NTCIR-14 Short Text Conversation Task (STC-3)

Dialogue Quality and Nugget Detection Subtasks

Recently, many reserachers are trying to build automatic helpdesk systems. However, there are very few methods to evaluate such systems. In STC-3, we aim to explore methods to evaluate task-oriented, multi-round, textual dialogue systems automatically. This dataset have the following features:

In this competition, we consider annotations ground truth, and participants are required to predict nugget type for each turn (Nugget Detection, or ND) and dialogue quality for each dialogue (Dialogue Quality, or DQ).



Comming soon


Dataset Overview

The Chinese dataset contains 4,090 (3,700 for training + 390 for testing) customer-helpdesk dialgoues which are crawled from Weibo. All of these dialogues are annotated by 19 annotators.

The English dataset contains 2062 dialogues (1,672 for training + 390 for testing) are manually translated from a subset of the Chinese dataset. The English dataset shares the same annotations with the Chinese dataset.


We hired 19 Chinese students from the department of Computer Science, Waseda University to annotate this dataset.

Format of the JSON file

Each file is in JSON format with UTF-8 encoding.

Following are the top-level fields:

Each element of the turns field contains the following fields:

Each element of annotations contains the following fields:

Nugget Types


Dialogue Quality

Scale: [2, 1, 0, -1, -2]



During the data annotaiton, we noticed that annotators’ assessment on dialgoues are highly subjective and are hard to consolidate them into one gold label. Thus, we proposed to preserve the diverse views in the annotations “as is” and leverage them at the step of evaluation measure calculation.

Instead of juding whether the estimated label is equal to the gold label, we compare the difference between the estiamted distributions and the gold distributions calculaed by 19 anntators’ annotations). Specifically, we employ these metrics for quality sub-task and nugget sub-task:

For the details about the metrics, please vistit:

Test and Submission

For some obvious reasons, we do not release the annotations of the test set. Instead, we require you to submit your prediction file to our server for evaluation. Also, we provide a evaluation script to help you calcualte these metrics for training set locally. For details, please visit Evaluation Page.


Have questions?

Please contact: zhaohao@fuji.waseda.jp

Conditions and Terms

See conditions and terms