Papers by Melvin Johnson
Explicit Alignment Objectives for Multilingual Bidirectional Encoders (2021.naacl-main)
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| Challenge: | Pre-trained cross-lingual encoders have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resourced languages. |
| Approach: | They propose a method to align multilingual encoders using two explicit alignment objectives that align the multilingual representations at different granularities. |
| Outcome: | The proposed method achieves gains of up to 1.1 average F1 score on sequence tagging and 27.3 average accuracy on retrieval over the XLM-R-large model. |
DOCmT5: Document-Level Pretraining of Multilingual Language Models (2022.findings-naacl)
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| Challenge: | DOCmT5 is a multilingual sequence-to-sequence language model pretraining with large-scale parallel documents. |
| Approach: | They propose a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. |
| Outcome: | The proposed model improves on baselines on document-level generation tasks. |
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (P18-1)
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Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, Macduff Hughes
| Challenge: | In recent years, the emergence of seq2seq models has revolutionized the field of machine translation by replacing traditional phrase-based approaches with neural machine translation (NMT) systems based on the encoder-decoder paradigm. |
| Approach: | They propose to use a convolutional seq2seq model to combine the strengths of the two approaches. |
| Outcome: | The proposed architectures outperform the existing models on the WMT’14 benchmark dataset. |
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents (2022.acl-long)
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| Challenge: | Document-level neural machine translation (DocNMT) is a powerful tool for integrating cross-sentence context into translations. |
| Approach: | They explore whether and how contextual modeling in DocNMT is transferable via multilingual modeling. |
| Outcome: | The proposed model can be used to transfer from teacher languages to student languages with no documents but sentence level data. |
MergeDistill: Merging Language Models using Pre-trained Distillation (2021.findings-acl)
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| Challenge: | Existing pre-trained multilingual language models often lack capacity and skewed data . this leads to inequitable representation of languages due to limited capacity and sub-optimal vocabularies. |
| Approach: | They propose a framework to merge pre-trained multilingual language models to maximize their assets with minimal dependencies. |
| Outcome: | The proposed framework outperforms teacher-trained models on multiple datasets and with a fixed model capacity. |
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021.acl-short)
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| Challenge: | Recent studies have shown that cross-lingual transfer learning in pre-trained multilingual models could be improved further by incorporating parallel data. |
| Approach: | They propose to integrate parallel data into mT5 pre-training to improve results on downstream multilingual and cross-lingual tasks. |
| Outcome: | The proposed model improves cross-lingual transfer significantly in small fine-tuning datasets and small model sizes. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints (2021.emnlp-main)
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| Challenge: | HintedBT provides hints (as source tags on the encoder) about the quality of each source-target pair. |
| Approach: | They propose a method which provides hints to the encoder and decoder to improve the quality of BT data by providing hints about the quality. |
| Outcome: | The proposed method improves translation quality and performance in three low/medium-resource language pairs. |
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation (2021.emnlp-main)
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Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson
| Challenge: | Recent advances in multilingual natural language processing have improved performance on benchmarks such as XTREME and XGLUE by 13 points . however, improvements have been easier to achieve in some tasks than others . |
| Approach: | They extend XTREME to XTRAME-R, which includes ten natural language understanding tasks and covers 50 typologically diverse languages. |
| Outcome: | The proposed framework improves the performance on the XTREME multilingual benchmark by 13 points compared to human-level performance on English transfer learning. |
Massively Multilingual Neural Machine Translation (N19-1)
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| Challenge: | Multilingual Neural Machine Translation models support translation from multiple source languages into multiple target languages. |
| Approach: | They perform extensive experiments in training massively multilingual NMT models involving up to 103 distinct languages and 204 translation directions simultaneously. |
| Outcome: | The proposed model outperforms the state-of-the-art in low resource settings while supporting up to 59 languages in 116 translation directions. |
Small and Practical BERT Models for Sequence Labeling (D19-1)
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| Challenge: | Existing models for morphosyntactic tagging have focused on building separate models for each language or for a small group of related languages. |
| Approach: | They propose a scheme to train a single multilingual sequence labeling model that is small and fast enough to run on a CPU. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages and low-level models on codemixed inputs. |