Hans Hanley, Zakir Durumeric
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hanshanley/tata | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| StanceClassifier | Not yet run | hanshanley/tata/train_tata.py code served (permissive licence) · get_code("5190998da7238ccf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Stance detection is important for understanding different attitudes and beliefs on the Internet. However, given that a passage's stance toward a given topic is often highly dependent on that topic, building a stance detection model that generalizes to unseen topics is difficult. In this work, we propose using contrastive learning as well as an unlabeled dataset of news articles that cover a variety of different topics to train topic-agnostic/TAG and topic-aware/TAW embeddings for use in downstream stance detection. Combining these embeddings in our full TATA model, we achieve state-of-the-art performance across several public stance detection datasets (0.771 F 1 -score on the Zero-shot VAST dataset). We release our code and data at https://github.com/hanshanley/tata.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.14450")
get_code_for_paper("2310.14450")
have("2310.14450")
Connect an agent — have() is free.