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Mass: Masked sequence to sequence pre-training for language generation
Pre-training and fine-tuning, eg, BERT, have achieved great success in language
understanding by transferring knowledge from rich-resource pre-training task to the low/zero …
understanding by transferring knowledge from rich-resource pre-training task to the low/zero …
Learning deep transformer models for machine translation
Transformer is the state-of-the-art model in recent machine translation evaluations. Two
strands of research are promising to improve models of this kind: the first uses wide …
strands of research are promising to improve models of this kind: the first uses wide …
Leveraging pre-trained checkpoints for sequence generation tasks
Unsupervised pre-training of large neural models has recently revolutionized Natural
Language Processing. By warm-starting from the publicly released checkpoints, NLP …
Language Processing. By warm-starting from the publicly released checkpoints, NLP …
Sparse is enough in scaling transformers
Large Transformer models yield impressive results on many tasks, but are expensive to
train, or even fine-tune, and so slow at decoding that their use and study becomes out of …
train, or even fine-tune, and so slow at decoding that their use and study becomes out of …
Exploring versatile generative language model via parameter-efficient transfer learning
Fine-tuning pre-trained generative language models to down-stream language generation
tasks has shown promising results. However, this comes with the cost of having a single …
tasks has shown promising results. However, this comes with the cost of having a single …
Very deep transformers for neural machine translation
We explore the application of very deep Transformer models for Neural Machine Translation
(NMT). Using a simple yet effective initialization technique that stabilizes training, we show …
(NMT). Using a simple yet effective initialization technique that stabilizes training, we show …
Decoder-only or encoder-decoder? interpreting language model as a regularized encoder-decoder
The sequence-to-sequence (seq2seq) task aims at generating the target sequence based
on the given input source sequence. Traditionally, most of the seq2seq task is resolved by …
on the given input source sequence. Traditionally, most of the seq2seq task is resolved by …
Explicit sparse transformer: Concentrated attention through explicit selection
Self-attention based Transformer has demonstrated the state-of-the-art performances in a
number of natural language processing tasks. Self-attention is able to model long-term …
number of natural language processing tasks. Self-attention is able to model long-term …
UniTE: Unified translation evaluation
Translation quality evaluation plays a crucial role in machine translation. According to the
input format, it is mainly separated into three tasks, ie, reference-only, source-only and …
input format, it is mainly separated into three tasks, ie, reference-only, source-only and …
Multilingual neural machine translation with language clustering
Multilingual neural machine translation (NMT), which translates multiple languages using a
single model, is of great practical importance due to its advantages in simplifying the training …
single model, is of great practical importance due to its advantages in simplifying the training …