Low-resource neural machine translation with morphological modeling

• URL: http://arxiv.org/abs/2404.02392v1
• Date: Wed, 3 Apr 2024 01:31:41 GMT
• Title: Low-resource neural machine translation with morphological modeling
• Authors: Antoine Nzeyimana,
• Abstract summary: Morphological modeling in neural machine translation (NMT) is a promising approach to achieving open-vocabulary machine translation. We propose a framework-solution for modeling complex morphology in low-resource settings. We evaluate our proposed solution on Kinyarwanda - English translation using public-domain parallel text.
• Score: 3.3721926640077804
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Morphological modeling in neural machine translation (NMT) is a promising approach to achieving open-vocabulary machine translation for morphologically-rich languages. However, existing methods such as sub-word tokenization and character-based models are limited to the surface forms of the words. In this work, we propose a framework-solution for modeling complex morphology in low-resource settings. A two-tier transformer architecture is chosen to encode morphological information at the inputs. At the target-side output, a multi-task multi-label training scheme coupled with a beam search-based decoder are found to improve machine translation performance. An attention augmentation scheme to the transformer model is proposed in a generic form to allow integration of pre-trained language models and also facilitate modeling of word order relationships between the source and target languages. Several data augmentation techniques are evaluated and shown to increase translation performance in low-resource settings. We evaluate our proposed solution on Kinyarwanda - English translation using public-domain parallel text. Our final models achieve competitive performance in relation to large multi-lingual models. We hope that our results will motivate more use of explicit morphological information and the proposed model and data augmentations in low-resource NMT.

Related papers

• Efficient Machine Translation with a BiLSTM-Attention Approach [0.0]
  This paper proposes a novel Seq2Seq model aimed at improving translation quality while reducing the storage space required by the model. The model employs a Bidirectional Long Short-Term Memory network (Bi-LSTM) as the encoder to capture the context information of the input sequence. Compared to the current mainstream Transformer model, our model achieves superior performance on the WMT14 machine translation dataset.
  arXiv  Detail & Related papers  (2024-10-29T01:12:50Z)
• Using Machine Translation to Augment Multilingual Classification [0.0]
  We explore the effects of using machine translation to fine-tune a multilingual model for a classification task across multiple languages. We show that translated data are of sufficient quality to tune multilingual classifiers and that this novel loss technique is able to offer some improvement over models tuned without it.
  arXiv  Detail & Related papers  (2024-05-09T00:31:59Z)
• TAMS: Translation-Assisted Morphological Segmentation [3.666125285899499]
  We present a sequence-to-sequence model for canonical morpheme segmentation. Our model outperforms the baseline in a super-low resource setting but yields mixed results on training splits with more data. While further work is needed to make translations useful in higher-resource settings, our model shows promise in severely resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-21T21:23:35Z)
• Unified Model Learning for Various Neural Machine Translation [63.320005222549646]
  Existing machine translation (NMT) studies mainly focus on developing dataset-specific models. We propose a versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks. OurNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs.
  arXiv  Detail & Related papers  (2023-05-04T12:21:52Z)
• Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning [3.2258463207097017]
  Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT) In GAN, similar to bilingual models, multilingual NMT only considers one reference translation for each sentence during model training. This article proposes Denoising Adversarial Auto-encoder-based Sentence Interpolation (DAASI) approach to perform sentence computation.
  arXiv  Detail & Related papers  (2023-03-31T12:34:14Z)
• Pre-Training a Graph Recurrent Network for Language Representation [34.4554387894105]
  We consider a graph recurrent network for language model pre-training, which builds a graph structure for each sequence with local token-level communications. We find that our model can generate more diverse outputs with less contextualized feature redundancy than existing attention-based models.
  arXiv  Detail & Related papers  (2022-09-08T14:12:15Z)
• Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies [72.56158036639707]
  Morphologically rich languages pose difficulties to machine translation. A large amount of differently inflected word surface forms entails a larger vocabulary. Some inflected forms of infrequent terms typically do not appear in the training corpus. Linguistic agreement requires the system to correctly match the grammatical categories between inflected word forms in the output sentence.
  arXiv  Detail & Related papers  (2022-03-25T10:13:20Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information [72.2412707779571]
  mRASP is an approach to pre-train a universal multilingual neural machine translation model. We carry out experiments on 42 translation directions across a diverse setting, including low, medium, rich resource, and as well as transferring to exotic language pairs.
  arXiv  Detail & Related papers  (2020-10-07T03:57:54Z)
• Learning Source Phrase Representations for Neural Machine Translation [65.94387047871648]
  We propose an attentive phrase representation generation mechanism which is able to generate phrase representations from corresponding token representations. In our experiments, we obtain significant improvements on the WMT 14 English-German and English-French tasks on top of the strong Transformer baseline.
  arXiv  Detail & Related papers  (2020-06-25T13:43:11Z)

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.