| Challenge: | Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort. |
| Approach: | They designed a neural language model to expand noisy abbreviated input where users often omit spaces and mid-word vowels. |
| Outcome: | The proposed recognizer can expand noisy abbreviated input even if a third of characters is omitted. |
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Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan, Meredith Morris, Michael Brenner
| Challenge: | Existing paradigms for text entry in augmentative and alternative communication (AAC) for people with severe motor impairments require 3-5 predictions to save keystrokes. |
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How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks (2021.findings-emnlp)
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| Challenge: | Recent studies have focused on rule-based and neural sequence-to-sequence (seq2sequ) TS is a technique that reduces text complexity for human consumption. |
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Coding Textual Inputs Boosts the Accuracy of Neural Networks (2020.emnlp-main)
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| Challenge: | a new approach to natural language processing uses arbitrary symbols to represent meaning . Soundex, MetaPhone, NYSIIS, logogram are used as inputs for NLP . |
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Shortformer: Better Language Modeling using Shorter Inputs (2021.acl-long)
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| Challenge: | Existing methods require computationally expensive relative position embeddings. |
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Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue (2022.acl-long)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
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Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)
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| Challenge: | Experimental results show text smoothing outperforms data augmentation methods by a substantial margin. |
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mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences (2023.findings-emnlp)
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Text Generation with Text-Editing Models (2022.naacl-tutorials)
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Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
| Challenge: | Text-editing models are a popular alternative to seq2seq for monolingual text generation tasks such as text summarization and style transfer. |
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Soundwave: Less is More for Speech-Text Alignment in LLMs (2025.acl-long)
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| Challenge: | Existing end-to-end speech large language models rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth. |
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