
# SCOPE Simile

## Dataset Description

- **Paper:** [Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation](https://aclanthology.org/2020.emnlp-main.524/)

## Dataset Summary


This dataset has been created for the purpose of generating similes from literal descriptive sentences.

The process involves a two-step approach: firstly, self-labeled similes are converted into literal sentences using structured common sense knowledge, and secondly, a seq2seq model is fine-tuned on these [literal sentence, simile] pairs to generate similes. The dataset was collected from Reddit, specifically from the subreddits WRITINGPROMPTS and FUNNY, using the phrase 'like a' to identify similes. The dataset consists of 87,843 self-labeled human-written similes, out of which 82,697 samples were used for training and 5,146 for validation. To convert the similes into literal versions, the shared property implied in the simile was identified using the COMET framework, and the top 5 common sense properties were selected to form possible literal versions. These versions were then ranked using perplexity scores from a pre-trained language model, GPT. A grammatical error correction model was used to correct any introduced errors. Independent test data was collected by scraping literal sentences from the same subreddits.

For the details of this dataset, we refer you to the original [paper](https://aclanthology.org/2020.emnlp-main.524/).

Metadata in Creative Language Toolkit ([CLTK](https://github.com/liyucheng09/cltk))
- CL Type: Simile
- Task Type: generation
- Size: 87k
- Created time: 2020
- Language: en

### Citation Information

If you find this dataset helpful, please cite:

```
 @inproceedings{chakrabarty-etal-2020-generating,
  title = "Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation",
  author = "Chakrabarty, Tuhin  and
    Muresan, Smaranda  and
    Peng, Nanyun",
  booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
  month = nov,
  year = "2020",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/2020.emnlp-main.524",
  pages = "6455--6469",
  abstract = "Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile                               go beyond plain expressions to give readers new insights and inspirations. In this paper, we tackle the problem of simile generation.                               Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first                                   propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their                             literal counterpart using structured common sense knowledge. We then propose to fine-tune a pre-trained sequence to sequence model,                                 BART (Lewis et al 2019), on the literal-simile pairs to gain generalizability, so that we can generate novel similes given a literal                               sentence. Experiments show that our approach generates 88{\%} novel similes that do not share properties with the training data. Human                             evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37{\%}                               of the time when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine-generated                             stories improves evocativeness and leads to better acceptance by human judges.",
}
```

### Contributions

If you have any queries, please open an issue or direct your queries to [mail](mailto:yucheng.li@surrey.ac.uk).
license:cc-by-2.0 region:us
{"license": "cc-by-2.0"}
TAGS
#license-cc-by-2.0 #region-us 


# SCOPE Simile

## Dataset Description

- Paper: Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

## Dataset Summary


This dataset has been created for the purpose of generating similes from literal descriptive sentences.

The process involves a two-step approach: firstly, self-labeled similes are converted into literal sentences using structured common sense knowledge, and secondly, a seq2seq model is fine-tuned on these [literal sentence, simile] pairs to generate similes. The dataset was collected from Reddit, specifically from the subreddits WRITINGPROMPTS and FUNNY, using the phrase 'like a' to identify similes. The dataset consists of 87,843 self-labeled human-written similes, out of which 82,697 samples were used for training and 5,146 for validation. To convert the similes into literal versions, the shared property implied in the simile was identified using the COMET framework, and the top 5 common sense properties were selected to form possible literal versions. These versions were then ranked using perplexity scores from a pre-trained language model, GPT. A grammatical error correction model was used to correct any introduced errors. Independent test data was collected by scraping literal sentences from the same subreddits.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Simile
- Task Type: generation
- Size: 87k
- Created time: 2020
- Language: en



If you find this dataset helpful, please cite:



### Contributions

If you have any queries, please open an issue or direct your queries to mail.
# SCOPE Simile ## Dataset Description

- Paper: Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation ## Dataset Summary


This dataset has been created for the purpose of generating similes from literal descriptive sentences.

The process involves a two-step approach: firstly, self-labeled similes are converted into literal sentences using structured common sense knowledge, and secondly, a seq2seq model is fine-tuned on these [literal sentence, simile] pairs to generate similes. The dataset was collected from Reddit, specifically from the subreddits WRITINGPROMPTS and FUNNY, using the phrase 'like a' to identify similes. The dataset consists of 87,843 self-labeled human-written similes, out of which 82,697 samples were used for training and 5,146 for validation. To convert the similes into literal versions, the shared property implied in the simile was identified using the COMET framework, and the top 5 common sense properties were selected to form possible literal versions. These versions were then ranked using perplexity scores from a pre-trained language model, GPT. A grammatical error correction model was used to correct any introduced errors. Independent test data was collected by scraping literal sentences from the same subreddits.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Simile
- Task Type: generation
- Size: 87k
- Created time: 2020
- Language: en



If you find this dataset helpful, please cite: ### Contributions

If you have any queries, please open an issue or direct your queries to mail.
TAGS
#license-cc-by-2.0 #region-us 
 # SCOPE Simile ## Dataset Description

- Paper: Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation ## Dataset Summary


This dataset has been created for the purpose of generating similes from literal descriptive sentences.

The process involves a two-step approach: firstly, self-labeled similes are converted into literal sentences using structured common sense knowledge, and secondly, a seq2seq model is fine-tuned on these [literal sentence, simile] pairs to generate similes. The dataset was collected from Reddit, specifically from the subreddits WRITINGPROMPTS and FUNNY, using the phrase 'like a' to identify similes. The dataset consists of 87,843 self-labeled human-written similes, out of which 82,697 samples were used for training and 5,146 for validation. To convert the similes into literal versions, the shared property implied in the simile was identified using the COMET framework, and the top 5 common sense properties were selected to form possible literal versions. These versions were then ranked using perplexity scores from a pre-trained language model, GPT. A grammatical error correction model was used to correct any introduced errors. Independent test data was collected by scraping literal sentences from the same subreddits.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Simile
- Task Type: generation
- Size: 87k
- Created time: 2020
- Language: en



If you find this dataset helpful, please cite: ### Contributions

If you have any queries, please open an issue or direct your queries to mail.
passage: TAGS
#license-cc-by-2.0 #region-us 
# SCOPE Simile## Dataset Description

- Paper: Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation## Dataset Summary


This dataset has been created for the purpose of generating similes from literal descriptive sentences.

The process involves a two-step approach: firstly, self-labeled similes are converted into literal sentences using structured common sense knowledge, and secondly, a seq2seq model is fine-tuned on these [literal sentence, simile] pairs to generate similes. The dataset was collected from Reddit, specifically from the subreddits WRITINGPROMPTS and FUNNY, using the phrase 'like a' to identify similes. The dataset consists of 87,843 self-labeled human-written similes, out of which 82,697 samples were used for training and 5,146 for validation. To convert the similes into literal versions, the shared property implied in the simile was identified using the COMET framework, and the top 5 common sense properties were selected to form possible literal versions. These versions were then ranked using perplexity scores from a pre-trained language model, GPT. A grammatical error correction model was used to correct any introduced errors. Independent test data was collected by scraping literal sentences from the same subreddits.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Simile
- Task Type: generation
- Size: 87k
- Created time: 2020
- Language: en



If you find this dataset helpful, please cite:### Contributions

If you have any queries, please open an issue or direct your queries to mail.