---
title: "Learning Contextually Informed Representations — Liu 2017"
description: "The paper addresses a trade-off in RST discourse parsing."
authors: ["Yang Liu", "Mirella Lapata"]
year: 2017
venue: "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D17-1133/"
doi: "10.18653/v1/d17-1133"
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/en/papers/liu-2017-learning-contextually-informed-representations-linear/"
---
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# Learning Contextually Informed Representations for Linear-Time Discourse Parsing

**Authors:** [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/), [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2017 · **Venue:** Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D17-1133/) · [PDF](https://aclanthology.org/D17-1133.pdf) · [DOI 10.18653/v1/d17-1133](https://doi.org/10.18653/v1/d17-1133) · [OpenAlex](https://openalex.org/W2759996821)

**Citations:** 28 citations (openalex-snapshot · 2026-10-14) · [16](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses a trade-off in RST discourse parsing. Cubic-time CKY-style parsers reach top accuracy but are slow on long documents. Linear-time parsers are fast but usually depend on local, hand-engineered features. The authors present CIDER, a greedy, bottom-up, two-stage parser. It first builds a discourse tree inside each sentence over EDUs, then joins sentences into a document-level tree. At each stage, two linear-chain CRFs make the decisions: one chooses which adjacent constituents to merge, and the other assigns the rhetorical relation. Unlike standard CRFs, pairwise scores are learned from observations rather than taken from a transition matrix. Constituent features come from LSTMs that read whole sentences and documents, using an "LSTM minus" span method, so they capture wider context and long-distance dependencies. At the document level, recursive neural networks add tree-shaped representations to the flat span representations. On the RST-DT test set, CIDER matches state-of-the-art cubic-time neural parsers and is the most balanced linear-time system. It is also fast and needs only POS tagging as preprocessing. This shows that global context can be obtained without high-order parsing.

## Key points
- Evaluated on the RST Discourse Treebank (RST-DT). CIDER with additional features scores 85.0 (span), 71.1 (nuclearity), and 59.0 (relation). Human agreement is 88.7 / 77.7 / 65.8.
- Among linear-time parsers, CIDER has the best nuclearity score. Its gains are statistically significant (p \< 0.05) over Heilman and Sagae (2015) across all metrics and over Feng and Hirst (2014) for relations.
- CIDER beats the cubic-time Li et al. (2014) on every metric. Against Li et al. (2016), it trails slightly on span and nuclearity (85.8 / 71.7) but leads on relations (58.9).
- Parsing takes about 3.80 s per document, compared with 9.88 s for Feng and Hirst (2014) and 26.00 s for Li et al. (2014). Preprocessing is limited to POS tagging, with no syntactic parser.
- Combining flat LSTM span representations with recursive tree-based ones gives the best results, with tree representations especially helpful for relation labeling.

*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

16 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 10 not yet.

- [Structured Dialogue Discourse Parsing](https://md.research.elamur.ai/free/en/papers/chi-2022-structured-dialogue-discourse-parsing/)
- [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 Distance-Aware Multi-Task Framework for Conversational Discourse Parsing](https://md.research.elamur.ai/free/en/papers/fan-2022-distance-aware-multi-task-framework/)
- [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://md.research.elamur.ai/free/en/papers/he-2021-multi-tasking-dialogue-comprehension-discourse/)
- [EusDisParser: improving an under-resourced discourse parser with cross-lingual data](https://md.research.elamur.ai/free/en/papers/iruskieta-2019-eusdisparser-improving-under-resourced-discourse/)
- [RST Discourse Parsing with Second-Stage EDU-Level Pre-training](https://md.research.elamur.ai/free/en/papers/yu-2022-rst-discourse-parsing-second-stage/)

## Cite

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

```bibtex
@inproceedings{liu2017learning,
  title = {{Learning Contextually Informed Representations for Linear-Time Discourse Parsing}},
  author = {Liu, Yang and Lapata, Mirella},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1133},
  url = {https://doi.org/10.18653/v1/d17-1133}
}
```

- [← previous Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network](https://md.research.elamur.ai/free/en/papers/mishra-2017-learning-cognitive-features-gaze-data/)
- [next → Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/2/)

## References

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