| Challenge: | Neural sequence-to-sequence models provide a powerful framework for learning to translate source texts into target texts. |
| Approach: | They propose a sequence tagging approach that casts text generation as a text editing task. |
| Outcome: | The proposed model outperforms strong seq2seq models on sentence fusion, sentence splitting, abstractive summarization, and grammar correction tasks and achieves state-of-the-art performance. |
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FELIX: Flexible Text Editing Through Tagging and Insertion (2020.findings-emnlp)
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| Challenge: | FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. |
| Approach: | They propose a flexible text-editing approach that decomposes a text-generating task into two sub-tasks: tagging and insertion. |
| Outcome: | The proposed model is efficient in low-resource settings and fast at inference time while being capable of modeling flexible input-output transformations. |
Transforming Sequence Tagging Into A Seq2Seq Task (2022.emnlp-main)
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| Challenge: | Pretrained, large, generative language models have had great success in a wide range of sequence tagging and structured prediction tasks. |
| Approach: | They propose to use a new format for casting input text sentences and their output labels into the input and target of a Seq2Seq model and introduce it to test their hypothesis. |
| Outcome: | The proposed format shows to be both simpler and more effective and devoid of hallucination. |
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction (2022.acl-long)
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| Challenge: | Currently, machine translation (MT) is the mainstream approach for GEC. |
| Approach: | They propose to ensemble Transformer-based encoders by majority votes on span-level edits . their best ensemble achieves a new SOTA result even without pre-training on synthetic datasets - "Troy-Blogs" and "Try-1BW". |
| Outcome: | The proposed model achieves a new SOTA result even without pre-training on synthetic datasets. |
EdiT5: Semi-Autoregressive Text Editing with T5 Warm-Start (2022.findings-emnlp)
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| Challenge: | Pre-trained seq2seq models have established strong baselines for text-to-text transduction tasks. |
| Approach: | They propose a semi-autoregressive text-editing approach that combines the strengths of non-auto-regressively text- editing and autoregressive decoding. |
| Outcome: | The proposed model is faster at inference times than conventional models while being capable of modeling flexible input-output transformations. |
Better, Faster, Stronger Sequence Tagging Constituent Parsers (N19-1)
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| Challenge: | Existing efforts to speed up constituent parsing have focused on chart-based or shift-reduce parsers. |
| Approach: | They propose to use auxiliary losses and sentence-level fine-tuning to mitigate greedy decoding issues. |
| Outcome: | The proposed model surpasses the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebank datasets and reduces their parsing time even further. |
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. |
Distillation of encoder-decoder transformers for sequence labelling (2023.findings-eacl)
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| Challenge: | despite the strong trend in NLP to explore the use of large language models, there is still limited work evaluating prompting and decoding mechanisms for SL tasks. |
| Approach: | They propose a hallucination-free framework for sequence tagging that is especially suited for distillation. |
| Outcome: | The proposed framework performs well across multiple sequence labelling datasets and in a few-shot learning scenario. |
Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
| Approach: | They propose a method for improving BERT's performance by using a label embedding technique while keeping almost the same computational cost. |
| Outcome: | The proposed method improves BERT's performance on six text classification benchmark datasets while keeping almost the same computational cost. |
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. |
| Approach: | They propose to use text-editing models to predict edit operations applied to the source sequence and to generate outputs word-by-word from scratch. |
| Outcome: | This paper provides an overview of the text-edit based models and their current state-of-the-art approaches. |
Revisiting Supertagging for faster HPSG parsing (2024.emnlp-main)
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| Challenge: | a new supertagger for HPSG-based treebanks is used to improve parsing speed and accuracy. |
| Approach: | They propose to integrate the best supertagger into an HPSG-based parser and compare it to an existing system. |
| Outcome: | The proposed system achieves 97.26% accuracy on 950 sentences from WSJ23 and 93.88% on the out-of-domain technical essay The Cathedral and the Bazaar. |