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This paper has presented a novel neural networkbased framework for task-oriented dialogue systems

A Network-based End-to-End Trainable Task-oriented Dialogue System.

EACL, pp.438-449, (2017)

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摘要

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing taskoriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each compon...更多

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简介
  • Building a task-oriented dialogue system such as a hotel booking or a technical support service is difficult because it is application-specific and there is usually limited availability of training data.
  • At the other end of the spectrum, sequence to sequence learning (Sutskever et al, 2014) has inspired several efforts to build end-to-end trainable, non-task-oriented conversational systems (Vinyals and Le, 2015; Shang et al, 2015; Serban et al, 2015b)
  • This family of approaches treats dialogue as a source to target sequence transduction problem, applying an encoder network (Cho et al, 2014) to encode a user query into a distributed vector representing its semantics, which conditions a decoder network to generate each system response.
  • They allow the creation of effective chatbot type systems but they lack any capability for supporting domain specific tasks, for example, being able to interact with databases (Sukhbaatar et al, 2015; Yin et al, 2015) and aggregate useful information into their responses
重点内容
  • Building a task-oriented dialogue system such as a hotel booking or a technical support service is difficult because it is application-specific and there is usually limited availability of training data
  • We propose a neural network-based model for task-oriented dialogue systems by balancing the strengths and the weaknesses of the two research communities: the model is end-to-end trainable1 but still modularly connected; it does not directly model the user goal, but it still learns to accomplish the required task by providing relevant and appropriate responses at each turn; it has an explicit representation of database (DB) attributes which it uses to achieve a high task success rate, but has a distributed representation of user intent
  • In this work we focus on text-based dialogue systems, we retain belief tracking at the core of our system because: (1) it enables a sequence of freeform natural language sentences to be mapped into a fixed set of slot-value pairs, which can be used to query a DB
  • This paper has presented a novel neural networkbased framework for task-oriented dialogue systems
  • We demonstrated that the pipe-lined parallel organisation of this collection framework enables good quality task-oriented dialogue data to be collected quickly at modest cost
  • The experimental assessment of the NN dialogue system showed that the learned model can interact efficiently and naturally with human subjects to complete an application-specific task
结论
  • This paper has presented a novel neural networkbased framework for task-oriented dialogue systems.
  • The authors demonstrated that the pipe-lined parallel organisation of this collection framework enables good quality task-oriented dialogue data to be collected quickly at modest cost.
  • The experimental assessment of the NN dialogue system showed that the learned model can interact efficiently and naturally with human subjects to complete an application-specific task.
  • To the best of the knowledge, this is the first end-to-end NNbased model that can conduct meaningful dialogues in a task-oriented application
表格
  • Table1: Tracker performance in terms of Precision, Recall, and F-1 score
  • Table2: Performance comparison of different model architectures based on a corpus-based evaluation
  • Table3: Human assessment of the NN system. The rating for comprehension/naturalness are both out of 5
  • Table4: A comparison of the NN system with a rule-based modular system (HDC)
  • Table5: Additional Rt term for delexicalised tokens when using weighted decoding (Equation 14). Not observed means the corresponding tracker has a highest probability on either not mentioned or dontcare value, while observed mean the highest probability is on one of the categorical values. A positive score encourages the generation of that token while a negative score discourages it
  • Table6: Some samples of real conversational logs between online judges and the end-to-end system
Download tables as Excel
基金
  • Tsung-Hsien Wen and David Vandyke are supported by Toshiba Research Europe Ltd, Cambridge
研究对象与分析
restaurants: 99
There are three informable slots (food, pricerange, area) that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) that the user can ask a value for once a restaurant has been offered. There are 99 restaurants in the DB. Based on this domain, we ran 3000 HITs (Human Intelligence Tasks) in total for roughly 3 days and collected 1500 dialogue turns

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