Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
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Summary
This paper first introduces syntactic information to help encode questions and then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them.
- Type
- preprint
- Published
- 2017-03-14
- Cited by
- 54
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W2601454101
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11056052
Keywords
Question answering, Adaptation (eye), Computer science, ENCODE, Baseline (sea)
References
- Parsing Natural Scenes and Natural Language with Recursive Neural Networks
- Teaching Machines to Read and Comprehend
- Convolutional Neural Networks for Sentence Classification
- Long Short-Term Memory Over Recursive Structures
- Long Short-Term Memory
- Accurate Unlexicalized Parsing
- Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
- The Stanford CoreNLP Natural Language Processing Toolkit
- MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
- The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
- On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
- Deep Residual Learning for Image Recognition
- GloVe: Global Vectors for Word Representation
- Modeling Biological Processes for Reading Comprehension
- Pointer Networks
- Machine Comprehension Using Match-LSTM and Answer Pointer
- Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference
- End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
- Multi-Perspective Context Matching for Machine Comprehension
Cited by
- ReasoNet: Learning to Stop Reading in Machine Comprehension
- Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference
- Ruminating Reader: Reasoning with Gated Multi-hop Attention
- Enhanced LSTM for Natural Language Inference
- Mnemonic Reader for Machine Comprehension
- Gated Self-Matching Networks for Reading Comprehension and Question Answering
- Reinforced Mnemonic Reader for Machine Reading Comprehension
- Phase Conductor on Multi-layered Attentions for Machine Comprehension
- Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation
- Natural Language Inference with External Knowledge
- Stochastic Answer Networks for Machine Reading Comprehension
- Contextualized Word Representations for Reading Comprehension
- Hierarchical Attention Flow for Multiple-Choice Reading Comprehension
- Machine Reading as Model Construction
- What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
- Neural Natural Language Inference Models Enhanced with External Knowledge
- Multilingual Extractive Reading Comprehension by Runtime Machine Translation
- A Fully Attention-Based Information Retriever
- Linguistically-Based Deep Unstructured Question Answering
- Mnemonic Reader: Machine Comprehension with Iterative Aligning and Multi-hop Answer Pointing
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