41 citations · 247 across the 35 of their papers we have counts for
35 papers
Multimodality of Ir fission studied by Langevin approach
Y. G. Huang, F. C. Gu, Y. J. Feng +5
[Background] The fission mechanism of sub-lead nuclides remains unclear, especially the types of fission modes involved and their corresponding shell effects. [Purpose] The aim is…
Glancing Future for Simultaneous Machine Translation
Shoutao Guo, Shaolei Zhang, Yang Feng
Simultaneous machine translation (SiMT) outputs translation while reading the source sentence. Unlike conventional sequence-to-sequence (seq2seq) training, existing SiMT methods ad…
Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappings
Shuhao Gu, Yang Feng
The many-to-many multilingual neural machine translation can translate between language pairs unseen during training, i.e., zero-shot translation. Improving zero-shot translation r…
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation
Yang Feng, Shuhao Gu, Dengji Guo +2
Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks gl…
GTM: A Generative Triple-Wise Model for Conversational Question Generation
Lei Shen, Fandong Meng, Jinchao Zhang +2
Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To a…
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency
Zekang Li, Jinchao Zhang, Zhengcong Fei +2
A good open-domain chatbot should avoid presenting contradictory responses about facts or opinions in a conversational session, known as its consistency capacity. However, evaluati…
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
Zekang Li, Jinchao Zhang, Zhengcong Fei +2
Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they gene…
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Chenze Shao, Yang Feng, Jinchao Zhang +2
In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregre…
Modeling Coverage for Non-Autoregressive Neural Machine Translation
Yong Shan, Yang Feng, Chenze Shao
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously. Despite its high efficiency, NAT usually suf…
Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation
Shuhao Gu, Yang Feng, Wanying Xie
Domain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both the general-domain and in-domain. However,…
Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation
Lei Shen, Haolan Zhan, Xin Shen +1
Open-domain multi-turn conversations mainly have three features, which are hierarchical semantic structure, redundant information, and long-term dependency. Grounded on these, sele…
WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track
Zekang Li, Zongjia Li, Jinchao Zhang +2
We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2 (Interactive Dialogue). In sub-ta…
Future-Guided Incremental Transformer for Simultaneous Translation
Shaolei Zhang, Yang Feng, Liangyou Li
Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios. The previous wait-k policy is concise and achi…
Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation
Shuhao Gu, Yang Feng
Neural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge…
Generating Diverse Translation from Model Distribution with Dropout
Xuanfu Wu, Yang Feng, Chenze Shao
Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate d…
Token-level Adaptive Training for Neural Machine Translation
Shuhao Gu, Jinchao Zhang, Fandong Meng +4
There exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Ne…
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation
Lei Shen, Yang Feng
Emotion-controllable response generation is an attractive and valuable task that aims to make open-domain conversations more empathetic and engaging. Existing methods mainly enhanc…
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Yong Shan, Zekang Li, Jinchao Zhang +4
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have…
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
Zeyang Lei, Zekang Li, Jinchao Zhang +5
Recently, variational auto-encoder (VAE) based approaches have made impressive progress on improving the diversity of generated responses. However, these methods usually suffer the…
Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog
Zekang Li, Zongjia Li, Jinchao Zhang +3
Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challeng…
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…