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Added new Chinese and Korean reading comprehension datasets
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README.md

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- [Entity linking](chinese/chinese.md#entity-linking)
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- [Chinese word segmentation](chinese/chinese_word_segmentation.md)
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- [Question answering](chinese/question_answering.md)
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### French
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- [Question Answering](portuguese/question_answering.md)
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### Korean
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- [Question Answering](korean/question_answering.md)
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### Nepali
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- [Machine Translation](nepali/nepali.md#machine-translation)

chinese/chinese.md

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| Tsai & Roth (2016) | 83.6 | [Cross-lingual wikification using multilingual embeddings](http://cogcomp.org/papers/TsaiRo16b.pdf) | |
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[Go back to the README](../README.md)
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## reading comprehension
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### Dureader Datasets
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See [here](https://ai.baidu.com/broad/introduction?dataset=dureader) to see the introduction.
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#### Baidu DuReader Dataset
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See [here](https://ai.baidu.com/broad/download?dataset=dureader) to download the Dataset.
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#### Disambiguation-Only Models
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See [here](https://ai.baidu.com/broad/leaderboard?dataset=dureader) to see the leaderboard.

chinese/question_answering.md

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# Question answering
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Question answering is the task of answering a question.
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### Table of contents
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- [Reading comprehension](#reading-comprehension)
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- [CMRC2018](#cmrc-2018)
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- [DRCD](#drcd)
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- [DuReader](#dureader)
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## Reading comprehension
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### CMRC 2018
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The [Chinese Machine Reading Comprehension (CMRC 2018)](https://www.aclweb.org/anthology/D19-1600/) is a SQuAD-like
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reading comprehension dataset that consists of 20,000 questions annotated on Wikipedia paragraphs by human experts. The
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dataset can be downloaded [here](https://github.com/ymcui/cmrc2018). Below we show the F1 and EM scores both on the
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test set and the challenge set.
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| Model | Test F1 | Test EM | Challenge F1 | Challenge EM | Paper |
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| ------------- | :-----:| :-----:| --- |
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| Human performance | 97.9 | 92.4 | 95.2 | 90.4 | [A Span-Extraction Dataset for Chinese Machine Reading Comprehension](https://www.aclweb.org/anthology/D19-1600/) |
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| Dual BERT (w / SQuAD; Cui et al., 2019) | 90.2 | 73.6 | 55.2 | 27.8 | [Cross-Lingual Machine Reading Comprehension](https://www.aclweb.org/anthology/D19-1169/) |
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| Dual BERT (Cui et al., 2019) | 88.1 | 70.4 | 47.9 | 23.8 | [Cross-Lingual Machine Reading Comprehension](https://www.aclweb.org/anthology/D19-1169/) |
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### DRCD
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The [Delta Reading Comprehension Dataset (DRCD)](https://arxiv.org/abs/1806.00920) is a SQuAD-like reading
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comprehension dataset that contains 30,000+ questions on 10,014 paragraphs from 2,108 Wikipedia articles. The dataset
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can be downloaded [here](https://github.com/DRCKnowledgeTeam/DRCD).
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| Model | F1 | EM | Paper |
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| ------------- | :-----:| :-----:| --- |
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| Human performance | 93.3 | 80.4 | [DRCD: a Chinese Machine Reading Comprehension Dataset](https://arxiv.org/abs/1806.00920) |
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| Dual BERT (w / SQuAD; Cui et al., 2019) | 91.6 | 85.4 | [Cross-Lingual Machine Reading Comprehension](https://www.aclweb.org/anthology/D19-1169/) |
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| Dual BERT (Cui et al., 2019) | 90.3 | 83.7 | [Cross-Lingual Machine Reading Comprehension](https://www.aclweb.org/anthology/D19-1169/) |
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### DuReader
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[DuReader](https://www.aclweb.org/anthology/W18-2605/) is a large-scale reading comprehension dataset that is based on
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the logs of Baidu Search and contains 200k questions, 420k answers, and 1M documents. For more information, refer to
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[its website](https://ai.baidu.com/broad/introduction?dataset=dureader) to see the introduction. You can download the
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dataset [here](https://ai.baidu.com/broad/download?dataset=dureader). The best models can be view on the
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[public leaderboard](https://ai.baidu.com/broad/leaderboard?dataset=dureader).

korean/question_answering.md

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# Question answering
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Question answering is the task of answering a question.
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### Table of contents
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- [Reading comprehension](#reading-comprehension)
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- [KorQuAD](#korquad)
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## Reading comprehension
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### KorQuAD
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The [Korean Question Answering Dataset (KorQuAD)](https://arxiv.org/abs/1909.07005) is a large-scale reading comprehension
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dataset in the style of SQuAD that consists of 70,000+ human-generated question answer pairs on Wikipedia articles. The
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data and public leaderboard are available [here](https://korquad.github.io/).

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