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Found 21 papers, 4 papers with code
Alternating Weak Triphone/BPE Alignment Supervision from Hybrid Model Improves End-to-End ASR
In this paper, alternating weak triphone/BPE alignment supervision is proposed to improve end-to-end model training.
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
To increase the accessibility of our model to non-English speakers, we propose to make use of cascaded machine translation and show that this approach can perform comparably to natively multilingual models while being easier to scale to a large number of languages.
Weak Alignment Supervision from Hybrid Model Improves End-to-end ASR
In contrast to the general one-hot cross-entropy losses, here we use a cross-entropy loss with a label smoothing parameter to regularize the supervision.
Improving Language Model Integration for Neural Machine Translation
The integration of language models for neural machine translation has been extensively studied in the past.
Does Joint Training Really Help Cascaded Speech Translation?
Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Is Encoder-Decoder Redundant for Neural Machine Translation?
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks.
Revisiting Checkpoint Averaging for Neural Machine Translation
Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models.
Self-Normalized Importance Sampling for Neural Language Modeling
Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transformer-Based Direct Hidden Markov Model for Machine Translation
The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks.
On Sampling-Based Training Criteria for Neural Language Modeling
As the vocabulary size of modern word-based language models becomes ever larger, many sampling-based training criteria are proposed and investigated.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unifying Input and Output Smoothing in Neural Machine Translation
Soft contextualized data augmentation is a recent method that replaces one-hot representation of words with soft posterior distributions of an external language model, smoothing the input of neural machine translation systems.
Towards a Better Understanding of Label Smoothing in Neural Machine Translation
In order to combat overfitting and in pursuit of better generalization, label smoothing is widely applied in modern neural machine translation systems.
Predicting and Using Target Length in Neural Machine Translation
Attention-based encoder-decoder models have achieved great success in neural machine translation tasks.
Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation
Mutual learning, where multiple agents learn collaboratively and teach one another, has been shown to be an effective way to distill knowledge for image classification tasks.
Diving Deep into Context-Aware Neural Machine Translation
Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e. g., document-level translation, or having meta-information.
Investigation of Large-Margin Softmax in Neural Language Modeling
After that, we apply the best norm-scaling setup in combination with various margins and conduct neural language models rescoring experiments in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification
Prominently used in support vector machines and logistic regressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to learn complex decision boundaries.
uniblock: Scoring and Filtering Corpus with Unicode Block Information
The preprocessing pipelines in Natural Language Processing usually involve a step of removing sentences consisted of illegal characters.
The RWTH Aachen University Machine Translation Systems for WMT 2019
For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39. 6{\%} BLEU on newstest2019.
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies.
Cross-Lingual Transfer
Low-Resource Neural Machine Translation
+3
Improving Neural Language Models with Weight Norm Initialization and Regularization
Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies.
