Word Representations in Factored Neural Machine Translation

Burlot, Franck; García-Martínez, Mercedes; Barrault, Loïc; Bougares, Fethi; Yvon, François

doi:10.18653/v1/w17-4703

Cited by 32 publications

(2 citation statements)

References 25 publications

(25 reference statements)

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“…Apart from the above works, there are some studies which use the factors in the target side (Burlot et al, 2017;García-Martínez et al, 2016a;García-Martínez et al, 2016b). In general, their approach is to predict the roots and other morphological tags of the target words instead of producing the surface forms.…”

Section: Related Workmentioning

confidence: 99%

Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation

Chakrabarty¹,

Dabre²,

Ding³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data. Integrating manually designed or automatically extracted features into the NMT framework is known to be beneficial. However, this study emphasizes that the relevance of the features is crucial to the performance. Specifically, we propose two methods, 1) self relevance and 2) word-based relevance, to improve the representation of features for NMT. Experiments are conducted on translation tasks from English to eight Asian languages, with no more than twenty thousand sentences for training. The proposed methods improve translation quality for all tasks by up to 3.09 BLEU points. Discussions with visualization provide the explainability of the proposed methods where we show that the relevance methods provide weights to features thereby enhancing their impact on low-resource machine translation.

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Section: Related Workmentioning

confidence: 99%

Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation

Chakrabarty¹,

Dabre²,

Ding³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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show abstract

“…While various word representations (Burlot et al, 2017) can be used in the first factor, our system predict at each timestep on the target side a normalized word and a PoS-tag. fully infl.…”

Section: Data Filteringmentioning

confidence: 99%

Limsi$@$wmt'17

Burlot

Safari

Labeau

et al. 2017

Proceedings of the Second Conference on Machine Translation

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This paper describes LIMSI's submissions to the news shared task at WMT'17 for English into Czech and Latvian, as well as related experiments. This year's novelties consist in the use of a neural machine translation system with a factored output predicting simultaneously a lemma decorated with morphological information and a fine-grained part-of-speech. Such a type of system drew our attention to the specific step of reinflection, where lemmas and parts-of-speech are transformed into fully inflected words. Finally, we ran experiments showing an efficient strategy for parameter initialization, as well as data filtering procedures.

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Addressing data sparsity for neural machine translation between morphologically rich languages

García-Martínez

Aransa

Bougares

et al. 2020

Machine Translation

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Translating between morphologically rich languages is still challenging for actual machine translation systems. In this paper, we experiment with various Neural Machine Translation (NMT) architectures to address the data sparsity problem caused by data availability (quantity), domain shift and the languages involved (Arabic and French). We showed that the Factored NMT (FNMT) model, which uses linguistically motivated factors, is able to outperform standard NMT systems using subword units by more than 1% BLEU point even when a large quantity of data is available. Our work shows the benefits of applying linguistic factors in NMT when faced with low and large resource conditions.

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Word Representations in Factored Neural Machine Translation

Cited by 32 publications

References 25 publications

Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation

Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation

Limsi$@$wmt'17

Addressing data sparsity for neural machine translation between morphologically rich languages

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