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.

Ex Input:
In this section, we consider 3 categories of data augmentation techniques that are effectively applicable to video datasets. To further increase training data size and diversity, we can create new audios via superimposing each original audio with additional noisy audios in time domain. To obtain diverse noisy audios, we use AudioSet, which consists of 632 audio event classes and a collection of over 2 million manually-annotated 10-second sound clips from YouTube videos BIBREF28. We consider applying the frequency and time masking techniques – which are shown to greatly improve the performance of end-to-end ASR models BIBREF23 – to our hybrid systems. Similarly, they can be applied online during each epoch of LF-MMI training, without the need for realignment.

Consider each utterance (i.e. after the audio segmentation in Section SECREF5), and we compute its log mel spectrogram with $\nu $ dimension and $\tau $ time steps:

Frequency masking is applied $m_F$ times, and each time the frequency bands $[f_0$, $f_0+ f)$ are masked, where $f$ is sampled from $[0, F]$ and $f_0$ is sampled from $[0, \nu - f)$.

Time masking is optionally applied $m_T$ times, and each time the time steps $[t_0$, $t_0+ t)$ are masked, where $t$ is sampled from $[0, T]$ and $t_0$ is sampled from $[0, \tau - t)$. Data augmentation ::: Speed and volume perturbation
Both speed and volume perturbation emulate mean shifts in spectrum BIBREF18, BIBREF19. To perform speed perturbation of the training data, we produce three versions of each audio with speed factors $0.9$, $1.0$, and $1.1$. The training data size is thus tripled. For volume perturbation, each audio is scaled with a random variable drawn from a uniform distribution $[0.125, 2]$.

Ex Output:
What are the best within-language data augmentation methods?


Ex Input:
Following recent works, we compare on two widely used datasets, the Penn Treebank (PTB) BIBREF28 as prepared by BIBREF29 and WikiText2 (WT-2) BIBREF20 .

Ex Output:
what data did they use?


Ex Input:
Visual Question Generation (VQG) is another emerging topic which aims to ask questions given an image. Motivated by this, BIBREF10 proposed open-ended VQG that aims to generate natural and engaging questions about an image.

Ex Output:

Do they survey visual question generation work?