Papers with neural machine translation model
Constructing Multilingual Code Search Dataset Using Neural Machine Translation (2023.acl-srw)
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| Challenge: | Existing datasets for code search are monolingual, but their query data are only in English. |
| Approach: | They construct a multilingual code search dataset in four natural and four programming languages using a neural machine translation model and apply back-translation data filtering to it. |
| Outcome: | The proposed model pre-trained with all natural and programming language data performs best under almost all settings. |
Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition (2020.acl-srw)
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| Challenge: | a limited number of North Korean to English translation models have been developed . a zero-shot approach is proposed to train a neural machine translation model using South Korean data . |
| Approach: | They propose a method to tokenize South Korean input sentences and decompose them into phonemes. |
| Outcome: | The proposed method improves the BLEU scores by +1.01 points compared with the baseline . the proposed method can learn North Korean to English translation and improve the linguistic accuracy. |
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)
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| Challenge: | Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities . |
| Approach: | They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach. |
| Outcome: | The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results. |
Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach (D19-1)
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| Challenge: | Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations . |
| Approach: | They propose automatic detection of adequacy errors in MT hypothesis for MT model evaluation by annotating missing and wrong translations in 15000 Chinese-English translation pairs. |
| Outcome: | The proposed model can detect missing and wrong translations in 15000 Chinese-English translation pairs. |
Translating Hanja Historical Documents to Contemporary Korean and English (2022.findings-emnlp)
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| Challenge: | The Annals of Joseon Dynasty contain the daily records of the Kings of Joseont, the 500-year kingdom preceding the modern nation of Korea. |
| Approach: | They propose a neural machine translation model that translates historical documents written in Hanja to more easily understandable Korean and to English. |
| Outcome: | The proposed model outperforms baseline models in terms of BLEU scores for both contemporary Korean and English translations. |
Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems (2020.emnlp-main)
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| Challenge: | Existing approaches to train character-level models require very deep architectures that are difficult and slow to train. |
| Approach: | They propose to fine tune a Transformer token-based model to get a model without token segmentation. |
| Outcome: | The proposed model improves translation quality and robustness to noise while requiring less token segmentation. |
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text (2021.acl-long)
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| Challenge: | a computational model for code-switching text is lacking in the corpus of real text. |
| Approach: | They propose a neural machine translation model to generate Hindi-English code-switched sentences using monolingual Hindi sentences. |
| Outcome: | The proposed model reduces perplexity on a language modeling task and improves on linguistic inference tasks. |
SENCORPUS: A French-Wolof Parallel Corpus (2020.lrec-1)
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| Challenge: | Currently, the corpus is being used to develop a neural machine translation model to translate French sentences into Wolof. |
| Approach: | They propose to build a bilingual parallel corpus between French and Wolof, a Niger-Congo language belonging to the Northern branch of the Atlantic group. |
| Outcome: | The proposed corpus contains 70,000 parallel sentences drawn on various sources from different domains. |
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language Bribri (2020.coling-main)
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| Challenge: | a small dataset of 5923 Bribri-Spanish pairs is used to train low-resource NMT models . |
| Approach: | They propose a Chibchan NMT model and dataset with an average performance of BLEU 16.91.7 for Bribri. |
| Outcome: | The proposed model improves on the Bribri dataset by 1.0 BLEU, but only when the new Spanish sentences belong to the same domain as the other Spanish examples. |
Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Experimental results show that multitask learning can support decoding in 24 depth configurations and is superior to individual training. |
| Approach: | They propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference. |
| Outcome: | The proposed model can support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method——LayerDrop. |
JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus (2020.lrec-1)
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| Challenge: | Recent machine translation algorithms rely on parallel corpora, but only some resource-rich language pairs can benefit from them. |
| Approach: | They construct a parallel corpus for English-Japanese, which has 8.7 million sentence pairs . they use a web crawler to automatically align parallel sentences in the corpus . |
| Outcome: | The proposed corpus includes a broader range of domains and can be trained with a pre-trained model. |
KazParC: Kazakh Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Statistical machine translation gained ground over rule-based machine translation in the late 1990s thanks to its ability to learn from large bilingual corpora. |
| Approach: | They propose to develop a parallel corpus for machine translation across Kazakh, English, Russian, and Turkish. |
| Outcome: | The proposed model outperforms Google Translate and Yandex Translate in terms of performance and evaluation metrics. |
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
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| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |