Blog
The latest from Google Research
TyDi QA: A Multilingual Question Answering Benchmark
Thursday, February 6, 2020
Posted by Jonathan Clark, Research Scientist, Google Research
Question answering technologies help people on a daily basis — when faced with a question, such as
“Is squid ink safe to eat?”
, users can ask a voice assistant or type a search and expect to
receive an answer
. Last year, we released the English-language
Natural Questions
dataset to the research community to provide a
challenge
that reflects the needs of real users. However, there are thousands of different languages, and many of those use very different approaches to construct meaning. For example, while English changes words to indicate one object (“book”) versus many (“books”), Arabic also has a third form to indicate if there are two of something ("كتابان",
kitaban
) beyond just singular ("كتاب",
kitab
) or plural ("كتب",
kutub
). In addition, some languages, such as Japanese, do not use spaces between words. Creating machine learning systems that can understand the many ways languages express meaning is challenging, and training such systems requires examples from the diverse the languages to which they will be applied.
To encourage research on multilingual question-answering, today we are releasing
TyDi QA
, a question answering corpus covering 11
Ty
pologically
Di
verse languages. Described in our paper, “
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
”, our corpus is inspired by
typological diversity
, a notion that different languages express meaning in structurally different ways. Because we selected a set of languages that are typologically distant from each other for this corpus, we expect models performing well on this dataset to generalize across a large number of the languages in the world.
A Typologically Diverse Collection of Languages
TyDi QA includes over 200,000 question-answer pairs from 11 languages representing a diverse range of linguistic phenomena and data challenges. Many of these languages use non-Latin alphabets, such as Arabic, Bengali, Korean, Russian, Telugu, and Thai. Others form words in complex ways, including Arabic, Finnish, Indonesian, Kiswahili, Russian. Japanese uses four alphabets (shown by the four colors in “
24
時間
での
サーキット
周回数
”) while the Korean alphabet itself is highly compositional. These languages also range from having much available data on the web (English and Arabic) to very little (Bengali and Kiswahili). We expect that systems that can address these challenges will be successful for a very large number of languages.
Creating Realistic Data
Many early QA datasets used by the research community were created by first showing paragraphs to people and then
asking them to write questions based on what could be answered from reading the paragraph. However, since people could see the answer while writing each question, this approach yielded questions that often contained the same words as the answer. As a result, machine learning algorithms trained on such data would favor word matching, oblivious to the more nuanced answers required to satisfy users’ needs.
To construct a more natural dataset, we collected questions from people who
wanted
an answer, but
did not
know the answer yet. To inspire questions, we showed people an interesting passage from Wikipedia written in their native language. We then had them ask a question,
any question
, as long as it was
not
answered by the passage and they
actually
wanted to know the answer. This is similar to how your own curiosity might spawn questions about interesting things you see while walking down the street. We encouraged our question writers to let their imaginations run. Does a passage about ice make you think about popsicles in summer? Great! Ask who invented popsicles. Importantly, questions were written directly in each language, not translated, so many questions are unlike those seen in an English-first corpus. One question in Bengali asks, “সফেদা ফল খেতে কেমন?” (
What does
sapodilla
taste like?)
Never heard of it? That’s probably because it’s grown much more commonly in India than the U.S.
For each of these questions, we performed a Google Search for the best-matching Wikipedia article in the appropriate language and asked a person to find and highlight the answer within that article. While we expected
some
interesting divergences between the question and answers when the question writers did not have the answers in front of them, combined with the astonishing breadth of linguistic phenomena in the world’s languages, we found that the situation was even more complex.
For example, in Finnish, there are fascinating examples in which the words
day
and
week
are represented very differently in the question and answer. To be successful in selecting this answer sentence out of an entire Wikipedia article, a system needs to be able to recognize the relationship among the Finnish words
viikonpäivät, seitsenpäiväinen,
and
viikko
Making Progress Together as a Research Community
It is our hope that this dataset will push the research community to innovate in ways that will create more helpful question-answering systems for users around the world. To track the community’s progress, we have established a
leaderboard
where participants can evaluate the quality of their machine learning systems and are also open-sourcing a
question answering system
that uses the data. Please visit the challenge website to view the leaderboard and learn more.
Acknowledgements
This dataset is the result of a team of many Googlers including (alphabetically) Dan Garrette, Eunsol Choi, Jennimaria Palomaki, Michael Collins, Tom Kwiatkowski, and Vitaly Nikolaev. The Finnish gloss above is by Jennimaria Palomaki.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Biology
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2022
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
