---
title: "Improving the Inference of Implicit — Rutherford 2015"
description: "Implicit discourse relations, which lack an overt connective, are the weakest part of PDTB-style discourse parsing because annotated training data is…"
authors: ["Attapol Rutherford", "Nianwen Xue"]
year: 2015
venue: "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/N15-1081/"
doi: "10.3115/v1/n15-1081"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rutherford-2015-improving-inference-implicit-discourse-relations/"
---
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# Improving the Inference of Implicit Discourse Relations via Classifying Explicit Discourse Connectives

**Authors:** [Attapol Rutherford](https://md.research.elamur.ai/free/en/authors/attapol-rutherford/), [Nianwen Xue](https://md.research.elamur.ai/free/en/authors/nianwen-xue/) · **Year:** 2015 · **Venue:** Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/N15-1081/) · [PDF](https://aclanthology.org/N15-1081.pdf) · [DOI 10.3115/v1/n15-1081](https://doi.org/10.3115/v1/n15-1081) · [OpenAlex](https://openalex.org/W2294607529)

**Citations:** 101 citations (openalex-snapshot · 2026-10-14) · [41](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Implicit discourse relations, which lack an overt connective, are the weakest part of PDTB-style discourse parsing because annotated training data is scarce. One remedy is to harvest explicit relations from raw text, remove the connective, and treat the result as extra implicit examples. Earlier attempts at this failed because many connectives change the meaning of a relation when removed. Rutherford and Xue address this by sorting connectives into classes using two statistics computed on the Penn Discourse Treebank 2.0. The first is the omission rate, meaning how often a connective appears as implicit. The second is the context differential, the Jensen-Shannon Divergence between word distributions of a connective's arguments in explicit and implicit uses. They then extract weakly labeled instances from English Gigaword only for "freely omissible" connectives. Adding these instances improves classification of the four top-level PDTB senses over a strong baseline without any new features, while other connective classes do not help. The work shows that distant supervision for implicit relations is viable when the data is filtered carefully.

## Key points
- **Data:** PDTB 2.0 is used to compute the selection statistics. English Gigaword v3 (about 2.9 billion words of newswire, 1994–2006) supplies the weakly labeled data.
- **Extraction:** The authors used the NLTK sentence segmenter, two regular-expression patterns that mirror implicit-relation argument shapes, and filtering rules (for example, excluding "because of"). This yielded more than 200,000 instances.
- **Connective classes:** Connectives were grouped as freely omissible, omissible, alternating I and II, and non-omissible.
- **Results with Gigaword data alone:** Freely omissible connectives reached a mean accuracy of 0.505, higher than any other class or all classes combined (0.490).
- **Results with implicit PDTB data added:** Freely omissible connectives scored 0.571, compared with 0.527–0.554 for the other settings. Gigaword data surpasses the baseline and explicit-PDTB data once roughly 12,000 weakly labeled instances are added. Covering all four senses was not sufficient on its own to improve results; both selection criteria were needed.

*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/en/shelves/linguistics/)

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

## Cited by

Cited by

41 papers of our library cite it (of the 140673 whose bibliographies we read); 26 of them have a page here, 15 not yet.

- [Learning Connective-based Word Representations for Implicit Discourse Relation Identification](https://md.research.elamur.ai/free/en/papers/braud-2016-learning-connective-based-word-representations/)
- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
- [Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph](https://md.research.elamur.ai/free/en/papers/dai-2018-improving-implicit-discourse-relation-classification/)
- [A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing](https://md.research.elamur.ai/free/en/papers/dai-2019-regularization-approach-incorporating-event-knowledge/)
- [CVAE-based Re-anchoring for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/dou-2021-cvae-based-re-anchoring-implicit/)
- [Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/ji-2015-closing-gap-domain-adaptation-explicit/)
- [Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives](https://md.research.elamur.ai/free/en/papers/kishimoto-2020-adapting-bert-implicit-discourse-relation/)
- [Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction](https://md.research.elamur.ai/free/en/papers/kurfal-2021-let-s-be-explicit-about/)
- [Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification](https://md.research.elamur.ai/free/en/papers/lan-2017-multi-task-attention-based-neural/)
- [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://md.research.elamur.ai/free/en/papers/liu-2016-recognizing-implicit-discourse-relations-repeated/)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://md.research.elamur.ai/free/en/papers/long-2022-facilitating-contrastive-learning-discourse-relational/)
- [Implicit Discourse Relation Identification for Open-domain Dialogues](https://md.research.elamur.ai/free/en/papers/ma-2019-implicit-discourse-relation-identification-open/)
- [Automatic Prediction of Discourse Connectives](https://md.research.elamur.ai/free/en/papers/malmi-2018-automatic-prediction-discourse-connectives/)
- [Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings](https://md.research.elamur.ai/free/en/papers/mihaylov-2016-discourse-relation-sense-classification-cross/)
- [An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/omura-2024-empirical-study-synthetic-data-generation/)
- [Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings](https://md.research.elamur.ai/free/en/papers/qin-2016-implicit-discourse-relation-recognition-context/)
- [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/qin-2017-adversarial-connective-exploiting-networks-implicit/)
- [A Systematic Study of Neural Discourse Models for Implicit Discourse Relation](https://md.research.elamur.ai/free/en/papers/rutherford-2017-systematic-study-neural-discourse-models/)
- [On the Need of Cross Validation for Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2017-need-cross-validation-discourse-relation/)
- [Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2019-acquiring-annotated-data-cross-lingual/)
- [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2019-learning-explicitate-connectives-seq2seq-network/)
- [Mining Discourse Markers for Unsupervised Sentence Representation Learning](https://md.research.elamur.ai/free/en/papers/sileo-2019-mining-discourse-markers-unsupervised-sentence/)
- [Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/wang-2023-prompt-based-logical-semantics-enhancement/)
- [Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/wu-2016-bilingually-constrained-synthetic-data-implicit/)
- [Using active learning to expand training data for implicit discourse relation recognition](https://md.research.elamur.ai/free/en/papers/xu-2018-active-learning-expand-training-data/)
- [Variational Neural Discourse Relation Recognizer](https://md.research.elamur.ai/free/en/papers/zhang-2016-variational-neural-discourse-relation-recognizer/)

## Cite

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

```bibtex
@inproceedings{rutherford2015improving,
  title = {{Improving the Inference of Implicit Discourse Relations via Classifying Explicit Discourse Connectives}},
  author = {Rutherford, Attapol and Xue, Nianwen},
  booktitle = {Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2015},
  doi = {10.3115/v1/n15-1081},
  url = {https://doi.org/10.3115/v1/n15-1081}
}
```

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