Papers by Danielle Saunders
Multi-representation ensembles and delayed SGD updates improve syntax-based NMT (P18-2)
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| Challenge: | Existing studies have shown that NMT models trained to generate target syntax exhibit improved sentence structure relative to those trained on plain-text. |
| Approach: | They propose an approach to decoding ensembles of models generating different representations, focusing on models generating syntax. |
| Outcome: | The proposed approach gives state-of-the-art performance on a difficult Japanese-English task. |
Using Context in Neural Machine Translation Training Objectives (2020.acl-main)
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| Challenge: | Neural Machine Translation (NMT) training is based on document-level metrics, not sentence-level BLEU. |
| Approach: | They propose to merge document-level metrics with batch-level documents to improve NMT training. |
| Outcome: | The proposed training is more robust for document-level metrics than sequence MRT and maximum-likelihood training. |
First the Worst: Finding Better Gender Translations During Beam Search (2022.findings-acl)
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| Challenge: | Neural language generation models optimized by likelihood tend towards 'safe' word choice. |
| Approach: | They propose to use beam search to improve gender diversity in n-best lists and rerank n best lists using gender features obtained from the source sentence to address this problem. |
| Outcome: | The proposed approach improves gender diversity in n-best lists and reranks n best lists using gender features obtained from the source sentence. |
Domain adapted machine translation: What does catastrophic forgetting forget and why? (2024.emnlp-main)
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| Challenge: | Neural Machine Translation (NMT) models can be specialized by domain adaptation, often fine-tuning on a dataset of interest. |
| Approach: | They propose a novel approach to understanding catastrophic forgetting during NMT adaptation by investigating the relationship between the data and the in-domain vocabulary coverage. |
| Outcome: | The proposed model can be specialized by fine-tuning on a domain of interest, but can fail to achieve the predicted quality of the target domain. |
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020.acl-main)
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| Challenge: | Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. |
| Approach: | They propose a lattice-rescoring scheme which allows a trade-off between general translation quality and bias reduction during adaptation and inference time. |
| Outcome: | The proposed approach outperforms all systems evaluated on WinoMT with no degradation of general test set BLEU. |
Domain Adaptive Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation models are effective when trained on broad domains with large datasets, such as news translation. |
| Approach: | They propose a novel approach for adaptive ensemble weighting for Neural Machine Translation by extending Bayesian Interpolation with source information. |
| Outcome: | The proposed approach improves performance on Spanish-English and English-German tasks without the need for the domain label. |