Deep Context Resolution

nlp;context resolution;coreference;deep learning;deep neural networks;dialog;conversation understanding

Chen, Junnanadvisor:Li, Ming ; affiliation1:Faculty of Mathematics ; Li, Ming ;

University of Waterloo

关键词: context resolution;    deep learning;    dialog;    nlp;    Master Thesis;    coreference;    conversation understanding;    deep neural networks;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/13301/3/Chen_Junnan.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Conversations depend on information from the context. To go beyond one-round conversation, a chatbot must resolve contextual information such as:1) co-reference resolution, 2) ellipsis resolution,and 3) conjunctive relationship resolution.There are simply not enough data to avoid these problems by trying to train a sequence-to-sequencemodel for multi-round conversation similar to that of one-round conversation.The contributions of this paper are: 1) We formulate the problem ofcontext resolution for conversation; 2) We present deep learning models, includingan end-to-end network for context resolution; 3) We propose a way of creating a huge amount of