Understanding and Improving Hidden Representations for Neural Machine Translation (N19-1)
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| Challenge: | Existing studies have explored some methods for understanding hidden representations, but they have not sought to improve the translation quality rationally according to their understanding. |
| Approach: | They propose to construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks. |
| Outcome: | The proposed methods achieve consistent improvements (up to +1.3 BLEU) on two widely-used datasets. |
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Improving Lexical Choice in Neural Machine Translation (N18-1)
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| Challenge: | False positives: the output layer rewards frequent words disproportionately, we argue . Falsibles: a model that learns word representations in continuous space tends to translate rare words . |
| Approach: | They propose to fix the norms of both vectors to a constant value and integrate a lexical module which is jointly trained with the rest of the model. |
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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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Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)
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| Challenge: | Modern neural machine translation models suffer limitation in compositional generalization, resulting in weakened translation performance on unseen compounds. |
| Approach: | They propose to introduce categorization to the contextualized representations to improve generalization by reducing sparsity and overfitting. |
| Outcome: | The proposed method reduces compositional generalization error rates by 24% on a dedicated MT dataset. |
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. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |
Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
Multi-layer Representation Fusion for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation systems require a number of stacked layers for deep models, but the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. |
| Approach: | They propose a multi-layer representation fusion approach to fusing stacked layers to learn a better representation from the stack. |
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Tied Multitask Learning for Neural Speech Translation (N18-1)
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| Challenge: | Recent efforts in endangered language documentation focus on collecting spoken language resources . BULB project uses mobile app to collect spoken resources accompanied by spoken translations . |
| Approach: | They propose a model where the second task decoder receives information from the first task . they apply regularization that encourages transitivity and invertibility to the model . |
| Outcome: | The proposed model improves performance on low-resource speech transcription and translation tasks. |
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization (D19-1)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
| Approach: | They propose to evaluate DA methods from two perspectives to determine their generalization ability . they find that DA method's test performance does not exhibit consistent improvements across translation tasks . |
| Outcome: | The proposed methods do not exhibit consistent improvements across translation tasks. |
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models (N18-2)
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| Challenge: | In this paper we explore the use of Learning Hidden Unit Contribution for neural machine translation. |
| Approach: | They propose to use Learning Hidden Unit Contribution for the task of neural machine translation. |
| Outcome: | The proposed method achieves improvements of up to 2.6 BLEU points over a general system . it also achieves up to 6 BLUE points if the initial system has been trained on out-of-domain data . |
A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
| Approach: | They propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side. |
| Outcome: | The proposed model outperforms standard seq2seq models by 2.1 BLEU points and other methods for incorporating target-side syntax by 0.7 BLUE points. |