In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: Our goal is to compare the efectiviness of the proposed approach against the baseline system described in BIBREF13 . We evaluate the false-reject (FR) and false-accept (FA) tradeoff across several end-to-end models of distinct sizes and computational complexities. As can be seen in the Receiver Operating Characteristic (ROC) curves in Figure FIGREF14 , the 2 largest end-to-end models, with 2-stage training, significantly outperform the recognition quality of the much larger and complex Baseline_1850K system. More specifically, E2E_318K_2stage and E2E_700K_2stage show up to 60% relative FR rate reduction over Baseline_1850K in most test conditions. 

Output: How do they measure the quality of detection?


Input: Consider Input: Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used. However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories.

Output: What are the weaknesses of their proposed interpretability quantification method?


Input: Consider Input: In terms of the future development of this technology, we emphasize the importance of answering four questions: 1) How to introduce unsupervised pre-training into NLG tasks with cross-modal context? 2) How to design a generic pre-training algorithm to fit a wide range of NLG tasks? 3) How to reduce the computing resources required for large-scale pre-training? 4) What aspect of knowledge do the pre-trained models provide for better language generation?


Output: Which future direction in NLG are discussed?