Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages
In this paper, we explore a simple solution to "Multi-Source Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way multilingual corpus without modifying the Neural Machine Translation (NMT) architecture or training procedure. We simply concatenate the source sentences to form a single long multi-source input sentence while keeping the target side sentence as it is and train an NMT system using this preprocessed corpus. We evaluate our method in resource poor as well as resource rich settings and show its effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using 5 source languages). We also compare against existing methods for MSNMT and show that our solution gives competitive results despite its simplicity. We also provide some insights on how the NMT system leverages multilingual information in such a scenario by visualizing attention.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTSentenceTranslationSimilar Papers 제목 키워드 기반
Relay Decoding: Concatenating Large Language Models for Machine Translation
Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handling both the source and target languages i…
Machine TranslationTranslationNeural Machine Translation with Source Dependency Representation
Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating represen…
Machine TranslationNMTTranslationIs Encoder-Decoder Redundant for Neural Machine Translation?
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks. For machine translation, despite the evolution from long short-term memory networks to Transformer networks, plus the introduction a…
DecoderLanguage ModelingLanguage ModellingMachine Translation+2Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation
Neural machine translation (NMT) has recently gained widespread attention because of its high translation accuracy. However, it shows poor performance in the translation of long sentences, which is a major issue in low-r…
Data AugmentationMachine TranslationNMTSentence+1CoMeT: Towards Code-Mixed Translation Using Parallel Monolingual Sentences
Code-mixed languages are very popular in multilingual societies around the world, yet the resources lag behind to enable robust systems on such languages. A major contributing factor is the informal nature of these langu…
Machine TranslationTranslation