mam10eks/team-1-task-1

TASK 1

Relevant for submission is trhe "two-step-transformer" of approach 3. Instructions for running in docker can be found below.

Approach 1 - multipart majority vote

steps:

• Find multipart spoiler with a majority vote of certain text features of the attributes postText and targetParagraphs.
• Run the transformer baseline on features that have not been classified as multipart.

goals:

• Reach a similar accuracy for multipart spoiler as the transformer baseline.
• Improve the efficiency due to less data that needs to be predicted by the transformer baseline.

code:

• accuracy calculation of certain text features
• majority voting

Approach 2 - text simplification

steps:

• Simplify the input text before predicting the spoiler type
• Either by just replacing difficult words (MILES)
• Or replacing words and reformulating (MUSS)
• Predict spoiler type using the baseline transformer

goals:

• Better accuracy due to a smaller set of words / reduced complexity

code:

• MILES transformer
• MUSS transformer

results:

• Only replacing difficult words did not enhance the accuracy.
• Due to problems with the libraries, the approach has been discarded.

Approach 3 - multipart classification model (extension of approach 1)

steps:

• Find multipart spoiler with a Gradient Boosting classification model trained on text features of the attributes postText and targetParagraphs.
• Run the transformer baseline on features that have not been classified as multipart.

goals:

• Reach a similar accuracy for multipart spoiler as the transformer baseline.
• Improve the efficiency due to less time for training and forward-pass of the Gradient Boosting classification model than the transformer baseline needs.

code:

• feature engineering

• feature engineering refactored

• feature engineering, training and evaulation of the Gradient Boosting Classifier

• two-step pipeline

results:

• Faster than baseline, as multipart spoilers are classified within seconds
• Probably similar or better accuracy, depending on the dataset

Running in Docker:

(Optional) Build:

1. cd statistical-model-multi-classification
2. docker build -t ghcr.io/tudbs-clickbait/team-1-task-1:two-step .
3. (optional) Login to GitHub registry
4. (optional, push image) docker push ghcr.io/tudbs-clickbait/team-1-task-1:two-step

Run:

1. docker run -v ${PWD}/data:/data ghcr.io/tudbs-clickbait/team-1-task-1:two-step --input=/data/validation_short.jsonl --output=/data/out.jsonl