GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity

Iknoor Singh, Yue Li, Melissa Thong, Carolina Scarton


Abstract
This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity. We propose an entity-enriched Siamese Transformer which computes news article similarity based on different sub-dimensions, such as the shared narrative, entities, location and time of the event discussed in the news article. Our system exploits a Siamese network architecture using a Transformer encoder to learn document-level representations for the purpose of capturing the narrative together with the auxiliary entity-based features extracted from the news articles. The intuition behind using all these features together is to capture the similarity between news articles at different granularity levels and to assess the extent to which different news outlets write about “the same events”. Our experimental results and detailed ablation study demonstrate the effectiveness and the validity of our proposed method.
Anthology ID:
2022.semeval-1.158
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1121–1128
Language:
URL:
https://aclanthology.org/2022.semeval-1.158
DOI:
10.18653/v1/2022.semeval-1.158
Bibkey:
Cite (ACL):
Iknoor Singh, Yue Li, Melissa Thong, and Carolina Scarton. 2022. GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1121–1128, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity (Singh et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.158.pdf
Video:
 https://aclanthology.org/2022.semeval-1.158.mp4
Code
 iknoorjobs/semeval-code