Sequence-to-sequence neural networks have been widely used in language-based applications as they have flexible capabilities to learn various language models. However, when seeking for the optimal language response through trained neural networks, current existing approaches such as beam-search decoder strategies are still not able reaching to promising performances. Instead of developing various decoder strategies based on a "regular sentence order" neural network (a trained model by outputting sentences from left-to-right order), we leveraged "reverse" order as additional language model (a trained model by outputting sentences from right-to-left order) which can provide different perspectives for the path finding problems. In this paper, we propose bidirectional strategies in searching paths by combining two networks (left-to-right and right-to-left language models) making a bidirectional beam search possible. Besides, our solution allows us using any similarity measure in our sentence selection criterion. Our approaches demonstrate better performance compared to the unidirectional beam search strategy.
Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy
Beijing University of Posts and Telecommunications（北京邮电大学）；Zhongguancun Academy（中关村学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems
Princeton University, Princeton NJ 08544, USA, , WWW home page: http://users/ iekeland/web/welcome.html Universit\' e de Paris-Sud, Laboratoire d'Analyse Num\' e rique, B\ a timent 425, F-91405 Orsay Cedex, France；Megatronix, Beijing, China Baidu, Inc., Beijing, China Shumei AI Research Institute, Beijing China Newcastle University, Newcastle Upon Tyne, United Kingdom
Synchronous Bidirectional Inference for Neural Sequence Generation
National Laboratory of Pattern Recognition, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Transformer with Bidirectional Decoder for Speech Recognition
Tsinghua University（清华大学）；ShanghaiTech University（上海科技大学）；Tsingmicro Intelligent Technology Co., Limited（清微智能科技有限公司）；Beijing National Research Center For Information Science And Technology（北京国家信息科学与技术研究中心）
