Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli
| Challenge: | OpenNMT is a community-built toolkit written in multiple languages with an emphasis on extensibility. |
| Approach: | They propose to use PyTorch to train custom sequence models for translation, summarization, language modeling, and other tasks. |
| Outcome: | The proposed toolkit is fast, extensible, and useful for both research and production. |
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| Challenge: | Speech synthesis is the task of generating speech waveforms with desired characteristics, including but not limited to textual content, speaker identity, and speaking styles. |
| Approach: | They propose a fairseq extension for speech synthesis that implements autoregressive and non-AR text-to-speech models and their multi-speaker variants. |
| Outcome: | The proposed extension can train autoregressive and non-AR models and their multi-speaker variants with less curated data and has automatic metrics to facilitate faster iteration and analysis. |
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
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| 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. |
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Inseq: An Interpretability Toolkit for Sequence Generation Models (2023.acl-demo)
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| Challenge: | Recent studies focused on classification tasks while largely overlooking generation settings due to a lack of dedicated tools. |
| Approach: | They propose to use Inseq to democratize access to interpretability analyses of sequence generation models by enabling intuitive extraction of models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. |
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FastSeq: Make Sequence Generation Faster (2021.acl-demo)
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Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang
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Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling (2020.lrec-1)
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| Challenge: | Neural models have attracted a lot of attention in the past few years due to their complexity and need to be customized to meet specific needs. |
| Approach: | They propose a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. |
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Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
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VizSeq: a visual analysis toolkit for text generation tasks (D19-3)
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| Challenge: | Several softwares for text evaluation are available that do not provide detailed examples. |
| Approach: | They propose a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks. |
| Outcome: | The proposed toolkit covers most common n-gram metrics and latest embedding-based metrics such as BERTScore. |
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation (2021.acl-demo)
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Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen
| Challenge: | TextBox is an open-source text generation framework that is modularized and extensible. |
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| Outcome: | The proposed framework implements 21 models on 9 benchmark datasets and is available under the Apache License 2.0 license. |
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)
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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang
| Challenge: | Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences. |
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Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |