• Corpus ID: 2129889

Learning End-to-End Goal-Oriented Dialog

@article{Bordes2016LearningEG,
  title={Learning End-to-End Goal-Oriented Dialog},
  author={Antoine Bordes and Jason Weston},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.07683}
}
Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. [] Key Result We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (Henderson et al., 2014a). We show similar result patterns on data extracted from an online concierge service.
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References

SHOWING 1-10 OF 35 REFERENCES

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

This work proposes a suite of new tasks that test the ability of models to answer factual questions, provide personalization, carry short conversations about the two, and finally to perform on natural dialogs from Reddit.

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding

The experiments on Microsoft Cortana conversational data show that the proposed memory network architecture can effectively extract salient semantics for modeling knowledge carryover in the multi-turn conversations and outperform the results using the state-of-the-art recurrent neural network framework (RNN) designed for single-turn SLU.

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

This work argues for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering, and classify these tasks into skill sets so that researchers can identify (and then rectify) the failings of their systems.

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

The recently proposed hierarchical recurrent encoder-decoder neural network is extended to the dialogue domain, and it is demonstrated that this model is competitive with state-of-the-art neural language models and back-off n-gram models.

Incremental on-line adaptation of POMDP-based dialogue managers to extended domains

It is shown that it is possible to effectively double the number of concepts understood by a system providing restaurant information using only 1000 adaptation dialogues with real users.

The Second Dialog State Tracking Challenge

The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions and ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.

On the Evaluation of Dialogue Systems with Next Utterance Classification

The performance of humans on Next-Utterance-Classification is investigated to validate the relevance of NUC as a method of evaluation and confirm the utility of this class of tasks for driving further research in automated dialogue systems.

The Fourth Dialog State Tracking Challenge

This edition of the fourth dialog state tracking challenge again focused on human-human dialogs, but introduced the task of cross-lingual adaptation of trackers, which received a total of 32 entries from 9 research groups.

An ISU Dialogue System Exhibiting Reinforcement Learning of Dialogue Policies: Generic Slot-Filling in the TALK In-car System

This prototype is the first "Information State Update" (ISU) dialogue system to exhibit reinforcement learning of dialogue strategies, and also has a fragmentary clarification feature.

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

A wide survey of publicly available datasets suitable for data-driven learning of dialogue systems is carried out and important characteristics of these datasets are discussed and how they can be used to learn diverse dialogue strategies.