@inproceedings{goel-bommidi-2022-semeval,
title = "Wolfies at {S}em{E}val-2022 Task 8: Feature extraction pipeline with transformers for Multi-lingual news article similarity",
author = "Goel, Nikhil and
Bommidi, Ranjith Reddy",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.159",
doi = "10.18653/v1/2022.semeval-1.159",
pages = "1129--1135",
abstract = "This work is about finding the similarity between a pair of news articles. There are seven different objective similarity metrics provided in the dataset for each pair and the news articles are in multiple different languages. On top of the pre-trained embedding model, we calculated cosine similarity for baseline results and feed-forward neural network was then trained on top of it to improve the results. We also built separate pipelines for each similarity metric for feature extraction. We could see significant improvement from baseline results using feature extraction and feed-forward neural network.",
}
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%0 Conference Proceedings
%T Wolfies at SemEval-2022 Task 8: Feature extraction pipeline with transformers for Multi-lingual news article similarity
%A Goel, Nikhil
%A Bommidi, Ranjith Reddy
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F goel-bommidi-2022-semeval
%X This work is about finding the similarity between a pair of news articles. There are seven different objective similarity metrics provided in the dataset for each pair and the news articles are in multiple different languages. On top of the pre-trained embedding model, we calculated cosine similarity for baseline results and feed-forward neural network was then trained on top of it to improve the results. We also built separate pipelines for each similarity metric for feature extraction. We could see significant improvement from baseline results using feature extraction and feed-forward neural network.
%R 10.18653/v1/2022.semeval-1.159
%U https://aclanthology.org/2022.semeval-1.159
%U https://doi.org/10.18653/v1/2022.semeval-1.159
%P 1129-1135
Markdown (Informal)
[Wolfies at SemEval-2022 Task 8: Feature extraction pipeline with transformers for Multi-lingual news article similarity](https://aclanthology.org/2022.semeval-1.159) (Goel & Bommidi, SemEval 2022)
ACL