End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge. The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer. The decoder first generates a sketch response with unfilled slots. Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers. We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem. As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation. Task-oriented dialogue systems aim to achieve specific user goals such as restaurant reservation or navigation inquiry within a limited dialogue turns via natural language. Traditional pipeline solutions are composed of natural language understanding, dialogue management and natural language generation BID32 BID28 , where each module is designed separately and expensively. In order to reduce human effort and scale up between domains, end-to-end dialogue systems, which input plain text and directly output system responses, have shown promising results based on recurrent neural networks BID10 BID14 and memory networks BID24 . These approaches have the advantages that the dialogue states are latent without hand-crafted labels and eliminate the needs to model the dependencies between modules and interpret knowledge bases (KB) manually.However, despite the improvement by modeling KB with memory network BID0 , end-to-end systems usually suffer from effectively incorporating external KB into the system response generation. The main reason is that a large, dynamic KB is equal to a noisy input and hard to encode and decode, which makes the generation unstable. Different from chit-chat scenario, this problem is especially harmful in task-oriented one, since the information in KB is usually the expected entities in the response. For example, in TAB0 the driver will expect to get the correct address to the gas station other than a random place such as a hospital. Therefore, pointer networks BID26 or copy mechanism BID8 ) is crucial to successfully generate system responses because directly copying essential words from the input source to the output not only reduces the generation difficulty, but it is also more like a human behavior. For example, in TAB0 , when human want to reply others the Valero's address, they will need to "copy" the information from the table to their response as well.Therefore, in the paper, we propose the global-to-local memory pointer (GLMP) networks, which is composed of a global memory encoder, a local memory decoder, and a shared external knowledge. Unlike existing approaches with copy ability BID9 BID8 , which the only information passed to decoder is the encoder hidden states, our model shares the external knowledge and leverages the encoder and the external knowledge to learn a global memory pointer and global contextual representation. Global memory pointer modifies the external knowledge by softly filtering words that are not necessary for copying. Afterward, instead of generating system responses directly, the local memory decoder first uses a sketch RNN to obtain sketch responses without slot values but sketch tags, which can be considered as learning a latent dialogue management to generate dialogue action template. Then the decoder generates local memory pointers to copy words from external knowledge and instantiate sketch tags.We empirically show that GLMP can achieve superior performance using the combination of global and local memory pointers. In simulated out-of-vocabulary (OOV) tasks in the bAbI dialogue dataset BID0 , GLMP achieves 92.0% per-response accuracy and surpasses existing end-to-end approaches by 7.5% in full dialogue. In the human-human dialogue dataset , GLMP is able to surpass the previous state of the art on both automatic and human evaluation, which further confirms the effectiveness of our double pointers usage. In the work, we present an end-to-end trainable model called global-to-local memory pointer networks for task-oriented dialogues. The global memory encoder and the local memory decoder are designed to incorporate the shared external knowledge into the learning framework. We empirically show that the global and the local memory pointer are able to effectively produce system responses even in the out-of-vocabulary scenario, and visualize how global memory pointer helps as well. As a result, our model achieves state-of-the-art results in both the simulated and the human-human dialogue datasets, and holds potential for extending to other tasks such as question answering and text summarization. One of the reviewers suggested us to compare our work to some existing dialogue framework such as PyDial 5 . To the best of our knowledge, in the PyDial framework, it requires to have the dialogue acts labels for the NLU module and the belief states labels for the belief tracker module. The biggest challenge is we do not have such labels in the SMD and bAbI datasets. Moreover, the semi tracker in PyDial is rule-based, which need to re-write rules whenever it encounters a new domain or new datasets. Even its dialogue management module could be a learning solution like policy networks, the input of the policy network is still the hand-crafted state features and labels. Therefore, without the rules and labels predefined in the NLU and belief tracker modules, PyDial could not learn a good policy network.Truly speaking, based on the data we have (not very big size) and the current state-of-the-art machine learning algorithms and models, we believe that a well and carefully constructed task-oriented dialogue system using PyDial in a known domain using human rules (in NLU and Belief Tracker) with policy networks may outperform the end-to-end systems (more robust). However, in this paper, without additional human labels and human rules, we want to explore the potential and the advantage of end-to-end systems. Besides easy to train, for multi-domain cases, or even zero-shot domain cases, we believe end-to-end approaches will have better adaptability compared to any rule-based systems. Delexicalized Generation: the nearest @poi_type is @poi , @distance away at @address Final Generation: the nearest grocery_store is willows_market , 3_miles away at 409_bollard_st
GLMP: Global memory encoder (context RNN, global pointer) and local memory decoder (sketch RNN, local pointer) that share external knowledge (MemNN) are proposed to strengthen response generation in task-oriented dialogue.
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