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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 ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.650/ Abstract: Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training. In practice, this is usually countered either by applying regularization methods (e.g. dropout, L2-regularization) or by providing huge amounts of training data. Additionally, Transformer and other architectures are known to struggle when generating very long sequences. For example, in machine translation, the neural-based systems perform worse on very long sequences when compared to the preceding phrase-based translation approaches (Koehn and Knowles, 2017). We present results which suggest that the issue might also be in the mismatch between the length distributions of the training and validation data combined with the aforementioned tendency of the neural networks to overfit to the training data. We demonstrate on a simple string editing task and a ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing
URL: https://dx.doi.org/10.48448/n5ds-km43
https://underline.io/lecture/37799-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 ...
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20
Sometimes We Want Ungrammatical Translations ...
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