GptGet

Effective Parallel Corpus Mining using Bilingual Sentence Embeddings

TL;DR

Proposes efficient parallel corpus mining using bilingual sentence embeddings, achieving 48.9% precision (en-fr).

cs.CL 🔴 Advanced 2018-08-01 41 views
Mandy Guo Qinlan Shen Yinfei Yang Heming Ge Daniel Cer Gustavo Hernandez Abrego Keith Stevens Noah Constant Yun-Hsuan Sung Brian Strope Ray Kurzweil
AI Reader Arxiv Page Download PDF
parallel corpus bilingual embeddings machine translation deep learning data mining

Key Findings

Methodology

The study uses a dual-encoder architecture with Deep Averaging Networks (DAN) to generate sentence embeddings. Hard negatives—semantically similar but non-translations—are introduced to improve model discrimination.

Key Results

  • Result 1: Achieved 48.9% precision (en-fr) and 54.9% (en-es) on UN corpus sentence-level matching.
  • Result 2: NMT models trained on mined data scored within 1-2 BLEU points of original corpus models.
  • Result 3: Document-level matching accuracy comparable to Uszkoreit et al., with lower computational cost.

Significance

This method significantly improves parallel corpus mining efficiency, addressing high computational costs in traditional methods and supporting high-quality machine translation data generation.

Technical Contribution

Introduced hard negative sampling to enhance bilingual embedding discrimination; developed dynamic normalization scoring for stable confidence calibration.

Novelty

First systematic introduction of hard negatives in parallel corpus mining, demonstrating improved translation quality.

Limitations

  • Limitation 1: Hard negative generation is time-consuming, covering only 20% of data.
  • Limitation 2: Adaptability to low-resource languages remains untested.

Future Work

Future directions include extending to multilingual corpus mining, optimizing hard negative generation, and exploring low-resource language applications.

AI Executive Summary

Parallel corpora are essential for machine translation, but traditional mining methods are computationally expensive and inefficient. This study proposes an efficient mining method using bilingual sentence embeddings generated by Deep Averaging Networks (DAN). Hard negatives—semantically similar but non-translations—are introduced to refine training. Experiments show sentence-level precision of 48.9% (en-fr) and 54.9% (en-es) on the UN corpus, with significantly reduced computational costs.

Additionally, NMT models trained on mined data performed nearly as well as those trained on original corpora, with BLEU scores only 1-2 points lower. This demonstrates the method's ability to efficiently mine parallel data while maintaining translation quality.

Despite the time-intensive process of generating hard negatives, this approach offers a promising pathway for parallel corpus mining. Future work could extend its application to multilingual scenarios and optimize negative sampling efficiency.

Deep Analysis

Background

Parallel corpora are critical for training neural machine translation (NMT) systems. Traditional methods like Uszkoreit et al. rely on complex distributed systems, which are computationally expensive. Recent research has explored lightweight embedding-based models.

Core Problem

Existing methods struggle with efficiency and fail to distinguish semantically similar but non-translated sentences, leading to lower data quality and suboptimal downstream translation performance.

Innovation

This study introduces a bilingual sentence embedding method using dual encoders and hard negative sampling. A dynamic normalization scoring mechanism further stabilizes confidence calibration.

Methodology

  • �� Dual-encoder architecture encodes source and target sentences separately.
  • �� Hard negatives introduced to improve discrimination between true translations and semantic distractors.
  • �� Dynamic normalization scoring calibrates confidence levels.
  • �� Approximate nearest neighbor (ANN) search used for sentence- and document-level matching.

Experiments

Experiments conducted on UN Parallel Corpus and ParaCrawl datasets evaluate sentence- and document-level precision. Comparisons made between random negatives and hard negatives, analyzing BLEU score impacts.

Results

Hard negatives significantly improved precision, achieving 48.9% (en-fr). NMT models trained on mined data performed within 1-2 BLEU points of original corpus models.

Applications

Ideal for large-scale parallel corpus mining, particularly for high-resource language pairs in machine translation training.

Limitations & Outlook

Hard negative generation is time-intensive, and the method's adaptability to low-resource languages remains untested. Future work should optimize efficiency and expand to multilingual scenarios.

Plain Language Accessible to non-experts

Imagine searching for matching books in two languages at a library. Each book has a tag summarizing its content. This method acts like a smart assistant that quickly identifies matching books while avoiding those that seem similar but aren't translations.

ELI14 Explained like you're 14

Imagine you have an English comic book and want to find its Spanish version. Instead of searching the whole store, this method works like a super-smart search tool that finds the right match fast! It even avoids picking books that look similar but aren't translations. Cool, right?

Glossary

Bilingual Sentence Embeddings

Transforms sentences into vector representations for cross-language matching.

Used to calculate sentence similarity for mining parallel corpora.

Hard Negatives

Semantically similar but non-translations used to refine model training.

Improves model discrimination to avoid false positives.

Deep Averaging Networks (DAN)

A simple embedding generation architecture averaging word embeddings.

Used in dual encoders to generate sentence embeddings.

BLEU Score

Metric for evaluating machine translation quality based on reference similarity.

Used to assess translation models trained on mined data.

Approximate Nearest Neighbor (ANN)

Efficient search algorithm for finding the most similar target sentences.

Applied in sentence- and document-level matching.

Open Questions Unanswered questions from this research

  • 1 How can hard negative generation efficiency be improved to cover more data?
  • 2 What is the method's adaptability to low-resource languages?

Applications

Immediate Applications

Machine Translation Data Mining

Quickly generate high-quality parallel corpora for translation model training.

Cross-Language Information Retrieval

Enhances retrieval accuracy for multilingual search systems.

Long-term Vision

Multilingual Corpus Construction

Extends to multilingual scenarios, supporting global language technology advancements.

Abstract

This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings. Our embedding models are trained to produce similar representations exclusively for bilingual sentence pairs that are translations of each other. This is achieved using a novel training method that introduces hard negatives consisting of sentences that are not translations but that have some degree of semantic similarity. The quality of the resulting embeddings are evaluated on parallel corpus reconstruction and by assessing machine translation systems trained on gold vs. mined sentence pairs. We find that the sentence embeddings can be used to reconstruct the United Nations Parallel Corpus at the sentence level with a precision of 48.9% for en-fr and 54.9% for en-es. When adapted to document level matching, we achieve a parallel document matching accuracy that is comparable to the significantly more computationally intensive approach of [Jakob 2010]. Using reconstructed parallel data, we are able to train NMT models that perform nearly as well as models trained on the original data (within 1-2 BLEU).

cs.CL