Apr 14, 2021 · TWEAC -- Transformer with Extendable Agent Classifiers -- achieves the best performance overall with 94% accuracy.

This repository contains the code for the paper TWEAC: Transformer with Extendable QA Agent Classifiers.

TWEAC: Transformer with Extendable QA Agent Classifiers. Published in arXiv preprint, 2021. Recommended citation: Gregor Geigle, Nils Reimers, ...

This work addresses the central research question of how to effectively and efficiently identify suitable QA agents for any given question, and shows that ...

Summary. Our proposed models manage to identify the correct agents with high precision in a realistic setup with 10 QA tasks while also scaling well to hundreds ...

We provide extensive insights on the scalability of TWEAC, demonstrating that it scales robustly to over 100 QA agents with each providing just 1000 examples of ...

Question answering systems should help users to access knowledge on a broad range of topics and to answer a wide array of different questions.

TWEAC: Transformer with Extendable QA Agent Classifiers. Preprint. Full-text ... consists of a single transformer-based model with a classification head for each ...

High-quality image restoration following human instructions. MV Conde, G ... TWEAC: transformer with extendable QA agent classifiers. G Geigle, N Reimers ...

Jul 10, 2023 · (2021) follow this approach and propose TWEAC. (Transformer with Extendable QA Agent Classi- fiers), a Transformer model with a classification.