Corpus ID: 85507296

1 System Overview Natural Language Understanding ASR Dialog Manager Context Intent Classifier Feedback Topic Dialog Modules Natural Language Generation Segmentation Noun Phrase

@inproceedings{Chen20181SO,
  title={1 System Overview Natural Language Understanding ASR Dialog Manager Context Intent Classifier Feedback Topic Dialog Modules Natural Language Generation Segmentation Noun Phrase},
  author={Chun-Yen Chen and Dian Yu and Weiming Wen and Y. M. Yang and Jiaping Zhang and Mingyang Zhou and Kevin Jesse and Author Chau and Antara Bhowmick and Shreenath Iyer and Giritheja Sreenivasulu and Runxiang Cheng and Ashwin Bhandare and Zhou Yu},
  year={2018}
}
Gunrock is a social bot designed to engage users in open domain conversations. We improved our bot iteratively using large scale user interaction data to be more capable and human-like. Our system engaged in over 40, 000 conversations during the semi-finals period of the 2018 Alexa Prize. We developed a context-aware hierarchical dialog manager to handle a wide variety of user behaviors, such as topic switching and question answering. In addition, we designed a robust threestep natural language… Expand

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SHOWING 1-10 OF 25 REFERENCES
A Neural Conversational Model
TLDR
A simple approach to conversational modeling which uses the recently proposed sequence to sequence framework, and is able to extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. Expand
Dialog Act Modeling for Conversational Speech
We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42`dialog acts' which were hand-labeled in 1155Expand
Learning End-to-End Goal-Oriented Dialog
TLDR
It is shown that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations and be compared to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge. Expand
Leveraging Linguistic Structure For Open Domain Information Extraction
TLDR
This work replaces this large pattern set with a few patterns for canonically structured sentences, and shifts the focus to a classifier which learns to extract self-contained clauses from longer sentences to determine the maximally specific arguments for each candidate triple. Expand
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
TLDR
A novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder and uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders is presented. Expand
Deep Reinforcement Learning for Dialogue Generation
TLDR
This work simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity, non-repetitive turns, coherence, and ease of answering. Expand
Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs
TLDR
It is argued that fictional dialogs offer a way to study how authors create the conversations but don't receive the social benefits (rather, the imagined characters do), and significant coordination across many families of function words in the large movie-script corpus is found. Expand
Automatic Online Evaluation of Intelligent Assistants
TLDR
This paper uses implicit feedback from users to predict whether users are satisfied with the intelligent assistant as well as its components, i.e., speech recognition and intent classification, and develops consistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants. Expand
Sequence to Sequence Learning with Neural Networks
TLDR
This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier. Expand
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
TLDR
The design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) is presented, which was the first Spoken Language Understanding Software Development Kit (SDK) for a virtual digital assistant, as far as the authors are aware. Expand
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