Cross-Lingual Bitext Mining
5 papers with code • 4 benchmarks • 1 datasets
Cross-lingual bitext mining is the task of mining sentence pairs that are translations of each other from large text corpora.
Benchmarks
These leaderboards are used to track progress in Cross-Lingual Bitext Mining
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Libraries
Use these libraries to find Cross-Lingual Bitext Mining models and implementations
BUCC
Most implemented papers
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
facebookresearch/LASER
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TACL 2019
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts.
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
facebookresearch/LASER
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ACL 2019
Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora.
Improving Neural Machine Translation Models with Monolingual Data
josephch405/curriculum-nmt
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ACL 2016
Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training.
Parallel Sentence Mining by Constrained Decoding
We present a novel method to extract parallel sentences from two monolingual corpora, using neural machine translation.
Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
AlexJonesNLP/alt-bitexts
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RANLP (BUCC) 2021
Obtaining high-quality parallel corpora is of paramount importance for training NMT systems.
