# Prerequisite Chain Learning on NLP to CV domain

Source: [Prerequisite Chain Learning on NLP to CV domain](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-cv-domain>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:43:05.485Z

Evaluation of Prerequisite Chain Learning performance on the NLP to CV domain dataset, reporting F1, Accuracy, Precision, and Recall.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Prerequisite Chain Learning |
| Dataset (including any stated split) | NLP to CV domain |
| Metric | F1 Score |
| Unit | SCALAR |
| Direction | Higher is better |
| Results loaded | 7 of 7 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [F1 Score](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-cv-domain.md?metric=F1%20Score>): Higher is better; unit: SCALAR.

- [Accuracy](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-cv-domain.md?metric=Accuracy>): Higher is better; unit: SCALAR.

- [Precision](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-cv-domain.md?metric=Precision>): Higher is better; unit: SCALAR.

- [Recall](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-cv-domain.md?metric=Recall>): Higher is better; unit: SCALAR.

## Recorded results

| Method | F1 Score | Result date | Report paper |
| --- | --- | --- | --- |
| CLS + Node2vec | 0.8172 | Not recorded | 68fa6a031e209131cea256c0 |
| CD-VGAE + P2V | 0.6754 | Not recorded | 68fa6a031e209131cea256c0 |
| CD-VGAE + BERT | 0.6391 | Not recorded | 68fa6a031e209131cea256c0 |
| VGAE + P2V | 0.6202 | Not recorded | 68fa6a031e209131cea256c0 |
| VGAE + BERT | 0.5885 | Not recorded | 68fa6a031e209131cea256c0 |
| CLS + P2V | 0.4881 | Not recorded | 68fa6a031e209131cea256c0 |
| CLS + BERT | 0.4277 | Not recorded | 68fa6a031e209131cea256c0 |

### CLS + Node2vec

F1 Score: 0.8172. Result ID: 698dfe0effc3901de099fe0a.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa62a01e209131ce9fa578.md>)

[Implementation](<http://snap.stanford.edu/node2vec>)

Recorded evaluation configuration: {"Base Embedding Model":"Node2vec","Learning Protocol":"Supervised / Upper Bound"}. This record does not assert independent reproduction.

### CD-VGAE + P2V

F1 Score: 0.6754. Result ID: 698dfe0effc3901de099fe09.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Implementation](<https://github.com/Yale-LILY/LectureBank/tree/master/LectureBankCD>)

Recorded evaluation configuration: {"Base Embedding Model":"P2V","Learning Protocol":"Zero-shot / Proposed"}. This record does not assert independent reproduction.

### CD-VGAE + BERT

F1 Score: 0.6391. Result ID: 698dfe0effc3901de099fe08.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Implementation](<https://github.com/Yale-LILY/LectureBank/tree/master/LectureBankCD>)

Recorded evaluation configuration: {"Base Embedding Model":"BERT","Learning Protocol":"Zero-shot / Proposed"}. This record does not assert independent reproduction.

### VGAE + P2V

F1 Score: 0.6202. Result ID: 698dfe0effc3901de099fe07.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa651f1e209131cea088b0.md>)

Recorded evaluation configuration: {"Base Embedding Model":"P2V","Learning Protocol":"Zero-shot / Baseline"}. This record does not assert independent reproduction.

### VGAE + BERT

F1 Score: 0.5885. Result ID: 698dfe0effc3901de099fe06.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa651f1e209131cea088b0.md>)

Recorded evaluation configuration: {"Base Embedding Model":"BERT","Learning Protocol":"Zero-shot / Baseline"}. This record does not assert independent reproduction.

### CLS + P2V

F1 Score: 0.4881. Result ID: 698dfe0effc3901de099fe05.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6b7a1e209131cea2e684.md>)

[Implementation](<https://github.com/artetxem/monoses>)

Recorded evaluation configuration: {"Base Embedding Model":"P2V","Learning Protocol":"Zero-shot / Baseline"}. This record does not assert independent reproduction.

### CLS + BERT

F1 Score: 0.4277. Result ID: 698dfe0effc3901de099fe04.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6a031e209131cea256c0.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa65c31e209131cea0c4ed.md>)

[Implementation](<https://github.com/google-research/bert>)

Recorded evaluation configuration: {"Base Embedding Model":"BERT","Learning Protocol":"Zero-shot / Baseline"}. This record does not assert independent reproduction.

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