mlsanityData to 7 Oct 2026

PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks

arXiv 2210.08855 ↗First posted 17 Oct 2022cs.CLcited by 7

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Abstract (arXiv)

Span identification aims at identifying specific text spans from text input and classifying them into pre-defined categories. Different from previous works that merely leverage the Subordinate (SUB) relation (i.e. if a span is an instance of a certain category) to train models, this paper for the first time explores the Peer (PR) relation, which indicates that two spans are instances of the same category and share similar features. Specifically, a novel Peer Data Augmentation (PeerDA) approach is proposed which employs span pairs with the PR relation as the augmentation data for training. PeerDA has two unique advantages: (1) There are a large number of PR span pairs for augmenting the training data. (2) The augmented data can prevent the trained model from over-fitting the superficial span-category mapping by pushing the model to leverage the span semantics. Experimental results on ten datasets over four diverse tasks across seven domains demonstrate the effectiveness of PeerDA. Notably, PeerDA achieves state-of-the-art results on six of them.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 52 of its 95 references that are in the map. First posted on arXiv 17 Oct 2022.

Builds on

52 of 95 references.

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Citing papers per year: 2022: 3; 2023: 4.

7 papers.

  1. 2023Reasons to Reject? Aligning Language Models with Judgmentscited by 15
  2. 2022ConNER: Consistency Training for Cross-lingual Named Entity Recognitioncited by 11
  3. 2023Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learningcited by 8
  4. 2023Easy-to-Hard Learning for Information Extractioncited by 5
  5. 2022Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representationscited by 3
  6. 2022From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Readercited by 2
  7. 2023mPMR: A Multilingual Pre-trained Machine Reader at Scalecited by 2