Kasumi Aoki


2020

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Learning with Contrastive Examples for Data-to-Text Generation
Yui Uehara | Tatsuya Ishigaki | Kasumi Aoki | Hiroshi Noji | Keiichi Goshima | Ichiro Kobayashi | Hiroya Takamura | Yusuke Miyao
Proceedings of the 28th International Conference on Computational Linguistics

Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., “Nikkei gains” is generated when “Nikkei drops” is expected. We investigate models trained on contrastive examples i.e., incorrect sentences or terms, in addition to correct ones to reduce such errors. We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as “gain” or “drop”. We then use learning methods with several losses that exploit contrastive examples. Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability of generating sentences with better lexical choice, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances on different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance. Human evaluation also supports the effectiveness of using contrastive examples.

2019

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Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels
Kasumi Aoki | Akira Miyazawa | Tatsuya Ishigaki | Tatsuya Aoki | Hiroshi Noji | Keiichi Goshima | Ichiro Kobayashi | Hiroya Takamura | Yusuke Miyao
Proceedings of the 12th International Conference on Natural Language Generation

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, because depending on users, it differs what they are interested in, so it is necessary to develop a method to generate various summaries according to users’ interests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation.

2018

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Generating Market Comments Referring to External Resources
Tatsuya Aoki | Akira Miyazawa | Tatsuya Ishigaki | Keiichi Goshima | Kasumi Aoki | Ichiro Kobayashi | Hiroya Takamura | Yusuke Miyao
Proceedings of the 11th International Conference on Natural Language Generation

Comments on a stock market often include the reason or cause of changes in stock prices, such as “Nikkei turns lower as yen’s rise hits exporters.” Generating such informative sentences requires capturing the relationship between different resources, including a target stock price. In this paper, we propose a model for automatically generating such informative market comments that refer to external resources. We evaluated our model through an automatic metric in terms of BLEU and human evaluation done by an expert in finance. The results show that our model outperforms the existing model both in BLEU scores and human judgment.