Why is a document relevant? Understanding the relevance scores in cross-lingual document retrieval

Modern cross-lingual document retrieval models are capable of finding documents relevant to the query. However, they do not have the capabilities for explaining why the document is relevant. This paper proposes a novel learning-to-rank model named LM-EMD that uses the multilingual BERT language model and Earth Mover's Distance (EMD) to measure the document's relevancy to the input query and provide interpretable insights into why a document is relevant. The model uses the query and document token's contextual embeddings generated with multilingual BERT to measure their distances in the embedding space, which are then used by EMD to calculate the document's relevance score and identify which document tokens contribute the most to its relevancy. We evaluate the model on five language pairs of varying degrees of similarity and analyze its performance. We find that the model (1) performs similar as the best performing comparing model on high-resource languages, (2) is less effective on low-resource languages, and (3) provides insight into why a document is relevant to the query. (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

This paper proposes a novel learning-to-rank model named LM-EMD that utilizes a multilingual BERT language model and Earth Mover's Distance (EMD) to measure the relevancy between a document and an input query. The model provides interpretable insights by analyzing the distances and identifying the contributing document tokens to the relevancy.