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.

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Competence-based Curriculum Learning for Neural Machine Translation (N19-1)

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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.
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.
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.
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)

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Challenge: Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings.
Approach: They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data.
Outcome: The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data.
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.
Dynamic Data Selection and Weighting for Iterative Back-Translation (2020.emnlp-main)

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Challenge: Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines.
Approach: They propose a data selection and weighting strategy to iterate back-translation models and apply it to it . they use a target language to back-transcribe monolingual data, which is of high quality and reflect the target domain.
Outcome: The proposed approach achieves 1.8 BLEU points over baselines on domain adaptation, low-resource, and high-resourced MT settings and on two language pairs.
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation (P18-2)

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Challenge: Existing methods to train neural machine translation (NMT) use a fixed training procedure where each sentence is sampled once during each epoch.
Approach: They propose to dynamically sample sentences to accelerate NMT training . a weight is assigned to each sentence based on the measured difference between training costs of two iterations.
Outcome: Empirical results show that the proposed method can significantly accelerate training and improve NMT performance.
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.
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation (D19-56)

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Challenge: Neural Machine Translation suffers from the lack of bilingual data in low-resource scenarios.
Approach: They propose to inject inductive biases into Neural Machine Translation (NMT) using auxiliary syntactic and semantic tasks.
Outcome: The proposed approach improves translation quality by reweighing training data of main and auxiliary tasks based on their contributions to generalisability of main task.

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