Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
Chinese
Size:
10K - 100K
ArXiv:
License:
Commit
•
48d5f78
1
Parent(s):
d10d2fe
Add mixed data files
Browse files- README.md +13 -5
- dataset_infos.json +11 -39
- mixed/test-00000-of-00001.parquet +3 -0
- mixed/train-00000-of-00001.parquet +3 -0
- mixed/validation-00000-of-00001.parquet +3 -0
README.md
CHANGED
@@ -62,16 +62,16 @@ dataset_info:
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sequence: string
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splits:
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- name: train
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num_bytes:
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num_examples: 3138
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- name: test
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num_bytes:
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num_examples: 1045
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- name: validation
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num_bytes:
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num_examples: 1046
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download_size:
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dataset_size:
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configs:
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- config_name: dialog
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data_files:
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@@ -81,6 +81,14 @@ configs:
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path: dialog/test-*
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- split: validation
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path: dialog/validation-*
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---
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# Dataset Card for C3
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sequence: string
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splits:
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- name: train
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+
num_bytes: 2710473
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num_examples: 3138
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- name: test
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num_bytes: 891579
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num_examples: 1045
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- name: validation
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num_bytes: 910759
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num_examples: 1046
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+
download_size: 3183780
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+
dataset_size: 4512811
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configs:
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- config_name: dialog
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data_files:
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path: dialog/test-*
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- split: validation
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path: dialog/validation-*
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- config_name: mixed
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data_files:
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- split: train
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path: mixed/train-*
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- split: test
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path: mixed/test-*
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- split: validation
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path: mixed/validation-*
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---
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# Dataset Card for C3
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dataset_infos.json
CHANGED
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"documents": {
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"feature": {
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-
"id": null,
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"_type": "Value"
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},
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"questions": {
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"feature": {
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"question": {
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"dtype": "string",
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-
"id": null,
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"_type": "Value"
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},
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"answer": {
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"dtype": "string",
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-
"id": null,
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"_type": "Value"
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},
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"choice": {
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"feature": {
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"dtype": "string",
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"dialog": {
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"description": "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.\nWe present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.\n",
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"documents": {
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"feature": {
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"dtype": "string",
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"_type": "Value"
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"_type": "Sequence"
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"questions": {
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"dataset_name": "c3",
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"config_name": "mixed",
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}
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"download_size": 3183780,
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},
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"dialog": {
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"description": "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.\nWe present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.\n",
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mixed/test-00000-of-00001.parquet
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mixed/train-00000-of-00001.parquet
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mixed/validation-00000-of-00001.parquet
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