Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreove...Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.展开更多
The conversation machine comprehension(MC)task aims to answer questions in the multi-turn conversation for a single passage.However,recent approaches don’t exploit information from historical conversations effectivel...The conversation machine comprehension(MC)task aims to answer questions in the multi-turn conversation for a single passage.However,recent approaches don’t exploit information from historical conversations effectively,which results in some references and ellipsis in the current question cannot be recognized.In addition,these methods do not consider the rich semantic relationships between words when reasoning about the passage text.In this paper,we propose a novel model GraphFlow+,which constructs a context graph for each conversation turn and uses a unique recurrent graph neural network(GNN)to model the temporal dependencies between the context graphs of each turn.Specifically,we exploit three different ways to construct text graphs,including the dynamic graph,static graph,and hybrid graph that combines the two.Our experiments on CoQA,QuAC and DoQA show that the GraphFlow+model can outperform the state-of-the-art approaches.展开更多
Opinion question machine reading comprehension(MRC)requires a machine to answer questions by analyzing corresponding passages.Compared with traditional MRC tasks where the answer to every question is a segment of text...Opinion question machine reading comprehension(MRC)requires a machine to answer questions by analyzing corresponding passages.Compared with traditional MRC tasks where the answer to every question is a segment of text in corresponding passages,opinion question MRC is more challenging because the answer to an opinion question may not appear in corresponding passages but needs to be deduced from multiple sentences.In this study,a novel framework based on neural networks is proposed to address such problems,in which a new hybrid embedding training method combining text features is used.Furthermore,extra attention and output layers which generate auxiliary losses are introduced to jointly train the stacked recurrent neural networks.To deal with imbalance of the dataset,irrelevancy of question and passage is used for data augmentation.Experimental results show that the proposed method achieves state-of-the-art performance.We are the biweekly champion in the opinion question MRC task in Artificial Intelligence Challenger 2018(AIC2018).展开更多
基金National key research and development program(2020AAA0108500)National Natural Science Foundation of China Project(No.U1836118)Key Laboratory of Rich Media Digital Publishing,Content Organization and Knowledge Service(No.:ZD2022-10/05).
文摘Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.
文摘The conversation machine comprehension(MC)task aims to answer questions in the multi-turn conversation for a single passage.However,recent approaches don’t exploit information from historical conversations effectively,which results in some references and ellipsis in the current question cannot be recognized.In addition,these methods do not consider the rich semantic relationships between words when reasoning about the passage text.In this paper,we propose a novel model GraphFlow+,which constructs a context graph for each conversation turn and uses a unique recurrent graph neural network(GNN)to model the temporal dependencies between the context graphs of each turn.Specifically,we exploit three different ways to construct text graphs,including the dynamic graph,static graph,and hybrid graph that combines the two.Our experiments on CoQA,QuAC and DoQA show that the GraphFlow+model can outperform the state-of-the-art approaches.
基金Project supported by the China Knowledge Centre for Engineering Sciences and Technology(No.CKCEST-2019-1-12)the National Natural Science Foundation of China(No.61572434)。
文摘Opinion question machine reading comprehension(MRC)requires a machine to answer questions by analyzing corresponding passages.Compared with traditional MRC tasks where the answer to every question is a segment of text in corresponding passages,opinion question MRC is more challenging because the answer to an opinion question may not appear in corresponding passages but needs to be deduced from multiple sentences.In this study,a novel framework based on neural networks is proposed to address such problems,in which a new hybrid embedding training method combining text features is used.Furthermore,extra attention and output layers which generate auxiliary losses are introduced to jointly train the stacked recurrent neural networks.To deal with imbalance of the dataset,irrelevancy of question and passage is used for data augmentation.Experimental results show that the proposed method achieves state-of-the-art performance.We are the biweekly champion in the opinion question MRC task in Artificial Intelligence Challenger 2018(AIC2018).
