论文标题
使用深层上下文化语言模型的表搜索
Table Search Using a Deep Contextualized Language Model
论文作者
论文摘要
预验证的情境化语言模型（例如BERT）在各种自然语言处理基准上取得了令人印象深刻的结果。受益于多个训练的任务和大规模培训语料库，经过验证的模型可以捕获复杂的句法关系。在本文中，我们使用深层上下文化的语言模型BERT进行临时表检索的任务。我们研究了如何考虑BERT的表结构和输入长度限制的表内容。我们还提出了一种方法，该方法结合了先前文献中有关桌子检索的特征，并与Bert共同训练它们。在公共数据集的实验中，我们表明我们的最佳方法可以优于以前的最新方法，而在不同的评估指标下具有很大利润的BERT基准。
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex syntactic word relations. In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval. We investigate how to encode table content considering the table structure and input length limit of BERT. We also propose an approach that incorporates features from prior literature on table retrieval and jointly trains them with BERT. In experiments on public datasets, we show that our best approach can outperform the previous state-of-the-art method and BERT baselines with a large margin under different evaluation metrics.
