| Challenge: | Existing studies have attributed SAN to being weak at learning positional information for sequence modeling due to lack of recurrence structure. |
| Approach: | They propose a word reordering detection task to quantify how well word order information is learned by SAN and RNN. |
| Outcome: | The proposed task quantifies how well word order information learned by SAN and RNN is learned. |
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Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures (D18-1)
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| Challenge: | Recent studies show that non-recurrent architectures outperform RNNs in neural machine translation. |
| Approach: | They hypothesize that CNNs and self-attentional networks could extract semantic features from source text. |
| Outcome: | The proposed architectures outperform RNNs on two tasks: subject-verb agreement and word sense disambiguation. |
Neural Machine Translation with Reordering Embeddings (P19-1)
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| Challenge: | Existing work exploits the reordering information in neural machine translation . experimental results show that the proposed methods can significantly improve the performance of the transformer translation system. |
| Approach: | They propose a reordering mechanism to learn the re ordering embedding of a word based on contextual information and stack them together with self-attention networks to learn sentence representation for machine translation. |
| Outcome: | The proposed method improves translation performance on English-to-German, NIST Chinese-to English, and WAT Japanese-toEnglish translation tasks. |
Fast and Accurate Reordering with ITG Transition RNN (C18-1)
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| Challenge: | Attention-based sequence-to-sequence neural networks learn to jointly align and translate. |
| Approach: | They propose to use a reordering RNN that shares the input encoder with the decoder to decouple re-ordering from translation. |
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Recurrent Positional Embedding for Neural Machine Translation (D19-1)
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| Challenge: | Existing translation systems that use positional embeddings only encode static order dependencies based on discrete numerical information, which may hinder the improvement of translation capacity. |
| Approach: | They propose a recurrent positional embedding approach based on word vectors that are learned by a neural network and integrated into existing multi-head self-attention models. |
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Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering (2022.findings-emnlp)
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| Challenge: | Existing non-autoregressive neural machine translation models that implicitly model dependencies are sub-optimal in handling word order errors. |
| Approach: | They propose to learn a non-autoregressive language model that can be combined with Viterbi decoding to achieve better reordering performance. |
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Self-Attention with Relative Position Representations (N18-2)
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| Challenge: | Recent approaches to sequence to sequence learning leverage recurrence, convolution, attention or combination of recurrent and convolutional neural networks. |
| Approach: | They propose an approach that extends the self-attention mechanism to consider representations of relative positions, or distances between sequence elements. |
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Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons (D19-1)
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| Challenge: | Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work. |
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Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study (2021.naacl-main)
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| Challenge: | Existing approaches to encode natural languages without orders are lacking. |
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Self-Attention with Cross-Lingual Position Representation (2020.acl-main)
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| Challenge: | Position encoding (PE) is used to preserve word order information for natural language processing tasks, generating fixed position indices for input sequences. |
| Approach: | They propose to augment SANs with cross-lingual position representations to model bilingually aware latent structure for the input sentence. |
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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. |