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IN22-Conv
IN-22 is a newly created comprehensive benchmark for evaluating machine translation performance in multi-domain, n-way parallel contexts across 22 Indic languages. IN22-Conv is the conversation domain subset of IN22. It is designed to assess translation quality in typical day-to-day conversational-style applications. The evaluation subset consists of 1503 sentences translated across 22 Indic languages enabling evaluation of MT systems across 506 directions.
Currently, we use it for sentence-level evaluation of MT systems but it can be repurposed for document translation evaluation as well.
Here is the domain distribution of our IN22-Conv evaluation subset.
domain | count |
hobbies | 120 |
daily_dialogue | 117 |
government | 116 |
geography | 114 |
sports | 100 |
entertainment | 97 |
history | 97 |
legal | 96 |
arts | 95 |
college_life | 94 |
tourism | 91 |
school_life | 87 |
insurance | 82 |
culture | 73 |
healthcare | 67 |
banking | 57 |
total | 1503 |
Please refer to the Appendix E: Dataset Card
of the preprint on detailed description of dataset curation, annotation and quality control process.
Dataset Structure
Dataset Fields
id
: Row number for the data entry, starting at 1.doc_id
: Unique identifier of the conversation.sent_id
: Unique identifier of the sentence order in each conversation.topic
: The specific topic of the conversation within the domain.domain
: The domain of the conversation.prompt
: The prompt provided to annotators to simulate the conversation.scenario
: The scenario or context in which the conversation takes place.speaker
: The speaker identifier in the conversation.turn
: The turn within the conversation.
Data Instances
A sample from the gen
split for the English language (eng_Latn
config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
{
"id": 1,
"doc_id": 0,
"sent_id": 1,
"topic": "Festivities",
"domain": "culture",
"prompt": "14th April a holiday",
"scenario": "Historical importance",
"speaker": 1,
"turn": 1,
"sentence": "Mom, let's go for a movie tomorrow."
}
When using a hyphenated pairing or using the all
function, data will be presented as follows:
{
"id": 1,
"doc_id": 0,
"sent_id": 1,
"topic": "Festivities",
"domain": "culture",
"prompt": "14th April a holiday",
"scenario": "Historical importance",
"speaker": 1,
"turn": 1,
"sentence_eng_Latn": "Mom, let's go for a movie tomorrow.",
"sentence_hin_Deva": "माँ, चलो कल एक फिल्म देखने चलते हैं।"
}
Sample Conversation
Speaker | Turn |
Speaker 1 | Mom, let's go for a movie tomorrow. I don't have to go to school. It is a holiday. |
Speaker 2 | Oh, tomorrow is the 14th of April right? Your dad will also have the day off from work. We can make a movie plan! |
Speaker 1 | That's a good news! Why is it a holiday though? Are all schools, colleges and offices closed tomorrow? |
Speaker 2 | It is Ambedkar Jayanti tomorrow! This day is celebrated annually to mark the birth of Dr. B. R Ambedkar. Have you heard of him? |
Speaker 1 | I think I have seen him in my History and Civics book. Is he related to our Constitution? |
Speaker 2 | Absolutely! He is known as the father of the Indian Constitution. He was a civil rights activist who played a major role in formulating the Constitution. He played a crucial part in shaping the vibrant democratic structure that India prides itself upon. |
... |
Usage Instructions
from datasets import load_dataset
# download and load all the pairs
dataset = load_dataset("ai4bharat/IN22-Conv", "all")
# download and load specific pairs
dataset = load_dataset("ai4bharat/IN22-Conv", "eng_Latn-hin_Deva")
Languages Covered
Assamese (asm_Beng) | Kashmiri (Arabic) (kas_Arab) | Punjabi (pan_Guru) |
Bengali (ben_Beng) | Kashmiri (Devanagari) (kas_Deva) | Sanskrit (san_Deva) |
Bodo (brx_Deva) | Maithili (mai_Deva) | Santali (sat_Olck) |
Dogri (doi_Deva) | Malayalam (mal_Mlym) | Sindhi (Arabic) (snd_Arab) |
English (eng_Latn) | Marathi (mar_Deva) | Sindhi (Devanagari) (snd_Deva) |
Konkani (gom_Deva) | Manipuri (Bengali) (mni_Beng) | Tamil (tam_Taml) |
Gujarati (guj_Gujr) | Manipuri (Meitei) (mni_Mtei) | Telugu (tel_Telu) |
Hindi (hin_Deva) | Nepali (npi_Deva) | Urdu (urd_Arab) |
Kannada (kan_Knda) | Odia (ory_Orya) |
Citation
If you consider using our work then please cite using:
@article{gala2023indictrans,
title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
author={Jay Gala and Pranjal A Chitale and A K Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Atul Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M Khapra and Raj Dabre and Anoop Kunchukuttan},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=vfT4YuzAYA},
note={}
}
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