Unpredictable Attributes in Market Comment Generation
Unpredictable Attributes in Market Comment Generation
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发表时间：
2021
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通讯作者：
Yumi Hamazono;Tatsuya Ishigaki;Yusuke Miyao;Hiroya Takamura;I. Kobayashi
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作者：
Yumi Hamazono;Tatsuya Ishigaki;Yusuke Miyao;Hiroya Takamura;I. Kobayashi
数据到文本有两种类型的数据集：一种使用在现实世界中获得的原始数据，另一种是为受控任务而实际构建的(artifi)。对于手动构造的数据集，直接输出文本是从其成对的输入数据生成的，因为该数据集构造得很好，没有任何额外的或不必要的(fi)关系。但是，对于使用真实数据和文本构建的数据集，可能无法从输入数据生成正确的输出文本。在这种情况下，我们必须提供额外的数据，例如数据或文本属性标签，以便从配对的输入生成预期的输出文本。本文讨论了在真实数据的数据到文本转换中附加输入标签的重要性。市场评论的内容和风格根据其媒介、市场状况和一天中的时间而变化。然而，由于作为输入数据的股票价格不包含任何上述信息，因此它不能仅从数据中适当地生成评论。因此，我们分析数据集，并为模型中的适当部分提供输入数据不可预测的附加标签。实验结果表明，在文本生成模型的训练过程中，应该将不可预测的属性作为输入的一部分。
There are two types of datasets for data-to-text: one uses raw data obtained in the real world, and the other is constructed artificially for a controlled task. A straightforwardly output text is generated from its paired input data for a manually constructed dataset because the dataset is well constructed without any excess or deficiencies. However, it may not be possible to generate a correct output text from the input data for a dataset constructed with real-world data and text. In such cases, we have to provide additional data, for example, data or text attribute labels, in order to generate the expected output text from the paired input. This paper discusses the importance of additional input labels in data-to-text for real-world data. The content and style of a market comment change depending on its medium, the market situation, and the time of day. However, as the stock price, which is the input data, does not contain any such aforementioned information, it cannot generate comments appropriately from the data alone. Therefore, we analyse the dataset and provide additional labels which are unpredictable with input data for the appropriate parts in the model. Thus, the accuracy of sentence generation is greatly improved compared to the case without the labels.The result suggests unpredictable attributes should be given as a part of the input in the training of the text generating model.