# Zero-shot transfer for implicit discourse relation classification

## TL;DR

This paper introduces a zero-shot transfer learning approach for classifying implicit discourse relations across multiple languages using only English training data and unannotated parallel text, demonstrating effective results on the TED-MDB corpus.

## Contribution

It proposes a novel zero-shot transfer method for implicit discourse relation classification that requires no annotated data in target languages.

## Key findings

- Achieved good classification results on seven languages using only English data.
- Demonstrated the effectiveness of unannotated parallel text for cross-lingual transfer.
- Showed that zero-shot transfer can be viable for discourse relation classification.

## Abstract

Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated training data exists only for a small number of languages, such as English and Chinese. We present a new system using zero-shot transfer learning for implicit discourse relation classification, where the only resource used for the target language is unannotated parallel text. This system is evaluated on the discourse-annotated TED-MDB parallel corpus, where it obtains good results for all seven languages using only English training data.