| Challenge: | Existing neural machine translation models adopt a monotonic decoding order of either left-to-right or right-to left. |
| Approach: | They propose a method that starts decoding target words from the right side of a median word and generates words on the left. |
| Outcome: | The proposed method outperforms baseline models on three datasets. |
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Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)
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| Challenge: | Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. |
| Approach: | They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context. |
| Outcome: | The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding. |
Mask-Predict: Parallel Decoding of Conditional Masked Language Models (D19-1)
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| Challenge: | a masked language model is used to train a model to predict subsets of mangled words . a parallel decoding algorithm can be used to generate translations in a constant number of iterations. |
| Approach: | They propose a model and a parallel decoding algorithm which train a machine to predict any subset of target words . they introduce conditional masked language models (CMLMs) which are trained with a mangled language model objective . |
| Outcome: | The proposed model improves state-of-the-art performance levels for non-autoregressive and parallel decoding models by over 4 BLEU on average. |
Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
Learning and Analyzing Generation Order for Undirected Sequence Models (2021.findings-emnlp)
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| Challenge: | Undirected neural sequence models generate monotonically from left to right in machine translation tasks. |
| Approach: | They train a policy that learns the generation order for a pre-trained, undirected translation model via reinforcement learning. |
| Outcome: | The proposed policy outperforms heuristic generation orders on three out of four language pairs. |
Neural Machine Translation Decoding with Terminology Constraints (N18-2)
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| Challenge: | Constrained neural machine translation systems can provide excellent quality but do not strictly enforce terminology. |
| Approach: | They propose a framework for constrained neural decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. |
| Outcome: | The proposed framework performs well on multiple translation tasks and motivates the need for constrained decoding with attentions to reduce misplacement and duplication when translating user constraints. |
Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade (2021.findings-acl)
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| Challenge: | Existing non-autoregressive neural machine translation models are slow to learn the dependency between output tokens. |
| Approach: | They propose to use fully non-autoregressive neural machine translation (NAT) to predict tokens with single forward of neural networks. |
| Outcome: | The proposed model achieves state-of-the-art results on three translation benchmarks with comparable performance to autoregressive and iterative NAT systems. |
A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)
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| Challenge: | Neural text generation is notorious for repetitive loops and tedious outputs. |
| Approach: | They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text . |
| Outcome: | The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality. |
Simple and Effective Noisy Channel Modeling for Neural Machine Translation (D19-1)
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| Challenge: | Previous work on noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. |
| Approach: | They propose to use a standard sequence to sequence model which utilizes the entire source and target sentences to estimate posterior probability of a target sequence y given a source sequence x. |
| Outcome: | The proposed model outperforms direct models on German-English translations by up to 3.2 BLEU on four language pairs. |
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
| Approach: | They propose a method that adds synthetic data to sentences with high prediction loss during training and a variety of sampling strategies targeting difficult-to-predict words. |
| Outcome: | The proposed method improves translation quality by up to 1.7 and 1.2 Bleu points over back-translation using random sampling for German-English and English-German, respectively. |
Encoding and Decoding Language in the Brain with Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
| Approach: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and scaling with language models. |
| Outcome: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and decoding with language models. |