---
title: "A closer look at the performance of neural — Hu 2020"
description: "Earlier studies (Marvin and Linzen, 2018; Futrell et al., 2018) concluded that neural language models (NLMs) fail to learn English reflexive anaphor…"
authors: ["Jennifer Hu", "Sherry Yong Chen", "Roger Levy"]
year: 2020
venue: "Proceedings of the Society for Computation in Linguistics 2020"
source: "https://aclanthology.org/2020.scil-1.39/"
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/hu-2020-closer-look-performance-neural-language/"
---
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# A closer look at the performance of neural language models on reflexive anaphor licensing

**Authors:** [Jennifer Hu](https://md.research.elamur.ai/free/authors/jennifer-hu/), Sherry Yong Chen, [Roger Levy](https://md.research.elamur.ai/free/authors/roger-levy/) · **Year:** 2020 · **Venue:** Proceedings of the Society for Computation in Linguistics 2020 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.scil-1.39/) · [PDF](https://aclanthology.org/2020.scil-1.39.pdf) · [OpenAlex](https://openalex.org/W2994726368)

**Citations:** 9 citations (openalex-snapshot · 2026-10-14) · [15](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Earlier studies (Marvin and Linzen, 2018; Futrell et al., 2018) concluded that neural language models (NLMs) fail to learn English reflexive anaphor licensing (RAL), meaning the rules that decide which noun phrase can serve as the antecedent of words like *himself* or *themselves*. Hu, Chen and Levy argue that those conclusions were partly caused by flaws in experimental design. One flaw was comparing different reflexives in the same context, even though *themselves*, *himself* and *herself* have very different corpus frequencies. Another was relying on profession nouns that are only stereotypically gendered and too rare for small-vocabulary models.

The authors built new test suites in which the target reflexive stays fixed and only the preceding context changes. Each item appears in three conditions: Baseline, Distractor and Ungrammatical. They test number agreement and rely on frequent nouns whose gender is built into the word itself, for example *man* and *woman*. The experiments separately probe the two licensing constraints, LOCALITY and C-COMMAND.

Under this paradigm, models show clearly more evidence of learning RAL than previously reported. The work shows that careful psycholinguistic controls are needed to fairly assess what NLMs can learn.

## Key points
- The evaluation counts an item as correct only when the target reflexive is least probable in the Ungrammatical condition, compared with both Distractor and Baseline. This sets chance at 33.33% rather than the usual 50%.
- Models tested include large-vocabulary models (BERT, TransXL, JRNN, GRNN) and small-vocabulary models (RNNG, TinyLSTM).
- In sentential complement items, all large-vocabulary models score near ceiling. RNNG beats TinyLSTM, though both score lower than the large models.
- A new experiment isolates C-COMMAND by placing a distractor inside a PP modifier within the same clause. Here all neural models except TinyLSTM perform well above chance, with RNNG at 62% versus 43% for TinyLSTM.
- With the older profession-noun materials, 11 of 12 nouns are stereotypically male. This produces a himself/herself asymmetry that largely disappears with lexically gendered nouns. Code and data are released on GitHub (jennhu/reflexive-anaphor-licensing).

*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

15 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 14 not yet.

- [Can language models capture syntactic associations without surface cues? A case study of reflexive anaphor licensing in English control constructions](https://md.research.elamur.ai/free/papers/lee-2022-can-language-models-capture-syntactic/)

## Cite

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

```bibtex
@inproceedings{hu2020closer,
  title = {{A closer look at the performance of neural language models on reflexive anaphor licensing}},
  author = {Hu, Jennifer and Chen, Sherry Yong and Levy, Roger},
  booktitle = {Proceedings of the Society for Computation in Linguistics 2020},
  year = {2020},
  doi = {10.7275/67qw-mf84},
  url = {https://doi.org/10.7275/67qw-mf84}
}
```

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