TASK DEFINITION: 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.
PROBLEM: We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., “If a surface has more friction, then an object will travel slower”), and asked to enter two objects, people, or situations to compare.

SOLUTION: Do all questions in the dataset allow the answers to pick from 2 options?

PROBLEM: Thus, to limit the number of different inputs to the classifier, we wish to reduce the number of distinct word recognition outputs that an attacker can induce, not just the number of words on which the model is “fooled”. We denote this property of a model as its sensitivity.

SOLUTION: What does the "sensitivity" quantity denote?

PROBLEM: Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. Neural text-to-speech systems have garnered large research interest in the past 2 years. The first to fully explore this avenue of research was Google's tacotron BIBREF1 system. The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 In the original Tacotron 2, the attention mechanism used was location sensitive attention BIBREF12 combined the original additive Seq2Seq BIBREF7 Bahdanau attention.

We propose to replace this attention with the simpler query-key attention from transformer model Following the logic above, we utilize a similar method from BIBREF6 that adds an additional guided attention loss to the overall loss objective, which acts to help the attention mechanism become monotoic as early as possible. As seen from FIGREF24 , an attention loss mask, INLINEFORM0 , is created applies a loss to force the attention alignment, INLINEFORM1 , to be nearly diagonal.

SOLUTION:
Which modifications do they make to well-established Seq2seq architectures?