| Challenge: | Existing datasets that only cover basic tags are limited in their scale or coverage of style tags. |
| Approach: | They propose a large-scale dataset that annotates speech utterances with rich style captions. |
| Outcome: | The proposed dataset scales speech utterances with rich style captions for the first time. |
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
| Approach: | They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation. |
| Outcome: | The proposed dataset is syntactically more diverse than existing datasets while maintaining good semantic similarity. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
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PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
| Approach: | They propose a large-scale evaluation tool for large language models that uses prompts . they evaluate 720 prompt templates for open-source LLM-based metrics on MT and summarization datasets a 6.6M evaluations. |
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AudioCaps: Generating Captions for Audios in The Wild (N19-1)
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| Challenge: | a dataset of 46K audio clips with human-written text pairs is used to generate captions for audio . the task of translating a multimedia input source into natural language has been extensively studied over the past few years . |
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VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)
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Wenrui Liu, Jionghao Bai, Xize Cheng, Jialong Zuo, Ziyue Jiang, Shengpeng Ji, Minghui Fang, Xiaoda Yang, Qian Yang, Zhou Zhao
| Challenge: | Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data. |
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Paraphrastic Representations at Scale (2022.emnlp-demos)
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| Challenge: | a new system allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. |
| Approach: | They propose a system that allows users to train their own paraphrastic sentence representations in a variety of languages. |
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Introducing a Large-Scale Dataset for Vietnamese POS Tagging on Conversational Texts (2020.lrec-1)
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| Challenge: | POS taggers are trained on informal texts which contain many informal inputs such as acronyms, abbreviations, out-of-vocabulary words, etc. |
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Learning to See through Sound: From VggCaps to Multi2Cap for Richer Automated Audio Captioning (2025.emnlp-main)
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| Challenge: | Existing AAC datasets suffer from short and simplistic captions, limiting expressiveness and semantic depth. |
| Approach: | They propose a multi-modal dataset that pairs audio with corresponding video and leverages large language models to generate rich, descriptive captions. |
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Paraphrasing with Large Language Models (D19-56)
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| Challenge: | Recent work has shown large language models are adept at text generation and fine-tuning for downstream NLP tasks. |
| Approach: | They propose a system that generates paraphrased examples in autoregressive fashion using a neural network without the need for techniques such as top-k word selection or beam search. |
| Outcome: | The proposed system generates paraphrased examples in autoregressive fashion without the need for techniques such as top-k word selection or beam search. |