| Challenge: | Existing NMT systems require specialized heuristics and large batch sizes. |
| Approach: | They propose a curriculum learning framework for NMT that reduces training time and costs . framework consists of a principled way of deciding which training samples are shown to the model . |
| Outcome: | The proposed framework can reduce training time and improve performance of recurrent neural network models and Transformers. |
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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (N19-1)
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| Challenge: | a heterogeneous training dataset can vary in characteristics such as domain, translation quality, and degree of difficulty. |
| Approach: | They propose to use reinforcement learning to learn an optimal curriculum for NMT training . they find it can beat uniform baselines and hand-designed, state-of-the-art curricula . |
| Outcome: | The proposed approach beats baselines and hand-designed curricula on English-to-French datasets. |
Data Selection Curriculum for Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Neural Machine Translation models are typically trained on heterogeneous data that are concatenated and randomly shuffled. |
| Approach: | They propose a two-stage curriculum training framework where a NMT model is fine-tuned on subsets of data, selected by deterministic scoring and online scoring. |
| Outcome: | The proposed framework improves on six language pairs comprising low- and high-resource languages and shows up to +2.2 BLEU improvement and faster convergence. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
| Outcome: | The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. |
Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. |
| Approach: | They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step. |
| Outcome: | The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed. |
Norm-Based Curriculum Learning for Neural Machine Translation (2020.acl-main)
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| Challenge: | Experimental results show that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x). |
| Approach: | They propose a norm-based curriculum learning method that measures difficulty, competence and weight of a sentence in a word embedding. |
| Outcome: | The proposed method outperforms baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x). |
Uncertainty-Aware Curriculum Learning for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages. |
| Approach: | They propose to use an uncertainty-aware curriculum learning approach to assess data difficulty and model competence to provide examples in an easy-to-hard order at different training stages. |
| Outcome: | The proposed approach outperforms baseline and related methods on translation quality and convergence speed. |
Curriculum Learning for Domain Adaptation in Neural Machine Translation (N19-1)
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| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
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| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
| Approach: | They propose a multi-candidate optimization framework for diverse NMT to deal with this defect. |
| Outcome: | The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. |
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT (2021.emnlp-main)
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| Challenge: | Statistical MT decomposes the translation task into distinct components that are learned separately. |
| Approach: | They show that neural machine translation models acquire different competences over the course of training . previous work shows how to improve some of the competences in NMT by using lexical translation probabilities, phrase memories, alignment information. |
| Outcome: | The proposed model improves translation quality and word-by-word translation, while learning complex reordering patterns. |