Bridging Background Knowledge Gaps in Translation with Automatic Explicitation

HyoJung Han, Jordan Lee Boyd-Graber, Marine Carpuat

Submission Type: Regular Long Paper

Submission Track: Machine Translation

Submission Track 2: Computational Social Science and Cultural Analytics

Keywords: Explicitation, translation, cross‑cultural NLP, pragmatic explicitation, multi-cultural NLP, explanatory translation

TL;DR: This work introduces techniques for automatically generating explicitations, motivated by WikiExpl, a dataset that we collect from Wikipedia and annotate with human translators, and evaluated by intrinsic and extrinsic ways.

Abstract: Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.

Submission Number: 4656