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Found 5 papers, 4 papers with code
Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documents
We propose a novel clustering pipeline to detect and characterize influence campaigns from documents.
Probabilistic Linguistic Knowledge and Token-level Text Augmentation
This paper investigates the effectiveness of token-level text augmentation and the role of probabilistic linguistic knowledge within a linguistically-motivated evaluation context.
Learning Transductions and Alignments with RNN Seq2seq Models
The paper studies the capabilities of Recurrent-Neural-Network sequence to sequence (RNN seq2seq) models in learning four transduction tasks: identity, reversal, total reduplication, and quadratic copying.
Random Text Perturbations Work, but not Always
We present three large-scale experiments on binary text matching classification task both in Chinese and English to evaluate the effectiveness and generalizability of random text perturbations as a data augmentation approach for NLP.
Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching
To investigate the role of linguistic knowledge in data augmentation (DA) for Natural Language Processing (NLP), we designed two adapted DA programs and applied them to LCQMC (a Large-scale Chinese Question Matching Corpus) for a binary Chinese question matching classification task.
