| Challenge: | Soft-attention based Neural Machine Translation models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation. |
| Approach: | They propose a hard-attention based NMT model which selects a subset of source tokens for each target token to effectively handle long sequence translation. |
| Outcome: | The proposed model performs better on long sequences and achieves significant improvement on English-German and English-French translation tasks compared to soft-attention based models. |
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| Challenge: | Existing NMT models are shallow in comparison to convolutional models used for both text and vision tasks. |
| Approach: | They propose to modify the attention mechanism to ease the optimization of deeper models by a simple modification to the seq2seq with attention paradigm. |
| Outcome: | The proposed model achieves consistent gains of 0.7-1.1 BLEU on the benchmark WMT’14 English-German and WMT'15 Czech-English tasks. |
Neural Hidden Markov Model for Machine Translation (P18-2)
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| Challenge: | Attention-based neural machine translation models selectively focus on specific source positions to produce a translation. |
| Approach: | They propose to replace the attention component with a neural hidden Markov model that selectively focuss on specific source positions to produce a translation. |
| Outcome: | The proposed model performs better than the state-of-the-art attention-based models on the GermanEnglish and ChineseEnglish translation tasks. |
Interrogating the Explanatory Power of Attention in Neural Machine Translation (D19-56)
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| Challenge: | Attention models are often used to justify the model’s decision in generating a token but it has not been rigorously established to what extent attention is a reliable source of information in NMT. |
| Approach: | They propose to use attention models to modify crucial aspects of the trained attention model to produce function and content words in the translation process. |
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Learning Source Phrase Representations for Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing approaches to machine translation have been shown to be effective for long sentences . however, the attentional network can't capture long-distance dependencies . |
| Approach: | They propose a multi-head attention mechanism which generates phrase representations from token representations and incorporates them into the Transformer translation model to enhance its ability to capture long-distance relationships. |
| Outcome: | The proposed model can be computed in parallel and improves on the WMT 14 tasks. |
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy . |
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Multi-Granularity Self-Attention for Neural Machine Translation (D19-1)
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| Challenge: | Existing neural machine translation models use a deep multi-head self-attention network with no explicit phrase information. |
| Approach: | They propose a neural network that combines multi-head self-attention and phrase modeling to train attention heads to attend to phrases in either n-gram or syntactic formalisms. |
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A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
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Exploiting Deep Representations for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models typically implement encoder and decoder as multiple layers, but only the top layers are leveraged in the subsequent process, which misses the opportunity to exploit useful information embedded in other layers. |
| Approach: | They propose to expose all of these signals with layer aggregation and multi-layer attention mechanisms and introduce an auxiliary regularization term to encourage different layers to capture diverse information. |
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Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation models are usually trained with word-level loss under teacher forcing algorithm . however, this method suffers from exposure bias due to high variance of gradient estimation . |
| Approach: | They propose a method with a differentiable sequence-level training objective . they use greedy search to alleviate the problem of exposure bias . |
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Recurrent Attention for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent research questions the importance of dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. |
| Approach: | They propose a novel mechanism to replace dot-product self-attention with a recurrent atteNtion mechanism that directly learns attention weights without token-to-token interaction. |
| Outcome: | The proposed model outperforms the Transformer model on translation tasks with fewer parameters and inference time. |