@article{wu-etal-2025-emergence,
    title = "The Emergence of Chunking Structures with Hierarchical {RNN}",
    author = "Wu, Zijun  and
      Deshmukh, Anup Anand  and
      Wu, Yongkang  and
      Lin, Jimmy  and
      Mou, Lili",
    journal = "Computational Linguistics",
    volume = "51",
    number = "3",
    month = sep,
    year = "2025",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-eacl/2025.cl-3.4/",
    doi = "10.1162/coli_a_00545",
    pages = "815--841",
    abstract = "In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This article introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks. Experiments on multiple datasets reveal a notable improvement of unsupervised chunking performance in both pretraining and finetuning stages. Interestingly, we observe that the emergence of the chunking structure is transient during the neural model{'}s downstream-task training. This study contributes to the advancement of unsupervised syntactic structure discovery and opens avenues for further research in linguistic theory.1"
}