WikiReading
This repository contains the three WikiReading datasets as used and described in WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia, Hewlett, et al, ACL 2016 (the English WikiReading dataset) and Byte-level Machine Reading across Morphologically Varied Languages, Kenter et al, AAAI-18 (the Turkish and Russian datasets).
Run get_data.sh
to download data the English WikiReading dataset.
Run get_ru_data.sh
and get_tr_data.sh
to get the Russian and Turkish version of the WikiReading data, respectively.
If you use the data or the results reported in the papers, please feel free to cite them.
@inproceedings {hewlett2016wikireading,
title = {{WIKIREADING}: A Novel Large-scale Language Understanding Task over {Wikipedia}},
booktitle = {Proceedings of the The 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016)},
author = {Daniel Hewlett and Alexandre Lacoste and Llion Jones and Illia Polosukhin and Andrew Fandrianto and Jay Han and Matthew Kelcey and David Berthelot},
year = {2016}
}
and
@inproceedings{byte-level2018kenter,
title={Byte-level Machine Reading across Morphologically Varied Languages},
author={Tom Kenter and Llion Jones and Daniel Hewlett},
booktitle={Proceedings of the The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)},
year={2018}
}
WikiReading Data
Train, validation, and test datasets are in TFRecord or streamed JSON (one JSON object per line). They are 45GB, 5GB, and 3GB respectively. For example test.tar.gz contains 15 files whose union is the whole test set. We split them to help speed up training/testing by parallelizing reads. Any one of the shards can be opened with a TFRecordReader or with your favorite JSON reader for every line. Download a sample TFRecord shard or a sample JSON shard of the validation set (1/15th) to play around with if disk space is limited.
English
file | size | description |
---|---|---|
train | 16,039,400 examples | TFRecord https://storage.googleapis.com/wikireading/train.tar.gz |
JSON https://storage.googleapis.com/wikireading/train.json.tar.gz | ||
validation | 1,886,798 examples | TFRecord https://storage.googleapis.com/wikireading/validation.tar.gz |
JSON https://storage.googleapis.com/wikireading/validation.json.tar.gz | ||
test | 941,280 examples | TFRecord https://storage.googleapis.com/wikireading/test.tar.gz |
JSON https://storage.googleapis.com/wikireading/test.json.tar.gz | ||
document.vocab | 176,978 tokens | vocabulary for tokens from Wikipedia documents |
answer.vocab | 10,876 tokens | vocabulary for tokens from answers |
raw_answer.vocab | 1,359,244 tokens | vocabulary for whole answers as they appear in WikiData |
type.vocab | 80 tokens | vocabulary for Part of Speech tags |
character.vocab | 12486 tokens | vocabulary for all characters that appear in the string sequences |
Russian
file | size | description |
---|---|---|
train | 4,259,667 examples | TFRecord https://storage.googleapis.com/wikireading/ru/train.tar.gz |
JSON https://storage.googleapis.com/wikireading/ru/train.json.tar.gz | ||
validation | 531,412 examples | TFRecord https://storage.googleapis.com/wikireading/ru/valid.tar.gz |
JSON https://storage.googleapis.com/wikireading/ru/valid.json.tar.gz | ||
test | 533,026 examples | TFRecord https://storage.googleapis.com/wikireading/ru/test.tar.gz |
JSON https://storage.googleapis.com/wikireading/ru/test.json.tar.gz | ||
document.vocab | 965,157 tokens | vocabulary for tokens from Wikipedia documents |
answer.vocab | 57,952 tokens | vocabulary for tokens from answers |
type.vocab | 56 tokens | vocabulary for Part of Speech tags |
character.vocab | 12,205 tokens | vocabulary for all characters that appear in the string sequences |
Turkish
file | size | description |
---|---|---|
train | 654,705 examples | TFRecord https://storage.googleapis.com/wikireading/tr/train.tar.gz |
JSON https://storage.googleapis.com/wikireading/tr/train.json.tar.gz | ||
validation | 81,622 examples | TFRecord https://storage.googleapis.com/wikireading/tr/valid.tar.gz |
JSON https://storage.googleapis.com/wikireading/tr/valid.json.tar.gz | ||
test | 82,643 examples | TFRecord https://storage.googleapis.com/wikireading/tr/test.tar.gz |
JSON https://storage.googleapis.com/wikireading/tr/test.json.tar.gz | ||
document.vocab | 215,294 tokens | vocabulary for tokens from Wikipedia documents |
answer.vocab | 11,123 tokens | vocabulary for tokens from answers |
type.vocab | 10 tokens | vocabulary for Part of Speech tags |
character.vocab | 6638 tokens | vocabulary for all characters that appear in the string sequences |
Features
Each instance contains these features (some features may be empty).
feature name | description |
---|---|
answer_breaks |
Indices into answer_ids and answer_string_sequence . |
Used to delimit multiple answers to a question, e.g. a list answer. | |
answer_ids |
answer.vocab ID sequence for words in the answer. |
answer_location |
Word indices into the document where any one token in the answer was found. |
answer_sequence |
document.vocab ID sequence for words in the answer. |
answer_string_sequence |
String sequence for the words in the answer. |
break_levels |
One integer [0,4] indicating a break level for each word in the document. |
* 0 = no separation between tokens | |
* 1 = tokens separated by space | |
* 2 = tokens separated by line break | |
* 3 = tokens separated by sentence break | |
* 4 = tokens separated by paragraph break | |
document_sequence |
document.vocab ID sequence for words in the document. |
full_match_answer_location |
Word indices into the document where all contiguous tokens in answer were found. |
paragraph_breaks |
Word indices into the document indicating a paragraph boundary. |
question_sequence |
document.vocab ID sequence for words in the question. |
question_string_sequence |
String sequence for the words in the question. |
raw_answer_ids |
raw_answer.vocab ID for the answer. |
raw_answers |
A string containing the raw answer. |
sentence_breaks |
Word indices into the document indicating a sentence boundary. |
string_sequence |
String sequence for the words in the document. character.vocab for char IDs. |
type_sequence |
type.vocab ID sequence for tags (POS, type, etc.) in the document. |