Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity

TL;DR

This paper introduces a reweighting strategy that identifies and emphasizes human-like sentences in synthetic data to improve sentence embeddings, leading to better generalization and performance.

Contribution

It proposes a novel reweighting approach using a classifier to distinguish and prioritize human-like sentences during training of sentence embeddings.

Findings

The reweighting method improves embedding quality on multiple datasets.

Synthetic data, when reweighted, enhances generalization of sentence embeddings.

The approach outperforms existing baselines in semantic similarity tasks.

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 much different from the ones written by human. We hypothesize that treating all these synthetic examples equally for training deep neural networks 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 existing baselines. Our implementation is publicly available at https://github.com/ddehun/coling2022_reweighting_sts.