# Zero-shot transfer for implicit discourse relation classification

arXiv: https://arxiv.org/abs/1907.12885 · HTML: https://mlsanity.com/paper/1907.12885/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Probing Multilingual Language Models for Discourse](https://mlsanity.com/paper/2106.04832/) (2021)
- [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022)
- [Implicit Discourse Relation Classification For Nigerian Pidgin](https://mlsanity.com/paper/2406.18776/) (2024)
- [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://mlsanity.com/paper/2204.00350/) (2022)
- [What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?](https://mlsanity.com/paper/2404.00999/) (2024)

## Abstract (arXiv)

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.