DinoStackAI/qasper-rag huggingface dataset:
Phase I: $\langle cc \rangle $ increases smoothly for $\wp < 0.4$, indicating that for this domain there is a small correlation between word neighborhoods. Full vocabularies are attained also for $\wp < 0.4$ Phase II: a drastic transition appears at the critical domain $\wp ^* \in (0.4,0.6)$, in which $\langle cc \rangle $ shifts abruptly towards 1. An abrupt change in $V(t_f)$ versus $\wp $ is also found (Fig. FIGREF16) for $\wp ^*$ Phase III: single-word languages dominate for $\wp > 0.6$. The maximum value of $\langle cc \rangle $ indicate that word neighborhoods are completely correlated
Compared to baselines SAN (Table 1) shows  improvement of 1.096% on EM and 0.689% F1. Compared to other published SQuAD results (Table 2) SAN is ranked second.
language identification, part-of-speech tagging, word segmentation, and preordering for statistical machine translation
paper acceptance prediction Named Entity Recognition (NER) author stance prediction
AQA diverges from well structured language in favour of less fluent, but more effective, classic information retrieval (IR) query operations
The baseline is a multi-task architecture inspired by another paper.
The network architecture has a multi-task Bi-Directional Recurrent Neural Network, with an unsupervised sequence labeling task and a low-dimensional embedding layer between tasks. There is a hidden layer after each successive task with skip connections to the senior supervised layers.
Stacks and joins outputs of previous frames with inputs of the current frame
reward 1 for successfully completing the task, with a discount by the number of turns, and reward 0 when fail
No, there has been previous work on recognizing social norm violation.
3 million webpages processed with a CCG parser for training, 220 queries for development, and 307 queries for testing
Fill-in-the-blank natural language questions
3 feature maps for a given tuple