Automatic Stance Detection Using End-to-End Memory Networks
Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, Alessandro Moschitti
Abstract
We present an effective end-to-end memory network model that jointly (i) predicts whether a given document can be considered as relevant evidence for a given claim, and (ii) extracts snippets of evidence that can be used to reason about the factuality of the target claim. Our model combines the advantages of convolutional and recurrent neural networks as part of a memory network. We further introduce a similarity matrix at the inference level of the memory network in order to extract snippets of evidence for input claims more accurately. Our experiments on a public benchmark dataset, FakeNewsChallenge, demonstrate the effectiveness of our approach.- Anthology ID:
- N18-1070
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 767–776
- Language:
- URL:
- https://aclanthology.org/N18-1070
- DOI:
- 10.18653/v1/N18-1070
- Cite (ACL):
- Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, and Alessandro Moschitti. 2018. Automatic Stance Detection Using End-to-End Memory Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 767–776, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Stance Detection Using End-to-End Memory Networks (Mohtarami et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/N18-1070.pdf
- Data
- FNC-1