Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
This addresses a bottleneck in discourse parsing for NLP researchers, but it is incremental as it builds on prior work exploiting translation-based data acquisition.
The paper tackled the problem of data scarcity in implicit discourse relation classification by investigating whether the choice of translation language and using multiple translations affect the quality of acquired data from cross-lingual explicitation, showing that this approach improves parsing performance.
Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connective as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca.~16k instances in the PDTB). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.