Outlier Detection for Improved Data Quality and Diversity in Dialog Systems · HackerPapers discussionshttps://hackerpaper.kaipfister.de/p/1904.03122/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMTOutlier Detection for Improved Data Quality and Diversity in Dialog Systems — mentioned by parnouxhttps://news.ycombinator.com/item?id=31830108urn:hackerpapers:mention:1904.03122:31830108Tue, 21 Jun 2022 23:02:59 GMT<p>In the example shown in the product tour, we use an approach based on Diversity Sampling. Basically we look for the datapoints that would be the most representative of the drifted domain (and therefore be outliers to the training domain). Here is a blog post that summarizes some of those techniques [1]. I also found this paper that describes a similar approach [2]. Happy to chat more about it. [1]: https://towardsdatascience.com/https-towardsdatascience-com-... [2]: https://arxiv.org/abs/1904.03122</p><p><a href="https://hackerpaper.kaipfister.de/p/1904.03122/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.CL