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# Joachim Bingel

3 paper cards in Mova's library · 2017–2018

Joachim Bingel wrote 3 of the papers Mova reads, in 2017–2018, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with [Anders Søgaard](https://md.mova.elamur.ai/free/en/docs/papers/authors/anders-sogaard/), [Marcel Bollmann](https://md.mova.elamur.ai/free/en/docs/papers/authors/marcel-bollmann/), Carolina Scarton.

[OpenAlex](https://openalex.org/A5028729586) · [ORCID](https://orcid.org/0000-0002-4113-3390) · [**h-index 16**](https://openalex.org/authors/A5028729586) · [1296 citations](https://openalex.org/authors/A5028729586) · [33 works](https://openalex.org/works?filter=author.id:A5028729586) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Joachim+Bingel) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Joachim+Bingel) · [dblp](https://dblp.org/search?q=Joachim+Bingel) · [ACL Anthology](https://aclanthology.org/search/?q=Joachim+Bingel) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Joachim+Bingel)

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 1

## Papers

1. **[Multi-task learning for historical text normalization: Size matters](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2018-bollmann-multi-task-learning-historical-text/)** *(2018)* The paper asks when multi-task learning helps historical normalisation. It tests an encoder-decoder model with and without auxiliary tasks on 10 datasets from 8 languages, from German and English to Hungarian, Icelandic, Slovene, Spanish, Portuguese and Swedish. Most datasets either always gain or always lose from…
2. **[Learning attention for historical text normalization by learning to pronounce](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-bollmann-learning-attention-historical-text-normalization/)** *(2017)* The paper treats historical normalisation as a character-level transduction with an encoder-decoder RNN, so no explicit character alignment is needed.
3. **[Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs](https://md.mova.elamur.ai/free/en/docs/papers/manchego-2017-learning-how-simplify-explicit-labeling/)** *(2017)* End-to-end simplification models learn from parallel sentence pairs and cannot say what they learned. The authors instead find the simplification operations (deletion, replacement, addition, reordering and others) automatically in a parallel corpus, by aligning complex and simple words, and label each source token…

## Co-authors (6)

- [Anders Søgaard](https://md.mova.elamur.ai/free/en/docs/papers/authors/anders-sogaard/) ×2
- [Marcel Bollmann](https://md.mova.elamur.ai/free/en/docs/papers/authors/marcel-bollmann/) ×2
- Carolina Scarton
- Fernando Alva-Manchego
- Gustavo Paetzold
- Lucia Specia
- [← previous Jin Zhao](https://md.mova.elamur.ai/free/en/docs/papers/authors/jin-zhao/)
- [next → Joachim Wagner](https://md.mova.elamur.ai/free/en/docs/papers/authors/joachim-wagner/)
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