The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform

Shiyu Chang - One of the best experts on this subject based on the ideXlab platform.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    Empirical Methods in Natural Language Processing, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    arXiv: Computation and Language, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. Our source code is available at: this https URL.

Xiaoxiao Guo - One of the best experts on this subject based on the ideXlab platform.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    Empirical Methods in Natural Language Processing, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    arXiv: Computation and Language, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. Our source code is available at: this https URL.

Yupeng Gao - One of the best experts on this subject based on the ideXlab platform.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    Empirical Methods in Natural Language Processing, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    arXiv: Computation and Language, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. Our source code is available at: this https URL.

Chuang Gan - One of the best experts on this subject based on the ideXlab platform.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    Empirical Methods in Natural Language Processing, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    arXiv: Computation and Language, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. Our source code is available at: this https URL.

Murray Campbell - One of the best experts on this subject based on the ideXlab platform.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    Empirical Methods in Natural Language Processing, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches.

  • Interactive Fiction game playing as multi paragraph reading comprehension with reinforcement learning
    arXiv: Computation and Language, 2020
    Co-Authors: Xiaoxiao Guo, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang
    Abstract:

    Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. Our source code is available at: this https URL.