5 research outputs found
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba
We present BIG-C (Bemba Image Grounded Conversations), a large multimodal
dataset for Bemba. While Bemba is the most populous language of Zambia, it
exhibits a dearth of resources which render the development of language
technologies or language processing research almost impossible. The dataset is
comprised of multi-turn dialogues between Bemba speakers based on images,
transcribed and translated into English. There are more than 92,000
utterances/sentences, amounting to more than 180 hours of audio data with
corresponding transcriptions and English translations. We also provide
baselines on speech recognition (ASR), machine translation (MT) and speech
translation (ST) tasks, and sketch out other potential future multimodal uses
of our dataset. We hope that by making the dataset available to the research
community, this work will foster research and encourage collaboration across
the language, speech, and vision communities especially for languages outside
the "traditionally" used high-resourced ones. All data and code are publicly
available: https://github.com/csikasote/bigc.Comment: accepted to ACL 202
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in
Natural Language Processing (NLP), recent years have seen a proliferation of
large, web-mined text datasets covering hundreds of languages. We manually
audit the quality of 205 language-specific corpora released with five major
public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource
corpora have systematic issues: At least 15 corpora have no usable text, and a
significant fraction contains less than 50% sentences of acceptable quality. In
addition, many are mislabeled or use nonstandard/ambiguous language codes. We
demonstrate that these issues are easy to detect even for non-proficient
speakers, and supplement the human audit with automatic analyses. Finally, we
recommend techniques to evaluate and improve multilingual corpora and discuss
potential risks that come with low-quality data releases.Comment: Accepted at TACL; pre-MIT Press publication versio
AfriQA:Cross-lingual Open-Retrieval Question Answering for African Languages
African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems -- those that retrieve answer content from other languages while serving people in their native language -- offer a means of filling this gap. To this end, we create AfriQA, the first cross-lingual QA dataset with a focus on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, AfriQA focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology
Untranscribed Radio Broadcast Audio Segments for Nyanja ASR
The audio collection of radio broadcast recordings segments for Nyanja.There are 6976 files amounting to 10 hours
AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
African languages have far less in-language content available digitally,
making it challenging for question answering systems to satisfy the information
needs of users. Cross-lingual open-retrieval question answering (XOR QA)
systems -- those that retrieve answer content from other languages while
serving people in their native language -- offer a means of filling this gap.
To this end, we create AfriQA, the first cross-lingual QA dataset with a focus
on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African
languages. While previous datasets have focused primarily on languages where
cross-lingual QA augments coverage from the target language, AfriQA focuses on
languages where cross-lingual answer content is the only high-coverage source
of answer content. Because of this, we argue that African languages are one of
the most important and realistic use cases for XOR QA. Our experiments
demonstrate the poor performance of automatic translation and multilingual
retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA
models. We hope that the dataset enables the development of more equitable QA
technology