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.
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Question: Finally, we combine our CNN and RNN models using a voting process. For each sentence in the test set, we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes. In case of a tie, we pick one of the most frequent classes randomly.

Answer: How does their simple voting scheme work?


Question: We use data from Mandarin Chinese and Cantonese. For each language, the data consists of a list of spoken words, recorded by the same speaker. The Mandarin dataset is from a female speaker and is provided by Shtooka, and the Cantonese dataset is from a male speaker and is downloaded from Forvo, an online crowd-sourced pronunciation dictionary.

Answer: What dataset is used for training?


Question: Let us take a closer look at what we mean by agreement between the two parse trees. Essentially, if we have two words in the English sentence denoted by i and i', aligned to words j and j' in the parallel Hindi sentence respectively, we can expect a dependency edge between i and i' in the English parse tree to correspond to an edge between j and j' in the Hindi parse tree, and vice versa. For an edge in the English parse tree, we term the corresponding edge or path in the Hindi parse tree as the projection or projected path of the English edge on the Hindi parse tree, and similarly there are projected paths from Hindi to English. The dual decomposition inference algorithm tries to bring the parse trees in the two languages through its constraints.

Answer:
How does enforcing agreement between parse trees work across different languages?