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Found 16 papers, 11 papers with code
Non-autoregressive Streaming Transformer for Simultaneous Translation
Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality.
Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation
Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem.
Rephrasing the Reference for Non-Autoregressive Machine Translation
In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output.
Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency.
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
We extend the alignment space to non-monotonic alignments to allow for the global word reordering and further consider all alignments that overlap with the target sentence.
One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation
Non-autoregressive neural machine translation (NAT) suffers from the multi-modality problem: the source sentence may have multiple correct translations, but the loss function is calculated only according to the reference sentence.
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation
The underlying cause is that training samples do not get balanced training in each model update, so we name this problem \textit{imbalanced training}.
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously.
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation
Meanwhile, we force the conventional decoder to simulate the behaviors of the seer decoder via knowledge distillation.
Modeling Coverage for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously.
Knowledge Distillation based Ensemble Learning for Neural Machine Translation
Under this framework, we introduce word-level ensemble learning and sequence-level ensemble learning for neural machine translation, where sequence-level ensemble learning is capable of aggregating translation models with different decoding strategies.
Generating Diverse Translation from Model Distribution with Dropout
Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation.
Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution.
Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously.
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails to exploit the target sequential information.
Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation
Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also suffers from exposure bias.
