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Found 7 papers, 1 papers with code
A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals
Regarding the rising number of people suffering from mental health illnesses in today's society, the importance of mental health cannot be overstated.
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy.
Multilingual BERT Post-Pretraining Alignment
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models.
Iterative Data Programming for Expanding Text Classification Corpora
Real-world text classification tasks often require many labeled training examples that are expensive to obtain.
Fine Grained Dataflow Tracking with Proximal Gradients
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel information leak detection.
Cryptography and Security
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.
Ranked #24 on
AMR Parsing
on LDC2017T10
Bootstrapping Conversational Agents With Weak Supervision
We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling.
