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Comparative Evaluation of Argument Extraction Algorithms in Discourse Relation Parsing

Summary

The paper addresses argument span extraction for explicit discourse relations in the Penn Discourse Treebank (PDTB). In this subtask, a parser must mark which tokens belong to Arg1 and Arg2 of a connective. Arg1 may sit in the connective's sentence (intra-sentential, SS) or in an earlier one (inter-sentential, PS). This raises a design question: should one model handle both cases, or should each case get its own model?

The authors start from the method of Ghosh et al. (2011), which labels each token with a CRF and uses a single model over a ±2 sentence window. They compare it with a pipeline that first predicts argument position (SS vs. PS) and then applies dedicated CRF models. For PS relations, this pipeline adds an immediately-previous-sentence heuristic to select the Arg1 candidate.

When position-classifier errors are counted against the separate-model parser, it still significantly beats the single-model parser for both arguments. The gain is largest for inter-sentential Arg1. The results show that splitting the task reduces its complexity. Token-level labeling also performs roughly on par with tree-node and constituent-level parsers.

Key points

  • PDTB statistics: of 18,459 explicit relations, 60.9% are SS, 39.1% are PS, and only 8 have Arg1 in a following sentence. In 44.2% of inter-sentential cases, Arg1 is exactly the whole immediately previous sentence.
  • Predicting SS vs. PS from syntactic category and connective position alone gives 95.0% majority accuracy. The trained argument position classifier reaches 98.12%.
  • On all relations, F1 scores with error propagation are:
    • single ±2 window parser (W5P): Arg2 77.79, Arg1 53.40
    • separate CRF models (SMP): Arg2 84.67, Arg1 58.25
    • separate models with heuristics (hSMP): Arg2 83.06, Arg1 56.71
  • On PS relations, Arg1 F1 improves from 29.01 (W5P) to 40.86 (SMP). Compared with the heuristics, the CRFs help significantly for Arg2 but not significantly for Arg1.
  • With training on sections 02-21 and testing on section 23, SMP scores Arg2/Arg1 F1 of 82.35/57.26. Lin et al. (2012) report 82.23/59.15 and Xu et al. (2012) report 81.00/60.69, though the authors consider this comparison not definitive.

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

Topics: Discourse analysis

Cited by

6 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 2 not yet.

Cite

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

@inproceedings{stepanov2013comparative,
  title = {{Comparative Evaluation of Argument Extraction Algorithms in Discourse Relation Parsing}},
  author = {Stepanov, Evgeny and Riccardi, Giuseppe},
  booktitle = {Proceedings of the 13th International Conference on Parsing Technologies (IWPT 2013)},
  year = {2013},
  url = {https://aclanthology.org/W13-5704/}
}

References

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  3. Robert Elwell and Jason Baldridge. 2008. Discourse connective argument identification with connective specific rankers. In Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2008). find
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