Papers with English-Japanese
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation (D19-52)
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| Challenge: | In the Japanese language different levels of honorific speech are used to convey respect, deference, humility, formality and social distance. |
| Approach: | They propose a method for controlling the level of formality of Japanese output . they use heuristics to identify honorific verb forms to classify Japanese sentences . |
| Outcome: | The proposed model can produce Japanese translations in different honorific speech styles for the same English input sentence. |
Data augmentation using back-translation for context-aware neural machine translation (D19-65)
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| Challenge: | A single sentence does not always convey information that is enough to translate it into other languages. |
| Approach: | They obtain large-scale pseudo parallel corpora by back-translating monolingual data and examine their impact on translation accuracy. |
| Outcome: | The large-scale pseudo parallel corpora obtained by back-translating monolingual data showed that the model trained with small parallel corporeals and large-sized pseudo parallels improved translation accuracy. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (D18-2)
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| Challenge: | Existing subword segmentation tools assume input is pre-tokenized into word sequences, but SentencePiece can train subword models directly from raw sentences. |
| Approach: | They propose a language-independent subword tokenizer and detokenizer for Neural-based text processing. |
| Outcome: | The proposed system achieves comparable accuracy to training from raw sentences. |
Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)
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| Challenge: | This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 . |
| Approach: | They propose a model for translation tasks of WAT 2019 that employs a Transformer model as the baseline and a deep layer model to improve translation quality. |
| Outcome: | The proposed methods can improve translation quality over traditional statistical machine translation (SMT) The proposed models can improve the translation quality of Japanese-English and Japanese-Chinese corpus. |
EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints (2021.tacl-1)
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| Challenge: | Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer while speeding up decoding dramatically compared to constrained beam search. |
| Approach: | They propose an Edit-Based TransfOrmer with Repositioning that integrates lexical preferences into output sequences by iterative editing hypotheses. |
| Outcome: | The proposed model uses soft lexical constraints more effectively than the Levenshtein Transformer while speeding up decoding dramatically compared to constrained beam search. |
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation (P18-2)
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| Challenge: | a beam search algorithm produces monotonic left-to-right order, meaning a hypothesis cannot be revisited . a proposed algorithm allows discarded hypotheses to be recovered in a later step. |
| Approach: | They propose to decode a beam search algorithm that considers multiple hypotheses simultaneously . they propose to maintain all found hypothese a single priority queue and a universal score function . |
| Outcome: | The proposed algorithm improves translations even for high-performance models in English-Japanese translation task. |
A Parallel Corpus of Arabic-Japanese News Articles (L18-1)
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| Challenge: | a large-scale parallel corpora with manually verified subsets of sentences has been used for machine translation between major language pairs. |
| Approach: | They describe the creation process and statistics of the Arabic-Japanese portion of the TUFS Media Corpus . they also report the first results of Arabic-japanese phrase-based machine translation trained on the corpus based on the Arabic corpus. |
| Outcome: | The proposed corpus is a document-level parallel corpus and sentence-level parser corpus . it is the first time that Arabic-Japanese translations have been trained on it . |
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture. |
| Approach: | They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier. |
| Outcome: | The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical |
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLWE) are used to perform multilingual natural language processing tasks. |
| Approach: | They propose a method that transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language’s average vector is zero. |
| Outcome: | The proposed method improves translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (2% to 44% test accuracy). |
Multi-Task Learning for Cross-Lingual Abstractive Summarization (2022.lrec-1)
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| Challenge: | Existing studies use pseudo cross-lingual abstractive summarization data to train neural encoder-decoders. |
| Approach: | They propose a multi-task learning framework for cross-lingual abstractive summarization that attaches a special token to the beginning of the input sentence to indicate the target task. |
| Outcome: | The proposed model achieves better performance than the model trained with only pseudo cross-lingual abstractive summarization data. |
Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation. |
| Approach: | They propose a supervised visual attention mechanism for multimodal neural machine translation that captures the relationship between a word and an image region more precisely than a conventional visual attention system. |
| Outcome: | The proposed model improves on English-German and German-English translation tasks and English-Japanese and Japanese-English tasks using the Flickr30k Entities JP dataset. |
SpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP (2020.coling-main)
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| Challenge: | Existing methods for automatic sentence alignment assume monotonic alignments, but they can handle non-monotonic alignments. |
| Approach: | They propose a method to automatically extract parallel sentences from noisy parallel documents by embeddings and encoding each source and target sentence. |
| Outcome: | The proposed method improves translation accuracy by 4.1 BLEU scores on English-Japanese . it can predict spans in target document from sentences in source document . |
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. |
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction (2020.coling-main)
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| Challenge: | Recent work has questioned the robustness of unsupervised bilingual dictionary induction methods on distant language pairs. |
| Approach: | They propose an iterative dimension reduction method to bridge this gap . they propose a method that initializes and self-learning and inducing a dictionary . |
| Outcome: | The proposed method achieves 13.64 55.53% accuracy between English and four distant languages. |
Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies (2026.findings-acl)
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| Challenge: | Simultaneous machine translation requires high-quality translations under strict real-time constraints. |
| Approach: | They extend the action space of simultaneous machine translation with four adaptive actions . they adapt these actions in a large language model framework and construct training references . |
| Outcome: | The proposed framework improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. |
Grammatical Error Correction for Code-Switched Sentences by Learners of English (2024.lrec-main)
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| Challenge: | Existing grammar error correction systems have been trained on monolingual data and not developed for CSW text. |
| Approach: | They propose a method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora and investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints. |
| Outcome: | The proposed model achieves an average increase of 1.57 F0.5 across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model’s performance on a monolingual dataset. |
JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus (2022.lrec-1)
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| Challenge: | Existing parallel corpora for English-Japanese are limited, limiting the accuracy of machine translation models. |
| Approach: | They propose a web-based English-Japanese parallel corpus with 21 million unique sentence pairs . this is more than twice as many as the previous corpus JParaCrawl v2.0 . |
| Outcome: | The proposed corpus boosts the accuracy of machine translation models on various domains. |