1,367 research outputs found
Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English
The necessity of using a fixed-size word vocabulary in order to control the
model complexity in state-of-the-art neural machine translation (NMT) systems
is an important bottleneck on performance, especially for morphologically rich
languages. Conventional methods that aim to overcome this problem by using
sub-word or character-level representations solely rely on statistics and
disregard the linguistic properties of words, which leads to interruptions in
the word structure and causes semantic and syntactic losses. In this paper, we
propose a new vocabulary reduction method for NMT, which can reduce the
vocabulary of a given input corpus at any rate while also considering the
morphological properties of the language. Our method is based on unsupervised
morphology learning and can be, in principle, used for pre-processing any
language pair. We also present an alternative word segmentation method based on
supervised morphological analysis, which aids us in measuring the accuracy of
our model. We evaluate our method in Turkish-to-English NMT task where the
input language is morphologically rich and agglutinative. We analyze different
representation methods in terms of translation accuracy as well as the semantic
and syntactic properties of the generated output. Our method obtains a
significant improvement of 2.3 BLEU points over the conventional vocabulary
reduction technique, showing that it can provide better accuracy in open
vocabulary translation of morphologically rich languages.Comment: The 20th Annual Conference of the European Association for Machine
Translation (EAMT), Research Paper, 12 page
Linguistically Motivated Subwords for English-Tamil Translation:University of Groningen’s Submission to WMT-2020
This paper describes our submission for the English-Tamil news translation task of WMT-2020. The various techniques and Neural Machine Translation (NMT) models used by our team are presented and discussed, including back-translation, fine-tuning and word dropout. Additionally, our experiments show that using a linguistically motivated subword segmentation technique (Ataman et al., 2017) does not consistently outperform the more widely used, non-linguistically motivated SentencePiece algorithm (Kudo and Richardson, 2018), despite the agglutinative nature of Tamil morphology
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages
Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality
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