MTNT: A Testbed For Machine Translation Of Noisy Text - ArXiv

Computer Science > Computation and Language arXiv:1809.00388 (cs) [Submitted on 2 Sep 2018] Title:MTNT: A Testbed for Machine Translation of Noisy Text Authors:Paul Michel, Graham Neubig View a PDF of the paper titled MTNT: A Testbed for Machine Translation of Noisy Text, by Paul Michel and Graham Neubig View PDF

Abstract:Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (this http URL) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT. The data is publicly available at this http URL.

Comments:	EMNLP 2018 Long Paper
Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:1809.00388 [cs.CL]
(or arXiv:1809.00388v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.1809.00388 Focus to learn more arXiv-issued DOI via DataCite

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From: Paul Michel [view email] [v1] Sun, 2 Sep 2018 20:43:09 UTC (515 KB) Full-text links:

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