---
title: "On the Information Conveyed by Discourse Markers — Asr 2013"
description: "The paper asks how much a discourse connective tells a reader about the relation between two text spans."
authors: ["Fatemeh Torabi Asr", "Vera Demberg"]
year: 2013
venue: "Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)"
source: "https://aclanthology.org/W13-2610/"
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/asr-2013-information-conveyed-discourse-markers/"
---
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# On the Information Conveyed by Discourse Markers

**Authors:** [Fatemeh Torabi Asr](https://md.research.elamur.ai/free/en/authors/fatemeh-torabi-asr/), [Vera Demberg](https://md.research.elamur.ai/free/en/authors/vera-demberg/) · **Year:** 2013 · **Venue:** Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · **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/W13-2610/) · [PDF](https://aclanthology.org/W13-2610.pdf) · [OpenAlex](https://openalex.org/W2113575228)

**Citations:** 24 citations (openalex-snapshot · 2026-10-14) · [14](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks how much a discourse connective tells a reader about the relation between two text spans. It frames this as a first step towards a surprisal-style model of discourse processing grounded in the Uniform Information Density (UID) hypothesis. Using only explicit relations in the Penn Discourse Treebank (PDTB), the authors run three analyses.

First, they define a markedness score based on normalized pointwise mutual information between relations and connectives. They use it to test whether relations that readers expect by default are signalled more ambiguously. Second, they sort out distinct kinds of ambiguity: connectives with several senses, and cases where two relations hold at once. Third, they propose an "enhancement" measure that splits a connective's information gain across the levels of the PDTB sense hierarchy.

The results fit the continuity and causality-by-default hypotheses: hard-to-predict contrastive relations get unambiguous markers, while expected ones tolerate vague markers. The work matters for psycholinguistic modelling and for automatic discourse parsing. It also raises the question of whether PDTB sense distinctions match what humans actually infer.

## Key points
- Data: explicit PDTB relations, with double-labelled connectives counted once per sense, giving 19,458 relation instances. Level-1 counts are Temporal 3,696, Contingency 3,741, Expansion 6,431 and Comparison 5,590.
- Markedness is computed by rescaling base-2 npmi to [0, 1] and weighting it by p(connective | relation).
- COMPARISON relations show the highest markedness and CONTINGENCY lower markedness. TEMPORAL relations are surprisingly low because their connectives also often mark CONTINGENCY.
- Ambiguity patterns differ by connective:
  - Polysemous connectives (while, since, when) add a little information at every hierarchy level.
  - Weak connectives (and, but, if) say almost nothing about subtypes.
  - Meanwhile, which marks inherently co-occurring relations, gives low gain everywhere.
- Summed over the three levels, the enhancement scores equal the total relative entropy between the relation distributions before and after the connective. Adversatives mainly help at level 1, while temporally directional connectives help most at deeper levels.

*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

14 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 10 not yet.

- [Factors Influencing the Implicitation of Discourse Relations across Languages](https://md.research.elamur.ai/free/en/papers/hoek-2015-factors-influencing-implicitation-discourse-relations/)
- [Reflections on the Penn Discourse TreeBank, Comparable Corpora, and Complementary Annotation](https://md.research.elamur.ai/free/en/papers/prasad-2014-reflections-penn-discourse-treebank-comparable/)
- [Examples and Specifications that Prove a Point: Identifying Elaborative and Argumentative Discourse Relations](https://md.research.elamur.ai/free/en/papers/scholman-2017-examples-specifications-that-prove-point/)
- [Modelling the Usage of Discourse Connectives as Rational Speech Acts](https://md.research.elamur.ai/free/en/papers/yung-2016-modelling-usage-discourse-connectives-as/)

## Cite

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

```bibtex
@inproceedings{asr2013information,
  title = {{On the Information Conveyed by Discourse Markers}},
  author = {Asr, Fatemeh Torabi and Demberg, Vera},
  booktitle = {Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)},
  year = {2013},
  url = {https://aclanthology.org/W13-2610/}
}
```

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## References

33 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Asher, N. and Lascarides, A. (1998). Bridging. Journal of Semantics, 15(1):83–113. [find](https://scholar.google.com/scholar?q=Asher%2C+N%2E+and+Lascarides%2C+A%2E+%281998%29%2E+Bridging%2E+Journal+of+Semantics%2C+15%281%29%3A83%E2%80%93113%2E)
2. Asr, F. T. and Demberg, V. (2012a). Implicitness of discourse relations. In Proceedings of COLING, Mumbai, India. [find](https://scholar.google.com/scholar?q=Asr%2C+F%2E+T%2E+and+Demberg%2C+V%2E+%282012a%29%2E+Implicitness+of+discourse+relations%2E+In+Proceedings+of+COLING%2C+Mumbai%2C+India%2E) [our card](https://md.research.elamur.ai/free/en/papers/asr-2012-implicitness-discourse-relations/)
3. Asr, F. T. and Demberg, V. (2012b). Measuring the strength of the discourse cues. In workshop on the Advances in Discourse Analysis and its Computational Aspects, Mumbai, India. [find](https://scholar.google.com/scholar?q=Asr%2C+F%2E+T%2E+and+Demberg%2C+V%2E+%282012b%29%2E+Measuring+the+strength+of+the+discourse+cues%2E+In+workshop+on+the+Advances+in+Discourse+Analysis+and+its+Computational+Aspects%2C+Mumbai%2C+India%2E)
4. Ben-Anath, D. (2006). The role of connectives in text comprehension. Teachers College, Columbia University Working Papers in TESOL & Applied Linguistics, 5(2). [find](https://scholar.google.com/scholar?q=Ben%2DAnath%2C+D%2E+%282006%29%2E+The+role+of+connectives+in+text+comprehension%2E+Teachers+College%2C+Columbia+University+Working+Papers+in+TESOL+%26+Applied+Linguistics%2C+5%282%29%2E)
5. Blakemore, D. (1992). Understanding utterances: An introduction to pragmatics. Blackwell Oxford. [find](https://scholar.google.com/scholar?q=Blakemore%2C+D%2E+%281992%29%2E+Understanding+utterances%3A+An+introduction+to+pragmatics%2E+Blackwell+Oxford%2E)
6. Blass, R. (1993). Are there logical relations in a text? Lingua, 90(1-2):91–110. [find](https://scholar.google.com/scholar?q=Blass%2C+R%2E+%281993%29%2E+Are+there+logical+relations+in+a+text%3F+Lingua%2C+90%281%2D2%29%3A91%E2%80%93110%2E)
7. Caron, J., Micko, H. C., and Thuring, M. (1988). Conjunctions and the recall of composite sentences. Journal of Memory and Language, 27(3):309–323. [find](https://scholar.google.com/scholar?q=Caron%2C+J%2E%2C+Micko%2C+H%2E+C%2E%2C+and+Thuring%2C+M%2E+%281988%29%2E+Conjunctions+and+the+recall+of+composite+sentences%2E+Journal+of+Memory+and+Language%2C+27%283%29%3A309%E2%80%93323%2E)
8. Degand, L. and Sanders, T. (2002). The impact of relational markers on expository text comprehension in l1 and l2. Reading and Writing, 15(7):739–757. [find](https://scholar.google.com/scholar?q=Degand%2C+L%2E+and+Sanders%2C+T%2E+%282002%29%2E+The+impact+of+relational+markers+on+expository+text+comprehension+in+l1+and+l2%2E+Reading+and+Writing%2C+15%287%29%3A739%E2%80%93757%2E)
9. Demberg, V. and Keller, F. (2008). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109(2):193–210. [find](https://scholar.google.com/scholar?q=Demberg%2C+V%2E+and+Keller%2C+F%2E+%282008%29%2E+Data+from+eye%2Dtracking+corpora+as+evidence+for+theories+of+syntactic+processing+complexity%2E+Cognition%2C+109%282%29%3A193%E2%80%93210%2E)
10. Frank, A. and Jaeger, T. (2008). Speaking rationally: Uniform information density as an optimal strategy for language production. Proceedings of the 28th meeting of the Cognitive Science Society. [find](https://scholar.google.com/scholar?q=Frank%2C+A%2E+and+Jaeger%2C+T%2E+%282008%29%2E+Speaking+rationally%3A+Uniform+information+density+as+an+optimal+strategy+for+language+production%2E+Proceedings+of+the+28th+meeting+of+the+Cognitive+Science+Society%2E)
11. Frank, S. (2009). Surprisal-based comparison between a symbolic and a connectionist model of sentence processing. In Proceedings of the 31st annual conference of the cognitive science society, pages 1139–1144. [find](https://scholar.google.com/scholar?q=Frank%2C+S%2E+%282009%29%2E+Surprisal%2Dbased+comparison+between+a+symbolic+and+a+connectionist+model+of+sentence+processing%2E+In+Proceedings+of+the+31st+annual+conference+of+the+cognitive+science+society%2C+pages+1)
12. Hale, J. (2001). A probabilistic earley parser as a psycholinguistic model. In Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies 2001, pages 1–8. [find](https://scholar.google.com/scholar?q=Hale%2C+J%2E+%282001%29%2E+A+probabilistic+earley+parser+as+a+psycholinguistic+model%2E+In+Second+meeting+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics+on+Language+technologies+20) [our card](https://md.research.elamur.ai/free/en/papers/hale-2001-probabilistic-earley-parser-as-psycholinguistic/)
13. Halliday, M. and Hasan, R. (1976). Cohesion in English. Longman (London). [find](https://scholar.google.com/scholar?q=Halliday%2C+M%2E+and+Hasan%2C+R%2E+%281976%29%2E+Cohesion+in+English%2E+Longman+%28London%29%2E)
14. Hernault, H., Bollegala, D., and Ishizuka, M. (2011). Semi-supervised discourse relation classification with structural learning. Computational Linguistics and Intelligent Text Processing, pages 340–352. [find](https://scholar.google.com/scholar?q=Hernault%2C+H%2E%2C+Bollegala%2C+D%2E%2C+and+Ishizuka%2C+M%2E+%282011%29%2E+Semi%2Dsupervised+discourse+relation+classification+with+structural+learning%2E+Computational+Linguistics+and+Intelligent+Text+Processing%2C+pages+340%E2%80%933)
15. Hobbs, J. R. (1979). Coherence and coreference. Cognitive science, 3(1):67–90. [find](https://scholar.google.com/scholar?q=Hobbs%2C+J%2E+R%2E+%281979%29%2E+Coherence+and+coreference%2E+Cognitive+science%2C+3%281%29%3A67%E2%80%9390%2E)
16. Kehler, A., Kertz, L., Rohde, H., and Elman, J. L. (2008). Coherence and coreference revisited. Journal of Semantics, 25(1):1–44. [find](https://scholar.google.com/scholar?q=Kehler%2C+A%2E%2C+Kertz%2C+L%2E%2C+Rohde%2C+H%2E%2C+and+Elman%2C+J%2E+L%2E+%282008%29%2E+Coherence+and+coreference+revisited%2E+Journal+of+Semantics%2C+25%281%29%3A1%E2%80%9344%2E)
17. Köhne, J. and Demberg, V. (2013). The time-course of processing discourse connectives. In Proceedings of the 35th Annual Meeting of the Cognitive Science Society. [find](https://scholar.google.com/scholar?q=Ko%CC%88hne%2C+J%2E+and+Demberg%2C+V%2E+%282013%29%2E+The+time%2Dcourse+of+processing+discourse+connectives%2E+In+Proceedings+of+the+35th+Annual+Meeting+of+the+Cognitive+Science+Society%2E)
18. Kuperberg, G., Paczynski, M., and Ditman, T. (2011). Establishing causal coherence across sentences: An ERP study. Journal of Cognitive Neuroscience, 23(5):1230–1246. [find](https://scholar.google.com/scholar?q=Kuperberg%2C+G%2E%2C+Paczynski%2C+M%2E%2C+and+Ditman%2C+T%2E+%282011%29%2E+Establishing+causal+coherence+across+sentences%3A+An+ERP+study%2E+Journal+of+Cognitive+Neuroscience%2C+23%285%29%3A1230%E2%80%931246%2E)
19. Levinson, S. (2000). Presumptive Meanings: The Theory of Generalized Conversational Implicature. The MIT Press. [find](https://scholar.google.com/scholar?q=Levinson%2C+S%2E+%282000%29%2E+Presumptive+Meanings%3A+The+Theory+of+Generalized+Conversational+Implicature%2E+The+MIT+Press%2E)
20. Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3):1126– 1177. [find](https://scholar.google.com/scholar?q=Levy%2C+R%2E+%282008%29%2E+Expectation%2Dbased+syntactic+comprehension%2E+Cognition%2C+106%283%29%3A1126%E2%80%93+1177%2E)
21. Levy, R. and Jaeger, T. F. (2007). Speakers optimize information density through syntactic reduction. In Advances in Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Levy%2C+R%2E+and+Jaeger%2C+T%2E+F%2E+%282007%29%2E+Speakers+optimize+information+density+through+syntactic+reduction%2E+In+Advances+in+Neural+Information+Processing+Systems%2E)
22. Lin, Z., Ng, H., and Kan, M. (2011). Automatically evaluating text coherence using discourse relations. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 997–1006. [find](https://scholar.google.com/scholar?q=Lin%2C+Z%2E%2C+Ng%2C+H%2E%2C+and+Kan%2C+M%2E+%282011%29%2E+Automatically+evaluating+text+coherence+using+discourse+relations%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%3A+Huma) [our card](https://md.research.elamur.ai/free/en/papers/lin-2011-automatically-evaluating-text-coherence-discourse/)
23. Millis, K. and Just, M. (1994). The influence of connectives on sentence comprehension. Journal of Memory and Language. [find](https://scholar.google.com/scholar?q=Millis%2C+K%2E+and+Just%2C+M%2E+%281994%29%2E+The+influence+of+connectives+on+sentence+comprehension%2E+Journal+of+Memory+and+Language%2E)
24. Mitchell, J., Lapata, M., Demberg, V., and Keller, F. (2010). Syntactic and semantic factors in processing difficulty: An integrated measure. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 196–206. [find](https://scholar.google.com/scholar?q=Mitchell%2C+J%2E%2C+Lapata%2C+M%2E%2C+Demberg%2C+V%2E%2C+and+Keller%2C+F%2E+%282010%29%2E+Syntactic+and+semantic+factors+in+processing+difficulty%3A+An+integrated+measure%2E+In+Proceedings+of+the+48th+Annual+Meeting+of+the+Associati)
25. Murray, J. (1995). Logical connectives and local coherence. Sources of Coherence in Reading, pages 107–125. [find](https://scholar.google.com/scholar?q=Murray%2C+J%2E+%281995%29%2E+Logical+connectives+and+local+coherence%2E+Sources+of+Coherence+in+Reading%2C+pages+107%E2%80%93125%2E)
26. Murray, J. (1997). Connectives and narrative text: The role of continuity. Memory and Cognition, 25(2):227–236. [find](https://scholar.google.com/scholar?q=Murray%2C+J%2E+%281997%29%2E+Connectives+and+narrative+text%3A+The+role+of+continuity%2E+Memory+and+Cognition%2C+25%282%29%3A227%E2%80%93236%2E)
27. Park, J. and Cardie, C. (2012). Improving implicit discourse relation recognition through feature set optimization. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 108–112. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Park%2C+J%2E+and+Cardie%2C+C%2E+%282012%29%2E+Improving+implicit+discourse+relation+recognition+through+feature+set+optimization%2E+In+Proceedings+of+the+13th+Annual+Meeting+of+the+Special+Interest+Group+on+Discourse) [our card](https://md.research.elamur.ai/free/en/papers/park-2012-improving-implicit-discourse-relation-recognition/)
28. Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A., and Webber, B. (2008). The Penn Discourse Treebank 2.0. In Proceedings of the 6th International Conference on Language Resources and Evaluation, pages 2961–2968. [find](https://scholar.google.com/scholar?q=Prasad%2C+R%2E%2C+Dinesh%2C+N%2E%2C+Lee%2C+A%2E%2C+Miltsakaki%2C+E%2E%2C+Robaldo%2C+L%2E%2C+Joshi%2C+A%2E%2C+and+Webber%2C+B%2E+%282008%29%2E+The+Penn+Discourse+Treebank+2%2E0%2E+In+Proceedings+of+the+6th+International+Conference+on+Language+Resource) [our card](https://md.research.elamur.ai/free/en/papers/disc-2008-prasad-penn-discourse-treebank-2-0/)
29. Roark, B., Bachrach, A., Cardenas, C., and Pallier, C. (2009). Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 324–333, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Roark%2C+B%2E%2C+Bachrach%2C+A%2E%2C+Cardenas%2C+C%2E%2C+and+Pallier%2C+C%2E+%282009%29%2E+Deriving+lexical+and+syntactic+expectation%2Dbased+measures+for+psycholinguistic+modeling+via+incremental+top%2Ddown+parsing%2E+In+Proceedings+)
30. Sanders, T. (2005). Coherence, causality and cognitive complexity in discourse. In Proceedings/Actes SEM-05, First International Symposium on the Exploration and Modelling of Meaning, pages 105–114. [find](https://scholar.google.com/scholar?q=Sanders%2C+T%2E+%282005%29%2E+Coherence%2C+causality+and+cognitive+complexity+in+discourse%2E+In+Proceedings%2FActes+SEM%2D05%2C+First+International+Symposium+on+the+Exploration+and+Modelling+of+Meaning%2C+pages+105%E2%80%93114%2E)
31. Segal, E., Duchan, J., and Scott, P. (1991). The role of interclausal connectives in narrative structuring: Evidence from adults’ interpretations of simple stories. Discourse Processes, 14(1):27–54. [find](https://scholar.google.com/scholar?q=Segal%2C+E%2E%2C+Duchan%2C+J%2E%2C+and+Scott%2C+P%2E+%281991%29%2E+The+role+of+interclausal+connectives+in+narrative+structuring%3A+Evidence+from+adults%E2%80%99+interpretations+of+simple+stories%2E+Discourse+Processes%2C+14%281%29%3A27%E2%80%9354%2E)
32. Versley, Y. (2011). Towards finer-grained tagging of discourse connectives. In Proceedings of the Workshop Beyound Semantics: Corpus-based Investigations of Pragmatic and Discourse Phenomena. [find](https://scholar.google.com/scholar?q=Versley%2C+Y%2E+%282011%29%2E+Towards+finer%2Dgrained+tagging+of+discourse+connectives%2E+In+Proceedings+of+the+Workshop+Beyound+Semantics%3A+Corpus%2Dbased+Investigations+of+Pragmatic+and+Discourse+Phenomena%2E)
33. Webber, B. (2013). What excludes an alternative in coherence relations? In Proceedings of the IWCS. [find](https://scholar.google.com/scholar?q=Webber%2C+B%2E+%282013%29%2E+What+excludes+an+alternative+in+coherence+relations%3F+In+Proceedings+of+the+IWCS%2E) [our card](https://md.research.elamur.ai/free/en/papers/webber-2013-what-excludes-alternative-coherence-relations/)
