Siamese CBOW: Optimizing Word Embeddings for Sentence Representations

TL;DR

The paper introduces Siamese CBOW, a neural network that trains word embeddings specifically for sentence representation by optimizing their averaging, leading to more effective sentence embeddings.

Contribution

It proposes a novel Siamese CBOW model that directly trains word embeddings for sentence averaging, improving sentence representation quality.

Findings

Effective across 20 diverse datasets

Outperforms traditional averaging methods

Robust and efficient sentence embeddings

Abstract

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sentence representation, and, thus, likely to be suboptimal. Siamese CBOW handles this problem by training word embeddings directly for the purpose of being averaged. The underlying neural network learns word embeddings by predicting, from a sentence representation, its surrounding sentences. We show the robustness of the Siamese CBOW model by evaluating it on 20 datasets stemming from a wide variety of sources.