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Found 10 papers, 4 papers with code
DESED: Dialogue-based Explanation for Sentence-level Event Detection
Many recent sentence-level event detection efforts focus on enriching sentence semantics, e. g., via multi-task or prompt-based learning.
KiPT: Knowledge-injected Prompt Tuning for Event Detection
Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts.
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning.
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer
The distances between the embedding at the masked position of input and prototypical embeddings are used as classification criterion.
Encoder-Decoder Neural Architecture Optimization for Keyword Spotting
Keyword spotting aims to identify specific keyword audio utterances.
Enhancing Neural Models with Vulnerability via Adversarial Attack
2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.
Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V1 6 metric)
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis
We firstly propose to explore the asking emphasis of a question as a key factor in DQI.
Ranked #1 on
Community Question Answering
on Quora Question Pairs
Review of Deep Learning
In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence.
An influence-based fast preceding questionnaire model for elderly assessments
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
