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.

Example Input: In this work, we propose to generate unanswerable questions by editing an answerable question and conditioning on the corresponding paragraph that contains the answer. So the generated unanswerable questions are more lexically similar and relevant to the context. Moreover, by using the answerable question as a prototype and its answer span as a plausible answer, the generated examples can provide more discriminative training signal to the question answering model.
Example Output: Does their approach require a dataset of unanswerable questions mapped to similar answerable questions?

Example Input: Experimental Protocol ::: Datasets ::: Training Dataset
(i) TTS System dataset: We trained our TTS system with a mixture of neutral and newscaster style speech. For a total of 24 hours of training data, split in 20 hours of neutral (22000 utterances) and 4 hours of newscaster styled speech (3000 utterances).

(ii) Embedding selection dataset: As the evaluation was carried out only on the newscaster speaking style, we restrict our linguistic search space to the utterances associated to the newscaster style: 3000 sentences.

Experimental Protocol ::: Datasets ::: Evaluation Dataset
The systems were evaluated on two datasets:

(i) Common Prosody Errors (CPE): The dataset on which the baseline Prostron model fails to generate appropriate prosody. This dataset consists of complex utterances like compound nouns (22%), “or" questions (9%), “wh" questions (18%). This set is further enhanced by sourcing complex utterances (51%) from BIBREF24.

(ii) LFR: As demonstrated in BIBREF25, evaluating sentences in isolation does not suffice if we want to evaluate the quality of long-form speech. Thus, for evaluations on LFR we curated a dataset of news samples. The news style sentences were concatenated into full news stories, to capture the overall experience of our intended use case.
Example Output: What dataset is used for train/test of this method?

Example Input: Method ::: Passage Ranking Model
The key component of our framework is the Ranker model, which is provided with a question $q$ and $K$ passages $\mathcal {P} = \lbrace p_1, p_2 ... p_K\rbrace $ from a pool of candidates, and outputs a chain of selected passages. Method ::: Cooperative Reasoner
To alleviate the noise in the distant supervision signal $\mathcal {C}$, in addition to the conditional selection, we further propose a cooperative Reasoner model, also implemented with the MatchLSTM architecture (see Appendix SECREF6), to predict the linking entity from the selected passages.
Example Output:
What are two models' architectures in proposed solution?