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1
On Homophony and Rényi Entropy ...
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2
Backtranslation in Neural Morphological Inflection ...
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3
Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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4
Translating Headers of Tabular Data: A Pilot Study of Schema Translation ...
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5
An Information-Theoretic Characterization of Morphological Fusion ...
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6
Analyzing the Surprising Variability in Word Embedding Stability Across Languages ...
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7
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings ...
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8
STaCK: Sentence Ordering with Temporal Commonsense Knowledge ...
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9
Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
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10
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation ...
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11
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
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12
Sequence Length is a Domain: Length-based Overfitting in Transformer Models ...
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13
Speechformer: Reducing Information Loss in Direct Speech Translation ...
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14
Data and Parameter Scaling Laws for Neural Machine Translation ...
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15
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders ...
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16
Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy ...
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17
Learning to Rewrite for Non-Autoregressive Neural Machine Translation ...
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18
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation ...
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19
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.666/ Abstract: Building neural machine translation systems to perform well on a specific target domain is a well-studied problem. Optimizing system performance for multiple, diverse target domains however remains a challenge. We study this problem in an adaptation setting where the goal is to preserve the existing system quality while incorporating data for domains that were not the focus of the original translation system. We find that we can improve over the performance trade-off offered by Elastic Weight Consolidation with a relatively simple data mixing strategy. At comparable performance on the new domains, catastrophic forgetting is mitigated significantly on strong WMT baselines. Combining both approaches improves the Pareto frontier on this task. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://dx.doi.org/10.48448/q156-0g50
https://underline.io/lecture/37661-improving-the-quality-trade-off-for-neural-machine-translation-multi-domain-adaptation
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20
Sometimes We Want Ungrammatical Translations ...
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