Efficiency-Effectiveness Tradeoff of Probabilistic Structured Queries for Cross-Language Information Retrieval

TL;DR

This paper revisits Probabilistic Structured Queries for cross-language information retrieval, introducing an efficient Python implementation and exploring how pruning translation probabilities affects the tradeoff between effectiveness and efficiency.

Contribution

It presents a new Python implementation of PSQ and demonstrates that multi-criteria pruning improves the effectiveness-efficiency tradeoff in CLIR.

Findings

Multi-criteria pruning enhances PSQ performance.

Efficient Python implementation is publicly available.

Pruning strategies significantly impact retrieval tradeoffs.

Abstract

Probabilistic Structured Queries (PSQ) is a cross-language information retrieval (CLIR) method that uses translation probabilities statistically derived from aligned corpora. PSQ is a strong baseline for efficient CLIR using sparse indexing. It is, therefore, useful as the first stage in a cascaded neural CLIR system whose second stage is more effective but too inefficient to be used on its own to search a large text collection. In this reproducibility study, we revisit PSQ by introducing an efficient Python implementation. Unconstrained use of all translation probabilities that can be estimated from aligned parallel text would in the limit assign a weight to every vocabulary term, precluding use of an inverted index to serve queries efficiently. Thus, PSQ's effectiveness and efficiency both depend on how translation probabilities are pruned. This paper presents experiments over a range of modern CLIR test collections to demonstrate that achieving Pareto optimal PSQ effectiveness-efficiency tradeoffs benefits from multi-criteria pruning, which has not been fully explored in prior work. Our Python PSQ implementation is available on GitHub(https://github.com/hltcoe/PSQ) and unpruned translation tables are available on Huggingface Models(https://huggingface.co/hltcoe/psq_translation_tables).