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Found 10 papers, 3 papers with code
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation
Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
You Can Do Better! If You Elaborate the Reason When Making Prediction
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus.
Sharp pointwise-in-time error estimate of L1 scheme for nonlinear subdiffusion equations
An essential feature of the subdiffusion equations with the $\alpha$-order time fractional derivative is the weak singularity at the initial time.
Numerical Analysis Numerical Analysis 65M06, 65M12, 65M15, 35R11 F.2.2
Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification
On one hand, CGPN learns to extract effective body part features for both holistic and partial person images.
Semi-supervised Visual Feature Integration for Pre-trained Language Models
In the framework, the visual features are obtained through a visualization and fusion mechanism.
Natural Language Inference
Natural Language Understanding
+2
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.
BERTSel: Answer Selection with Pre-trained Models
we are the first to explore the performance of fine-tuning BERT for answer selection.
LCQMC:A Large-scale Chinese Question Matching Corpus
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
