3 citations · 7 across the 6 of their papers we have counts for
6 papers · 1 filter
Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models
Yida Zhao, Chao Lou, Kewei Tu
Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. While prior work has been focusing on adding…
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers
Chao Lou, Zixia Jia, Zilong Zheng +1
Accommodating long sequences efficiently in autoregressive Transformers, especially within an extended context window, poses significant challenges due to the quadratic computation…
AMR Parsing with Causal Hierarchical Attention and Pointers
Chao Lou, Kewei Tu
Translation-based AMR parsers have recently gained popularity due to their simplicity and effectiveness. They predict linearized graphs as free texts, avoiding explicit structure m…
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
Tianyu Yu, Chengyue Jiang, Chao Lou +12
Large language models (LLMs) have shown impressive ability for open-domain NLP tasks. However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which…
Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints
Chao Lou, Kewei Tu
Neural QCFG is a grammar-based sequence-tosequence (seq2seq) model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suf…
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
Chao Lou, Songlin Yang, Kewei Tu
Nested named entity recognition (NER) has been receiving increasing attention. Recently, (Fu et al, 2021) adapt a span-based constituency parser to tackle nested NER. They treat ne…