Papers by Marcos Treviso
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
| Challenge: | Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows. |
| Approach: | They propose to use data, time, storage, or energy to improve model performance. |
| Outcome: | The proposed methods and findings provide guidance for conducting NLP under limited resources and point towards promising research directions for developing more efficient methods. |
Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese (2020.lrec-1)
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| Challenge: | Using automated analysis of connected speech is a promising direction for diagnosing cognitive impairments. |
| Approach: | They propose to use a novel model to segment impaired speech transcriptions . they propose to include a Linear Chain CRF and a self-attention mechanism . |
| Outcome: | The proposed system performs better than the existing model with three new datasets used to diagnose cognitive impairments. |
The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics (2023.acl-short)
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| Challenge: | Neural metrics for machine translation evaluation are considered "black boxes" lexical overlap-based metrics are popular for evaluation of translation systems and algorithms . |
| Approach: | They develop and compare several neural explainability methods to understand translation errors . they aim to better understand the correspondence between token-level explanations and human annotated error spans . |
| Outcome: | The proposed methods leverage token-level information that can be directly attributed to translation errors. |
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation (2023.acl-long)
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| Challenge: | Existing methods for analyzing and training NLP models have not been integrated to combine their complementary advantages. |
| Approach: | They introduce a framework for selective rationalization and counterfactual text generation that leverages CREST to regularize selective rationales and a loss function that regularizes selective rationals. |
| Outcome: | The proposed framework generates valid counterfactuals that are more natural than those produced by previous methods and can be used for data augmentation at scale. |
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors (2024.findings-emnlp)
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Marcos Treviso, Nuno Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan Stigt, Andre Martins
| Challenge: | Neural machine translation systems produce translations with errors and anomalies . understanding these errors can help improve the translation quality and user experience . |
| Approach: | They propose an open large language model (LLM) built on top of TowerBase to provide free-text explanations for translation errors in order to guide the generation of a corrected translation. |
| Outcome: | The proposed model improves translation quality and user experience by allowing translators to provide free-text explanations for errors and anomalies. |
OpenKiwi: An Open Source Framework for Quality Estimation (P19-3)
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| Challenge: | Existing open-source frameworks for QE are based on complex ensemble systems, complicated architectures, or require not well-documented pretraining and fine-tuning of some components. |
| Approach: | They introduce OpenKiwi, a Pytorch-based framework for translation quality estimation. |
| Outcome: | The proposed framework performs state-of-the-art on word-level and sentence-level tasks and is near state-outperforming on sentence- and word-based tasks. |