laughbot-transformer

Extending laughbot project (https://github.com/katepark/laughbot) and paper (https://cs.stanford.edu/~katepark/Laughbot.pdf) to transformer model finetuned with same dataset of the Switchboard Corpora (3000 transcripts from phone conversations between two speakers)

Dataset: Any line of a transcript preceding indication of laughter (often transcribed as "[laughter]") classified as a positive "punchline." Else, "unfunny". Example

Transcript	Class
A: Uh-huh. Well, you must have a relatively clean conscience then.	Punchline
B: [Laughter]	-

Num Examples

• Train: 23658 (38.7% "punchline")
• Val: 2966
• Test: 2893

Model	Model Description	Accuracy	Precision	Recall	F1-score
Positive Baseline	Always predict positive class ("punchline")	37.2	37.2	100.0	54.2
Random Weighted Baseline	Predict positive class 37.2% of the time matching overall dataset distribution	37.2	37.2	37.2	37.2
Logistic regression (language only)	Logistic Regression trained on language features(ngrams, parts of speech, sentiment, line length)	70.6	62.7	51.4	56.5
RNN (audio only)	RNN trained on acoustic features (MFCC vectors, Energy level)	71.7	63.5	55.9	59.4
Paper Final Model (RNN+LogReg)	Extract final hidden state vector from RNN trained on acoustic features. Concatenate with language features. Train logistic regression model on combined feature vector	73.9	66.5	60.3	63.2
Finetuned Transformer (this repo)	distilbert-base-uncased finetuned on tokenized transcripts, no audio features	74.1	73.3	73.6	73.7

Transformers rock!

Overfitting...

Dataset	Accuracy	Precision	Recall	F1-score
Train	89.9	89.9	89.9	89.9
Val	72.2	71.7	72.1	71.8

You can play with the model here https://huggingface.co/goldenk/distilbert-base-uncased-finetuned-switchboard-2
Open in Colab: https://colab.research.google.com/github/katepark/laughbot-transformer/blob/main/laughbot_transformer_scratch.ipynb

Future Work

• joke text-generator fed into existing humor detection
• better dataset than switchboard for humor detection

Citation

This work drew from Hugging Face's NLP with Transformers

  @book{tunstall2022natural,
    title={Natural Language Processing with Transformers: Building Language Applications with Hugging Face},
    author={Tunstall, Lewis and von Werra, Leandro and Wolf, Thomas},
    isbn={1098103246},
    url={https://books.google.ch/books?id=7hhyzgEACAAJ},
    year={2022},
    publisher={O'Reilly Media, Incorporated}
  }