Large datasets are essential for  many NLP tasks. Current publicly available open-domain dialogue datasets offer a trade-off between size and quality (e.g. DailyDialog vs. Opensubtitles). We aim to close this gap by building a high-quality dataset consisting of 14.8M utterances in English  . We extract and process dialogues from publicly available online books . We present a detailed description of our pipeline and heuristics and an error analysis of extracted dialogues. Better response quality can be achieved in zero-shot and finetuning settings by training on our data than on the larger but much noisier Opensubtitles dataset. Researchers can easily build their versions of the dataset by adjusting various trade-off parameters. The code can be extended to further languages with limited effort URL
Large datasets are essential for neural modeling of many NLP tasks. Current publicly available open-domain dialogue datasets offer a trade-off between quality (e.g. , DailyDialog) and size (e.g., Opensubtitles). We narrow this gap by building a high-quality dataset of 14.8M utterances in English , and smaller datasets in German, Dutch, Spanish, Portuguese, Italian, and Hungarian . We extract and process dialogues from public-domain books made available by Project Gutenberg. We describe our dialogue extraction pipeline, analyze the effects of the various heuristics used, and present an error analysis of extracted dialogues. Finally, we conduct experiments showing that better response quality can be achieved in zero-shot and finetuning settings by training on our data than on the larger but much noisier Opensubtitles dataset. Our open-source pipeline URL can be extended to further languages with little additional effort. Researchers can also build their versions of existing datasets by adjusting various trade-off parameters. We also built a web demo for interacting with our models: URL
A
neural modeling of
D
size and
R
DailyDialog vs.
, DailyDialog) and size (e.g.,
R
aim to close
narrow
D
consisting
A
, and smaller datasets in German, Dutch, Spanish, Portuguese, Italian, and Hungarian
R
publicly available online books . We present a detailed description of our pipeline and heuristics and
public-domain books made available by Project Gutenberg. We describe our dialogue extraction pipeline, analyze the effects of the various heuristics used, and present
R
Better
Finally, we conduct experiments showing that better
R
Researchers can easily
Our open-source pipeline URL can be extended to further languages with little additional effort. Researchers can also
R
the dataset
existing datasets
R
The code can be extended to further languages with limited effort
We also built a web demo for interacting with our models: