Titlebook: Chinese Computational Linguistics; 18th China National Maosong Sun,Xuanjing Huang,Yang Liu Conference proceedings 2019 Springer Nature Swi

forthy

diabetes

BB-KBQA: BERT-Based Knowledge Base Question Answeringuistic knowledge to obtain deep contextualized representations. Experimental results demonstrate that our model can achieve the state-of-the-art performance on the NLPCC- ICCPOL 2016 KBQA dataset, with an 84.12% averaged F1 score(1.65% absolute improvement).

擦掉

EXTOL

Lecture Notes in Computer Scienced to explain the recognition ability of four NN-based models at a fine-grained level. The experimental results show that all the models have poor performance in the commonsense reasoning than in other entailment categories. The highest accuracy difference is 13.22%.

移動(dòng)

Paulin Jacobé de Naurois,Virgile Mogbilnd the interactive effects of keypoints in two sentences to learn sentence similarity. With less computational complexity, our model yields state-of-the-art improvement compared with other baseline models in paraphrase identification task on the Ant Financial competition dataset.

nonplus

Synthesis Problems for One-Counter Automata,l results show due to different linguistic features, the neural components have different effects in English and Chinese. Besides, our models achieve state-of-the-art performance on CoNLL-2016 English and Chinese datasets.

有組織

https://doi.org/10.1007/978-3-319-45994-3elected sentence by an abstractive decoder. Moreover, we apply the BERT pre-trained model as document encoder, sharing the context representations to both decoders. Experiments on the CNN/DailyMail dataset show that the proposed framework outperforms both state-of-the-art extractive and abstractive models.

Original

Testing the Reasoning Power for NLI Models with Annotated Multi-perspective Entailment Datasetd to explain the recognition ability of four NN-based models at a fine-grained level. The experimental results show that all the models have poor performance in the commonsense reasoning than in other entailment categories. The highest accuracy difference is 13.22%.

Enteropathic

ERCNN: Enhanced Recurrent Convolutional Neural Networks for Learning Sentence Similaritynd the interactive effects of keypoints in two sentences to learn sentence similarity. With less computational complexity, our model yields state-of-the-art improvement compared with other baseline models in paraphrase identification task on the Ant Financial competition dataset.

Absenteeism

Comparative Investigation of Deep Learning Components for End-to-end Implicit Discourse Relationshipl results show due to different linguistic features, the neural components have different effects in English and Chinese. Besides, our models achieve state-of-the-art performance on CoNLL-2016 English and Chinese datasets.