Intelligence Science and Technology Course, Graduate School of Informatics, Kyoto University

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Kyoto University Commonsense Inference Dataset (KUCI) †

This dataset is a Japanese commonsense inference dataset built from a web corpus of 715 million sentences.
It consists of 104k multiple-choice questions that ask contingency between basic events.
The problems are generated from the event pairs verified by crowdsourcing.
Here is an example:

電池の減りはやはり早いので、 (The battery drains so fast that)
    a. 実際の半導体製造装置は実現しません (we cannot actually make the semiconductor manufacturing equipment)
    b. 今回は期間限定でのお届けになります (it is a limited-time offer this time)
    c. 原子炉を手動停止する (we manually shut down a nuclear reactor)
    d. 充電用にＵＳＢケーブル買います (I buy a USB cable for charging)

The task is to select the most appropriate choice as the continuing sentence.
In this case, d is a correct answer.

Data format †

The data format is jsonl as follows.

{
    "id": "0", 
    "label": "d", 
    "agreement": "2",
    "context": "電池 の 減り は やはり 早い ので 、", 
    "choice_a": "実際 の 半導体 製造 装置 は 実現 し ませ ん",
    "choice_b": "今回 は 期間 限定 で の お 届け に なり ます", 
    "choice_c": "原子 炉 を 手動 停止 する",
    "choice_d": "充電 用 に ＵＳＢ ケーブル 買い ます"
} ...

"agreement" is the result of contingency verification for each pair of a context and a correct choice, which is among {2, 3, 4}.
"context" and "choice_{a, b, c, d}" have already been divided into morphemes using Juman++ 2.0.0-rc3.

Download †

We prepared training, development, and test set.
Please refer to [1] for the detailed statistics.

• Download (10.4MB)

Please concact us by e-mail at "nl-resource at nlp.ist.i.kyoto-u.ac.jp" for questions.

Demo †

You can try some training examples on the demo site below.

• Demo (in Japanese)

Update history †

• Renamed "Kyoto University Commonsense Inference Dataset" - October 7, 2020
• Release ver1.0 - October 6, 2020

References †

• [1] Kazumasa Omura, Daisuke Kawahara and Sadao Kurohashi: A Method for Building a Commonsense Inference Dataset based on Basic Events, In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020).

     

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