MOSS: End-to-End Dialog System Framework with Modular Supervision

A major bottleneck in training end-to-end task-oriented dialog system is the lack of data. To utilize limited training data more efficiently, we propose Modular Supervision Network (MOSS), an encoder-decoder training framework that could incorporate supervision from various intermediate dialog system modules including natural language understanding, dialog state tracking, dialog policy learning, and natural language generation. With only 60% of the training data, MOSS-all (i.e., MOSS with supervision from all four dialog modules) outperforms state-of-the-art models on CamRest676. Moreover, introducing modular supervision has even bigger benefits when the dialog task has a more complex dialog state and action space. With only 40% of the training data, MOSS-all outperforms the state-of-the-art model on a complex laptop network troubleshooting dataset, LaptopNetwork, that we introduced. LaptopNetwork consists of conversations between real customers and customer service agents in Chinese. Moreover, MOSS framework can accommodate dialogs that have supervision from different dialog modules at both the framework level and model level. Therefore, MOSS is extremely flexible to update in a real-world deployment

Learning End-to-End Goal-Oriented Dialog with Multiple Answers

In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks.Comment: EMNLP 2018. permuted-bAbI dialog tasks are available at - https://github.com/IBM/permuted-bAbI-dialog-task

On End-to-End Learning of Neural Goal-Oriented Dialog Systems

Goal-oriented dialog systems assist users to complete tasks such as restaurant reservations and flight ticket booking. Deep neural networks have opened up the possibility of end-to-end learning of the entire goal-oriented dialog system directly from data. End-to-end learning enables automatic adaptation of the different parts of the dialog system accounting for how changes in one part affect the others. Since the entire dialog system is learned directly from the data, the design of the dialog system need not make any assumptions about the domain. This makes it possible to build dialog systems for new domains with different training data, without much domain-specific hand-crafting of the dialog system. With deep neural networks which can potentially capture the complexity of human dialog in natural language, learning neural goal-oriented dialog systems end-to-end holds the promise of bringing dialog systems into our everyday lives. In this thesis, we identify some of the challenges in end-to-end learning of neural goal-oriented dialog systems and propose methods to address them. We look at four challenges: 1) The challenge posed by the presence of a large number of named entities in goal-oriented dialog tasks. We propose a method to build neural embeddings for named entities on the fly and store them in a key-value table with neural embeddings as keys and the actual named entities as values. The proposed method allows for comparison and retrieval, using neural embeddings as well as actual named entities, which leads to significant improvement in performance, especially in the presence of out-of-vocabulary named entities. 2) The challenge of performing supervised learning of goal-oriented dialog systems with multiple valid next utterances. We propose a method to learn to use different parts of the neural network to encode different predictions of the next utterances with learning of one not interfering with the learning of the others. Our experiments show considerable improvement in the generalization performance. 3) The challenge of handling new user behaviors during deployment of a trained dialog system. We propose a method that learns to anticipate failures and efficiently transfers dialogs to human agents in order to make sure the overall task success of the users remains high. Our experiments show that using our proposed method it is possible to achieve very high user task success while minimally using human agents. 4) The challenge of requiring large amounts of training data for each new dialog task of interest. We show that by selectively learning from a related task's data that is already available, we can improve the performance on a new task of interest that has only a limited amount of training data.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169752/1/rjana_1.pd