Word Alignment
84 papers with code • 7 benchmarks • 4 datasets
Word Alignment is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other.
Source: Neural Network-based Word Alignment through Score Aggregation
Benchmarks
These leaderboards are used to track progress in Word Alignment
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WMT 2016 News
MUSE
Europarl ConcoDisco Dataset
Bangla Word Analogy
Most implemented papers
Word Translation Without Parallel Data
facebookresearch/MUSE
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ICLR 2018
We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.
Addressing the Rare Word Problem in Neural Machine Translation
atpaino/deep-text-corrector
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IJCNLP 2015
Our experiments on the WMT14 English to French translation task show that this method provides a substantial improvement of up to 2. 8 BLEU points over an equivalent NMT system that does not use this technique.
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
masoudjs/simalign
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Findings of the Association for Computational Linguistics 2020
We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e. g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
amazon-research/multiatis
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EMNLP 2020
We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus.
Word Alignment by Fine-tuning Embeddings on Parallel Corpora
neulab/awesome-align
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EACL 2021
In addition, we demonstrate that we are able to train multilingual word aligners that can obtain robust performance on different language pairs.
Sparse Attention with Linear Units
Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants.
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
natspeech/natspeech
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NeurIPS 2021
Non-autoregressive text-to-speech (NAR-TTS) models such as FastSpeech 2 and Glow-TTS can synthesize high-quality speech from the given text in parallel.
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback
ZhangShiyue/ChrEn
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ACL 2021
The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment.
Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
showlab/demovlp
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Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.
AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translation
Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks.
