Papers by Marc’Aurelio Ranzato
Facebook AI’s WAT19 Myanmar-English Translation Task Submission (D19-52)
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Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
| Challenge: | Using back-translation, we can improve generalization by using noisy channel re-ranking and ensembling. |
| Approach: | They propose to use BPE-based transformer models to leverage monolingual data to improve generalization and use noisy channel re-ranking and ensembling to improve results. |
| Outcome: | The proposed system improves on the baseline system trained exclusively on the provided small parallel dataset, and the human evaluation and BLEU score are higher. |
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English (D19-1)
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Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc’Aurelio Ranzato
| Challenge: | a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. |
| Approach: | They propose to use a dataset to evaluate methods trained on low-resource language pairs . they report baseline performance using supervised, weakly supervised and semi-supervised settings . |
| Outcome: | The proposed evaluation datasets show that current state-of-the-art methods perform poorly on this benchmark, posing a challenge to the research community working on low-resource MT. |
The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation (2022.tacl-1)
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Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, Angela Fan
| Challenge: | a lack of good evaluation benchmarks hinders progress in low-resource and multilingual machine translation . despite advances in translation quality for a handful of languages, many low-source languages are not even supported by most popular translation engines. |
| Approach: | They propose a high-quality evaluation benchmark for machine translation using 3001 sentences from Wikipedia . they aim to improve evaluation of models on long tail of low-resource languages . |
| Outcome: | The proposed evaluation benchmarks are based on 3001 sentences extracted from Wikipedia . the results show that the models can be used to evaluate multilingual systems . |
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. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |
On The Evaluation of Machine Translation Systems Trained With Back-Translation (2020.acl-main)
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| Challenge: | Back-translation is a data augmentation technique that can be used to improve neural machine translation systems. |
| Approach: | They propose to combine back-translation with a language model score to measure fluency. |
| Outcome: | The proposed method improves translation quality of natural text and translationese according to professional translators. |
Classical Structured Prediction Losses for Sequence to Sequence Learning (N18-1)
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| Challenge: | Recent work on training neural attention models at the sequence level has focused on a series of objective functions commonly used for structured prediction. |
| Approach: | They propose to use objective functions commonly used to train linear models for structured prediction to train neural attention models at the sequence-level using either reinforcement learning-style methods or beam search optimization. |
| Outcome: | The proposed model outperforms beam search optimization on German-English translation and abstractive summarization tasks. |
Discriminative Reranking for Neural Machine Translation (2021.acl-long)
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| Challenge: | reranking models allow the integration of rich features to select a better output hypothesis within an n-best list or lattice. |
| Approach: | They use discriminative reranking to train a large transformer architecture to train an ranked list of hypotheses. |
| Outcome: | Experiments on four WMT directions show that discriminative reranking improves translation quality. |
The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)
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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
| Challenge: | Despite the interconnected world we live in, people in different places talk about different things in different parts of the world. |
| Approach: | They propose a metric to quantify the effect of local context in machine translation and propose measurable results. |
| Outcome: | The proposed metric can be used to quantify the effect of local context on the use of language in machine translation systems on low resource languages. |