activity
20142026
most citedDistilling Task-Specific Knowledge from BERT into Simple Neural Networks

335 citations · 1.7k across the 86 of their papers we have counts for

collaborators
Showing 2024 · cs.CLShow all

6 papers · 2 filters

cs.CL2024

Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing

Behzad Shayegh, Hobie H. -B. Lee, Xiaodan Zhu +2

We address unsupervised dependency parsing by building an ensemble of diverse existing models through post hoc aggregation of their output dependency parse structures. We observe t…

cs.CL2024

LLMR: Knowledge Distillation with a Large Language Model-Induced Reward

Dongheng Li, Yongchang Hao, Lili Mou

Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks. However, these models are typical…

cs.CL2024

Action Controlled Paraphrasing

Ning Shi, Zijun Wu

Recent studies have demonstrated the potential to control paraphrase generation, such as through syntax, which has broad applications in various downstream tasks. However, these me…

cs.CL2024

A Dual-View Approach to Classifying Radiology Reports by Co-Training

Yutong Han, Yan Yuan, Lili Mou

Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In th…

cs.CL2024

EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation

Yuqiao Wen, Behzad Shayegh, Chenyang Huang +2

The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alt…

cs.CL2024★ 1 cited

Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing

Behzad Shayegh, Yuqiao Wen, Lili Mou

We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature. We propose to build an…