Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase (2021.eacl-main)
Copied to clipboard
| Challenge: | a data augmentation technique is used to boost performance on spoken language understanding tasks. |
| Approach: | They propose a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. |
| Outcome: | The proposed method performs well on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity. |
Similar Papers
Syntactic Data Augmentation Increases Robustness to Inference Heuristics (2020.acl-main)
Copied to clipboard
| Challenge: | Pretrained neural models lack sensitivity to word order on controlled challenge sets . augmentation methods that improve accuracy on standard training sets may be a problem . |
| Approach: | They propose to augment standard training sets with syntactically informative examples by applying syntastic transformations to sentences from the MNLI corpus. |
| Outcome: | The proposed method improved BERT’s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73 without affecting performance on the MNLI test set. |
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish (2022.lrec-1)
Copied to clipboard
Cedric Lothritz, Bertrand Lebichot, Kevin Allix, Lisa Veiber, Tegawende Bissyande, Jacques Klein, Andrey Boytsov, Clément Lefebvre, Anne Goujon
| Challenge: | Pre-trained Language Models such as BERT are ubiquitous in NLP but are scarce for low-resource languages such as Luxembourgish. |
| Approach: | They propose a BERT model for Luxembourgish language that they use to augment pre-training datasets by partially translating text data from a closely related language. |
| Outcome: | The proposed model outperforms the baseline model and the mBERT model in Luxembourgish. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks (2021.naacl-main)
Copied to clipboard
| Challenge: | Cross-encoders perform full-attention over the input pair, while bi-encoding requires substantial training data and fine-tuning over the target task to achieve competitive performance. |
| Approach: | They propose a data augmentation strategy that uses cross-encoders to label larger set of input pairs to augment training data for bi-encoding. |
| Outcome: | The proposed approach improves on multiple tasks and domain adaptation tasks by up to 37 points compared to the original bi-encoder performance. |
A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
Copied to clipboard
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
| Approach: | They present a comprehensive survey of data augmentation for NLP by summarizing the literature in a structured manner. |
| Outcome: | The proposed methods are used for popular NLP applications and tasks and highlight current challenges and directions for future research. |
DABERT: Dual Attention Enhanced BERT for Semantic Matching (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models for semantic sentence matching lack the ability to capture subtle differences. |
| Approach: | They propose to use a Transformer-based pre-trained language model to capture fine-grained differences in sentence pairs by introducing a dual attention module and a fusion module to learn the aggregation of difference and affinity features. |
| Outcome: | The proposed method is able to capture fine-grained differences in sentence pairs. |
Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity (2024.lrec-main)
Copied to clipboard
| Challenge: | Using labeled NLI datasets for learning sentence embeddings leads to improved performance for natural language understanding tasks. |
| Approach: | They compare two data augmentation techniques for learning better sentence embeddings . they use a cross-lingual transfer technique that exploits English resources as training data to yield non-English sentence embeds as zero-shot inference . |
| Outcome: | The proposed techniques yield better performance on Japanese and Korean sentences. |
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022.acl-short)
Copied to clipboard
| Challenge: | End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations. |
| Approach: | They propose a method that augments transcriptions by sampling from suffix memory and translating them into target languages. |
| Outcome: | The proposed method delivers up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on languages on CoVoST 2 and Europarl-ST. |
Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
Copied to clipboard
| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Using self-training or text-to-speech (TTS) to improve low-resource ASR performance is costly and can lead to catastrophic forgetting. |
| Approach: | They examine whether data augmentation techniques could help improve low-resource ASR performance . they use self-training to generate transcriptions, which are combined with original data to train new system . |
| Outcome: | The proposed approach yields a 20.5% reduction in WER compared to a system trained on 24 minutes of manually transcribed speech. |