Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines

TL;DR

This paper introduces a universal multi-lingual sentence encoder designed for search engines, trained on diverse datasets including user search data and NLI datasets, to improve semantic similarity scoring across various query lengths and languages.

Contribution

The paper proposes a multi-task training approach that combines heterogeneous datasets to develop a robust, unbiased multi-lingual sentence encoder for large-scale search applications.

Findings

Effective multi-task training leverages diverse datasets.

The encoder improves semantic similarity scoring across languages.

The approach reduces bias from click data and handles various query lengths.

Abstract

In this paper, we present a multi-lingual sentence encoder that can be used in search engines as a query and document encoder. This embedding enables a semantic similarity score between queries and documents that can be an important feature in document ranking and relevancy. To train such a customized sentence encoder, it is beneficial to leverage users search data in the form of query-document clicked pairs however, we must avoid relying too much on search click data as it is biased and does not cover many unseen cases. The search data is heavily skewed towards short queries and for long queries is small and often noisy. The goal is to design a universal multi-lingual encoder that works for all cases and covers both short and long queries. We select a number of public NLI datasets in different languages and translation data and together with user search data we train a language model using a multi-task approach. A challenge is that these datasets are not homogeneous in terms of content, size and the balance ratio. While the public NLI datasets are usually two-sentence based with the same portion of positive and negative pairs, the user search data can contain multi-sentence documents and only positive pairs. We show how multi-task training enables us to leverage all these datasets and exploit knowledge sharing across these tasks.