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Found 6 papers, 5 papers with code
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
A syntactic language model (SLM) incrementally generates a sentence with its syntactic tree in a left-to-right manner.
Augmenting Transformers with Recursively Composed Multi-grained Representations
More interestingly, the hierarchical structures induced by ReCAT exhibit strong consistency with human-annotated syntactic trees, indicating good interpretability brought by the CIO layers.
Constituency Grammar Induction
Natural Language Inference
+1
A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification
As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self-interpretable classification model.
Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation
We propose to use a top-down parser as a model-based pruning method, which also enables parallel encoding during inference.
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
Interactive Question Clarification in Dialogue via Reinforcement Learning
In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.
