Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity

TaeHee Kim, ChaeHun Park, Jimin Hong, Radhika Dua, Edward Choi, Jaegul Choo


Abstract
Semantically meaningful sentence embeddings are important for numerous tasks in natural language processing. To obtain such embeddings, recent studies explored the idea of utilizing synthetically generated data from pretrained language models(PLMs) as a training corpus. However, PLMs often generate sentences different from the ones written by human. We hypothesize that treating all these synthetic examples equally for training can have an adverse effect on learning semantically meaningful embeddings. To analyze this, we first train a classifier that identifies machine-written sentences and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences. Based on this, we propose a novel approach that first trains the classifier to measure the importance of each sentence. The distilled information from the classifier is then used to train a reliable sentence embedding model. Through extensive evaluation on four real-world datasets, we demonstrate that our model trained on synthetic data generalizes well and outperforms the baselines.
Anthology ID:
2022.coling-1.429
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4853–4863
Language:
URL:
https://aclanthology.org/2022.coling-1.429
DOI:
Bibkey:
Cite (ACL):
TaeHee Kim, ChaeHun Park, Jimin Hong, Radhika Dua, Edward Choi, and Jaegul Choo. 2022. Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4853–4863, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity (Kim et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.429.pdf
Code
 ddehun/coling2022_reweighting_sts
Data
MRPCPAWS