---
title: "Learning to Explicitate Connectives with Seq2Seq — Shi 2019"
description: "Identifying implicit discourse relations, where no connective such as \"because\" or \"but\" signals the link between two text spans, is a major bottleneck…"
authors: ["Wei Shi", "Vera Demberg"]
year: 2019
venue: "Proceedings of the 13th International Conference on Computational Semantics - Long Papers"
source: "https://aclanthology.org/W19-0416/"
doi: "10.18653/v1/w19-0416"
arxiv: "1811.01697"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/shi-2019-learning-explicitate-connectives-seq2seq-network/"
---
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# Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification

**Authors:** [Wei Shi](https://md.research.elamur.ai/free/authors/wei-shi/), [Vera Demberg](https://md.research.elamur.ai/free/authors/vera-demberg/) · **Year:** 2019 · **Venue:** Proceedings of the 13th International Conference on Computational Semantics - Long Papers · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/W19-0416/) · [PDF](https://aclanthology.org/W19-0416.pdf) · [DOI 10.18653/v1/w19-0416](https://doi.org/10.18653/v1/w19-0416) · [arXiv 1811.01697](https://arxiv.org/abs/1811.01697) · [OpenAlex](https://openalex.org/W2963766765)

**Citations:** 26 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Identifying implicit discourse relations, where no connective such as "because" or "but" signals the link between two text spans, is a major bottleneck in discourse parsing. Shi and Demberg tackle this with a model that copies how PDTB annotators worked: annotators first inserted a fitting connective and only then chose a relation label. The model pairs a classifier with a seq2seq network. The network reads [Arg1; Arg2] and learns to output [Arg1; implicit connective; Arg2], using the connectives annotated in PDTB 2.0. Its parts are a bidirectional LSTM encoder over Word2Vec embeddings, a separate LSTM decoder with Luong-style global attention, a gated-interaction layer, and a context memory network that retrieves similar training instances. The classifier combines the original argument encoding, the "explicitated" decoder representation, and the memory-derived knowledge vector. The authors report state-of-the-art results on the standard 11-way PDTB classification, including under cross-validation, and strong results on top-level 4-way and binary tasks. This suggests that generating a connective as an auxiliary task produces more discriminative representations of the arguments.

## Key points
- **Auxiliary task:** reconstruct the arguments with the annotated implicit connective inserted, so the input and output sequences differ. This distinguishes the model from a plain autoencoder and from the adversarial connective approach of Qin et al. (2017).
- **Data:** PDTB 2.0 implicit relations, with 12,345 training, 1,156 development and 1,011 test instances at the top level. Only 55 Temporal instances appear in the test set.
- **Top-level 4-way results:** 61.42% accuracy (the best reported) and 46.40 macro-F1 (second best).
- **One-vs-all binary F1:** Comparison 41.83, Contingency 62.07, Expansion 69.58, Temporal 35.72. The model beats prior systems on Comparison and Contingency.
- **Analysis and outlook:** memory attention tends to select training examples with the same relation. The gains are not explained merely by a larger parameter count. The authors see potential for connective prediction and argument generation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/)

## Cited by

Cited by

10 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 5 not yet.

- [Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives](https://md.research.elamur.ai/free/papers/kishimoto-2020-adapting-bert-implicit-discourse-relation/)
- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://md.research.elamur.ai/free/papers/pyatkin-2023-design-choices-crowdsourcing-implicit-discourse/)
- [DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations](https://md.research.elamur.ai/free/papers/scholman-2022-discogem-crowdsourced-corpus-genre-mixed/)
- [Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/papers/shi-2019-acquiring-annotated-data-cross-lingual/)
- [Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains](https://md.research.elamur.ai/free/papers/shi-2019-next-sentence-prediction-helps-implicit/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{shi2019learning,
  title = {{Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification}},
  author = {Shi, Wei and Demberg, Vera},
  booktitle = {Proceedings of the 13th International Conference on Computational Semantics - Long Papers},
  year = {2019},
  doi = {10.18653/v1/w19-0416},
  eprint = {1811.01697},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/w19-0416}
}
```

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