基于压缩与推理的长文本多项选择答题方法

Long Text Multiple Choice Answer Method Based on Compression and Reasoning

多项选择作为机器阅读理解中的一项重要任务,在自然语言处理(natural language processing,NLP)领域受到了广泛关注。由于数据中需要处理的文本长度不断增长,长文本多项选择成为了一项新的挑战。然而,现有的长文本处理方法容易丢失文本中的有效信息,导致结果不准确。针对上述问题,提出了一种基于压缩与推理的长文本多项选择答题方法(Long Text Multiple Choice Answer Method Based on Compression and Reasoning,LTMCA),通过训练评判模型识别相关句子,将相关句拼接成短文本输入到推理模型进行推理。为了提高评判模型的精度,在评判模型中增加了文章与选项之间的交互以补充文章对选项的注意力,有针对性地进行相关语句识别,更加准确地完成多项选择答题任务。在本文构建的CLTMCA中文长文本多项选择数据集上进行了实验验证,结果表明本文方法能够有效地解决BERT在处理长文本多项选择任务时的限制问题,相比于其他方法,在各项评价指标上均取得了较高的提升。

As a significant task in Machine Reading Comprehension,multiple choice has received widespread attention in Natural Language Processing(NLP).Since the length of text that needs to be processed in data continues to be longer,long text multiple choice becomes a new challenge.However,existing long text processing methods tend to lose useful information in the text,leading to inaccurate results.To solve the above problems,this paper proposes the Long Text Multiple Choice Answer Method Based on Compression and Reasoning(LTMCA),which identifies relevant sentences by training the judgment model,and combines the relevant sentences to form a short text to be input into the inference model for inference.In order to improve the accuracy of the evaluation model,the interaction between the essay and the options is added to the evaluation model to supplement the essay's attention to the options,and the relevant statements are identified in a targeted way to complete the multiple-choice answer task more accurately.Experimental verification is carried out on the CLTMCA Chinese long text multiple choice dataset constructed in this paper,and the results show that the proposed method can effectively solve the problems of the BERT in handling the long text multiple choice task.Compared with other methods,this method has greatly improved in various evaluation indicators.