Datasets:

Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
albertvillanova HF staff commited on
Commit
48d5f78
1 Parent(s): d10d2fe

Add mixed data files

Browse files
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: 2710513
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  num_examples: 3138
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  - name: test
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- num_bytes: 891619
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  num_examples: 1045
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  - name: validation
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- num_bytes: 910799
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  num_examples: 1046
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- download_size: 5481785
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- dataset_size: 4512931
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  configs:
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  - config_name: dialog
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  data_files:
@@ -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:
64
  - 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
@@ -8,53 +8,40 @@
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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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  "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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  "feature": {
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  "config_name": "mixed",
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  "version": {
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  "version_str": "1.0.0",
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  "major": 1,
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  "minor": 0,
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  "patch": 0
@@ -62,41 +49,26 @@
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  "splits": {
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  "train": {
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  "name": "train",
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- "num_bytes": 2710513,
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  "num_examples": 3138,
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- "dataset_name": "c3"
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  },
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  "test": {
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- "dataset_name": "c3"
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  },
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  "validation": {
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  "name": "validation",
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- "num_bytes": 910799,
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  "num_examples": 1046,
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- "dataset_name": "c3"
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- }
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  }
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- "dataset_size": 4512931,
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- "size_in_bytes": 9994716
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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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  "documents": {
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  "answer": {
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+ "builder_name": "parquet",
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+ "dataset_name": "c3",
 
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  "config_name": "mixed",
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  "version": {
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  "version_str": "1.0.0",
 
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  "name": "validation",
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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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