The Experts below are selected from a list of 6081 Experts worldwide ranked by ideXlab platform

Caiming Xiong - One of the best experts on this subject based on the ideXlab platform.

  • global to local Memory Pointer networks for task oriented dialogue
    arXiv: Computation and Language, 2019
    Co-Authors: Richard Socher, Caiming Xiong
    Abstract:

    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.

  • global to local Memory Pointer networks for task oriented dialogue
    International Conference on Learning Representations, 2019
    Co-Authors: Richard Socher, Caiming Xiong
    Abstract:

    A system and corresponding method are provided for generating responses for a dialogue between a user and a computer. The system includes a Memory storing information for a dialogue history and a knowledge base. An encoder may receive a new utterance from the user and generate a global Memory Pointer used for filtering the knowledge base information in the Memory. A decoder may generate at least one local Memory Pointer and a sketch response for the new utterance. The sketch response includes at least one sketch tag to be replaced by knowledge base information from the Memory. The system generates the dialogue computer response using the local Memory Pointer to select a word from the filtered knowledge base information to replace the at least one sketch tag in the sketch response.

Min Yang - One of the best experts on this subject based on the ideXlab platform.

  • a template guided hybrid Pointer network for knowledge basedtask oriented dialogue systems
    arXiv: Computation and Language, 2021
    Co-Authors: Dingmin Wang, Ziyao Chen, Li Zhong, Yunzhe Tao, Min Yang
    Abstract:

    Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid Pointer network for the knowledge-based task-oriented dialogue system, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a Memory Pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.

Yang Min - One of the best experts on this subject based on the ideXlab platform.

  • A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems
    2021
    Co-Authors: Wang Dingmin, Li Zhong, Chen Ziyao, He Wanwei, Tao Yunzhe, Yang Min
    Abstract:

    Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid Pointer network for the knowledge-based task-oriented dialogue system, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a Memory Pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.Comment: DialDoc workshop@ACL-IJCNLP-202

Li Zhong - One of the best experts on this subject based on the ideXlab platform.

  • a template guided hybrid Pointer network for knowledge basedtask oriented dialogue systems
    arXiv: Computation and Language, 2021
    Co-Authors: Dingmin Wang, Ziyao Chen, Li Zhong, Yunzhe Tao, Min Yang
    Abstract:

    Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid Pointer network for the knowledge-based task-oriented dialogue system, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a Memory Pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.

  • A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems
    2021
    Co-Authors: Wang Dingmin, Li Zhong, Chen Ziyao, He Wanwei, Tao Yunzhe, Yang Min
    Abstract:

    Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid Pointer network for the knowledge-based task-oriented dialogue system, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a Memory Pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.Comment: DialDoc workshop@ACL-IJCNLP-202

Wu Chien-sheng - One of the best experts on this subject based on the ideXlab platform.

  • Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
    2019
    Co-Authors: Wu Chien-sheng, Socher Richard, Xiong Caiming
    Abstract:

    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.Comment: ICLR 201

  • Learning to Memorize in Neural Task-Oriented Dialogue Systems
    2019
    Co-Authors: Wu Chien-sheng
    Abstract:

    In this thesis, we leverage the neural copy mechanism and Memory-augmented neural networks (MANNs) to address existing challenge of neural task-oriented dialogue learning. We show the effectiveness of our strategy by achieving good performance in multi-domain dialogue state tracking, retrieval-based dialogue systems, and generation-based dialogue systems. We first propose a transferable dialogue state generator (TRADE) that leverages its copy mechanism to get rid of dialogue ontology and share knowledge between domains. We also evaluate unseen domain dialogue state tracking and show that TRADE enables zero-shot dialogue state tracking and can adapt to new few-shot domains without forgetting the previous domains. Second, we utilize MANNs to improve retrieval-based dialogue learning. They are able to capture dialogue sequential dependencies and memorize long-term information. We also propose a recorded delexicalization copy strategy to replace real entity values with ordered entity types. Our models are shown to surpass other retrieval baselines, especially when the conversation has a large number of turns. Lastly, we tackle generation-based dialogue learning with two proposed models, the Memory-to-sequence (Mem2Seq) and global-to-local Memory Pointer network (GLMP). Mem2Seq is the first model to combine multi-hop Memory attention with the idea of the copy mechanism. GLMP further introduces the concept of response sketching and double Pointers copying. We show that GLMP achieves the state-of-the-art performance on human evaluation.Comment: HKUST MPhil Thesis. 93 page