---
title: "Massive Choice, Ample Tasks (MaChAmp) — Goot 2021"
description: "MaChAmp is a toolkit for fine-tuning pre-trained contextualized embeddings on one or many NLP tasks simultaneously, while keeping the setup simple."
authors: ["Rob van der Goot", "Ahmet Üstün", "Alan Ramponi", "Ibrahim Sharaf", "Barbara Plank"]
year: 2021
venue: "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations"
source: "https://aclanthology.org/2021.eacl-demos.22/"
doi: "10.18653/v1/2021.eacl-demos.22"
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/goot-2021-massive-choice-ample-tasks-machamp/"
---
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# Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

**Authors:** [Rob van der Goot](https://md.research.elamur.ai/free/en/authors/rob-van-der-goot/), [Ahmet Üstün](https://md.research.elamur.ai/free/en/authors/ahmet-ustun/), [Alan Ramponi](https://md.research.elamur.ai/free/en/authors/alan-ramponi/), Ibrahim Sharaf, [Barbara Plank](https://md.research.elamur.ai/free/en/authors/barbara-plank/) · **Year:** 2021 · **Venue:** Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.eacl-demos.22/) · [PDF](https://aclanthology.org/2021.eacl-demos.22.pdf) · [DOI 10.18653/v1/2021.eacl-demos.22](https://doi.org/10.18653/v1/2021.eacl-demos.22) · [OpenAlex](https://openalex.org/W3030416750)

**Citations:** 73 citations (openalex-snapshot · 2026-10-14) · [104](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
MaChAmp is a toolkit for fine-tuning pre-trained contextualized embeddings on one or many NLP tasks simultaneously, while keeping the setup simple. It is built on AllenNLP and can load any Hugging Face encoder. A shared encoder feeds separate task-specific decoders. Users describe datasets and tasks in a JSON configuration file. The toolkit covers many task types:

- tagging, lemmatization, BIO tagging and multi-label tagging,
- classification,
- dependency parsing,
- masked language modeling,
- seq2seq generation.

The authors chose the default hyperparameters through a grid search over GLUE and EWT using mBERT. They then checked that single-task models are close to published results, which holds for UD but less so for GLUE and machine translation. Finally, they compared several multi-dataset strategies on all UD treebanks and GLUE. Joint training helps when the datasets share the same tasks, as in UD, but not when the tasks differ, as in GLUE. Smoothing how often each dataset is sampled is the most consistently useful option. The work matters because it lowers the effort needed to run multi-task and multilingual experiments and makes them easier to reproduce.

## Key points
- **Supported task types:** seq, string2string, seq_bio (CRF decoder), multiseq, dependency (deep biaffine parser with Chu-Liu/Edmonds decoding), mlm, classification and seq2seq (RNN decoder with attention).
- **Masked language modeling:** follows the BERT setup. 15% of tokens are selected; of these, 80% become [MASK], 10% become a random token and 10% stay unchanged. Next sentence prediction is left out.
- **Hyperparameter defaults:** tuned on the GLUE classification tasks and EWT 2.6 with mBERT. Training sets were capped at 20,000 sentences.
- **Average UD LAS by setup:**

  | Setup | UD LAS |
  |---|---|
  | Single | 72.22 |
  | All | 72.82 |
  | Smoothed (α = 0.5) | 73.74 |
  | Separate decoders | 73.69 |

- **Average GLUE accuracy by setup:**

  | Setup | GLUE accuracy |
  |---|---|
  | Single | 82.38 |
  | All | 80.96 |
  | Smoothed | 81.87 |

  Multi-task training only helped RTE among the GLUE tasks.
- **Effect by treebank size:** the largest gain is for treebanks under 1k sentences, where LAS rises from 15.1 to 45.4 with smoothing. For treebanks over 10k sentences the change is marginal (80.1 → about 81.0).

## Interesting ideas
- **One dataset per batch, with selective loss.** Each batch draws from a single dataset, and only the losses of tasks present in that batch are computed. This lets corpora with different annotation layers be mixed without filling in missing labels.
- **Tunable sampling smoothing (α).** A parameter controls how much small datasets are upsampled relative to large ones. In these experiments it lifted low-resource treebanks a lot, and it would suit any collection of very unevenly sized corpora.
- **Dataset or language embeddings.** Telling the model which dataset an instance comes from can help on top of contextual encoders, especially for medium-sized data. It is a cheap way to handle domain or genre variation among treebanks of one language.
- **Picking a proxy model for languages with no training data.** For zero-shot treebanks, the source parser was chosen by word overlap between the first 10 test sentences and each candidate training set. This simple selection rule can be reused wherever no target-language training data exists.
- **Predicting edit rules instead of labels.** For lemmatization, the model predicts a token-to-lemma transformation rather than the lemma itself, which shrinks the label vocabulary sharply. This should help morphologically rich languages.

*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: [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/)

## Cited by

Cited by

104 papers of our library cite it (of the 140884 whose bibliographies we read); 8 of them have a page here, 96 not yet.

- [Cross-Genre Learning for Old English Poetry POS Tagging](https://md.research.elamur.ai/free/en/papers/aclbulk-2025-miani-cross-genre-learning-old-english/)
- [SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://md.research.elamur.ai/free/en/papers/farha-2022-semeval-2022-task-6-isarcasmeval/)
- [Cross-Lingual and Cross-Domain Transfer Learning for POS Tagging in Historical Germanic Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/miani-2026-cross-lingual-cross-domain-transfer/)
- [MesoTree: Annotated Linguistic Resources for Quantitative Comparative Linguistic Analysis and NLP in Mesoamerica](https://md.research.elamur.ai/free/en/papers/pugh-2026-mesotree-annotated-linguistic-resources-quantitative/)
- [ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution](https://md.research.elamur.ai/free/en/papers/straka-2023-fal-corpipe-crac-2023-larger/)
- [UppsalaNLP at EvaLatin 2026: Multilingual parsing for Latin](https://md.research.elamur.ai/free/en/papers/stymne-2026-uppsalanlp-evalatin-2026-multilingual-parsing/)
- [Data-driven Parsing Evaluation for Child-Parent Interactions](https://md.research.elamur.ai/free/en/papers/ud-2023-liu-data-driven-parsing-evaluation-child/)
- [CAIT: A Syntactic Parsing Toolkit for Child–Adult InTeractions](https://md.research.elamur.ai/free/en/papers/ud-2026-padovani-cait-syntactic-parsing-toolkit-child/)

## Cite

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

```bibtex
@inproceedings{goot2021massive,
  title = {{Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP}},
  author = {Goot, Rob van der and Üstün, Ahmet and Ramponi, Alan and Sharaf, Ibrahim and Plank, Barbara},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-demos.22},
  url = {https://doi.org/10.18653/v1/2021.eacl-demos.22}
}
```

- [← previous Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/)
- [next → MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/3/)

## References

213 references, 32 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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213. Dataset size RTE 2k MRPC 4k CoLa 9k SST-2 67k QNLI 105k QQP 364k MNLI 393k MNLI-mis 393k SNLI 550k Single All Smoothed 67.1 69.3 72.9 85.5 81.6 82.8 74.7 70.2 72.7 88.4 88.2 87.6 85.2 82.3 83.1 90.5 90.1 90.3 80.2 79.2 78.8 80.8 79.7 80.1 88.9 88.1 88.4 *Table 5: The scores (accuracy) per dataset on… [find](https://scholar.google.com/scholar?q=Dataset+size+RTE+2k+MRPC+4k+CoLa+9k+SST%2D2+67k+QNLI+105k+QQP+364k+MNLI+393k+MNLI%2Dmis+393k+SNLI+550k+Single+All+Smoothed+67%2E1+69%2E3+72%2E9+85%2E5+81%2E6+82%2E8+74%2E7+70%2E2+72%2E7+88%2E4+88%2E2+87%2E6+85%2E2+82%2E3+83%2E1+90%2E5+9)