Titlebook: Natural Language Processing and Chinese Computing; 9th CCF Internationa Xiaodan Zhu,Min Zhang,Ruifang He Conference proceedings 2020 Spring

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教義

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Generating Emotional Social Chatbot Responses with a Consistent Speaking Style achieved promising results, the issue of the speaking style inconsistency still exists. In this paper, we propose a Style-Aware Emotional Dialogue System (SEDS) to enhance speaking style consistency through detecting user’s emotions and modeling speaking styles in emotional response generation. Spe

恫嚇

An Interactive Two-Pass Decoding Network for Joint Intent Detection and Slot Fillingwo tasks focus on modeling the semantic correlations between the intent and slots and applying the information of one task to guide the other task, which helps them to promote each other. However, most existing joint approaches only unidirectionally utilize the intent information to guide slot filli

恫嚇

RuKBC-QA: A Framework for Question Answering over Incomplete KBs Enhanced with Rules Injection-QA). To alleviate this problem, a framework, RuKBC-QA, is proposed to integrate methods of rule-based knowledge base completion (KBC) into general QA systems. Three main components are included in our framework, namely, a rule miner that mines logic rules from the KB, a rule selector that selects m

表狀態(tài)

Syntax-Guided Sequence to Sequence Modeling for Discourse Segmentationining process or heavily rely on powerful pre-trained word vectors. Under this condition, a simpler but more robust segmentation method is needed. In this work, we take a deeper look into intra-sentence dependencies to investigate if the syntax information is totally useless, or to what extent it ca

合并

Macro Discourse Relation Recognition via Discourse Argument Pair Graphation, but also make it difficult to deal with long-distance dependencies when the discourse arguments are paragraph-level or document-level. To address the above issues, we propose a GCN-based neural network model on discourse argument pair graph to transform discourse relation recognition into a n

Respond

Dependency Parsing with Noisy Multi-annotation Datang real-life texts, parsing performance degrades dramatically. Besides the domain adaptation technique, which has made slow progress due to its intrinsic difficulty, one straightforward way is to annotate a certain scale of syntactic data given a new source of texts. However, it is well known that a

CHART

Joint Bilinear End-to-End Dependency Parsing with Prior Knowledgearsing model, including POS tagger and Joint Bilinear Model (JBM). Based on prior POS knowledge from dataset, we use POS tagging results to guide the training of JBM. To narrow the gap between edge and label prediction, we pass the knowledge hidden in label prediction procedure in JBM. Motivated by

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