Back translation data augmentation paper Back-translation is translating target language to source language and mixing both original source sentence and back-translated sentence to train a model In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. In light of this growing interest, we need more comprehensive studies comparing different augmentation approaches. In this paper, we first translate the sentences from English to The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. 1. Our experiments are based onstrong baseline models trained on the public bi-text ofthe WMT competition. However, when applying data augmentation methods, the size of the synthetic data is frequently chosen by the authors without explicit explanations. FM means fuzzy matching data. ). Low-resource language pairs with a paucity of parallel data pose challenges for machine translation in terms of both adequacy and fluency. In this work, we compare three popular approaches: lexical replacements, linguistic theories, and back-translation (BT), in the context Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. BT and FT stand for back and forward translation, respectively. , 2018)ofback-translation inseveral ways. In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. Browse State-of-the-Art Datasets ; Methods; More Newsletter RC2022. In this paper, we propose a novel data augmentation approach In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. Moreover, we propose a novel procedure to filter the synthetic sentence pairs during the augmentation process, ensuring the high quality of the data. After filtering low-quality data from a larger monolingual corpus, it is ready for training an intermediate target-to-source model. Instant dev environments Issues. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. We back-translated the minority class in each dataset, which is always the subjective documents. 6 years ago • 10 min read By Ayoosh Kathuria. We also propose Deep Back-Translation, a novel NLP data augmentation technique and apply it to benchmark datasets. [22] to predict fake news using a BERT-based model. This paper proposes a novel way of utilizing a monolingual corpus on the source side to assist Neural Machine Translation (NMT) In this regard, we present in this paper a new deep learning-based method that fuses a Back Translation method, and a Paraphrasing technique for data augmentation. 5 for back-translation operations to generate diverse training samples, thereby enhancing the model's generalization capabilities. In this paper, we propose a novel data augmentation approach for NMT, which is independent of any additional training data. Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT). ,2022). Association for Computational Linguistics, Hong Kong, China, 35–44. One of our main goals is to show the nature of DA, i. Finally, we present another augmentation strategy utilizing Deep Networks to augment data for the training of other Deep In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. In this paper, we propose to leverage back translation to augment the training This project employs the back-translation augmentation technique, which translates text to another language and then back to the original language. Automate any workflow Codespaces. To the best of our knowledge, this paper is the first work to present such an extensive comparison of 3. The proposed models process text augmentation using GANs compared to methods like Easy Data Augmentation and back translation. To employ a complete text augmentation training strategy we need to understand the full effect of back translation. Data Augmentation For each language, augmentation and training were done via back-translation into the respective language using AWS translation. Weextend previous analysis (Sennrich et al. ,2016a;Imamura et al. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden In this paper, we present a hybrid data augmentation method to extend the original data and increase ABSA performance. An effective method of generating auxiliary data is back-translation of target language sentences. To overcome this challenge, we present two data augmentation techniques, one that builds comparable corpora (i. Our We tackle Aspect Term Extraction (ATE), a task that automatically recognizes aspect terms conditioned on the under-standing of word-level semantics. {li-specia-2019-improving, title = "Improving Neural The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. To promote the diversity and quality of synthetic data generated by back-translation, in this paper, we Back Translation (BT) [21]. The Tune-B model is finetuned with MTNT data merging both Fr→En and En→Fr directions while the Tune-S is fine-tuned only with Fr→En data. • Section 3 presents the methodology and model design for CANTONMT Since back translation English->other language -> English seems like quite a useful data augmentation technique , I wanted to experiment with it. The In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is In this study, we first obtain large-scale pseudo parallel corpora by back-translating monolingual data, and then investigate its impact on the translation accuracy of context-aware NMT models. This paper has two main contributions: It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, often requiring large amounts of auxiliary data to achieve competitive results. Our experiments on six dif-ferent text classification tasks demonstrate that LM-CPPF outperforms the previous SOTA methods of data augmentation in prompt-based fine-tuning, including Easy Data Augmentation (EDA) (Wei and Zou,2019), Back Translation (BT) (Sugiyama and Yoshinaga,2019), and multiple templates (Jian et al. However, due to the syntactic and semantic differences between different languages, back-translation can lead to loss or distortion of the original information. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of In this paper, we propose a general framework for data augmentation in low-resource machine translation that not only uses target-side monolingual data, but also pivots through a related high-resource language HRL. In this work, we aim to BackTranslation-Based-Data-Augmentation The demo notebook uses the Stanford Sentiment Treebank v2 (SST2) present here The methodology is simply to translate the sentence into a pivot language and the use that translated sentence to come back to the original language. : relative improvement to the baseline without data augmentation (Wang and Yang, 2015) Alternatively, we can leverage pre-trained classic word embeddings such as word2vec, GloVe, and fasttext to perform similarity Request PDF | On Dec 1, 2018, Tomoki Hayashi and others published Back-Translation-Style Data Augmentation for end-to-end ASR | Find, read and cite all the research you need on ResearchGate The results prove that using back-translation to expand the data is particularly helpful on a smaller dataset, it also can reduce the unbalanced distribution of samples and improve the classification performance. Bayer et al. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. In contrast to previous work, which The augmented data of the paper "Parallel Data Augmentation for Formality Style Transfer" (ACL 2020). In this work, we compare three popular approaches: lexical replacements, linguistic theories, and back-translation (BT), in the context Additionally, this paper integrates Translation Memory (TM) with NMT, increasing the amount of data available to the generator. This signifies that multi-layer back translation augmentation generates more diversified data than single-level back translation augmentation. In our case, it will be from English to Neural Machine Translation (NMT) for low-resource languages is still a challenging task in front of NLP researchers. 14. Data Augmentation via Back translation is a technique used by MT researchers when tackling low-resource languages. Paper tables with annotated results for Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of polyphonic character, and a Phoneme Multilingual neural machine translation (MNMT) models are theoretically attractive for low- and zero-resource language pairs with the impact of cross-lingual knowledge transfer. These methods suffer from issues associated with a domain information gap, which leads to translation errors for low frequency and out-of-vocabulary terminology. In this work, we reformulate Sugiyama and Yoshinaga (2019) employed the back-translation on a context-aware NMT model to augment the training data. , why data augmentation works. Inspired by the This paper describes a translation model for ancient Chinese to modern Chinese and English for the Evahan 2023 competition, a subtask of the Ancient Language Translation 2023 challenge. We utilize Special Character Insertion (SCI) in The augmented data of the paper "Parallel Data Augmentation for Formality Style Transfer" (ACL 2020). ro source was used by Busioc et al. [1] propose a This paper investigates the development and evaluation of machine translation models from Cantonese to English, where we propose a novel approach to tackle low-resource language translations. This paper proposes a dictionary-based In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. When training machine learning models, data augmentation acts as a regularizer and helps to avoid Code-switching (CSW) text generation has been receiving increasing attention as a solution to address data scarcity. Back-translation has proved helpful in other areas of NLP as a paraphrasing method to augment text, but because of the data structure of ABSA, it has not been used at its full potential in this field yet. Write better code with AI Security. Back translation is a data augmentation technique that has proven to be a quite effective in NLP. However, the prevalent use of lexical substitution in data augmentation has Data Augmentation Layer Based on Large Language Models The Data Augmentation Layer is a key component of the model proposed in this paper, designed to leverage the large language model GPT-3. The F 1 -score for the offensive class, as well as the weighted average of the Many Data Augmentation (DA) methods have been proposed for neural machine translation. The idea is simple: translating a sentence in one language to another and then back to the original language. 2019. Existing data augmentation approaches for neural machine 3. Although the result is not remarkable, with an accuracy of 64. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. In our 2021 experiment [7 Text Data Augmentation Using Generative Adversarial Networks, Back Translation and EDA Premanand Ghadekar1, Manomay Jamble1, Aditya Jaybhay1,Bhavesh Jagtap1, Aniruddha Joshi1and Harshvardhan More1 The results are summarized under three broad categories – generic data augmentation techniques, advanced data augmentation methods and combined augmentations – composite methods that leverage two or more augmentation techniques simultaneously. Temporary translation: translate each of the original training labeled data into a different language. First, we propose In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. This method generates a larger However, when applying data augmentation methods, the size of the synthetic data is frequently chosen by the authors without explicit explanations. Data augmentation not only helps to grow the dataset but it also Multilingual neural machine translation (MNMT) models are theoretically attractive for low- and zero-resource language pairs with the impact of cross-lingual knowledge transfer. If paper metadata matches the PDF, but the we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Our pipeline investigates different word-embedding-based architectures for classification of Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation . E. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new This paper proposes a general framework of data augmentation for low-resource machine translation not only using target-side monolingual data, but also by pivoting through a related high-resource language. 3 DOI: 10. , 2016a; Poncelas et al. - styfeng/DataAug4NLP. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of polyphonic character, and a Phoneme Exploring back-translation augmentation for question answering Longpre et al. Back translation [3] is an impor-tant sentence-level data augmentation method. In this work, we deploy a standard data augmentation methodology by back-translation to a new language translation direction Cantonese-to-English. In this study, we first obtain large-scale pseudo parallel corpora by back-translating monolingual data, and then investigate its impact on the translation accuracy of context-aware NMT models. Additionally, we propose Back-translation Data Augmentation (BTrA) for both textual and visual data to enhance contrastive learning, where different back-translation schemes are designed to generate a larger number of positive and negative samples. In this paper, we propose a novel data augmentation method. Existing works measure the superiority of DA methods in terms of their performance on a specific test set, but we find that some DA methods do not exhibit consistent improve-ments across translation tasks. Conversion of Chinese Grapheme-to-Phoneme (G2P) plays an important role in Mandarin Chinese Text-To-Speech (TTS) systems, where one of the biggest challenges is the task of polyphone disambiguation. Experimental results on two public datasets demonstrate our method achieves state-of-the-art High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering 22 Aug 2024 · Hengjie Liu, Ruibo Hou, Yves Lepage · Edit social preview. To improve effectiveness of the available BT data, we introduce HintedBT -- a family of techniques which provides hints (through tags) to the encoder and decoder. Conversion of Chinese Grapheme-to-Phoneme (G2P) Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Code-switching (CSW) text generation has been receiving increasing attention as a solution to address data scarcity. 91%, no data augmentation techniques were used. The automatic and human evaluation of our augmented data Data augmentation is an effective technique to reduce overfitting that consists of creating an additional slightly modified version of the available data. In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. ALL author names, the title, and the abstract match the PDF. 18653/v1/D19-6504 Corpus ID: 207979868; Data augmentation using back-translation for context-aware neural machine translation @inproceedings{Sugiyama2019DataAU, title={Data augmentation using back-translation for context-aware neural machine translation}, author={Amane Sugiyama and Naoki Yoshinaga}, booktitle={Conference on Empirical Data Augmentation For Chinese Text Classification Using Back-Translation. Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 1651, The 2020 second International Conference on Artificial Intelligence Technologies and Application (ICAITA) 2020 21-23 August 2020, Dalian, China In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of polyphonic character, and a Phoneme In this paper, we design a multi-channel data augmentation method including three parallel strategies as shown in Fig. Jun Ma 1 and Langlang Li 1. When looking at the application examples, we noticed that the changes in the sentences are subtle and manage to keep their original meanings well, but often they do not offer much diversity, which can be a problem depending In this paper, we propose Chinese text data augmentation based on back-translation, which is used to generate corpus to enrich the sentence pattern or lexical fe atures of text data, and improve Back-translation for data augmentation The data augmentation in this study follows the exist-ing back-translation strategies for NMT (Sennrich et al. First In this paper, we investigate back-translation for neural machine translation at a large scale by adding hundreds of millions of back-translated sentences to the bitext. Moreover, because the neural machine translation model has different like back-translation, easy data augmentation and other traditional methods. In NLP, Back Translation is one of such augmentation technique that Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. We use the original parallel data to train a seq2seq model in the formal-to-informal direction. When the amount of data is insufficient, the classification accuracy will be greatly Multilingual neural machine translation (MNMT) models are theoretically attractive for low- and zero-resource language pairs with the impact of cross-lingual knowledge transfer. Imp. 9 BLEU points over the baseline and up to 3. Based on the observation, this paper makes an Data Augmentation for Bounding Boxes: Scaling and Translation. - styfeng/DataAug4NLP Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (W-NUT @ EMNLP '19) WMT'15/'19 en/fr, MTNT, IWSLT'17, MuST-C: This paper proposes new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small and explores the effect of utilizing noise from external data in the form of speech transcripts and shows that it could help robustness. We explore prompt-based data augmentation approaches that leverage large-scale language models such as ChatGPT. This is the second part in a series of articles we are doing covering on implementing adapting the image CANTONMT: Investigating Back-Translation and Model-Switch Mechanisms for Cantonese-English Neural Machine Translation • 3 • Section 2 introduces some technical backgrounds and related works on MT, LLMs, back-translation, data augmentation, and Cantonese NLP. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Back Translation Data Augmentation is a general data augmentation strategy, which can generate Semantically different sentences while preserving information. 4: (1) the neural machine translation (NMT) model, which can generate more diverse data via beam search; (2) the translation API, which is more accurate and convenient; (3) the paraphrase generation model, which is essentially similar to traditional A back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data, and a data balance strategy to improve the accuracy of some typical polyphonic characters in the training set with imbalanced distribution or data scarcity. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to The back-and-forth data augmentation was only completed using one set of translation language, English to German, where English was the base language and German was the target language. 10 Conclusion. We present the models we fine-tuned using the limited amount of real data and the synthetic In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. , code pairs with similar functionality), and another that augments existing parallel data with multiple reference Instruction back translation is a scalable method to build a high-quality instruction following language model by automatically labeling human written text with corresponding instructions. ,2018;Edunov et al. 18653/v1/D19-6504 Corpus ID: 207979868; Data augmentation using back-translation for context-aware neural machine translation @inproceedings{Sugiyama2019DataAU, title={Data augmentation using back-translation for context-aware neural machine translation}, author={Amane Sugiyama and Naoki Yoshinaga}, booktitle={Conference on Empirical Back-translation — data augmentation by translating target monolingual data — is a crucial component in modern neural machine translation (NMT). Data augmentation using back-translation for context-aware neural machine translation. The back-and-forth data augmentation was only completed using one set of translation language, English to German, where English was the base language and German was the target language. It typically translates from the target to the source language to ensure high-quality translation results. By applying this loss, our model learns to generate more similar multimodal graph representations for instance pairs with the same label, while pushing away those pairs that have different labels. With the help of context, the pronoun in no-pronoun We propose to assist the training of context-aware NMT mod-els using pseudo parallel data which is automati-cally generated by back-translating a large mono-lingual data (x 3, Figure 1). Our approach, AdMix, consists of two parts: 1) introduce faint discrete noise For low-resource language pairs this is not the case, resulting in poor translation quality. The results indicate One major challenge of translating code between programming languages is that parallel training data is often limited. 2. 6M sentence pairs by back translation strategy. , 2018) except that we assume a context-aware model for the forward-translation; the monolin-gual data for back-translation must be a set of doc- Download Citation | On Jan 1, 2019, Amane Sugiyama and others published Data augmentation using back-translation for context-aware neural machine translation | Find, read and cite all the research The back translation concept is straightforward and consists of three main steps. Skip to content. Find and fix vulnerabilities Actions. In this work, we aim to Additionally, we propose Back-translation Data Augmentation (BTrA) for both textual and visual data to enhance contrastive learning, where different back-translation schemes are designed to generate a larger number of positive and negative samples. , zero pronouns in translating Japanese to English or epicene pro- A dictionary-based data augmentation method for cross-domain NMT that synthesizes a domain-specific dictionary with general domain corpora to automatically generate a large-scale pseudo-IND parallel corpus and is able to further improve the performance of the back-translation based and IND-finetuned NMT models. Inspired by the back-translation technique proposed in the field of machine translation, a neural text-to-encoder model is built which predicts a sequence of hidden states extracted by a pre-trained E2E-ASR encoder from asequence of characters. In this paper, we will investigate how each language’s back translation Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. In conclusion, this research has shown that using GANs to augment text data can. 1 Multilingual Back-Translation. g. Navigation Menu Toggle navigation. While other techniques often rely on either augmentation on the token level or the sentence level The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. (2021) post a survey on DA for text classification only. During the training of our model, we applied various data augmentation techniques and used SiKu-RoBERTa as part of our model architecture. For the choice of the Easy Data Augmentation (EDA) method, we opted for back-translation. Navigation Menu Toggle Request PDF | On Dec 1, 2018, Tomoki Hayashi and others published Back-Translation-Style Data Augmentation for end-to-end ASR | Find, read and cite all the research you need on ResearchGate Paper tables with annotated results for Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation. About Trends Portals This repository builds on the idea of back translation as a data augmentation method. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. Other language pairs were not investigated in this research. This process can introduce variations while retaining the original meaning, such as rephrasing sentences or changing word order, which helps create more diverse training data. significantly improve Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. If paper metadata matches the PDF, but the we propose a novel data augmentation approach that targets low-frequency words show that our method improves translation quality by up to 2. This is the second part in a series of articles we are doing covering on Code-switching (CSW) text generation has been receiving increasing attention as a solution to address data scarcity. Back translation refers to the method of using machine translation to automatically translate target language monolingual data into source language data, which is a commonly used data augmentation ALL author names, the title, and the abstract match the PDF. Secondly, we explore the effect of utilizing noise from The method proposed in this paper combines the back translation method with the low-frequency word replacement method in an organic way. The goal of EDA is to generate new, semantically similar sentences from an DOI: 10. Then we feed formal sentences to this In the paper, Wang and Yang proposed to use k-nearest-neighbor (KNN) and cosine similarity to find the similar word for replacement. Inspired by the These categories thus tend to be too limited or general, e. 2 BLEU over back-translation level data augmentation and word-level data augmentation according to granularity. We analyze the quality of the synthetic data generated, evaluate its performance gains and compare all of . From experiments, ensembled models trained on The results show that the Generative models give the best overall performance advantage over the EDA or back translation accuracy. Collection of papers and resources for data augmentation for NLP. This paper investigates the Data Augmentation for Bounding Boxes: Scaling and Translation. It involves a two-step translation process: first, In the paper, Ribeiro et al. Existing approaches mainly focus on English-centric directions and always underperform compared to their pivot-based counterparts for non-English directions. In this work, we compare three popular approaches: lexical replacements, linguistic theories, and back-translation (BT), in the context In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Furthermore, we propose two novel semantic-level protein augmentation methods, namely Integrated Gradients Data augmentation is a technique that enhances the performance of data-hungry models by generating synthetic data instead of collecting new ones. The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. The Factual. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of scenario, we envision that a system of back translations can provide transformers with the generalized data that they need to train larger and larger models. In Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019), Andrei Popescu-Belis, Sharid Loáiciga, Christian Hardmeier, and Deyi Xiong (Eds. discuss “curating our input data and learning regime to encourage representations that are not biased by any one domain or distribution” . Existing approaches mainly focus on Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Sign in Product GitHub Copilot. In contrast, word-level Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora. In this work, we reformulate back-translation in the scope of cross-entropy optimization of an NMT model, clarifying its underlying mathematical assumptions and approximations beyond its heuristic usage. We employ Data augmentation is an effective method for the performance enhancement of neural machine translation (NMT) by generating additional bilingual data. Due to the capacity of enriching linguistic phenomena for learning, data augmentation contributes to the establishment of robust ATE models. Plan and track work Code Review. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and we investigate the use of monolingual data for NMT. We appended back-translated subjective documents to the training set. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the LRL, making use of available resources to better approximate the In this paper, we perform a comprehensive study of NLP data augmentation techniques, compar-ing their relative performance under different settings. Paper tables with annotated results for Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation . Our in-depth analyses indicate that both By applying this loss, our model learns to generate more similar multimodal graph representations for instance pairs with the same label, while pushing away those pairs that have different labels. It involves translating text from one language to another, and then translating the translated text back to the orig-inal language. Back-translation is a widely used data augmentation technique which leverages target monolingual data. 2 Data augmentation Due to the extremely unbalanced distribution of the data set, the model tends to be over- tting, predicting the most frequent category. This two-dimensional approach enhances data in a manner that could lead to a “1 + 1 > 2” effect for strictly data-driven neural machine translation. The purpose of Back-Translation is to identify errors and inconsistencies in the machine transla-tion by using data augmentation to enhance performance. In this survey, we will provide an inclusive overview of DA methods in NLP. Typically, since not enough data is Back-translation has been proven to be an effective data augmentation method that translates target monolingual data into source-side to improve the performance of Neural Machine Translation (NMT), especially in low-resource scenarios. Association for Computational Linguistics Data Augmentation using Back-translation for Context-aware Neural Machine Translation: A single sentence does not always convey in- formation required to translate it into other lan- guages; we sometimes need to add or special- ize words that are omitted or ambiguous in the source languages (e. To create a synthetic parallel corpus, we compare 3 methods using different prompts. By expanding the This paper extends data augmentation techniques previously used for images and texts to proteins and then benchmarks these techniques on a variety of protein-related tasks, providing the first comprehensive evaluation of protein augmentation. Back-translation is to translate sentences into other lan- In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. e. One similar work to ours that studied the scaling behaviour of back-translation is proposed by Poncelas, Shterionov, Way, de Buy Wenniger, and Passban (Citation 2018). , back-translation and model-based techniques. PDF Abstract We experimented with using automatic back-translations of the monolingual News corpus as additional training data, pervasive dropout, and target-bidirectional models. A well-executed back-translation can modify vocabulary, syntax, word order, length, and other In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data We also propose Deep Back-Translation, a novel NLP data augmentation technique and apply Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Browse State-of-the-Art Datasets ; Methods; More The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. Text classification is a basic task in natural language processing. Paper Code Collection of papers and resources for data augmentation for NLP. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend Amane Sugiyama and Naoki Yoshinaga. This paper investigates the The surge of interest in data augmentation within the realm of NLP has been driven by the need to address challenges posed by hate speech domains, the dynamic nature of social media vocabulary, and the demands for large-scale neural networks requiring extensive training data. Style augmentation . This method generates a larger In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Inspired by the back-translation technique proposed in the field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to predict the pronunciation of Back translation is a data augmentation technique that has proven to be a quite effective in NLP. and there is not enough training data for Cantonese. A simple and e ective method is back-translation [16]. By double tuning we mean we fine-tune the There is also an unique aspect of back translation compared to other data augmentation methods. it occurred to me that languages from very different language families (but very well supported for economic reasons such as Chinese, Russian, Spanish, Korean, Arabic) could make for a diverse set of effects occurring In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. It is grounded on the potential advantages that the back-translated instances generally appear as In this study, we first obtain large-scale pseudo parallel corpora by back-translating monolingual data, and then investigate its impact on the translation accuracy of context-aware NMT models. In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. The project uses the NLPAug library to perform back Translation-based algorithms (Back Translation (BT) and Deep Back Translation (DeepBT)) use translations to alter the original texts. %0 Conference Proceedings %T Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation %A Li, Zhenhao %A Specia, Lucia %Y Xu, Wei %Y Ritter, Alan %Y Baldwin, Tim %Y Rahimi, Afshin %S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) %D 2019 %8 November %I In the paper, Wang and Yang proposed to use k-nearest-neighbor (KNN) and cosine similarity to find the similar word for replacement. - lancopku/Augmented_Data_for_FST We augment a total of 1. We decided to use data augmentation technology to solve this problem. Data Augmentation Language Modeling +3. Compared to the previous paper, the data set is small, with only 845 statements. Table 3: BLEU scores of models fine-tuned on different data in the Fr→En direction. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. This study obtains large-scale pseudo parallel corpora by back-translating monolingual data, and investigates its impact on the translation accuracy of context-aware In this paper, we propose to leverage back translation to augment the training data for ATE. We implement Scale and Translate augmentation techniques, and what to do if a portion of your bounding box is outside the image after the augmentation. - lancopku/Augmented_Data_for_FST. Manage code changes Discussions. We evaluated context-aware NMT models trained with small parallel corpora and the large-scale pseudo parallel corpora on English-Japanese and English-French datasets to demonstrate the Data Augmentation Back-Translation. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. In this paper, we explore Back-Translation (BT) as a data augmentation method to enhance such datasets by artificially introducing semantics-preserving variations. Neural Machine Translation (NMT) models have been proved strong when PDF | On Jan 1, 2019, Zhenhao Li and others published Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation | Find, read and cite all the research you need Back translation, as a technique for extending a dataset, is widely used by researchers in low-resource language translation tasks. [1 In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data. Finally, we present another augmentation strategy utilizing Deep Networks to augment data for the training of other Deep Data Augmentation by Backtranslation (DAB) ヽ( •_-)ᕗ - vietai/dab. Collaborate Exploring back-translation augmentation for question answering Longpre et al. It typically translates from the target to 4. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden Collection of papers and resources for data augmentation for NLP. Back translation, as a technique for extending a dataset, is widely used by researchers in low-resource language translation tasks. To facilitate contrastive learning, we propose a data augmentation method called Back-translation Augmentation (BTrA). 2 Back-Translation Back-Translation is a technique used to improve the quality of machine-translated text. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora. fwyhf utlpdi qjnrwtgr jumtj zqs hkzb jvubh aeaf isgimq fiuy