Zero-Shot Open-Book Question Answering
Explore this paper's citation graph
Summary
A solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot) and attempts to find the yes-no-none answers and text answers in the same pass.
- Type
- preprint
- Published
- 2021-11-22
- Cited by
- 11
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W3215363805
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:244488674
Keywords
Question answering, Open domain, Computer science, Questions and answers, Information retrieval
References
- The Structure and Performance of an Open-Domain Question Answering System
- An Analysis of the AskMSR Question-Answering System
- MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
- Building Watson: An Overview of the DeepQA Project
- Dynamic Memory Networks for Visual and Textual Question Answering
- Summarization of Yes/No Questions Using a Feature Function Model
- RACE: Large-scale ReAding Comprehension Dataset From Examinations
- TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
- Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
- CoQA: A Conversational Question Answering Challenge
- QuAC: Question Answering in Context
- QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
- Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming
- Natural Questions: A Benchmark for Question Answering Research
- Careful Selection of Knowledge to Solve Open Book Question Answering
- Bidirectional Attention Flow for Machine Comprehension
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- SQuAD: 100,000+ Questions for Machine Comprehension of Text
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Cited by
- Alexa, Predict My Flight Delay
- Do Generative Large Language Models need billions of parameters?
- Can a student Large Language Model perform as well as it's teacher?
- Can pruning make Large Language Models more efficient?
- Retrieval-Augmented Generation Approach: Document Question Answering using Large Language Model
- Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes
- A Multilingual Intelligent Document Processing System
- Does Synthetic Data Make Large Language Models More Efficient?
- Flight Delay Prediction Using Deep Learning and Conversational Voice-Based Agents
- PreAdapter: Pre-training Language Models on Knowledge Graphs
- Modern Approaches in Natural Language Processing
Related papers
No related papers recorded.