---
title: "Text Modular Networks — paper by Khot et al. (2021)"
description: "Using distant supervision and no annotated decompositions, the authors then build… 3.7% F1. 43 references, cited by 46 papers here."
authors: ["Tushar Khot", "Daniel Khashabi", "Kyle Richardson", "Peter Clark", "Ashish Sabharwal"]
year: 2021
venue: "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/2021.naacl-main.99/"
doi: "10.18653/v1/2021.naacl-main.99"
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/khot-2021-text-modular-networks-learning-decompose/"
---
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# Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models

**Authors:** [Tushar Khot](https://md.research.elamur.ai/free/en/authors/tushar-khot/), [Daniel Khashabi](https://md.research.elamur.ai/free/en/authors/daniel-khashabi/), [Kyle Richardson](https://md.research.elamur.ai/free/en/authors/kyle-richardson/), [Peter Clark](https://md.research.elamur.ai/free/en/authors/peter-clark/) and [Ashish Sabharwal](https://md.research.elamur.ai/free/en/authors/ashish-sabharwal/) · **Year:** 2021 · **Venue:** [Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2021](https://md.research.elamur.ai/free/en/venues/naacl/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.naacl-main.99/) · [PDF](https://aclanthology.org/2021.naacl-main.99.pdf) · [DOI 10.18653/v1/2021.naacl-main.99](https://doi.org/10.18653/v1/2021.naacl-main.99) · [OpenAlex](https://openalex.org/W3082583995)

**Citations:** 48 citations (OpenAlex, 2026-10-14) · [46](https://md.research.elamur.ai/free/en/papers/khot-2021-text-modular-networks-learning-decompose/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Text Modular Networks (TMNs) tackle complex question answering by breaking a hard question into a chain of simpler questions that existing models can already answer. Earlier decomposition methods were built for one dataset and produced sub-questions without regard to which solver would receive them. TMNs instead learn how each existing sub-model "speaks." A BART model is trained on a sub-model's own dataset, such as SQuAD, to generate questions from a context, an answer and vocabulary hints. Using distant supervision and no annotated decompositions, the authors then build step-by-step decomposition examples and train a next-question generator. This generator picks a sub-model and writes the next sub-question until it emits an end symbol. The resulting system, MODULARQA, combines a neural single-span QA model with a symbolic calculator. It covers five reasoning types: composition, conjunction, comparison, difference and complementation. It is the first decomposition-based interpretable system that works on both DROP and HotpotQA. It comes close to black-box models in accuracy while being more robust and sample-efficient, and its explanations are faithful and written in natural language.

## Key points
- On DROP, MODULARQA scores only 3.7% F1 below the black-box NumNet+V2. With 20% of the training data it outperforms NumNet+V2 (87.0 vs 85.4 F1), although NumNet+V2 leads at 100% (91.6 vs 87.8).
- Evaluated on 239 in-scope questions from the contrast set for DROP introduced by Gardner et al. (2020), MODULARQA reaches 55.7 EM and 63.3 F1, compared with 45.2 EM and 56.2 F1 for NumNet+V2.
- It clearly beats the DROP-specific NMN-D and roughly matches S-NMN on HotpotQA. It trails DecompRC and Quark on HotpotQA, and the authors note that both rely on dataset-specific design or supporting-fact annotations.
- Human judges rated its explanations as more trustworthy, understandable and preferable than those of the split-based DecompRC in 67%–78% of cases.
- Error analysis:
  - On HotpotQA, 65% of errors come from intermediate questions that have multiple or yes/no answers.
  - On DROP, errors stem mainly from decomposition (53.3%) and the QA sub-models (33.33%).
  - Code is available at github.com/allenai/modularqa.

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

## Institutions

- [Allen Institute for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/allen-institute-for-artificial-intelligence/) — Research institute, Seattle, United States · 219 works · 89,712 citations

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## Cite

Cite as: Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark and Ashish Sabharwal. 2021. Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

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

```bibtex
@inproceedings{khot2021text,
  title = {{Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models}},
  author = {Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark and Ashish Sabharwal},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.99},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.99}
}
```

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- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/)

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*Updated 24 October 2026*