Papers by Nuno M. Guerreiro
SPECTRA: Sparse Structured Text Rationalization (2021.emnlp-main)
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| Challenge: | Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize rationale extraction. |
| Approach: | They propose a framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer. |
| Outcome: | The proposed framework outperforms previous studies on performance and plausibility of extracted rationales. |
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation (2023.acl-long)
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| Challenge: | Neural machine translation models can unpredictably produce severely pathological translations, known as hallucinations, that seriously undermine user trust. |
| Approach: | They propose a fully unsupervised, plug-in detector that can be used with any attention-based NMT model. |
| Outcome: | The proposed detector outperforms existing models and is competitive with detectors that employ external models trained on millions of samples. |
Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) is becoming more accurate, but hallucinations are extremely pathological . previous work focused on artificial settings where the problem is amplified, disregarding some common types of hallucines . |
| Approach: | They propose a method for alleviating hallucinations at test time that significantly reduces the hallucinic rate. |
| Outcome: | The proposed method significantly reduces the hallucinatory rate in a natural setting. |
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. |