Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.
Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus.
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
On one hand, CGPN learns to extract effective body part features for both holistic and partial person images.
In the framework, the visual features are obtained through a visualization and fusion mechanism.
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.
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.