multilinguality:monolingual size_categories:n<1K language:en license:other region:us
[Analogy Question](https://aclanthology.org/2021.acl-long.280/)
@inproceedings{ushio-etal-2021-bert,
    title = "{BERT} is to {NLP} what {A}lex{N}et is to {CV}: Can Pre-Trained Language Models Identify Analogies?",
    author = "Ushio, Asahi  and
      Espinosa Anke, Luis  and
      Schockaert, Steven  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.280",
    doi = "10.18653/v1/2021.acl-long.280",
    pages = "3609--3624",
    abstract = "Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as {``}eye is to seeing what ear is to hearing{''}, sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has not yet received much attention in the language model era. In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. We find that off-the-shelf language models can identify analogies to a certain extent, but struggle with abstract and complex relations, and results are highly sensitive to model architecture and hyperparameters. Overall the best results were obtained with GPT-2 and RoBERTa, while configurations using BERT were not able to outperform word embedding models. Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations.",
}
---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- n<1K
pretty_name: Analogy Question
---

# Dataset Card for "relbert/analogy_questions"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/2021.acl-long.280/](https://aclanthology.org/2021.acl-long.280/)
- **Dataset:** Analogy Questions

### Dataset Summary
This dataset contains 5 different word analogy questions used in [Analogy Language Model](https://aclanthology.org/2021.acl-long.280/).

- original analogy questions

| name      | Size (valid/test) | Num of choice | Num of relation group | Original Reference                                                         |
|-----------|------------------:|--------------:|----------------------:|:--------------------------------------------------------------------------:|
| `u2`      | 24/228            | 5,4,3         | 9                     | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
| `u4`      | 48/432            | 5,4,3         | 5                     | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
| `google`  | 50/500            | 4             | 2                     | [Mikolov et al., (2013)](https://www.aclweb.org/anthology/N13-1090.pdf)    |
| `bats`    | 199/1799          | 4             | 3                     | [Gladkova et al., (2016)](https://www.aclweb.org/anthology/N18-2017.pdf)   |

- extra analogy questions

| name                                | Size (valid/test)   | Num of choice (valid/test)   | Num of relation group (valid/test)   | Original Reference                                                                                                     |
|:------------------------------------|:--------------------|:-----------------------------|:-------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
| `semeval2012_relational_similarity` | 79/-                | 3/-                          | 79/-                                 | [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) |
| `t_rex_relational_similarity`       | 496/183             | 74/48                        | 60/19                                | [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity)             |
| `conceptnet_relational_similarity`  | 1112/1192           | 19/17                        | 18/16                                | [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity)   |
| `nell_relational_similarity`        | 400/600             | 5/7                          | 4/6                                  | [relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity)               |
| `scan` | 178/1616            | 3,36,136,10,45,78,15,21,55,120,153,91,28/3,36,136,10,45,78,15,21,55,120,153,91,28 | 2/2                                  | [relbert/scientific_and_creative_analogy](https://huggingface.co/datasets/relbert/scientific_and_creative_analogy) |


## Dataset Structure
### Data Instances
An example of `test` looks as follows.
```
{
    "stem": ["raphael", "painter"],
    "answer": 2,
    "choice": [["andersen", "plato"],
                ["reading", "berkshire"],
                ["marx", "philosopher"],
                ["tolstoi", "edison"]]
}
```
The `stem` is the query word pair, `choice` has word pair candidates,
and `answer` indicates the index of correct candidate which starts from `0`.
All data is lowercased except Google dataset.

### Citation Information
```
@inproceedings{ushio-etal-2021-bert-is,
    title ={{BERT} is to {NLP} what {A}lex{N}et is to {CV}: {C}an {P}re-{T}rained {L}anguage {M}odels {I}dentify {A}nalogies?},
    author={Ushio, Asahi and
            Espinosa-Anke, Luis and
            Schockaert, Steven and
            Camacho-Collados, Jose},
    booktitle={Proceedings of the {ACL}-{IJCNLP} 2021 Main Conference},
    year={2021},
    publisher={Association for Computational Linguistics}
}
```

### LICENSE
The LICENSE of all the resources are under [CC-BY-NC-4.0](./LICENSE). Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.
