41 citations · 247 across the 35 of their papers we have counts for
10 papers · 1 filter
Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
Yang Feng, Wanying Xie, Shuhao Gu +4
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 pro…
Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation
Chenze Shao, Jinchao Zhang, Yang Feng +2
Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously. However, in the context…
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Yong Shan, Yang Feng, Jinchao Zhang +2
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produ…
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
Zhengxin Yang, Jinchao Zhang, Fandong Meng +3
Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations. The widely used method for document-level translation…
Improving Multi-Head Attention with Capsule Networks
Shuhao Gu, Yang Feng
Multi-head attention advances neural machine translation by working out multiple versions of attention in different subspaces, but the neglect of semantic overlapping between subsp…
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations
Zekang Li, Cheng Niu, Fandong Meng +3
Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in…
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Chenze Shao, Yang Feng, Jinchao Zhang +3
Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails…
Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables
Lei Shen, Yang Feng, Haolan Zhan
Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It's practical th…
Bridging the Gap between Training and Inference for Neural Machine Translation
Wen Zhang, Yang Feng, Fandong Meng +2
Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the…
Improving Domain Adaptation Translation with Domain Invariant and Specific Information
Shuhao Gu, Yang Feng, Qun Liu
In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper,…