---
title: "Neural End-to-End Learning for Computational — Eger 2017"
description: "The paper addresses end-to-end computational argumentation mining (AM): starting from raw text, a system must find argumentative spans, label them…"
authors: ["Steffen Eger", "Johannes Daxenberger", "Iryna Gurevych"]
year: 2017
venue: "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P17-1002/"
doi: "10.18653/v1/p17-1002"
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/eger-2017-neural-end-end-learning-computational/"
---
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# Neural End-to-End Learning for Computational Argumentation Mining

**Authors:** [Steffen Eger](https://md.research.elamur.ai/free/en/authors/steffen-eger/), [Johannes Daxenberger](https://md.research.elamur.ai/free/en/authors/johannes-daxenberger/), [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) · **Year:** 2017 · **Venue:** Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P17-1002/) · [PDF](https://aclanthology.org/P17-1002.pdf) · [DOI 10.18653/v1/p17-1002](https://doi.org/10.18653/v1/p17-1002) · [OpenAlex](https://openalex.org/W2609776793)

**Citations:** 167 citations (openalex-snapshot · 2026-10-14) · [96](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses end-to-end computational argumentation mining (AM): starting from raw text, a system must find argumentative spans, label them (premise, claim, major claim), link them, and classify the links (support/attack, for/against). Earlier end-to-end systems chained separately trained classifiers built on hand-crafted features and then imposed global consistency through an ILP model. That approach propagates errors and must be re-engineered for every new annotation scheme. The authors instead test neural, token-level framings on the persuasive essays (PE) corpus of Stab and Gurevych (2017):

- dependency parsing,
- BiLSTM sequence tagging with a combined label that encodes component distance,
- multi-task tagging with AM subtasks as auxiliary objectives,
- the LSTM-ER entity/relation model of Miwa and Bansal (2016).

Parsing performed poorly and was unstable. The BiLSTM taggers were robust, and multi-task learning helped. Several neural models beat the feature-based ILP state of the art without any feature or constraint engineering. The result matters because it shows that AM can be learned jointly and adapted more cheaply to new argumentation theories.

## Key points
- **Data.** The PE corpus has 402 essays, of which 80 are held out for testing. It contains 751 major claims, 1506 claims, 3832 premises and 5338 relations, and more than 90% of the relations are supporting.
- **Essay vs. paragraph level.** Paragraphs average 66 tokens and essays 368. Predicting at paragraph level is therefore much easier, because there are fewer candidate components to link.
- **STag labels.** Each token in the STag framing receives a four-part label: a BIO tag, the component type, the distance (counted in components) to the linked component, and the relation type. The taggers tested were BL, BLC and BLCC (BiLSTM-CRF-CNN).
- **Stability.** Measured by C-F1 across runs, STag BLCC scored 60.62±3.54 on essays and 64.74±1.97 on paragraphs. The LSTM-Parser reached only 9.40±13.57 on essays and often failed to learn at all; it scored 56.24±2.87 on paragraphs.
- **Segmentation.** On BIO segmentation, STag BLCC and LSTM-ER both scored around 90% F1. This is above the ILP system (86.67) and above the reported human upper bound (88.6).

## Interesting ideas
- **Relations folded into tag labels.** Writing the relative distance to the linked unit, plus the relation type, into each token's tag turns a structure-prediction task into ordinary tagging. This trick could carry over to other span-linking problems, such as coreference-like or discourse links in Ukrainian texts.
- **Lossless re-linking into a tree.** A structure that is almost a tree can be converted reversibly: claims are re-attached to a single major claim and a synthetic root is added. Tree-based tools can then be applied without losing information.
- **Natural subtasks as auxiliary tasks.** Adding subtasks such as component identification or relation identification as auxiliary tasks improved the main tagger. This is a cheap way to obtain the benefits of joint modeling while still treating the subtasks separately.
- **Matching model capacity to data and document length.** The authors recommend constrained local taggers for long documents with many entities, and more general joint models like LSTM-ER for short texts. They also suggest reporting variance across random seeds, since this exposed how fragile the parsers were.
- **Diagnostics beyond the headline score.** Two analyses were especially revealing. One measured relation accuracy only in cases where both linked components were predicted correctly. The other evaluated segmentation alone against the human upper bound. Together they show where each model actually loses points.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

96 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 92 not yet.

- [Argument Mining: A Survey](https://md.research.elamur.ai/free/en/papers/lawrence-2019-argument-mining-survey/)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://md.research.elamur.ai/free/en/papers/reimers-2019-classification-clustering-arguments-contextualized-word/)
- [Cross-topic Argument Mining from Heterogeneous Sources](https://md.research.elamur.ai/free/en/papers/stab-2018-cross-topic-argument-mining-heterogeneous/)
- [TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic](https://md.research.elamur.ai/free/en/papers/verma-2026-ttlab-daleel-2026-star-ar/)

## Cite

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

```bibtex
@inproceedings{eger2017neural,
  title = {{Neural End-to-End Learning for Computational Argumentation Mining}},
  author = {Eger, Steffen and Daxenberger, Johannes and Gurevych, Iryna},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-1002},
  url = {https://doi.org/10.18653/v1/p17-1002}
}
```

- [← previous Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/)
- [next → Neural Machine Translation with Extended Context](https://md.research.elamur.ai/free/en/papers/tiedemann-2017-neural-machine-translation-extended-context/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/3/)

## References

44 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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