Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models

TL;DR

This paper introduces TRIP, a novel pre-training method leveraging document-level trilingual corpora to enhance multilingual models, significantly improving performance on document translation and summarization tasks.

Contribution

It pioneers the use of trilingual parallel corpora in pre-training and introduces Grafting, a new method to accelerate monolingual and bilingual objectives.

Findings

Achieves state-of-the-art results on multilingual document translation benchmarks.

Improves cross-lingual summarization performance.

Demonstrates up to 3.11 d-BLEU and 8.9 ROUGE-L gains.

Abstract

Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use `bilingual corpora' to denote parallel corpora with `bilingual translation pairs' in many different language pairs, each consisting of two sentences/documents with the same meaning written in different languages. We use `trilingual corpora' to denote parallel corpora with `trilingual translation pairs' in many different language combinations, each consisting of three sentences/documents.} and sometimes synthetic document-level bilingual corpora. This hampers the performance with cross-lingual document-level tasks such as document-level translation. Therefore, we propose to mine and leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training. We present \textbf{Tri}angular Document-level \textbf{P}re-training (\textbf{TRIP}), which is the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. Experiments show that TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.