Papers by Laurent Sartran
Better Document-Level Machine Translation with Bayes’ Rule (2020.tacl-1)
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| Challenge: | Existing document translation models are based on autoregressive language models, but they are not able to be learned from monolingual documents. |
| Approach: | They propose to use Bayes' rule to create document translation models that can be learned from only parallel sentences and monolingual documents. |
| Outcome: | The proposed model outperforms existing document translation approaches and is based on a novel left-to-right beam-search algorithm. |
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale (2022.tacl-1)
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| Challenge: | a novel class of Transformer language models that combine expressive power, scalability, and strong performance of Transformers and recursive syntactic compositions. |
| Approach: | They introduce Transformer Grammars, a class of Transformer language models that combine expressive power and recursive syntactic compositions. |
| Outcome: | The proposed model outperforms strong baselines on sentence-level language modeling perplexity and syntax-sensitive language evaluation metrics. |
Measuring Progress in Fine-grained Vision-and-Language Understanding (2023.acl-long)
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| Challenge: | X-VLM models lack "fine-grained" understanding of relationships, verbs and numbers in images . pretraining on large-scale image–text data from the Web has facilitated rapid progress on many vision-and-language tasks . |
| Approach: | They investigate models that outperform other baselines on fine-grained data . they highlight importance of novel losses and rich data sources for learning fine-grain skills . |
| Outcome: | The proposed model outperforms baseline models on four fine-grained benchmarks . the model outpersforms other baseline models and even degrades performance . |
SynJax: Structured Probability Distributions for JAX (2023.emnlp-demo)
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| Challenge: | a number of deep learning libraries have been developed to account for structured objects . a vectorized implementation of inference algorithms for structured distributions is difficult to implement . |
| Approach: | SynJax provides vectorized implementations of inference algorithms for structured distributions . authors propose to use a vectorized version of the algorithms to model structure in data . similar structures appear in biology and chemistry . |
| Outcome: | SynJax provides an efficient vectorized implementation of inference algorithms for structured distributions. |