Papers › Neural Language Modeling by Jointly Learning Syntax and Lexicon
Neural Language Modeling by Jointly Learning Syntax and Lexicon
Yikang Shen, Zhouhan Lin, Chin-wei Huang, Aaron Courville
We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Constituency Grammar Induction | PTB Diagnostic ECG Database | PRPN (tuned) | Max F1 (WSJ) | 47.9 | #22 of 24 | Archive leaderboard | report |
| Constituency Grammar Induction | PTB Diagnostic ECG Database | PRPN (tuned) | Mean F1 (WSJ) | 47.3 | #22 of 24 | Archive leaderboard | report |
| Constituency Grammar Induction | PTB Diagnostic ECG Database | PRPN | Max F1 (WSJ) | 38.1 | #24 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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