| { | |
| "paper_id": "2022", | |
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| "date_generated": "2023-01-19T07:13:08.785864Z" | |
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| "title": "On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing", | |
| "authors": [ | |
| { | |
| "first": "Itsuki", | |
| "middle": [], | |
| "last": "Okimura", | |
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| "institution": "The University of Tokyo", | |
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| }, | |
| "email": "okimura@weblab.t.u-tokyo.ac.jp" | |
| }, | |
| { | |
| "first": "Machel", | |
| "middle": [], | |
| "last": "Reid", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "The University of Tokyo", | |
| "location": {} | |
| }, | |
| "email": "machelreid@weblab.t.u-tokyo.ac.jp" | |
| }, | |
| { | |
| "first": "Makoto", | |
| "middle": [], | |
| "last": "Kawano", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "The University of Tokyo", | |
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| }, | |
| "email": "kawano@weblab.t.u-tokyo.ac.jp" | |
| }, | |
| { | |
| "first": "Yutaka", | |
| "middle": [], | |
| "last": "Matsuo", | |
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| "affiliation": { | |
| "laboratory": "", | |
| "institution": "The University of Tokyo", | |
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| "email": "matsuo@weblab.t.u-tokyo.ac.jp" | |
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| "abstract": "With in the broader scope of machine learning, data augmentation is a common strategy to improve generalization and robustness of machine learning models. While data augmentation has been widely used within computer vision, its use in the NLP has been comparably rather limited. The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear. In this paper, we look to tackle this by evaluating the impact of 12 data augmentation methods on multiple datasets when finetuning pre-trained language models. We find minimal improvements when data sizes are constrained to a few thousand, with performance degradation when data size is increased. We also use various methods to quantify the strength of data augmentations, and find that these values, though weakly correlate with downstream performance, correlate negatively or positively depending on the task. Furthermore, we find a glaring lack of consistently performant data augmentations. This all alludes to the difficulty of data augmentations for NLP tasks and we are inclined to believe that static data augmentations are not broadly applicable given these properties.", | |
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| "text": "With in the broader scope of machine learning, data augmentation is a common strategy to improve generalization and robustness of machine learning models. While data augmentation has been widely used within computer vision, its use in the NLP has been comparably rather limited. The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear. In this paper, we look to tackle this by evaluating the impact of 12 data augmentation methods on multiple datasets when finetuning pre-trained language models. We find minimal improvements when data sizes are constrained to a few thousand, with performance degradation when data size is increased. We also use various methods to quantify the strength of data augmentations, and find that these values, though weakly correlate with downstream performance, correlate negatively or positively depending on the task. Furthermore, we find a glaring lack of consistently performant data augmentations. This all alludes to the difficulty of data augmentations for NLP tasks and we are inclined to believe that static data augmentations are not broadly applicable given these properties.", | |
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| "section": "Abstract", | |
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| "text": "Data augmentation may be useful in situations where the data size is insufficient for the number of parameters in the model, resulting in overtraining (Perez and Wang, 2017) . It has been pointed out that data augmentation does not degrade the expressive power of the model and achieves an improvement in the generalization performance of the model without adjusting the hyperparameters (Hern\u00e1ndez-Garc\u00eda and K\u00f6nig, 2018) . While data augmentation is standard in the field of computer vision, it is not fully used in natural language processing. Two factors can be cited for this. The first reason is that there has been insufficient unified validation of data augmentation methods for a wide range of datasets and data sizes. Another reason is that it is still unclear what kind of data augmentation is effective for learning. In natural language processing, it is difficult to judge whether a data augmentation method is good or bad without relying on experiments, and it is necessary to search for effective data augmentations by trial and error (Feng et al., 2021) . If it is possible to predict whether a data augmentation is effective for learning before training, it would be possible to search for data augmentations more efficiently.", | |
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| "end": 173, | |
| "text": "(Perez and Wang, 2017)", | |
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| "start": 387, | |
| "end": 421, | |
| "text": "(Hern\u00e1ndez-Garc\u00eda and K\u00f6nig, 2018)", | |
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| "end": 1068, | |
| "text": "(Feng et al., 2021)", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "This paper examines the performance impact of data augmentation methods that have been proposed for natural language processing on various datasets. Through this experiment, we will verify whether the data augmentation method can contribute to the improvement of performance on multiple datasets and problem settings. We also use various measures of the strength of a given data augmentation, and investigate its relationship with performance after learning. We find that although data augmentation strength (i.e. how significantly it perturbs the input) is correlated with the change in downstream performance to a given degree, its sign and degree often varies significantly. Based on this, we believe that static data augmentations are not a wise choice for NLP tasks with a reasonable amount of data, and may need to be combined with data-dependent modeling innovations to be broadly applicable to future work.", | |
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| "section": "Introduction", | |
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| "text": "Data Augmentation for NLP Data augmentation has been explored in NLP recently with EDA (Wei and Zou, 2019) , as well as NL-augmenter (Dhole et al., 2021) . Masked language modeling can be considered to be data augmentation (Devlin et al., 2019) , while dictionary-derived augmentation methods have been employed recently for aug-menting multilingual language models with large improvements Reid et al., 2021; Reid and Artetxe, 2022) . However, Longpre et al. (2020) showed that two data augmentation methods in natural language processing had small effects on pre-trained language models. We further expand the scope of this study to examine the performance impact of 12 different data augmentation methods.", | |
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| "text": "NL-augmenter (Dhole et al., 2021)", | |
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| "section": "Related Work", | |
| "sec_num": "2" | |
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| "text": "Evaluating Data Augmentation In the field of computer vision, researchers have been studying what kind of data augmentation contributes to the performance (Taylor and Nitschke, 2018; Perez and Wang, 2017) . And some studies have been done to create metrics on data augmentation and evaluate the relationship with the performance of the model after training. Gontijo-Lopes et al. 2020proposed two indices, affinity and diversity, to quantify how data augmentation improves the generalization of the model, and pointed out that data augmentation methods that are evaluated as having high affinity and diversity will lead to better performance in computer vision. Meanwhile, it is still unclear what characteristics of data augmentation methods are effective in the field of natural language processing.", | |
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| "text": "(Taylor and Nitschke, 2018;", | |
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| "text": "Perez and Wang, 2017)", | |
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| "section": "Related Work", | |
| "sec_num": "2" | |
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| { | |
| "text": "In this section, we briefly go over metrics we use to evaluate the strength of our data augmentations of a given task as well as strategies for training using data augmentations.", | |
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| "section": "Evaluation Metrics and Training Strategies", | |
| "sec_num": "3" | |
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| "text": "In this subsection, we briefly discuss our two training strategies for incorporating data augmentation. Given an i.", | |
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| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "i.d. dataset D = {(x 1 , y 1 ), (x 2 , y 2 ), . . . , (x N , y N )} containing N examples", | |
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| "text": "where each x i represents an input, and y i represents the assigned label corresponding to x i . Oftentimes, we simply fit a given model on this dataset. However, given a data augmentation function f (x i ) =x i , wherex i represents an augmented input, we can also augment this dataset to improve the diversity of inputs which should hopefully lead to better model generalization and robustness. That is, we now have augmented dataset", | |
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| "text": "D = {(x 1 , y 1 ), . . . , (x N ,\u0177 N )}.", | |
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| "section": "Training Strategy", | |
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| "text": "We now explain the following finetuning methods: Normal training Finetuning our models on D 1-step training Finetuning our models jointly on augmented datasetD and original dataset D-this method is commonly employed in computer vision. 2-step training To mitigate the distribution shift introduced by the augmentation, but still allowing the model to learn from the augmented dataset, we look at two-step finetuning where we first finetune onD and then finetune on D.", | |
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| "text": "We also look to analyse whether there are certain trends among the strength of augmentation methods and their impact on downstream performance. To do this, we measure the strength of augmentation methods using the following metrics:", | |
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| "section": "Data Augmentation Strength", | |
| "sec_num": "3.2" | |
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| "text": "Semantic Similarity We use semantic similarity (Cer et al., 2017) as a measure of strength of data augmentation. For example, if a given example is perturbed in a more significant manner, we assume that it's semantic similarity will decrease, therefore indicating a \"stronger\" data augmentation. We use SentenceBERT (Reimers and Gurevych, 2019) to measure the cosine similarity between sentence representation of the original example x i and sentence representation of augmented examplex i .", | |
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| "section": "Data Augmentation Strength", | |
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| "text": "BLEU We use BLEU (Papineni et al., 2002; Post, 2018) as a metric that works on discrete tokens (therefore more sensitive to exact token matches), that is not model dependent as our semantic similarity measure is. That is, a lower BLEU score represents a stronger data augmentation.", | |
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| "section": "Data Augmentation Strength", | |
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| "text": "BERTScore We also use text generation metric BERTScore (Zhang* et al., 2020) , which measures cosine-similarity at a token-level, rather than on a sequence-level like our semantic similarity measure.", | |
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| "text": "In our analyses (Sec. 5), we measure the correlation between these measures and the \u00b1 change in performance.", | |
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| "section": "Data Augmentation Strength", | |
| "sec_num": "3.2" | |
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| "text": "In our experiments, we compared the performance of the model when trained with 12 typical data augmentation methods with that of the model trained without data augmentation. Our data augmentations methods are sourced from NL-Augmenter 1 (Dhole et al., 2021) and nlpaug 2 (Ma, 2019) . We provide additional details in Appendix B.", | |
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| "section": "Data Augmentation Methods", | |
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| "text": "In experiments, we use three datasets for different language tasks, MRPC (Dolan and Brockett, 2005), SICK (Marelli et al., 2014) , and SST-2 (Socher et al., 2013) . MRPC is a dataset in which the task is to predict whether a sentence-pair is semantically equivalent. SICK is a dataset that contains a task to infer the connotation between a given premise and an explanation. In this experiment, it is a binary classification problem whether the meaning of the explanatory sentence is contained in the meaning of the premise sentence or not. SST-2 is a binary classification problem in which a dataset for sentiment analysis of sentences is created from movie reviews, are classified as positive or negative. For MRPC and SICK, we extended the data to the second sentence in the experiment, and the combination of the first sentence, the extended second sentence pair, and the original label was used as the augmented data set. For SST-2, the combination of the augmented sentence and the original label was used as the augmented data set.", | |
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| "section": "Datasets", | |
| "sec_num": "4.2" | |
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| "text": "In this experiment, we used the GPT-2 (345M) (Radford et al., 2019) and BERT-large (Devlin et al., 2018) as pre-trained language models. We train models on a single NVIDIA V100 16GB GPU. We measured the performance of training on the original dataset as a baseline, and compared the performance of fine-tuning on the training dataset with the augmented data. We train models until convergence, and perform early stopping where we use a patience of 3 epochs for all models.", | |
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| "section": "Models", | |
| "sec_num": "4.3" | |
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| "text": "Performance Changes Due to Data Augmentation Table 1 shows the scores for single-step and 2-step training on the data set with data augmentation (see Appendix D for per-task results). For both training strategies, we also measure the impact of data size, experimenting with various data sizes (10%, 50%, and 100% of the full dataset). When all data was used for training, we found that no data augmentation that improved scores on average for both the language model and the masked language model, except for the 2-step training with BERT with synonym substitution. This indicates that although data-augmentation has the tendency to help at a smaller scale, perhaps mitigating effects of (lack of) data diversity, as the data scale grows we notice that performance degrades where the augmentations most likely add more noise to the dataset.", | |
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| "text": "Table 1", | |
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| "section": "Results", | |
| "sec_num": "5" | |
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| "text": "Relationship between Data Augmentation Intensity and Post-training Performance The correlation coefficients measured by the difference in F1 scores between the data augmentation intensity obtained by the language model and the masked language model and the baseline for each model and learning method are shown in Table2. A positive value indicates that a weaker (i.e. more similar) data augmentation results in better performance. When we use 1-step training, this correlation is generally positive -this indicates that when using naive data combination, then a more similar (i.e. weaker augmentation) is generally more effective. This supports our hypothesis about distribution shift negatively impact augmentation. However, this finding varies significantly when switching to 2-step training depending on model and dataset. Given the relatively strong performance of 2-step training, this indicates that strength of data augmentation can have varying effects when using various training schedules/models.", | |
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| "sec_num": "5" | |
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| "text": "When all the original training data was used for training in the three datasets tested in this study, the effect of data augmentation on performance improvement was small, and the performance on the test data deteriorated in many cases. There are two possible reasons for this. The first is that the augmented data may have become noise. It is almost inevitable that data augmentation will result in the augmentation of sentences whose labels cannot be preserved. If some of the augmented sentences are incorrectly labeled, the quality of the dataset will deteriorate to some extent. Therefore, in a setting where a relatively large number of data can be prepared, such as using all the training data, the negative impact of the decrease in data quality is stronger than the positive impact of the increase in the number of data. The second reason is that the knowledge that can be obtained by data augmentation may have already been acquired through prior learning. This is also pointed out by Longpre et al. (2020) . Therefore, for data 1-step GPT2 1-step BERT 2-step GPT-2 2-step BERT 100% 50% 10% 100% 50% 10% 100% 50% 10% 100% 50% 10% baseline Sentence similarity BLEU BERTScore GPT-2 BERT GPT-2 BERT GPT-2 BERT 1-step 2-step 1-step 2-step 1-step 2-step 1-step 2-step 1-step 2-step 1-step 2-step MRPC 0. augmentation in a specific domain, it is possible that data augmentation based on knowledge about the domain, such as substitution based on a list of words that can be substituted in the domain, which cannot be obtained by pre-training with a general corpus, may be effective. On the other hand, when the number of data used for training was limited, we observed some cases where the performance improved even when using a pre-training model. Therefore, in domains where only a few hundred examples are available, performance improvement can be expected by augmenting the existing data.", | |
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| "text": "1-step BERT 2-step GPT-2 2-step BERT 100% 50% 10% 100% 50% 10% 100% 50% 10% 100% 50% 10% baseline", | |
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| "section": "Discussion", | |
| "sec_num": "6" | |
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| "text": "In addition, in 1-step learning, the weaker the data augmentation, the better the performance. However, in 2-step learning, the relationship between the strength of consistent data augmentation and performance depended on the type of data set. This suggests that in 2-step learning, the effective strength of data augmentation may differ depending on the characteristics of the data set. For example, in MRPC, the difference between the data augmentation intensity and the F1 score of the baseline was negatively correlated because even trivial changes are likely to produce data that become noise in learning. In SICK and SST-2, even if some of the content changes, the labels of the sentences are retained as long as the words indicating relevance and emotion remain the same. In this case, the various sentences created by strong data reinforcement in two-stage learning contribute to the learning pro-cess, allowing clean data to be learned in the second half. This may be why the difference between the strength of the data reinforcement and the F1 score from the baseline may have been positively correlated in some cases. Therefore, by comparing the augmentation intensity determined by the proposed index, it may be possible to efficiently search for promising data augmentation methods before actual training. However, more work needs to be done to effectively use these methods in a practical setting.", | |
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| "section": "Discussion", | |
| "sec_num": "6" | |
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| "text": "In this paper, we observed that most of the data augmentation methods did not improve performance when training on datasets with thousands of examples, but some of them improved performance when training on datasets with hundreds of examples. This suggests that, depending on the task and the data size, data augmentation may be effective even when a pre-trained language model is used for training. We also defined data augmentation intensity, a measure to evaluate whether data augmentation produces sentences that are different from the original sentences, and evaluated the relationship between this measure and the performance after training. As a result, the data augmentation intensity showed different correlations with the change in performance after training depending on the target dataset. For tasks with enough data, this indi-cates the limited applicability and predictability of static data augmentations. In future work, we believe the NLP community should look at modeling or adaptive learning methods (Dery et al., 2022) to account for these differences in data.", | |
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| "text": "(Dery et al., 2022)", | |
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| "section": "Conclusion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "https://github.com/GEM-benchmark/ NL-Augmenter", | |
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| "text": "https://github.com/makcedward/nlpaug", | |
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| "title": "SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation", | |
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| "first": "Daniel", | |
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