---
title: "Few-Shot Anaphora Resolution in Scientific — Le 2022"
description: "Anaphora resolution usually needs annotated data for each new domain."
authors: ["Nghia T. Le", "Fan Bai", "Alan Ritter"]
year: 2022
venue: "Findings of the Association for Computational Linguistics: EMNLP 2022"
source: "https://aclanthology.org/2022.findings-emnlp.197/"
doi: "10.18653/v1/2022.findings-emnlp.197"
arxiv: "2210.03690"
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/le-2022-few-shot-anaphora-resolution-scientific/"
---
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# Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts

**Authors:** [Nghia T. Le](https://md.research.elamur.ai/free/authors/nghia-t-le/), Fan Bai, [Alan Ritter](https://md.research.elamur.ai/free/authors/alan-ritter/) · **Year:** 2022 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2022 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.findings-emnlp.197/) · [PDF](https://aclanthology.org/2022.findings-emnlp.197.pdf) · [DOI 10.18653/v1/2022.findings-emnlp.197](https://doi.org/10.18653/v1/2022.findings-emnlp.197) · [arXiv 2210.03690](https://arxiv.org/abs/2210.03690) · [OpenAlex](https://openalex.org/W4385573688)

**Citations:** 7 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Anaphora resolution usually needs annotated data for each new domain. This is especially costly for chemical synthesis protocols, where phrases like "the mixture" refer back to several earlier substances and non-expert crowd annotators lack the necessary expertise. The authors test whether few-shot in-context learning can handle this task. They identify two obstacles: paragraph-length examples leave room for only a few demonstrations per prompt, and performance varies strongly with the choice and order of demonstrations.

Their method, MICE (Mixtures of In-Context Experts), works in four steps:
- It frames the task as extractive question answering.
- It builds hundreds of prompts from permutations of a tiny training set.
- It runs each prompt through GPT-J.
- It merges the predicted antecedents in a mixture model, with gating weights based on embedding similarity between the test input and each prompt's demonstrations.

On the ChEMU-Ref corpus, MICE beats a strong prompt-retrieval baseline (KATE) by roughly 30% in F1. Its automatic labels can also train compact BERT-based students without loss of accuracy. The authors present this as the first evidence that in-context learning is effective for this task.

## Key points
- **Ensemble size:** MICE creates up to k^d experts. For example, 16 training examples with 2 demonstrations per prompt gives 256 prompts. Within GPT-J's 2048-token window, a single prompt holds at most 8 demonstrations.
- **Main result:** With 32 training examples, one prompt reaches 38.6 F1, while MICE with 256 prompts reaches 53.9 F1. Gains level off after about 75 prompts.
- **MICE-Sampling variant:** This Monte Carlo approximation weights a binary "was this antecedent generated" indicator by the gating probabilities. It worked better than first-token LM probabilities and is simpler to implement.
- **Data:** The authors used ChEMU-Ref and merged its one-to-many relations into a single MULTIPLE-ANTECEDENT relation. They kept nominal anaphors (about 90% of the total) and dropped IUPAC-named compounds. The test split contains 898 anaphors, and five training sets were sampled for each k-shot setting.
- **Distillation and release:** Distillation into BERT-based models reduces MICE's high inference cost. Code and data are available at github.com/nle18/mice.

*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/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cited by

Cited by

13 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 8 not yet.

- [Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions](https://md.research.elamur.ai/free/papers/alkan-2026-do-lexical-contextual-coreference-resolution/)
- [Lions: 1 and Tigers: 2 and Bears: 3, Oh My! Literary Coreference Annotation with LLMs](https://md.research.elamur.ai/free/papers/hicke-2024-lions-1-tigers-2-bears/)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://md.research.elamur.ai/free/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
- [Are Large Language Models Robust Coreference Resolvers?](https://md.research.elamur.ai/free/papers/le-2023-large-language-models-robust-coreference/)
- [Multilingual coreference resolution as text generation](https://md.research.elamur.ai/free/papers/skachkova-2024-multilingual-coreference-resolution-as-text/)

## Cite

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

```bibtex
@inproceedings{le2022few,
  title = {{Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts}},
  author = {Le, Nghia T. and Bai, Fan and Ritter, Alan},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-emnlp.197},
  eprint = {2210.03690},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.findings-emnlp.197}
}
```

- [← previous FeTaQA: Free-form Table Question Answering](https://md.research.elamur.ai/free/papers/nan-2022-fetaqa-free-form-table-question/)
- [next → Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/papers/liu-2022-few-shot-parameter-efficient-fine/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/papers/2022/page/2/)

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