| Challenge: | a new version of Bicleaner detects noisy sentences in parallel corpora . the tool is based on pre-trained transformer-based language models fine-tuned on a binary classification task. |
| Approach: | They propose to use Bicleaner AI to detect noisy sentences in parallel corpora . they use pre-trained transformer-based language models fine-tuned on a binary classification task . |
| Outcome: | The proposed tool improves translation quality and reduces manual cleaning steps. |
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| Challenge: | Standard decoders for neural machine translation generate a single token per timestep, which slows inference . a series of controlled experiments demonstrates that SynST decodes sentences 5x faster than the baseline autoregressive Transformer. |
| Approach: | They propose a syntactically supervised Transformer that generates all target tokens in one shot . synST is a variant of the Transformer architecture that autoregressively predicts a chunked parse tree . |
| Outcome: | The proposed method decodes sentences 5x faster than the baseline method on En-De and En-Fr datasets while achieving higher BLEU scores. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
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One Sentence One Model for Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation. |
| Approach: | They propose a dynamic neural network which learns a general network as usual and fine-tunes it for each test sentence. |
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Fixing Translation Divergences in Parallel Corpora for Neural MT (D18-1)
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| Challenge: | Existing methods to detect translation divergences from parallel corpora are noisy and limited in size. |
| Approach: | They propose an unsupervised method for detecting translation divergences in parallel sentences . they use a neural network that computes cross-lingual sentence similarity scores . |
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Domain Adaptation of Neural Machine Translation by Lexicon Induction (P19-1)
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| Challenge: | Neural machine translation (NMT) is sensitive to domain shift, resulting in failure for sentences with large numbers of unknown words and lack of supervision for domain-specific words. |
| Approach: | They propose an unsupervised method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. |
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Improving Machine Translation with Phrase Pair Injection and Corpus Filtering (2022.emnlp-main)
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| Challenge: | In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation systems. |
| Approach: | They propose to combine Phrase Pair Injection and Corpus Filtering to boost performance of Neural Machine Translation systems. |
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An Effective Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
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Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)
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| Challenge: | In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence. |
| Approach: | They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences. |
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Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)
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| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
| Approach: | They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation. |
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Evaluating Discourse Phenomena in Neural Machine Translation (N18-1)
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| Challenge: | Existing models for machine translation have been evaluated with standard automatic metrics, but are poorly adapted to evaluating discourse phenomena. |
| Approach: | They propose to use BLEU to train multi-encoder NMT models on English subtitles to test their ability to exploit previous source and target sentences. |
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