When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?

• URL: http://arxiv.org/abs/2204.12165v1
• Date: Tue, 26 Apr 2022 09:07:51 GMT
• Title: When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
• Authors: Zhuoyuan Mao, Chenhui Chu, Raj Dabre, Haiyue Song, Zhen Wan, Sadao Kurohashi
• Abstract summary: This work proposes a word-level contrastive objective to leverage word alignments for many-to-many NMT. Analyses reveal that in many-to-many NMT, the encoder's sentence retrieval performance highly correlates with the translation quality.
• Score: 33.28706502928905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous methods, which are unavailable for most language pairs. Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT. This work proposes a word-level contrastive objective to leverage word alignments for many-to-many NMT. Empirical results show that this leads to 0.8 BLEU gains for several language pairs. Analyses reveal that in many-to-many NMT, the encoder's sentence retrieval performance highly correlates with the translation quality, which explains when the proposed method impacts translation. This motivates future exploration for many-to-many NMT to improve the encoder's sentence retrieval performance.

Related papers

• Towards Effective Disambiguation for Machine Translation with Large Language Models [65.80775710657672]
  We study the capabilities of large language models to translate "ambiguous sentences" Experiments show that our methods can match or outperform state-of-the-art systems such as DeepL and NLLB in four out of five language directions.
  arXiv  Detail & Related papers  (2023-09-20T22:22:52Z)
• Learning Homographic Disambiguation Representation for Neural Machine Translation [20.242134720005467]
  Homographs, words with the same spelling but different meanings, remain challenging in Neural Machine Translation (NMT) We propose a novel approach to tackle issues of NMT in the latent space. We first train an encoder (aka " homographic-encoder") to learn universal sentence representations in a natural language inference (NLI) task. We further fine-tune the encoder using homograph-based syn-set WordNet, enabling it to learn word-set representations from sentences.
  arXiv  Detail & Related papers  (2023-04-12T13:42:59Z)
• Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning [24.64700139151659]
  Current Neural machine translation (NMT) systems suffer from a lack of reliability. We present a consistency-aware meta-learning (CAML) framework derived from the model-agnostic meta-learning (MAML) algorithm to address it. We conduct experiments on the NIST Chinese to English task, three WMT translation tasks, and the TED M2O task.
  arXiv  Detail & Related papers  (2023-03-20T09:41:28Z)
• DICTDIS: Dictionary Constrained Disambiguation for Improved NMT [50.888881348723295]
  We present DictDis, a lexically constrained NMT system that disambiguates between multiple candidate translations derived from dictionaries. We demonstrate the utility of DictDis via extensive experiments on English-Hindi and English-German sentences in a variety of domains including regulatory, finance, engineering.
  arXiv  Detail & Related papers  (2022-10-13T13:04:16Z)
• Exploring Unsupervised Pretraining Objectives for Machine Translation [99.5441395624651]
  Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT) Most approaches adapt masked-language modeling (MLM) to sequence-to-sequence architectures, by masking parts of the input and reconstructing them in the decoder. We compare masking with alternative objectives that produce inputs resembling real (full) sentences, by reordering and replacing words based on their context.
  arXiv  Detail & Related papers  (2021-06-10T10:18:23Z)
• Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models [72.56058378313963]
  We bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table. We find that NMT models learn patterns from simple to complex and distill essential bilingual knowledge from the training examples.
  arXiv  Detail & Related papers  (2020-04-28T03:44:34Z)
• 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)
• Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation [61.88012735215636]
  Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. UNMT can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. In this paper, we empirically introduce a simple method to translate between thirteen languages using a single encoder and a single decoder.
  arXiv  Detail & Related papers  (2020-04-21T17:26:16Z)
• Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation [8.87546236839959]
  We propose a morphological word segmentation method on the source-side for Neural machine translation (NMT) It incorporates morphology knowledge to preserve the linguistic and semantic information in the word structure while reducing the vocabulary size at training time. It can be utilized as a preprocessing tool to segment the words in agglutinative languages for other natural language processing (NLP) tasks.
  arXiv  Detail & Related papers  (2020-01-02T10:05:02Z)

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.