---
title: "End-to-end Argument Mining with Cross-corpora Multi-task"
description: "Morio et al. propose MT-AM, which learns from several differently annotated corpora at once. 69 references, cited by 19 papers here."
authors: ["Gaku Morio", "Hiroaki Ozaki", "Terufumi Morishita", "Kohsuke Yanai"]
year: 2022
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2022.tacl-1.37/"
doi: "10.1162/tacl_a_00481"
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/morio-2022-end-end-argument-mining-cross/"
---
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# End-to-end Argument Mining with Cross-corpora Multi-task Learning

**Authors:** [Gaku Morio](https://md.research.elamur.ai/free/en/authors/gaku-morio/), Hiroaki Ozaki, [Terufumi Morishita](https://md.research.elamur.ai/free/en/authors/terufumi-morishita/) and Kohsuke Yanai · **Year:** 2022 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2022](https://md.research.elamur.ai/free/en/venues/tacl/2022/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.tacl-1.37/) · [PDF](https://aclanthology.org/2022.tacl-1.37.pdf) · [DOI 10.1162/tacl\_a\_00481](https://doi.org/10.1162/tacl_a_00481) · [OpenAlex](https://openalex.org/W4285233520)

**Citations:** 24 citations (OpenAlex, 2026-10-14) · 19 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Argument mining (AM) recovers the structure of an argument from text. It finds argumentative spans, assigns them types such as Claim or Premise, and links them with labelled relations such as Support or Attack. Annotated AM data is small and costly to produce. Existing corpora also follow incompatible schemes for spans, component labels and relation types, so parsers are usually trained on one limited dataset. Morio et al. propose MT-AM, which learns from several differently annotated corpora at once. Their baseline is a span-biaffine parser over a Longformer encoder that handles all three subtasks jointly. MT-AM first pre-trains this parser on auxiliary corpora together with the target corpus, then fine-tunes it on the target alone. Across five public AM corpora, MT-AM usually beat single-corpus training, and its gains grew as the target training set shrank. Pairwise auxiliary/target experiments showed that the choice of auxiliary corpus matters. The study offers a practical recipe for building argument parsers in domains or languages with little annotated data.

## Key points
- **Corpora:** MTC, AAEC, CDCP, AbstRCT and AASD. They differ in span style (segment-based units vs. selectively extracted spans), label sets and graph structure.
- **Model:** a span-biaffine architecture with biaffine scoring (Dozat and Manning) and pairwise relation classification. Longformer is used to encode long, multi-sentence inputs. Span identification, component classification and relation classification are trained end-to-end.
- **Low-resource tests:** the target data was subsampled to 1%, 10% and 100%. MT-AM helped most when target data was scarce. For example, AbstRCT as an auxiliary corpus clearly improved root-component detection on AASD at 10%.
- **Transferability:** the authors explain the gains with three hypotheses: data/training sufficiency, annotation compatibility and semantic compatibility. They acknowledge these are not independent and remain unproven.
- **Resources:** code is at github.com/hitachi-nlp/graph_parser, and all corpora are publicly available. Published in TACL vol. 10, pp. 639–658 (2022).

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

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## Cite

Cite as: Gaku Morio, Hiroaki Ozaki, Terufumi Morishita and Kohsuke Yanai. 2022. End-to-end Argument Mining with Cross-corpora Multi-task Learning. Transactions of the Association for Computational Linguistics.

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

```bibtex
@article{morio2022end,
  title = {{End-to-end Argument Mining with Cross-corpora Multi-task Learning}},
  author = {Gaku Morio, Hiroaki Ozaki, Terufumi Morishita and Kohsuke Yanai},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2022},
  doi = {10.1162/tacl\_a\_00481},
  url = {https://doi.org/10.1162/tacl_a_00481}
}
```

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## References

69 references, 24 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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*Updated 1 November 2026*