|
--- |
|
annotations_creators: |
|
- other |
|
language_creators: |
|
- other |
|
language: |
|
- zh |
|
license: |
|
- unknown |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 100K<n<1M |
|
source_datasets: |
|
- original |
|
task_categories: |
|
- text-classification |
|
- multiple-choice |
|
task_ids: |
|
- topic-classification |
|
- semantic-similarity-scoring |
|
- natural-language-inference |
|
- multiple-choice-qa |
|
paperswithcode_id: clue |
|
pretty_name: 'CLUE: Chinese Language Understanding Evaluation benchmark' |
|
tags: |
|
- coreference-nli |
|
- qa-nli |
|
dataset_info: |
|
- config_name: afqmc |
|
features: |
|
- name: sentence1 |
|
dtype: string |
|
- name: sentence2 |
|
dtype: string |
|
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|
dtype: |
|
class_label: |
|
names: |
|
'0': '0' |
|
'1': '1' |
|
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|
dtype: int32 |
|
splits: |
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num_bytes: 378726 |
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num_examples: 3861 |
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|
num_bytes: 3396535 |
|
num_examples: 34334 |
|
- name: validation |
|
num_bytes: 426293 |
|
num_examples: 4316 |
|
download_size: 1195044 |
|
dataset_size: 4201554 |
|
- config_name: tnews |
|
features: |
|
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|
dtype: string |
|
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|
dtype: |
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|
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'5': '106' |
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'6': '107' |
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'7': '108' |
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'8': '109' |
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'9': '110' |
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'10': '112' |
|
'11': '113' |
|
'12': '114' |
|
'13': '115' |
|
'14': '116' |
|
- name: idx |
|
dtype: int32 |
|
splits: |
|
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|
num_bytes: 810974 |
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num_examples: 10000 |
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- name: train |
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num_bytes: 4245701 |
|
num_examples: 53360 |
|
- name: validation |
|
num_bytes: 797926 |
|
num_examples: 10000 |
|
download_size: 5123575 |
|
dataset_size: 5854601 |
|
- config_name: iflytek |
|
features: |
|
- name: sentence |
|
dtype: string |
|
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|
dtype: |
|
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'73': '73' |
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'74': '74' |
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'75': '75' |
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'76': '76' |
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'77': '77' |
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'78': '78' |
|
'79': '79' |
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'80': '80' |
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'81': '81' |
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'82': '82' |
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'83': '83' |
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'84': '84' |
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'85': '85' |
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'86': '86' |
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'87': '87' |
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'88': '88' |
|
'89': '89' |
|
'90': '90' |
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'91': '91' |
|
'92': '92' |
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'93': '93' |
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'94': '94' |
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'95': '95' |
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'96': '96' |
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'97': '97' |
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'98': '98' |
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'99': '99' |
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'100': '100' |
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'101': '101' |
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'102': '102' |
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'103': '103' |
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'104': '104' |
|
'105': '105' |
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'106': '106' |
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'107': '107' |
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'108': '108' |
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'109': '109' |
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'110': '110' |
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'111': '111' |
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'112': '112' |
|
'113': '113' |
|
'114': '114' |
|
'115': '115' |
|
'116': '116' |
|
'117': '117' |
|
'118': '118' |
|
- name: idx |
|
dtype: int32 |
|
splits: |
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- name: test |
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num_bytes: 2105688 |
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num_examples: 2600 |
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num_bytes: 10028613 |
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num_examples: 12133 |
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- name: validation |
|
num_bytes: 2157123 |
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num_examples: 2599 |
|
download_size: 6505938 |
|
dataset_size: 14291424 |
|
- config_name: cmnli |
|
features: |
|
- name: sentence1 |
|
dtype: string |
|
- name: sentence2 |
|
dtype: string |
|
- name: label |
|
dtype: |
|
class_label: |
|
names: |
|
'0': neutral |
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'1': entailment |
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'2': contradiction |
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- name: idx |
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dtype: int32 |
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num_examples: 12241 |
|
download_size: 31404066 |
|
dataset_size: 72123991 |
|
- config_name: cluewsc2020 |
|
features: |
|
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dtype: int32 |
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dtype: |
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names: |
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'0': 'true' |
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'1': 'false' |
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- name: target |
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struct: |
|
- name: span1_text |
|
dtype: string |
|
- name: span2_text |
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dtype: string |
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dtype: int32 |
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dtype: int32 |
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splits: |
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- name: validation |
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num_examples: 304 |
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download_size: 281384 |
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dataset_size: 1007159 |
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- config_name: csl |
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features: |
|
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dtype: int32 |
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- name: corpus_id |
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download_size: 3234594 |
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dataset_size: 21407229 |
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dataset_size: 42400 |
|
--- |
|
|
|
# Dataset Card for "clue" |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** https://www.cluebenchmarks.com |
|
- **Repository:** https://github.com/CLUEbenchmark/CLUE |
|
- **Paper:** [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://aclanthology.org/2020.coling-main.419/) |
|
- **Point of Contact:** [Zhenzhong Lan](mailto:lanzhenzhong@westlake.edu.cn) |
|
- **Size of downloaded dataset files:** 189.48 MB |
|
- **Size of the generated dataset:** 463.81 MB |
|
- **Total amount of disk used:** 653.29 MB |
|
|
|
### Dataset Summary |
|
|
|
CLUE, A Chinese Language Understanding Evaluation Benchmark |
|
(https://www.cluebenchmarks.com/) is a collection of resources for training, |
|
evaluating, and analyzing Chinese language understanding systems. |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Languages |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
#### afqmc |
|
|
|
- **Size of downloaded dataset files:** 1.14 MB |
|
- **Size of the generated dataset:** 4.01 MB |
|
- **Total amount of disk used:** 5.15 MB |
|
|
|
An example of 'validation' looks as follows. |
|
``` |
|
{ |
|
"idx": 0, |
|
"label": 0, |
|
"sentence1": "双十一花呗提额在哪", |
|
"sentence2": "里可以提花呗额度" |
|
} |
|
``` |
|
|
|
#### c3 |
|
|
|
- **Size of downloaded dataset files:** 3.05 MB |
|
- **Size of the generated dataset:** 14.96 MB |
|
- **Total amount of disk used:** 18.02 MB |
|
|
|
An example of 'train' looks as follows. |
|
``` |
|
This example was too long and was cropped: |
|
|
|
{ |
|
"answer": "比人的灵敏", |
|
"choice": ["没有人的灵敏", "和人的差不多", "和人的一样好", "比人的灵敏"], |
|
"context": "[\"许多动物的某些器官感觉特别灵敏,它们能比人类提前知道一些灾害事件的发生,例如,海洋中的水母能预报风暴,老鼠能事先躲避矿井崩塌或有害气体,等等。地震往往能使一些动物的某些感觉器官受到刺激而发生异常反应。如一个地区的重力发生变异,某些动物可能通过它们的平衡...", |
|
"id": 1, |
|
"question": "动物的器官感觉与人的相比有什么不同?" |
|
} |
|
``` |
|
|
|
#### chid |
|
|
|
- **Size of downloaded dataset files:** 132.75 MB |
|
- **Size of the generated dataset:** 261.38 MB |
|
- **Total amount of disk used:** 394.13 MB |
|
|
|
An example of 'train' looks as follows. |
|
``` |
|
This example was too long and was cropped: |
|
|
|
{ |
|
"answers": { |
|
"candidate_id": [3, 5, 6, 1, 7, 4, 0], |
|
"text": ["碌碌无为", "无所作为", "苦口婆心", "得过且过", "未雨绸缪", "软硬兼施", "传宗接代"] |
|
}, |
|
"candidates": "[\"传宗接代\", \"得过且过\", \"咄咄逼人\", \"碌碌无为\", \"软硬兼施\", \"无所作为\", \"苦口婆心\", \"未雨绸缪\", \"和衷共济\", \"人老珠黄\"]...", |
|
"content": "[\"谈到巴萨目前的成就,瓜迪奥拉用了“坚持”两个字来形容。自从上世纪90年代克鲁伊夫带队以来,巴萨就坚持每年都有拉玛西亚球员进入一队的传统。即便是范加尔时代,巴萨强力推出的“巴萨五鹰”德拉·佩纳、哈维、莫雷罗、罗杰·加西亚和贝拉乌桑几乎#idiom0000...", |
|
"idx": 0 |
|
} |
|
``` |
|
|
|
#### cluewsc2020 |
|
|
|
- **Size of downloaded dataset files:** 0.27 MB |
|
- **Size of the generated dataset:** 0.98 MB |
|
- **Total amount of disk used:** 1.23 MB |
|
|
|
An example of 'train' looks as follows. |
|
``` |
|
{ |
|
"idx": 0, |
|
"label": 1, |
|
"target": { |
|
"span1_index": 3, |
|
"span1_text": "伤口", |
|
"span2_index": 27, |
|
"span2_text": "它们" |
|
}, |
|
"text": "裂开的伤口涂满尘土,里面有碎石子和木头刺,我小心翼翼把它们剔除出去。" |
|
} |
|
``` |
|
|
|
#### cmnli |
|
|
|
- **Size of downloaded dataset files:** 29.95 MB |
|
- **Size of the generated dataset:** 68.78 MB |
|
- **Total amount of disk used:** 98.73 MB |
|
|
|
An example of 'train' looks as follows. |
|
``` |
|
{ |
|
"idx": 0, |
|
"label": 0, |
|
"sentence1": "从概念上讲,奶油略读有两个基本维度-产品和地理。", |
|
"sentence2": "产品和地理位置是使奶油撇油起作用的原因。" |
|
} |
|
``` |
|
|
|
### Data Fields |
|
|
|
The data fields are the same among all splits. |
|
|
|
#### afqmc |
|
- `sentence1`: a `string` feature. |
|
- `sentence2`: a `string` feature. |
|
- `label`: a classification label, with possible values including `0` (0), `1` (1). |
|
- `idx`: a `int32` feature. |
|
|
|
#### c3 |
|
- `id`: a `int32` feature. |
|
- `context`: a `list` of `string` features. |
|
- `question`: a `string` feature. |
|
- `choice`: a `list` of `string` features. |
|
- `answer`: a `string` feature. |
|
|
|
#### chid |
|
- `idx`: a `int32` feature. |
|
- `candidates`: a `list` of `string` features. |
|
- `content`: a `list` of `string` features. |
|
- `answers`: a dictionary feature containing: |
|
- `text`: a `string` feature. |
|
- `candidate_id`: a `int32` feature. |
|
|
|
#### cluewsc2020 |
|
- `idx`: a `int32` feature. |
|
- `text`: a `string` feature. |
|
- `label`: a classification label, with possible values including `true` (0), `false` (1). |
|
- `span1_text`: a `string` feature. |
|
- `span2_text`: a `string` feature. |
|
- `span1_index`: a `int32` feature. |
|
- `span2_index`: a `int32` feature. |
|
|
|
#### cmnli |
|
- `sentence1`: a `string` feature. |
|
- `sentence2`: a `string` feature. |
|
- `label`: a classification label, with possible values including `neutral` (0), `entailment` (1), `contradiction` (2). |
|
- `idx`: a `int32` feature. |
|
|
|
### Data Splits |
|
|
|
| name |train |validation|test | |
|
|-----------|-----:|---------:|----:| |
|
|afqmc | 34334| 4316| 3861| |
|
|c3 | 11869| 3816| 3892| |
|
|chid | 84709| 3218| 3231| |
|
|cluewsc2020| 1244| 304| 290| |
|
|cmnli |391783| 12241|13880| |
|
|
|
## Dataset Creation |
|
|
|
### Curation Rationale |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Source Data |
|
|
|
#### Initial Data Collection and Normalization |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
#### Who are the source language producers? |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Annotations |
|
|
|
#### Annotation process |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
#### Who are the annotators? |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Personal and Sensitive Information |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
## Considerations for Using the Data |
|
|
|
### Social Impact of Dataset |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Discussion of Biases |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Other Known Limitations |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
## Additional Information |
|
|
|
### Dataset Curators |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Licensing Information |
|
|
|
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
|
|
|
### Citation Information |
|
|
|
``` |
|
@inproceedings{xu-etal-2020-clue, |
|
title = "{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark", |
|
author = "Xu, Liang and |
|
Hu, Hai and |
|
Zhang, Xuanwei and |
|
Li, Lu and |
|
Cao, Chenjie and |
|
Li, Yudong and |
|
Xu, Yechen and |
|
Sun, Kai and |
|
Yu, Dian and |
|
Yu, Cong and |
|
Tian, Yin and |
|
Dong, Qianqian and |
|
Liu, Weitang and |
|
Shi, Bo and |
|
Cui, Yiming and |
|
Li, Junyi and |
|
Zeng, Jun and |
|
Wang, Rongzhao and |
|
Xie, Weijian and |
|
Li, Yanting and |
|
Patterson, Yina and |
|
Tian, Zuoyu and |
|
Zhang, Yiwen and |
|
Zhou, He and |
|
Liu, Shaoweihua and |
|
Zhao, Zhe and |
|
Zhao, Qipeng and |
|
Yue, Cong and |
|
Zhang, Xinrui and |
|
Yang, Zhengliang and |
|
Richardson, Kyle and |
|
Lan, Zhenzhong", |
|
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", |
|
month = dec, |
|
year = "2020", |
|
address = "Barcelona, Spain (Online)", |
|
publisher = "International Committee on Computational Linguistics", |
|
url = "https://aclanthology.org/2020.coling-main.419", |
|
doi = "10.18653/v1/2020.coling-main.419", |
|
pages = "4762--4772", |
|
} |
|
``` |
|
|
|
|
|
### Contributions |
|
|
|
Thanks to [@thomwolf](https://github.com/thomwolf), [@JetRunner](https://github.com/JetRunner) for adding this dataset. |