| Challenge: | Several NLP tasks are instances of set generation. |
| Approach: | They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality. |
| Outcome: | The proposed method improves performance on four benchmark datasets with no additional annotations. |
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| Challenge: | Neural sequence-to-sequence models are autoregressive, meaning they factor the joint probability of the output sequence into the product of probabilities over the next to-ken. |
| Approach: | They propose a non-autoregressive sequence generation model using latent variables . they use generative flow to model complex distributions using neural networks . |
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Set Learning for Generative Information Extraction (2023.emnlp-main)
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| Challenge: | Recent efforts to employ sequence-to-sequence models to solve IE tasks have been focused on a single problem: structured objects are an unordered set, resulting in a potential order bias. |
| Approach: | They propose a sequence-to-sequence (Seq2Sequen) model that considers multiple permutations of structured objects to optimize set probability approximately. |
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Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)
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| Challenge: | Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models. |
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A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)
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| Challenge: | Multi-label classification (MLC) aims to assign multiple labels to each sample. |
| Approach: | They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order. |
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OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification (2022.emnlp-main)
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| Challenge: | Extreme multi-label text classification (XMTC) is a task of finding the most relevant subset labels from an extremely large label set. |
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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. |
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A Sequence-to-Sequence&Set Model for Text-to-Table Generation (2023.findings-acl)
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| Challenge: | Existing models for text-to-table generation are order-insensitive, but suffer from errors . a novel sequence-tosequence&set model generates table body rows in parallel . |
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Simple and effective data augmentation for compositional generalization (2024.naacl-long)
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| Approach: | They propose to use data augmentation methods to generate additional training data by sampling from an augmentation distribution to generalize to the out-of-distribution test data. |
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Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources (2021.eacl-main)
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| Challenge: | a hybrid approach to lemmatization enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
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Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog (2021.naacl-main)
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| Challenge: | Semantic parsing using sequence-to-sequence models is stymied by higher compute requirements and higher latency. |
| Approach: | They propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. |
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