Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
Copied to clipboard
Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
Similar Papers
Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
| Outcome: | The proposed methods improve translation and summarization by 6.9% and 7.5% respectively. |
Rethinking Data Augmentation in Text-to-text Paradigm (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches to augment training data are limited or marginal, or even diminishing or adverse especially given original training corpus is relatively sufficient or the backbone classifiers are PLM based. |
| Approach: | They propose to integrate text-to-text language models and construct a new two-phase framework for augmentation using two novel schemes. |
| Outcome: | The proposed framework synthesizes new samples benefiting from the knowledge learned from pre-trained language models on two public classification datasets and shows remarkable gains. |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
Copied to clipboard
Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |
Vec2Sent: Probing Sentence Embeddings with Natural Language Generation (2020.coling-main)
Copied to clipboard
| Challenge: | a new unsupervised probing task is able to retrieve black-box sentence embeddings . a variety of problems surround probing tasks, including manual construing . |
| Approach: | They propose a method to generate black-box sentence embeddings by conditionally generating from them . they also illustrate how the language generated from different encoders differs . |
| Outcome: | The proposed probing task improves the performance of black-box sentence embeddings . the proposed task is based on a conditional natural language generation approach . |
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
Copied to clipboard
| Challenge: | End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks. |
| Approach: | They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation. |
| Outcome: | The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference. |
Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey (2025.emnlp-main)
Copied to clipboard
| Challenge: | Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over speech attributes. |
| Approach: | They provide a review of controllable TTS methods from traditional control techniques to emerging approaches using natural language prompts. |
| Outcome: | The proposed methods are based on models, strategies, and features, and summarize challenges, datasets, and evaluations. |
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
Copied to clipboard
| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
| Outcome: | The proposed methods outperform the state-of-the-art on four core tasks. |
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content (D19-55)
Copied to clipboard
| Challenge: | Social media texts are considered important language resources for several NLP tasks, but their use of non-standard words makes it difficult to process and analyze UGC. |
| Approach: | They propose to use a Neural Machine Translation approach to normalize lexical variants to their canonical forms to overcome performance drop in UGC. |
| Outcome: | The proposed approach overcomes a data bottleneck in Dutch, a low-resource language. |