- This repository contains the code and resources for multi-level complexity-controllable MT.
- Multi-level complexity-controllable MT is MT controlling the complexity of the output at three or more levels.
- This dataset is based on Newsela data. In accordance with our contract, we have applied to Newsela for permission to publish the dataset. Please wait until the permission is granted.
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data: Test dataset for Japanese-to-English multi-level complexity-controllable MT. Our test dataset consists of ja.txt and en.txt, which have sentence alignments across languages.- ja.txt
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Each line is separated into 2 elements by
\t:'set_id"\t"Japanese_sentence' -
The 'set_id' is defined in our dataset for aligning sentences between Japanese and English.
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'Japanese_sentence' was manually translated from English sentences in the Newsela corpus.
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- en.txt
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Each line is separated into 6 elements separated by
\t:'set_id"\t"article_title"\t"grade_level"\t"readability_level"\t"paragraph_index"\t"sentence_index_within_the_paragraph' -
The 'set_id' is shared between ja.txt and en.txt. The other factors are the same in the Newsela corpus.
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Note that en.txt does NOT include original English sentences in the Newsela corpus. You should obtain the English sentences from the Newsela corpus by using article_title, paragraph_index, and sentence_index_within_the_paragraph.
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- ja.txt
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src: Codes of 2 benchmark multi-level complexity-controllable NMT models we evaluated : a pipleline NMT model and a multi-task NMT model (coming soon...).
- To use our dataset, please first obtain access to the Newsela corpus and then extract the English sentences identified by en.txt.
- Please use Python 3 to run our codes.
@InProceedings{tani-EtAl:2022:LREC,
author = {Tani, Kazuki and Yuasa, Ryoya and Takikawa, Kazuki and Tamura, Akihiro and Kajiwara, Tomoyuki and Ninomiya, Takashi and Kato, Tsuneo},
title = {A Benchmark Dataset for Multi-Level Complexity-Controllable Machine Translation},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {6744--6752},
abstract = {This paper presents a new benchmark test dataset for multi-level complexity-controllable machine translation (MLCC-MT), which is MT controlling the complexity of the output at more than two levels. In previous research, MLCC-MT models have been evaluated on a test dataset automatically constructed from the Newsela corpus, which is a document-level comparable corpus with document-level complexity. The existing test dataset has the following three problems: (i) A source language sentence and its target language sentence are not necessarily an exact translation pair because they are automatically detected. (ii) A target language sentence and its simplified target language sentence are not necessarily exactly parallel because they are automatically aligned. (iii) A sentence-level complexity is not necessarily appropriate because it is transferred from an article-level complexity attached to the Newsela corpus. Therefore, we create a benchmark test dataset for Japanese-to-English MLCC-MT from the Newsela corpus by introducing an automatic filtering of data with inappropriate sentence-level complexity, manual check for parallel target language sentences with different complexity levels, and manual translation. Moreover, we implement two MLCC-NMT frameworks with a Transformer architecture and report their performance on our test dataset as baselines for future research. Our test dataset and codes are released.},
url = {https://aclanthology.org/2022.lrec-1.726}
}
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