New Protocols and Negative Results for Textual Entailment Data Collection
Samuel R. Bowman; Jennimaria Palomaki; Livio Baldini Soares; Emily Pitler · 2020 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding. However, the crowdsourcing protocol that was used to collect this data has known issues and was not explicitly optimized for either of these purposes, so it is likely far from ideal. We propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples. Using these alternatives and a fifth baseline protocol, we collect and compare
Excerpt shown for reference under fair use — read the full paper at the publisher.
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Metadata source: arXiv