xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages

• URL: http://arxiv.org/abs/2306.12907v1
• Date: Thu, 22 Jun 2023 14:20:15 GMT
• Title: xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages
• Authors: Mingda Chen, Kevin Heffernan, Onur \c{C}elebi, Alex Mourachko, Holger Schwenk
• Abstract summary: We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xSIM++. In comparison to xSIM, we show that xSIM++ is better correlated with the downstream BLEU scores of translation systems trained on mined bitexts.
• Score: 15.351726952216369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xSIM++. In comparison to xSIM, this improved proxy leverages rule-based approaches to extend English sentences in any evaluation set with synthetic, hard-to-distinguish examples which more closely mirror the scenarios we encounter during large-scale mining. We validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages, and subsequently train NMT systems on the mined data. In comparison to xSIM, we show that xSIM++ is better correlated with the downstream BLEU scores of translation systems trained on mined bitexts, providing a reliable proxy of bitext mining performance without needing to run expensive bitext mining pipelines. xSIM++ also reports performance for different error types, offering more fine-grained feedback for model development.

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