Anna Currey


2020

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Distilling Multiple Domains for Neural Machine Translation
Anna Currey | Prashant Mathur | Georgiana Dinu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource. The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity). In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage. We show that this model can improve translation on both high- and low-resource domains over strong multi-domain baselines. In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.

2019

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Zero-Resource Neural Machine Translation with Monolingual Pivot Data
Anna Currey | Kenneth Heafield
Proceedings of the 3rd Workshop on Neural Generation and Translation

Zero-shot neural machine translation (NMT) is a framework that uses source-pivot and target-pivot parallel data to train a source-target NMT system. An extension to zero-shot NMT is zero-resource NMT, which generates pseudo-parallel corpora using a zero-shot system and further trains the zero-shot system on that data. In this paper, we expand on zero-resource NMT by incorporating monolingual data in the pivot language into training; since the pivot language is usually the highest-resource language of the three, we expect monolingual pivot-language data to be most abundant. We propose methods for generating pseudo-parallel corpora using pivot-language monolingual data and for leveraging the pseudo-parallel corpora to improve the zero-shot NMT system. We evaluate these methods for a high-resource language pair (German-Russian) using English as the pivot. We show that our proposed methods yield consistent improvements over strong zero-shot and zero-resource baselines and even catch up to pivot-based models in BLEU (while not requiring the two-pass inference that pivot models require).

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Incorporating Source Syntax into Transformer-Based Neural Machine Translation
Anna Currey | Kenneth Heafield
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

Transformer-based neural machine translation (NMT) has recently achieved state-of-the-art performance on many machine translation tasks. However, recent work (Raganato and Tiedemann, 2018; Tang et al., 2018; Tran et al., 2018) has indicated that Transformer models may not learn syntactic structures as well as their recurrent neural network-based counterparts, particularly in low-resource cases. In this paper, we incorporate constituency parse information into a Transformer NMT model. We leverage linearized parses of the source training sentences in order to inject syntax into the Transformer architecture without modifying it. We introduce two methods: a multi-task machine translation and parsing model with a single encoder and decoder, and a mixed encoder model that learns to translate directly from parsed and unparsed source sentences. We evaluate our methods on low-resource translation from English into twenty target languages, showing consistent improvements of 1.3 BLEU on average across diverse target languages for the multi-task technique. We further evaluate the models on full-scale WMT tasks, finding that the multi-task model aids low- and medium-resource NMT but degenerates high-resource English-German translation.

2018

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Multi-Source Syntactic Neural Machine Translation
Anna Currey | Kenneth Heafield
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses. This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; the resulting representations are then combined using a hierarchical attention mechanism. The proposed model improves over both seq2seq and parsed baselines by over 1 BLEU on the WMT17 English-German task. Further analysis shows that our multi-source syntactic model is able to translate successfully without any parsed input, unlike standard parsed methods. In addition, performance does not deteriorate as much on long sentences as for the baselines.

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Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation
Anna Currey | Kenneth Heafield
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016; Luong et al., 2016). However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domains for which a reliable parser is not available. In this paper, we introduce an unsupervised tree-to-sequence (tree2seq) model for neural machine translation; this model is able to induce an unsupervised hierarchical structure on the source sentence based on the downstream task of neural machine translation. We adapt the Gumbel tree-LSTM of Choi et al. (2018) to NMT in order to create the encoder. We evaluate our model against sequential and supervised parsing baselines on three low- and medium-resource language pairs. For low-resource cases, the unsupervised tree2seq encoder significantly outperforms the baselines; no improvements are seen for medium-resource translation.

2017

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Copied Monolingual Data Improves Low-Resource Neural Machine Translation
Anna Currey | Antonio Valerio Miceli Barone | Kenneth Heafield
Proceedings of the Second Conference on Machine Translation

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The University of Edinburgh’s Neural MT Systems for WMT17
Rico Sennrich | Alexandra Birch | Anna Currey | Ulrich Germann | Barry Haddow | Kenneth Heafield | Antonio Valerio Miceli Barone | Philip Williams
Proceedings of the Second Conference on Machine Translation

2016

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Using Related Languages to Enhance Statistical Language Models
Anna Currey | Alina Karakanta | Jon Dehdari
Proceedings of the NAACL Student Research Workshop