Richer Syntactic Dependencies for Structured Language Modeling
Ciprian Chelba, Peng Xu
Abstract
The paper investigates the use of richer syntactic dependencies in the structured language model (SLM). We present two simple methods of enriching the dependencies in the syntactic parse trees used for intializing the SLM. We evaluate the impact of both methods on the perplexity (PPL) and word-error-rate(WER, N-best rescoring) performance of the SLM. We show that the new model achieves an improvement in PPL and WER over the baseline results reported using the SLM on the UPenn Treebank and Wall Street Journal (WSJ) corpora, respectively.
Create a lesson
Related papers
SpecCompact: Accelerating LLM Agent Context Compaction with Slot-Parallel Semantic Speculative Verification
Yulu An, Yifei Feng, Xiangyang Yin
Balancing hierarchical representation and quantization: A performance trade-off in text embeddings
Kai Ponel, Marco Polignano, Erasmo Purificato et al.
Persona Perspectives: Adopting Viewpoints Reorganizes Representations in LLMs
Austin C. Kozlowski, Avital Mintz, Madison Lee
Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants
Peidi Dong, Yunze Xiao, Yu-Lin Tsai et al.
LooM: Loop-Invariant Memory for Causal Encoder-Decoder Language Models
Lele Yang, Muxi Diao, Zhonghao Yan et al.
SAUCE: Summary Analysis Using Conversation Entailment
Man-Ling Sung, Hemanth Kandula, Jeff Ma et al.