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Found 5 papers, 0 papers with code
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
In this paper, we propose a novel ungrammatical-syntax-based in-context example selection strategy for GEC.
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding
To address them, we propose Mixture-of-Prompt-Experts with Block-Aware Prompt Fusion (MoPE-BAF), a novel multi-modal soft prompt framework based on the unified vision-language model (VLM).
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently.
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation
Specially, we present localness modeling with a Gaussian bias to enable the model to focus on answer-surrounded context, and propose a mask attention mechanism to make the syntactic structure of input passage accessible in question generation process.
Ranked #5 on
Question Generation
on SQuAD1.1
Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data
This paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE.
