Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures

• URL: http://arxiv.org/abs/2010.11351v1
• Date: Wed, 21 Oct 2020 23:45:18 GMT
• Title: Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures
• Authors: M. Li, H. Bai, L. Tan, K. Xiong, M. Li, J. Lin
• Abstract summary: We learn a categorical variational autoencoder based on off-the-shelf pre-trained language models. We empirically demonstrate that these finetuned models could be further improved by Latte-Mix.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Measuring sentence semantic similarity using pre-trained language models such as BERT generally yields unsatisfactory zero-shot performance, and one main reason is ineffective token aggregation methods such as mean pooling. In this paper, we demonstrate under a Bayesian framework that distance between primitive statistics such as the mean of word embeddings are fundamentally flawed for capturing sentence-level semantic similarity. To remedy this issue, we propose to learn a categorical variational autoencoder (VAE) based on off-the-shelf pre-trained language models. We theoretically prove that measuring the distance between the latent categorical mixtures, namely Latte-Mix, can better reflect the true sentence semantic similarity. In addition, our Bayesian framework provides explanations for why models finetuned on labelled sentence pairs have better zero-shot performance. We also empirically demonstrate that these finetuned models could be further improved by Latte-Mix. Our method not only yields the state-of-the-art zero-shot performance on semantic similarity datasets such as STS, but also enjoy the benefits of fast training and having small memory footprints.

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