Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs.
There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal.
In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora.
Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation.
Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French-to-English and German-to-English translation.
The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively.
Our implementation is released as an open source project.
Neural machine translation (NMT) has recently become the dominant paradigm to machine translation BID32 .
As opposed to the traditional statistical machine translation (SMT), NMT systems are trained end-to-end, take advantage of continuous representations that greatly alleviate the sparsity problem, and make use of much larger contexts, thus mitigating the locality problem.
Thanks to this, NMT has been reported to significantly improve over SMT both in automatic metrics and human evaluation BID34 .Nevertheless
, for the same reasons described above, NMT requires a large parallel corpus to be effective, and is known to fail when the training data is not big enough BID18 . Unfortunately
, the lack of large parallel corpora is a practical problem for the vast majority of language pairs, including low-resource languages (e.g. Basque) as well as many combinations of major languages (e.g. German-Russian). Several authors
have recently tried to address this problem using pivoting or triangulation techniques as well as semi-supervised approaches BID14 , but these methods still require a strong cross-lingual signal.In this work, we eliminate the need of cross-lingual information and propose a novel method to train NMT systems in a completely unsupervised manner, relying solely on monolingual corpora. Our approach builds
upon the recent work on unsupervised cross-lingual embeddings BID1 BID35 . Thanks to a shared
encoder for both translation directions that uses these fixed cross-lingual embeddings, the entire system can be trained, with monolingual data, to reconstruct its input. In order to learn
useful structural information, noise in the form of random token swaps is introduced in this input. In addition to denoising
, we also incorporate backtranslation Figure 1: Architecture of the proposed system. For each sentence in language
L1, the system is trained alternating two steps: denoising, which optimizes the probability of encoding a noised version of the sentence with the shared encoder and reconstructing it with the L1 decoder, and on-the-fly backtranslation, which translates the sentence in inference mode (encoding it with the shared encoder and decoding it with the L2 decoder) and then optimizes the probability of encoding this translated sentence with the shared encoder and recovering the original sentence with the L1 decoder. Training alternates between sentences
in L1 and L2, with analogous steps for the latter. BID28 into the training procedure to
further improve results. Figure 1 summarizes this general schema
of the proposed system.In spite of the simplicity of the approach, our experiments show that the proposed system can reach up to 15.56 BLEU points for French → English and 10.21 BLEU points for German → English in the standard WMT 2014 translation task using nothing but monolingual training data. Moreover, we show that combining this method
with a small parallel corpus can further improve the results, obtaining 21.81 and 15.24 BLEU points with 100,000 parallel sentences, respectively. Our manual analysis confirms the effectiveness
of the proposed approach, revealing that the system is learning non-trivial translation relations that go beyond a word-by-word substitution.The remaining of this paper is organized as follows. Section 2 analyzes the related work. Section 3
then describes the proposed method.
The experimental settings are discussed in Section
4, while Section 5 presents and discusses the obtained results. Section 6 concludes the paper.
We discuss the quantitative results in Section 5.1, and present a qualitative analysis in Section 5.2.
In this work, we propose a novel method to train an NMT system in a completely unsupervised manner.
We build upon existing work on unsupervised cross-lingual embeddings BID1 BID35 , and incorporate them in a modified attentional encoder-decoder model.
By using a shared encoder with these fixed cross-lingual embeddings, we are able to train the system from monolingual corpora alone, combining denoising and backtranslation.The experiments show the effectiveness of our proposal, obtaining significant improvements in the BLEU score over a baseline system that performs word-by-word substitution in the standard WMT 2014 French-English and German-English benchmarks.
Our manual analysis confirms the quality of the proposed system, showing that it is able to model complex cross-lingual relations and produce high-quality translations.
Moreover, we show that combining our method with a small parallel corpus can bring further improvements, showing its potential interest beyond the strictly unsupervised scenario.Our work opens exciting opportunities for future research, as our analysis reveals that, in spite of the solid results, there is still a considerable room for improvement.
In particular, we observe that the performance of a comparable supervised NMT system is considerably below the state of the art, which suggests that the architectural modifications introduced by our proposal (Section 3.1) are also limiting its potential performance.
For that reason, we would like to explore progressively relaxing these constraints during training as discussed in Section 5.1.
Additionally, we would like to incorporate character level information into the model, which we believe that could be very helpful to address some of the adequacy issues observed in our manual analysis (Section 5.2).
Finally, we would like to explore other neighborhood functions for denoising, and analyze their effect in relation to the typological divergences of different language pairs.
We introduce the first successful method to train neural machine translation in an unsupervised manner, using nothing but monolingual corpora