best method shows an improvement of 36.3% over the baseline. Our best method is applied to posts from the 10 subreddits to create a corpus consisting of 10K dialogues (3.3M tokens), 570 of which are blindly compared against dialogues in 3 other datasets, Blended Skill Talk, Daily Dialogue, and Topical Chat. In general, our dialogues are found to be more engaging but slightly less natural than ones in the other datasets, while it costs a fraction of human labor and money to create our corpus compared to the others. To the best of our knowledge, it is the first work to create a large multi-turn dialogue corpus from discussion forums that can advance neural-based dialogue systems.

Venue / Year

Proceedings of the International Conference on Computational Linguistics / 2022


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