Towards String-To-Tree Neural Machine Translation
Roee Aharoni,Yoav Goldberg +1 more
- Vol. 2, pp 132-140
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TLDR
This paper presented a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees, which resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset.Abstract:
We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.read more
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Neural Network Methods in Natural Language Processing
Yoav Goldberg,Graeme Hirst +1 more
TL;DR: Neural networks are a family of powerful machine learning models as mentioned in this paper, and they have been widely used in natural language processing applications such as machine translation, syntactic parsing, and multi-task learning.
Proceedings ArticleDOI
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
TL;DR: The authors proposed a simple and effective approach to incorporate syntactic structure into neural attention-based encoder-decoder models for machine translation by using graph convolutional networks (GCNs).
Proceedings ArticleDOI
Graph-to-Sequence Learning using Gated Graph Neural Networks
TL;DR: This article proposed a new model that encodes the full structural information contained in the graph, allowing nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work.
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Neural Machine Translation
TL;DR: A comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges.
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Graph-to-Sequence Learning using Gated Graph Neural Networks
TL;DR: This work proposes a new model that encodes the full structural information contained in the graph, couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work.
References
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Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Proceedings Article
Sequence to Sequence Learning with Neural Networks
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
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Sequence to Sequence Learning with Neural Networks
TL;DR: This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
Proceedings ArticleDOI
Neural Machine Translation of Rare Words with Subword Units
TL;DR: This paper introduces a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units, and empirically shows that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.3 BLEU.