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TARGRES, or Tree-based Argumetation Resolution, is a method for determining the robustness of a collaboratively discussed thesis, where arguments and counter-arguments can be represented as a tree. Based on the imbalance between pro-thesis and anti-thesis argumentation robustness, TARGRES ultimately confirms or debunks the thesis - all, of course, based on opinions reflected in the data.
Debate resolution helps shape social and scientific progress since the dawn of time. In today's era this is not only factual offline - it's also one of the backbones of the internet:
- StackOverflow and over a hundred siblings
- a vast portion of Reddit
- Quora
... and the many forums in between. The whole ever-present world of Q&A was built to thrive on the resolution of various rarely one-sided debates. The goal of TARGRES is to help lead to intelligence that can receive a debate as input, and output the most robust answer - and likely the right one.
Through a custom scraped dataset of discussions from the Kialo website, this study aims to understand and rank the robustness of argumentations in a discussion, with a combination of textual context, localized social impact (feedback from ratings) and ramifications (pros & cons) stemming from a given argumentation.
TARGRES Paper has the complete research, mathematical constructs in support of TARGRES and model benchmarks.
In TARGRES.ipynb you'll find the sequential documented guide on using the algorithm.
The only necessary step:
pip install -r requirements.txt
The notebook assumes a CUDA-enabled pytorch installation to run BERT's embedding process. A GPU with ~2.5k CUDA cores and 12GB RAM (compute 3.7) took ~1h30m to embed a batch of 100k rows.
After a successful installation, the execution of TARGRES.ipynb is sequential. Scraping and preprocessing can be entirely skipped, as section "5. Intelligence Architecture" will load the dataset from data/clean_claims_df.pkl.
- An argument-type-identifier should come for individual argument analysis. Which is potentially a full-fledged project, by itself.
- Real-time impact involves strong single-argument strength analysis.
- An open interface to interact with TARGRES.
- Automate releases (e.g. through GitHub Actions).
- Normalize model fairness, as done in scikit-fairness.
- Either team up with Kialo itself, or deduce userbase demographics.
- Extrapolate TARGRES to external data (StackExchange or Reddit, e.g. /r/changemyview)
- Labeling, adapting and running against a well structured base argument to start. Eventually a one-fits-most implementation could be thought.