Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation

• URL: http://arxiv.org/abs/2010.01667v1
• Date: Sun, 4 Oct 2020 19:42:40 GMT
• Title: Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation
• Authors: Luyu Gao, Xinyi Wang, Graham Neubig
• Abstract summary: multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs. Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages.
• Score: 104.10726545151043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To improve the performance of Neural Machine Translation~(NMT) for low-resource languages~(LRL), one effective strategy is to leverage parallel data from a related high-resource language~(HRL). However, multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs. In this paper, we aim to improve the effectiveness of multilingual transfer for NMT models that translate \emph{into} the LRL, by designing a better decoder word embedding. Extending upon a general-purpose multilingual encoding method Soft Decoupled Encoding~\citep{SDE}, we propose DecSDE, an efficient character n-gram based embedding specifically designed for the NMT decoder. Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages.

Related papers

• Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation [62.202893186343935]
  We explore what it would take to adapt Large Language Models for low-resource languages. We show that parallel data is critical during both pre-training andSupervised Fine-Tuning (SFT) Our experiments with three LLMs across two low-resourced language groups reveal consistent trends, underscoring the generalizability of our findings.
  arXiv  Detail & Related papers  (2024-08-23T00:59:38Z)
• Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer [92.80671770992572]
  Cross-lingual transfer is a central task in multilingual NLP. Earlier efforts on this task use parallel corpora, bilingual dictionaries, or other annotated alignment data. We propose a simple yet effective method, SALT, to improve the zero-shot cross-lingual transfer.
  arXiv  Detail & Related papers  (2023-09-19T19:30:56Z)
• Improving Multilingual Neural Machine Translation System for Indic Languages [0.0]
  We propose a multilingual neural machine translation (MNMT) system to address the issues related to low-resource language translation. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models.
  arXiv  Detail & Related papers  (2022-09-27T09:51:56Z)
• Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval [66.69799641522133]
  State-of-the-art neural (re)rankers are notoriously data hungry. Current approaches typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders. We show that two parameter-efficient approaches to cross-lingual transfer, namely Sparse Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more effective zero-shot transfer.
  arXiv  Detail & Related papers  (2022-04-05T15:44:27Z)
• Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages [1.0965065178451106]
  We propose the tagged-multilingual NMT model and improve the self-learning algorithm to handle these two problems. Experimental results on IWSLT show that the adjusted tagged-multilingual NMT separately obtains 9.41 and 7.85 BLEU scores over the multilingual NMT.
  arXiv  Detail & Related papers  (2021-10-02T02:50:53Z)
• Improving Multilingual Translation by Representation and Gradient Regularization [82.42760103045083]
  We propose a joint approach to regularize NMT models at both representation-level and gradient-level. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
  arXiv  Detail & Related papers  (2021-09-10T10:52:21Z)
• Enabling Zero-shot Multilingual Spoken Language Translation with Language-Specific Encoders and Decoders [5.050654565113709]
  Current end-to-end approaches to Spoken Language Translation rely on limited training resources. Our proposed method extends a MultiNMT architecture based on language-specific encoders-decoders to the task of Multilingual SLT.
  arXiv  Detail & Related papers  (2020-11-02T16:31:14Z)
• Multi-task Learning for Multilingual Neural Machine Translation [32.81785430242313]
  We propose a multi-task learning framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data. We show that the proposed approach can effectively improve the translation quality for both high-resource and low-resource languages.
  arXiv  Detail & Related papers  (2020-10-06T06:54:12Z)
• Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation [81.7786241489002]
  Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations. We argue that multilingual NMT requires stronger modeling capacity to support language pairs with varying typological characteristics. We propose random online backtranslation to enforce the translation of unseen training language pairs.
  arXiv  Detail & Related papers  (2020-04-24T17:21:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.