Guillaume Lample
12 papers in the PaperMetrix corpus
Papers by this author
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Massively Multilingual Word Embeddings
2016 · arXiv (Cornell University)
We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do …
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Neural Architectures for Named Entity Recognition
2016
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
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Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
2016
Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W Black, Lori Levin, Chris Dyer. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: …
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Word Translation Without Parallel Data
2017 · arXiv (Cornell University)
State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed …
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Unsupervised Machine Translation Using Monolingual Corpora Only
2017 · arXiv (Cornell University)
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet …
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What you can cram into a single vector: Probing sentence embeddings for\n linguistic properties
2018 · arXiv (Cornell University)
Although much effort has recently been devoted to training high-quality\nsentence embeddings, we still have a poor understanding of what they are\ncapturing. "Downstream" tasks, often based on sentence classification, are\ncommonly used to evaluate the quality of …
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XNLI: Evaluating Cross-lingual Sentence Representations
2018 · arXiv (Cornell University)
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, Veselin Stoyanov. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
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The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English
2019
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc’Aurelio Ranzato. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on …
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Cross-lingual Language Model Pretraining
2019 · arXiv (Cornell University)
Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods …
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Phrase-Based & Neural Unsupervised Machine Translation
2018
Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This …
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
2018
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. "Downstream" tasks, often based on sentence classification, are commonly used to evaluate …
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LLaMA: Open and Efficient Foundation Language Models
2023 · arXiv (Cornell University)
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly …