Towards End-to-End Open Conversational Machine Reading
- URL: http://arxiv.org/abs/2210.07113v1
- Date: Thu, 13 Oct 2022 15:50:44 GMT
- Title: Towards End-to-End Open Conversational Machine Reading
- Authors: Sizhe Zhou (1, 2, 3), Siru Ouyang (1, 2, 3), Zhuosheng Zhang (1, 2,
3), Hai Zhao (1, 2, 3) ((1) Department of Computer Science and Engineering,
Shanghai Jiao Tong University, (2) Key Laboratory of Shanghai Education
Commission for Intelligent Interaction and Cognitive Engineering, Shanghai
Jiao Tong University, (3) MoE Key Lab of Artificial Intelligence, AI
Institute, Shanghai Jiao Tong University)
- Abstract summary: We model OR-CMR as a unified text-to-text task in a fully end-to-end style.
Experiments on the OR-ShARC dataset show the effectiveness of our proposed end-to-end framework.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In open-retrieval conversational machine reading (OR-CMR) task, machines are
required to do multi-turn question answering given dialogue history and a
textual knowledge base. Existing works generally utilize two independent
modules to approach this problem's two successive sub-tasks: first with a
hard-label decision making and second with a question generation aided by
various entailment reasoning methods. Such usual cascaded modeling is
vulnerable to error propagation and prevents the two sub-tasks from being
consistently optimized. In this work, we instead model OR-CMR as a unified
text-to-text task in a fully end-to-end style. Experiments on the OR-ShARC
dataset show the effectiveness of our proposed end-to-end framework on both
sub-tasks by a large margin, achieving new state-of-the-art results. Further
ablation studies support that our framework can generalize to different
backbone models.
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