Papers by Gerard I. Gállego
Measuring the Mixing of Contextual Information in the Transformer (2022.emnlp-main)
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| Challenge: | Experimentally, we show that ALTI provides more faithful explanations and increased robustness than gradient-based methods. |
| Approach: | They propose to measure token-to-token interactions within each layer and then use them to aggregate model predictions. |
| Outcome: | The proposed method provides more faithful explanations and increased robustness than gradient-based methods. |
SpeechAlign: A Framework for Speech Translation Alignment Evaluation (2024.lrec-main)
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| Challenge: | Speech-to-Speech and Speech- to-Text translation are currently dynamic areas of research. |
| Approach: | They propose a framework to evaluate source-target alignment in speech models . they introduce a speech gold alignment dataset and introduce two new metrics . |
| Outcome: | The proposed framework evaluates source-target alignment quality within speech models. |
Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer (2022.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) relies on source sentence and target prefix attributions for each input token. |
| Approach: | They propose an interpretability method that tracks input tokens’ attributions for both contexts and extends it to any encoder-decoder Transformer-based model. |
| Outcome: | The proposed method can be extended to any encoder-decoder Transformer-based model and provides insights into their behaviour. |
Evaluating Gender Bias in Speech Translation (2022.lrec-1)
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| Challenge: | Existing evaluation techniques for gender biases are lacking in the field of machine translation. |
| Approach: | They propose to use a free evaluation set to evaluate gender bias in speech translation. |
| Outcome: | The proposed set is the speech version of WinoMT, an MT challenge set. |
On the Locality of Attention in Direct Speech Translation (2022.acl-srw)
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| Challenge: | Recent advances in NLP have created problems with the complexity of the self-attention layer. |
| Approach: | They propose to substitute standard self-attention with a local efficient one to avoid the computation of attention weights. |
| Outcome: | The proposed model matches the baseline performance and improves efficiency by skipping the computation of weights that standard attention discards. |
Explaining How Transformers Use Context to Build Predictions (2023.acl-long)
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| Challenge: | Existing methods for analyzing input attributions for a model's prediction are unclear how prior words affect the model' s decision throughout the layers. |
| Approach: | They propose a procedure to analyze models for language generation using the Transformer and a comparison of their results with evidence of the linguistic phenomena. |
| Outcome: | The proposed method consistently aligns better than gradient-based and perturbation-based baselines and generates human-like source-target alignments for building predictions. |
Multiformer: A Head-Configurable Transformer-Based Model for Direct Speech Translation (2022.naacl-srw)
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| Challenge: | Existing approaches to address speech tasks with a self-attention mechanism are expensive and lead to information loss. |
| Approach: | They propose a Transformer-based model which uses different attention mechanisms on each head to bias the self-attention towards the extraction of more diverse token interactions. |
| Outcome: | The proposed model outperforms baseline models by 0.7 BLEU in the speech task. |