论文标题
通过序列到序列体系结构和验证的语言模型进行会话问题重新进行了
Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models
论文作者
论文摘要
本文介绍了对会话问题重新印象（CQR）的实证研究，该研究具有序列到序列体系结构和验证的语言模型（PLM）。我们利用PLM来解决CQR任务中共同目标（最大可能性估计）中实现的强大的令牌独立性假设。在以任务为导向的对话系统的CQR基准测试中，我们评估了最近引入的CANARD数据集上的微调PLM作为一项内域任务，并使用TREC 2019铸造轨道的数据作为外域任务来验证模型。在检查具有不同参数数量的各种体系结构时，我们证明了最近的文本到文本传输变压器（T5）与类似的变压器体系结构相比，在Canard和Cast上取得了最佳的结果。
This paper presents an empirical study of conversational question reformulation (CQR) with sequence-to-sequence architectures and pretrained language models (PLMs). We leverage PLMs to address the strong token-to-token independence assumption made in the common objective, maximum likelihood estimation, for the CQR task. In CQR benchmarks of task-oriented dialogue systems, we evaluate fine-tuned PLMs on the recently-introduced CANARD dataset as an in-domain task and validate the models using data from the TREC 2019 CAsT Track as an out-domain task. Examining a variety of architectures with different numbers of parameters, we demonstrate that the recent text-to-text transfer transformer (T5) achieves the best results both on CANARD and CAsT with fewer parameters, compared to similar transformer architectures.
