TUdatalib Upgrade

Am 2. Juni erfolgte ein TUdatalib Upgrade auf eine neue Softwareversion. Dieses Upgrade bringt wichtige Neuerungen mit sich. Eine Übersicht finden Sie in der Dokumentation
On June 2nd, TUdatalib was upgraded to a new software version. This upgrade introduced major changes to the system. Please see our documentation for an overview.

File access restricted

Weights for Aspect-Controlled Neural Argument Generation

Files

reddit_seqlen256_v1.7z.001 (Weights of Argument Generation Model (Reddit), Part 1) (3.81 GB)

reddit_seqlen256_v1.7z.002 (Weights of Argument Generation Model (Reddit), Part 2) (3.81 GB)

reddit_seqlen256_v1.7z.003 (Weights of Argument Generation Model (Reddit), Part 3) (3.22 GB)

cc_seqlen256_v1.7z.003 (Weights of Argument Generation Model (CC), Part 3) (3.23 GB)

cc_seqlen256_v1.7z.002 (Weights of Argument Generation Model (CC), Part 2) (3.81 GB)

Date

2020-05-07

Type

Authors

Schiller, Benjamin
Daxenberger, Johannes

Gurevych, Iryna

Description

This collection includes trained weights for the controlled argument generation models which were fine-tuned on Common-Crawl and Reddit-Comments dumps. For more information, please refer to the paper and the GitHub repository linked in the paper. DISCLAIMER: All weights provided as downloads may be used for research purposes only. The user acknowledges and agrees that the data is provided on an “as-is” basis and that the licensor makes no representations or warranties of any kind.

Keywords

argument generation, controlled argument generation, aspect detection

Identifier

https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2328

Related Resources

Is Cited By

https://arxiv.org/abs/2005.00084

Project(s)

PTJ | 03VP02540 | ArgumenText
DFG | GU798/25-1 | Offenes Argument-Min

Collections

Argument Mining

License

Except where otherwise noted, this license is described as In Copyright