XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
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.
The paper proposes a cross-lingual data augmentation method to improve the language inference and question answering tasks. The core idea is to replace a port of the input text (such as one of the sentence in a sentence pair in the language inference tasks) with its translation in another language. The authors empirically show that deploying the XLDA data augment improves the baseline methods for both the XNLI language inference data sets and the SQuAD task. The paper is generally easy to follow and the experiments show promising results. Nevertheless, I have a concern with the comparison baseline used in the paper, as follows. The authors indicate that their work is close to back-translation at the end of the related work section. Also, the authors provide their hypotheses about the possible disadvantages of using back-translation as data augmentation, compared to the proposed work here. However, the authors did not provide experimental studies to support that. To me, the proposed data augmentation strategy is very similar to the back-translation, so I would expect the authors to use that as a comparison baseline in their experimental studies. Without those results, it is difficult to judge the contributions of the proposed approach. Also, the paper could be significantly improved if the authors could provide some analysis/observations on why replacing one of the sentences in the sentence pair for the inference tasks helps? In this sense, using some other data augmentation methods as comparison baselines would be very helpful. For example, simply drop some words in the sentence pair. I found the observations in the paper interesting, but the lack of comparison baselines makes me think the paper in its current form is not ready yet.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposes a cross-lingual data augmentation method to improve the language inference and question answering tasks. The core idea is to replace a port of the input text (such as one of the sentence in a sentence pair in the language inference tasks) with its translation in another language. The authors empirically show that deploying the XLDA data augment improves the baseline methods for both the XNLI language inference data sets and the SQuAD task. The paper is generally easy to follow and the experiments show promising results. Nevertheless, I have a concern with the comparison baseline used in the paper, as follows. The authors indicate that their work is close to back-translation at the end of the related work section. Also, the authors provide their hypotheses about the possible disadvantages of using back-translation as data augmentation, compared to the proposed work here. However, the authors did not provide experimental studies to support that. To me, the proposed data augmentation strategy is very similar to the back-translation, so I would expect the authors to use that as a comparison baseline in their experimental studies. Without those results, it is difficult to judge the contributions of the proposed approach. Also, the paper could be significantly improved if the authors could provide some analysis/observations on why replacing one of the sentences in the sentence pair for the inference tasks helps? In this sense, using some other data augmentation methods as comparison baselines would be very helpful. For example, simply drop some words in the sentence pair. I found the observations in the paper interesting, but the lack of comparison baselines makes me think the paper in its current form is not ready yet.
assistant
The paper proposes a cross-lingual data augmentation method to improve the language inference and question answering tasks. The core idea is to replace a port of the input text (such as one of the sentence in a sentence pair in the language inference tasks) with its translation in another language. The authors empirically show that deploying the XLDA data augment improves the baseline methods for both the XNLI language inference data sets and the SQuAD task. The paper is generally easy to follow and the experiments show promising results. Nevertheless, I have a concern with the comparison baseline used in the paper, as follows. The authors indicate that their work is close to back-translation at the end of the related work section. Also, the authors provide their hypotheses about the possible disadvantages of using back-translation as data augmentation, compared to the proposed work here. However, the authors did not provide experimental studies to support that. To me, the proposed data augmentation strategy is very similar to the back-translation, so I would expect the authors to use that as a comparison baseline in their experimental studies. Without those results, it is difficult to judge the contributions of the proposed approach. Also, the paper could be significantly improved if the authors could provide some analysis/observations on why replacing one of the sentences in the sentence pair for the inference tasks helps? In this sense, using some other data augmentation methods as comparison baselines would be very helpful. For example, simply drop some words in the sentence pair. I found the observations in the paper interesting, but the lack of comparison baselines makes me think the paper in its current form is not ready yet.