Papers by David Vilar
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits (2021.findings-emnlp)
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| Challenge: | Training data for machine translation (MT) is often sourced from multiple large corpora that are multi-faceted in nature. |
| Approach: | They propose to optimize the balance between translationese and natural training data to relieve system developers from manual schedule design. |
| Outcome: | The proposed model relieves system developers from manual schedule design. |
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models (N18-2)
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| Challenge: | In this paper we explore the use of Learning Hidden Unit Contribution for neural machine translation. |
| Approach: | They propose to use Learning Hidden Unit Contribution for the task of neural machine translation. |
| Outcome: | The proposed method achieves improvements of up to 2.6 BLEU points over a general system . it also achieves up to 6 BLUE points if the initial system has been trained on out-of-domain data . |
A Natural Diet: Towards Improving Naturalness of Machine Translation Output (2022.findings-acl)
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| Challenge: | MT evaluation often focuses on accuracy and fluency without paying much attention to translation style. |
| Approach: | They propose a method for training machine translation systems to achieve a more natural style by contrasting training data according to the naturalness of the target side. |
| Outcome: | The proposed method achieves lexical richness on par with human translations, and is preferred by human experts when compared to baseline translations. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
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| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
Controlling Machine Translation for Multiple Attributes with Additive Interventions (2021.emnlp-main)
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| Challenge: | A standard approach for exerting control in MT is to prepend the input with a special tag to signal the desired output attribute. |
| Approach: | They propose a vector-valued approach which allows for fine-grained control over multiple attributes simultaneously via a weighted linear combination of the corresponding vectors. |
| Outcome: | The proposed approach achieves better control over a wider range of tasks than tagging and even fine-tuning a model trained without annotations. |
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (N18-1)
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| Challenge: | Existing approaches to neural machine translation have computational complexities that are either linear or exponential in the number of constraints. |
| Approach: | They propose an algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. |
| Outcome: | The proposed algorithm can place constraints and improve results in simulated post-editing tasks. |