Neural Models for Key Phrase Extraction and Question Generation 利用GNN进行encode的工作,非常细节。 其实在使用GNN之前,作者设计了一个非常细节的deep alignment network,就是将passage和answer的表示(包含bert向量,glove向量,词汇特征等)进行多次反复的交互,具体的方式可以详读paper。 在deep alignment network之后,就...
We introduce two neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. And, the second one remedies the first one by restructuring the BERT employment into ...
Question Generation using RoBERTa 促进问题生成的多样性有很多解决方案,总体来说可以分为两类,一类是以CVAE为代表的在训练阶段引入潜变量来促进多样性的方法,另一类是以随机解码为代表的在推断阶段优化解码方式的方法。本文采用的是Holtzman et al., 2020提出的top-p核采样方法(nucleus sampling),因为该方法简单,高效...
This idea is proposed in the "A Recurrent BERT-based Model for Question Generation"paper. See section 4.3 As the answer aware models need answers for generating question, we need something which can extract answer like spans from the text. This can be done using various methods like NER, nou...
python nlp machine-learning information-retrieval ai transformers pytorch question-answering summarization language-model semantic-search squad bert rag gpt-3 large-language-models llm generative-ai chatgpt retrieval-augmented-generation Updated Jan 9, 2025 Python PaddlePaddle / PaddleNLP Star 12.3k Co...
The following year, Zhejiang University’s team [27] proposed a novel model capable of extracting image features from the middle layer of VGG16 and extracting question features using Bert, which won first place in the ImageCLEF2019 VQA-Med task. Kornuta et al. [28] proposed a modular ...
[27] proposed a novel model capable of extracting image features from the middle layer of VGG16 and extracting question features using Bert, which won first place in the ImageCLEF2019 VQA-Med task. Kornuta et al. [28] proposed a modular pipeline architecture that utilized transfer learning ...
Searches were carried out sequentially using Google Scholar, ACL, ACM, DBLP, and Springer databases, as well as by considering citations made by or to several articles collected. We used the keyword“question generation”. We deliberately use fairly general keywords instead of more specific ones ...
内容提示: What BERT Sees: Cross-Modal Transfer forVisual Question GenerationThomas Scialom ?‡∗ Patrick Bordes ‡∗ Paul-Alexis Dray ?Jacopo Staiano ? Patrick Gallinari ‡‡Sorbonne Université, CNRS, LIP6, F-75005 Paris, France?reciTAL, Paris, France{thomas, paul-alexis, jacopo}@...
nlpnatural-language-processingtransformersnatural-language-generationnlgbertquestion-generationt5 UpdatedFeb 6, 2024 Python Intelligent Q&A system(第七届中软杯,智能问答系统) question-answeringquestion-generation UpdatedApr 3, 2022 HTML Mimix: A Text Generation Tool and Pretrained Chinese Models ...