SemEval-2022 Task 8: Multilingual news article similarity

Chen, Xi; Zeynali, Ali; Camargo, Chico Q.; Flöck, Fabian; Gaffney, Devin; Grabowicz, Przemyslaw A.; Hale, Scott A.; Jurgens, David; Samory, Mattia

doi:10.18653/v1/2022.semeval-1.155

Cited by 20 publications

(29 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SemEval 2022 Task 8 is a multilingual news article similarity task(X. Chen, Zeynali, Camargo, Flöck, Gaffney, Grabowicz, Hale, Jurgens, and Samory 2022). There are mainly 3 difficulties in the task compared with regular text similarity task: (1) the task is interested in the real worldhappenings covered in the news articles, not their style of writing, political spin, tone, or any other more subjective design.…”

Section: Introductionmentioning

confidence: 99%

YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity

Nai¹,

Wang²,

Zhang³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

View full text Add to dashboard Cite

This paper describes the system submitted by our team (YNU-HPCC) to SemEval-2022 Task 8: Multilingual news article similarity. This task requires participants to develop a system which could evaluate the similarity between multilingual news article pairs. We propose an approach that relies on Transformers to compute the similarity between pairs of news. We tried different models namely BERT, ALBERT, ELECTRA, RoBERTa, M-BERT and Compared their results. At last, we chose M-BERT as our System, which has achieved the best Pearson Correlation Coefficient score of 0.738.

show abstract

Section: Introductionmentioning

confidence: 99%

YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity

Nai¹,

Wang²,

Zhang³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

View full text Add to dashboard Cite

show abstract

“…The significance of the problem of multilingual news article similarity (Chen et al, 2022) lies towards the applicability in this area by scoring a pair of news articles based on their similarity in coverage of the event without focusing on the subjectivity of the content.…”

Section: Introductionmentioning

confidence: 99%

IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News Similarity

Joshi¹,

Taunk²,

Varma³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

View full text Add to dashboard Cite

The task of multilingual news article similarity entails determining the degree of similarity of a given pair of news articles in a language-agnostic setting. This task aims to determine the extent to which the articles deal with the entities and events in question without much consideration of the subjective aspects of the discourse. Considering the superior representations being given by these models as validated on other tasks in NLP across an array of high and low-resource languages and this task not having any restricted set of languages to focus on, we adopted using the encoder representations from these models as our choice throughout our experiments. For modeling the similarity task by using the representations given by these models, a Siamese architecture was used as the underlying architecture. In experimentation, we investigated on several fronts including features passed to the encoder model, data augmentation and ensembling among our major experiments. We found data augmentation to be the most effective working strategy among our experiments.

show abstract

“…The task (Chen et al, 2022) aims to design a system that can find the similarities of multi-language news articles. The task focuses on the consistency of actual events reported in two news articles, but not the subjective factors such as writing style or political factors.…”

Section: Introductionmentioning

confidence: 99%

ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity

Chen¹,

Chen²,

Sun³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

View full text Add to dashboard Cite

This article introduces a system to solve the SemEval 2022 Task 8: Multilingual News Article Similarity. The task focuses on the consistency of events reported in two news articles. The system consists of a pre-trained model(e.g., INFOXLM and XLM-RoBERTa) to extract multilingual news features, following fully-connected networks to measure the similarity. In addition, data augmentation and Ten Folds Voting are used to enhance the model. Our final submitted model is an ensemble of three base models, with a Pearson value of 0.784 on the test dataset.

show abstract

SemEval-2022 Task 8: Multilingual news article similarity

Cited by 20 publications

References 51 publications

YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity

YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity

IIIT-MLNS at SemEval-2022 Task 8: Siamese Architecture for Modeling Multilingual News Similarity

ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity

Contact Info

Product

Resources

About