HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article Similarity

Sai Sandeep Sharma Chittilla, Talaat Khalil


Abstract
This paper describes our submission to SemEval-2022 Multilingual News Article Similarity task. We experiment with different approaches that utilize a pre-trained language model fitted with a regression head to predict similarity scores for a given pair of news articles. Our best performing systems include 2 key steps: 1) pre-training with in-domain data 2) training data enrichment through machine translation. Our final submission is an ensemble of predictions from our top systems. While we show the significance of pre-training and augmentation, we believe the issue of language coverage calls for more attention.
Anthology ID:
2022.semeval-1.162
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:
1151–1156
Language:
URL:
https://aclanthology.org/2022.semeval-1.162
DOI:
10.18653/v1/2022.semeval-1.162
Bibkey:
Cite (ACL):
Sai Sandeep Sharma Chittilla and Talaat Khalil. 2022. HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article Similarity. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1151–1156, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article Similarity (Chittilla & Khalil, SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.162.pdf
Video:
 https://aclanthology.org/2022.semeval-1.162.mp4