---
title: "AStitchInLanguageModels — Madabushi 2021"
description: "Pre-trained language models such as BERT build meaning largely by composing words and sub-words."
authors: ["Harish Tayyar Madabushi", "Edward Gow-Smith", "Carolina Scarton", "Aline Villavicencio"]
year: 2021
venue: "Findings of the Association for Computational Linguistics: EMNLP 2021"
source: "https://aclanthology.org/2021.findings-emnlp.294/"
doi: "10.18653/v1/2021.findings-emnlp.294"
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/papers/madabushi-2021-astitchinlanguagemodels-dataset-methods-exploration-idiomaticity/"
---
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# AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models

**Authors:** [Harish Tayyar Madabushi](https://md.research.elamur.ai/free/authors/harish-tayyar-madabushi/), [Edward Gow-Smith](https://md.research.elamur.ai/free/authors/edward-gow-smith/), [Carolina Scarton](https://md.research.elamur.ai/free/authors/carolina-scarton/), [Aline Villavicencio](https://md.research.elamur.ai/free/authors/aline-villavicencio/) · **Year:** 2021 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2021 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.findings-emnlp.294/) · [PDF](https://aclanthology.org/2021.findings-emnlp.294.pdf) · [DOI 10.18653/v1/2021.findings-emnlp.294](https://doi.org/10.18653/v1/2021.findings-emnlp.294) · [OpenAlex](https://openalex.org/W3197690918)

**Citations:** 33 citations (openalex-snapshot · 2026-10-14) · [53](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

Pre-trained language models such as BERT build meaning largely by composing words and sub-words. This makes them weak at handling idiomatic multiword expressions (MWEs). Earlier resources mostly list isolated phrases with compositionality scores, or attach context only to expressions that are always literal or always idiomatic. They also offer at most one literal and one figurative sense.

The authors introduce AStitchInLanguageModels, a resource in English and Portuguese. It pairs naturally occurring sentences, along with their neighbouring context, with noun compounds drawn from the NCS dataset. Each usage is labelled with a fine-grained sense, and paraphrases are supplied for every meaning. On top of this data they define two tasks:

- **Task 1:** detecting idiomatic usage, both coarse and fine-grained.
- **Task 2:** checking how well sentence embeddings capture idiomaticity.

Both tasks are run in zero-, one- and few-shot setups. The authors also test the idiom principle by adding dedicated MWE tokens to a contextual model.

The models detect idioms reasonably once they have seen the expressions, but they struggle with unseen ones. Adding single MWE tokens helps on idiomatic sentences, but the overall gains are modest. The resource and models are openly released.

## Key points

- Twelve annotators collected 20–30 examples per MWE, aiming for 7–10 per sense. The senses are Idiomatic, Non-Idiomatic, Proper Noun and "Meta Usage", which covers a literal MWE used inside a metaphor.
- Task 2 Subtask A (pre-training only, MWE subset of Test) measures Spearman ρ for English and Portuguese:

  | Setting | EN | PT |
  |---|---|---|
  | All Tokenized without further pre-training | 0.395 | 0.274 |
  | All Tokenized with pre-training | 0.459 | 0.369 |

- Task 2 Subtask B (fine-tuning, MWE subset): Select Tokenized scored best for Portuguese (0.416 vs. 0.312 default). Default tokenization was slightly better for English (0.627).
- When evaluated on the complete test set, where regular STS data is also present, additional pre-training on MWE-tokenized text seldom outperformed MWE tokens initialised at random. Fine-tuning from random MWE embeddings performed comparably, which suggests a sample-efficient way to learn these embeddings.
- Data, splits, models and code are available at github.com/H-TayyarMadabushi/AStitchInLanguageModels.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

53 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 43 not yet.

- [Automatic Evaluation and Analysis of Idioms in Neural Machine Translation](https://md.research.elamur.ai/free/papers/baziotis-2023-automatic-evaluation-analysis-idioms-neural/)
- [No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models](https://md.research.elamur.ai/free/papers/cheng-2024-no-context-needed-contextual-quandary/)
- [ID10M-JAM: Stress-Testing Idiom Identification Under Challenging Context](https://md.research.elamur.ai/free/papers/hashiloni-2026-id10m-jam-stress-testing-idiom/)
- [SemEval-2025 Task 1: AdMIRe - Advancing Multimodal Idiomaticity Representation](https://md.research.elamur.ai/free/papers/pickard-2025-semeval-2025-task-1-admire/)
- [A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding](https://md.research.elamur.ai/free/papers/selamet-2026-parallel-cross-lingual-benchmark-multimodal/)
- [MultiCoPIE: A Multilingual Corpus of Potentially Idiomatic Expressions for Cross-lingual PIE Disambiguation](https://md.research.elamur.ai/free/papers/sentsova-2025-multicopie-multilingual-corpus-potentially-idiomatic/)
- [ITUNLP at MWE-2026 AdMIRe 2: A Zero-Shot LLM Pipeline for Multimodal Idiom Understanding and Ranking](https://md.research.elamur.ai/free/papers/site-2026-itunlp-mwe-2026-admire-2/)
- [ID10M: Idiom Identification in 10 Languages](https://md.research.elamur.ai/free/papers/tedeschi-2022-id10m-idiom-identification-10-languages/)
- [How Are Idioms Processed Inside Transformer Language Models?](https://md.research.elamur.ai/free/papers/tian-2023-how-are-idioms-processed-inside/)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://md.research.elamur.ai/free/papers/zeng-2023-iekg-commonsense-knowledge-graph-idiomatic/)

## Cite

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

```bibtex
@inproceedings{madabushi2021astitchinlanguagemodels,
  title = {{AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models}},
  author = {Madabushi, Harish Tayyar and Gow-Smith, Edward and Scarton, Carolina and Villavicencio, Aline},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
  year = {2021},
  doi = {10.18653/v1/2021.findings-emnlp.294},
  url = {https://doi.org/10.18653/v1/2021.findings-emnlp.294}
}
```

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