Titlebook: Image and Graphics; 9th International Co Yao Zhao,Xiangwei Kong,David Taubman Conference proceedings 2017 Springer Nature Switzerland AG 20

FOVEA

Yang Yu,Zhiqiang Gong,Ping Zhong,Jiaxin Shan pairs, the passage context, and current question. It can be applied to a variety of scenarios with human-machine dialogue. The major challenge of this task is to require the model to consider the relevant conversation history while understanding the passage. Existing methods usually simply prepend

ORE

Zhongliang Yang,Yu-Jin Zhang,Sadaqat ur Rehman,Yongfeng HuangTourism-domain Knowledge Graph (TKG), which is helpful to manage tourism information and develop downstream applications such as tourism search engine, recommendation and Q & A. However, the existing TKG is deficient, and there are few open methods to promote the construction and widespread applicat

relieve

Youxia Cao,Bo Jiang,Jin Tang,Bin Luoadvantage of lots of semantic information contained in golden temporal relation labels, which is lost by the discrete one-hot labels. To alleviate the loss of semantic information, we propose learning Temporal semantic information of the golden labels by Auxiliary Contrastive Learning (TempACL). Dif

Arboreal

Jing Wang,Hong Zhu,Shan Xue,Jing Shiations in natural language processing tasks such as reading comprehension, question and answer systems. The main approach is to compute the interaction between text representations and sentence pairs through an attention mechanism, which can extract the semantic information between sentence pairs we

推遲

Yuwei Qiu,Huimin Ma,Lei Gaommon practice is applying the translation model as a feature extractor. However, there exist several discrepancies between the translation model and the QE model. The translation model is trained in an autoregressive manner, while the QE model is performed in a non-autoregressive manner. Besides, th

Panacea

brachial-plexus

Perennial長(zhǎng)期的

ORE

hypnogram

Jingwen Yan,Zhenguo Yuan,Tingting Xie,Huimin Zhaothree verbs of communication (., . and .) were chosen as the research objects. The Corpus of China English (CCE) was built with 13.9 million tokens. All the colligational patterns of the three verbs were extracted from the CCE by the tool Colligator, and analyzed in comparison with those from the Br