---
title: "Speaker-Aware Discourse Parsing on Multi-Party — Yu 2022"
description: "This paper addresses discourse parsing for conversations with several participants."
authors: ["Nan Yu", "Guohong Fu", "Min Zhang"]
year: 2022
venue: "Proceedings of the 29th International Conference on Computational Linguistics"
source: "https://aclanthology.org/2022.coling-1.477/"
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/yu-2022-speaker-aware-discourse-parsing-multi/"
---
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# Speaker-Aware Discourse Parsing on Multi-Party Dialogues

**Authors:** [Nan Yu](https://md.research.elamur.ai/free/authors/nan-yu/), [Guohong Fu](https://md.research.elamur.ai/free/authors/guohong-fu/), [Min Zhang](https://md.research.elamur.ai/free/authors/min-zhang/) · **Year:** 2022 · **Venue:** Proceedings of the 29th International Conference on Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.coling-1.477/) · [PDF](https://aclanthology.org/2022.coling-1.477.pdf) · [OpenAlex](https://openalex.org/W6929059507)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
This paper addresses discourse parsing for conversations with several participants. The task is to build an SDRT-style dependency structure that links utterances (EDUs) and labels each link with a relation such as QAP or Elab. Earlier neural parsers focus mainly on conversational context. At best, they add a binary cue indicating whether two EDUs share a speaker, which leaves unmodeled how distinct participants interact. The authors make the parser speaker-aware in two ways. First, SSP-BERT gives BERT an extra pre-training stage on sampled EDU pairs, teaching it to guess whether both utterances come from one person. Second, SCIJE embeds each dialogue's speaker sequence, encodes it with a BiGRU, and merges these speaker vectors with node-centric context features (BERT over tokens, BiGRU over EDUs). An encoder–decoder then predicts links and relation labels. On STAC and Molweni, the full model beats all previously reported systems. This suggests that explicit speaker structure is an inexpensive but valuable signal for analyzing dialogue structure.

## Key points
- Evaluation uses two benchmarks, STAC and Molweni, with Link and Link&Rel F-measure. Molweni's discourse portion has 500 dialogues. For a fair comparison, the model of He et al. (2021) was rerun on the discourse-parsing test split.
- Ablations on Molweni: removing SCIJE lowers Link&Rel by roughly 1.7%, and removing SSP-BERT lowers Link by about 0.4%.
- Ablations on STAC: dropping SCIJE costs about 0.4% Link&Rel, and dropping SSP-BERT costs about 1.3% Link&Rel.
- Speaker modeling helps more on Molweni, which the authors attribute to its dialogues involving more distinct speakers. SCIJE improves accuracy specifically on linked EDU pairs from different speakers in both datasets.
- Code is released at github.com/yunan4nlp/SA-DPMD.

*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); 3 of them have a page here, 7 not yet.

- [The DISRPT 2023 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.research.elamur.ai/free/papers/braud-2023-disrpt-2023-shared-task-elementary/)
- [Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition](https://md.research.elamur.ai/free/papers/fan-2023-improving-dialogue-discourse-parsing-reply/)
- [Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing](https://md.research.elamur.ai/free/papers/li-2023-task-aware-self-supervised-framework/)

## Cite

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

```bibtex
@inproceedings{yu2022speaker,
  title = {{Speaker-Aware Discourse Parsing on Multi-Party Dialogues}},
  author = {Yu, Nan and Fu, Guohong and Zhang, Min},
  booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
  year = {2022},
  doi = {10.48448/nmpk-7y25},
  url = {https://doi.org/10.48448/nmpk-7y25}
}
```

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