---
title: "A Neural Approach to Discourse Relation Signal Detection (2020)"
description: "Discourse relations such as CONDITION, CONCESSION or CAUSE are often marked by connectives. 56 references, cited by 5 papers here."
authors: ["Amir Zeldes", "Yang Liu"]
year: 2020
venue: "Dialogue & Discourse"
source: "https://aclanthology.org/2020.dnd-11.5/"
doi: "10.5087/dad.2020.201"
arxiv: "2001.02380"
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/zeldes-2020-neural-approach-discourse-relation-signal/"
---
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# A Neural Approach to Discourse Relation Signal Detection

**Authors:** [Amir Zeldes](https://md.research.elamur.ai/free/en/authors/amir-zeldes/) and [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/) · **Year:** 2020 · **Venue:** [Dialogue & Discourse](https://md.research.elamur.ai/free/en/venues/dnd/) ([Dialogue & Discourse 2020](https://md.research.elamur.ai/free/en/venues/dnd/2020/)) · **Publisher:** [University of Illinois Chicago](https://md.research.elamur.ai/free/en/publishers/university-of-illinois-chicago/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.dnd-11.5/) · [PDF](https://aclanthology.org/2020.dnd-11.5.pdf) · [DOI 10.5087/dad.2020.201](https://doi.org/10.5087/dad.2020.201) · [arXiv 2001.02380](https://arxiv.org/abs/2001.02380) · [OpenAlex](https://openalex.org/W3047327766)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/zeldes-2020-neural-approach-discourse-relation-signal/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Discourse relations such as CONDITION, CONCESSION or CAUSE are often marked by connectives. Corpus-wide frequency or PMI counts give each word type a single score. They cannot tell whether a given occurrence of "and" or "to" actually carries a relation, and they cannot flag words that point the wrong way. Zeldes and Liu address this by training a distantly supervised neural relation classifier, an RNN over contextualized character and word embeddings, on the English GUM corpus, which is annotated with 20 RST relations. The model sees only relation labels and never the signal annotations. To score a token, they mask it and measure how the softmax probability of the gold relation changes. This metric, ∆s ("delta-softmax"), runs from -1 to 1, and negative values mark distractors. They compare the scores with human token-anchored signal annotations using recall@k. The model separates discourse-relevant uses of frequent, ambiguous words from irrelevant ones. It misses relational cues and signals that lie outside the related unit pair. The work provides graded signal strength per instance and a diagnostic of what discourse classifiers still lack.

## Key points
- Data: GUM, a multi-genre English web corpus with 20 RST relations. A subset carries human signal annotations anchored to specific tokens, and the authors propose a taxonomy of anchorable signal types.
- ∆s works like permutation importance at the token level. It is the shift in gold-relation probability when a word is masked, bounded between -1 and 1, and it yields both signals and "distractors."
- Evaluation is framed as information retrieval: recall@k against human anchors. It is complemented by a tokenwise signal-strength visualization and an error analysis of disagreements in both directions.
- ∆s distributions separate uses of the same word:
  - "to" is bimodal, with prepositional uses scoring low and infinitival PURPOSE uses scoring high.
  - Capitalized "If" almost always marks CONDITION, while lowercase "if" sometimes heads unsegmented object clauses such as "see if."
- Limitations found:
  - The model ignores antecedents of pronouns, lexical relations (synonymy, meronymy, antonymy) and count comparisons.
  - It captures only endocentric signals.
  - The many exocentric signals suggest a natural accuracy ceiling for classifying relations from head EDU pairs alone.

## Interesting ideas
- Mask each input token and read off the change in the gold label's probability, giving a signed, per-instance importance score instead of a per-type average.
- Treat negative contributions as first-class "distractors," words whose presence makes the correct label harder to identify.
- Learn which words signal a relation using only relation labels (distant supervision), then validate against a small human-anchored subset with recall@k.
- Plot the per-type distribution of instance scores (boxplots) to expose ambiguity, bimodality and surface cues such as capitalization of "If" vs "if".
- Use the gap between model and human signals as a list of missing information: antecedents, lexical relations and quantity comparisons.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Apply the same masking probe to gparse attachments and labels, so that every parse decision can be explained by the words that supported or undermined it.
- **I2** (answer): Score words in maths word problems and fable questions for distractor strength, to locate misleading phrasing and build harder negative-control test items.
- **I3** (frames): Induce cue inventories for discourse relations and speech acts from relation-labelled text, and check them against a few hand-anchored examples before trusting them.
- **I4** (tokens): Keep case and sentence position as tagger features for connective-like words, and use score distributions to decide which tokens need contextual disambiguation rules.
- **I5** (world): Feed explicit coreference links, WordNet relations and compared measures from the text world into relation and event-order decisions, since a pure neural classifier does not recover them.

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

## Institutions

- [Georgetown University](https://md.research.elamur.ai/free/en/universities/georgetown-university/) — University, Washington, United States · 69 works · 4,417 citations

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🧭 Topics (1): [🧵 Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · 394 works

## Cite

Cite as: Amir Zeldes and Yang Liu. 2020. A Neural Approach to Discourse Relation Signal Detection. Dialogue & Discourse.

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

```bibtex
@inproceedings{zeldes2020neural,
  title = {{A Neural Approach to Discourse Relation Signal Detection}},
  author = {Amir Zeldes and Yang Liu},
  booktitle = {Dialogue \& Discourse},
  year = {2020},
  doi = {10.5087/dad.2020.201},
  eprint = {2001.02380},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.5087/dad.2020.201}
}
```

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*Updated 24 October 2026*