We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| manga-uofa/uchrnn | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| train | Not yet run | manga-uofa/uchrnn/baseline/Unsupervised-Chunking/model4_hrnn.py code served (permissive licence) · get_code("f69f589778408167") |
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In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.04919")
get_code_for_paper("2309.04919")
have("2309.04919")
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