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:

While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. 

We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language.
XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark.
With improvements of up to 4.8, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu.
XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language.
On the SQuAD question answering task, we see that XLDA provides a 1.0 performance increase on the English evaluation set.
Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.
Recent work on pretraining natural language processing systems Radford et al., 2018; Howard & Ruder, 2018; Peters et al., 2018; McCann et al., 2017) has led to improvements across a wide variety of natural language tasks (Wang et al., 2018; Rajpurkar et al., 2016; Conneau et al., 2018) .
For several of these tasks, data can be plentiful for high-resource languages like English, Chinese, German, Spanish and French, but both the collection and proliferation of data is limited for low-resource languages like Urdu.
Even when a large language model is pretrained on large amounts of multilingual data Lample & Conneau, 2019) , languages like English can contain orders of magnitude more data in common sources for pretraining like Wikipedia.
One of the most common ways to leverage multilingual data is to use transfer learning.
Word embeddings such as Word2Vec (Mikolov et al., 2013b) or GloVe (Pennington et al., 2014) use large amounts of unsupervised data to create task-agnostic word embeddings which have been shown to greatly improve downstream task performance.
Multilingual variants of such embeddings (Bojanowski et al., 2017) have also shown to be useful at improving performance on common tasks across several languages.
More recently, contextualized embeddings such as CoVe, ElMo, ULMFit and GPT (McCann et al., 2017; Peters et al., 2018; Howard & Ruder, 2018; Radford et al., 2018) have been shown to significantly improve upon aforementioned static embeddings.
BERT ) employs a similar strategy by using a masked version of the language modeling objective.
Unlike other approaches, BERT also provides a multilingual contextual representation which is enabled by its shared sub-word vocabulary and multilingual training data.
Often, for languages for which large amounts of data is not available, aforementioned techniques for creating embeddings (static or contextualized) is not possible and additional strategies need to be employed.
Mrs. Cavendish is in her mother-in-law 's room .
La Sra.
Cavendish ha abandonado el edificio .
(b) Figure 1 :
a) Comparing the standard monolingual approach, a naive multilingual approach that aggregates examples in various languages in a way that each example is solely in one language, and cross-lingual data augmentation (XLDA).
Prediction is always in a single language.
b) Two examples of XLDA inputs using the XNLI dataset.
We demonstrate the effectiveness of cross-lingual data augmentation (XLDA) as a simple technique that improves generalization across multiple languages and tasks.
XLDA can be used with both pretrained and randomly initialized models without needing to explicitly further align the embeddings.
To apply XLDA to any natural language input, we simply take a portion of that input and replace it with its translation in another language.
This makes XLDA compatible with recent methods for pretraining (Lample & Conneau, 2019; .
Additionally, the approach seamlessly scales for many languages and improves performance on all high-and low-resource languages tested including English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Chinese, Hindi, Swahili and Urdu.
This paper makes the following contributions:
• We propose cross-lingual data augmenation (XLDA), a new technique for improving the performance of NLP systems that simply replaces part of the natural language input with its translation in another language.
• We present experiments that show how XLDA can be used to improve performance for every language in XNLI, and in three cases XLDA leads to state-of-the-art performance.
• We demonstrate the ability of our method to improve exact-match and F1 on the SQuAD question-answering dataset as well.
We introduce XLDA, cross-lingual data augmentation, a method that improves the training of NLP systems by replacing a segment of the input text with its translation in another language.
We show how reasoning across languages is crucial to the success of XLDA.
We show the effectiveness of the approach with both pretrained models and randomly initialized models.
We boost performance on all languages in the XNLI dataset, by up to 4.8%, and achieve state of the art results on 3 languages including the low resource language Urdu.
Further investigation is needed to understand the causal and linguistic relationship between XLDA and performance on downstream tasks.
Translating portions of the input during training can improve cross-lingual performance.