In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.
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Question: We have implemented the following interfaces for Macaw:

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File IO: This interface is designed for experimental purposes, such as evaluating the performance of a conversational search technique on a dataset with multiple queries. This is not an interactive interface.

Standard IO: This interactive command line interface is designed for development purposes to interact with the system, see the logs, and debug or improve the system.

Telegram: This interactive interface is designed for interaction with real users (see FIGREF4). Telegram is a popular instant messaging service whose client-side code is open-source. We have implemented a Telegram bot that can be used with different devices (personal computers, tablets, and mobile phones) and different operating systems (Android, iOS, Linux, Mac OS, and Windows). This interface allows multi-modal interactions (text, speech, click, image). It can be also used for speech-only interactions. For speech recognition and generation, Macaw relies on online APIs, e.g., the services provided by Google Cloud and Microsoft Azure. In addition, there exist multiple popular groups and channels in Telegram, which allows further integration of social networks with conversational systems. For example, see the Naseri and Zamani's study on news popularity in Telegram BIBREF12. 
 Question: What interface does Macaw currently have?

Answer: Extractive


Question: Irony Classifier: We implement a CNN classifier trained with our irony dataset. Sentiment Classifier for Irony: We first implement a one-layer LSTM network to classify ironic sentences in our dataset into positive and negative ironies. Sentiment Classifier for Non-irony: Similar to the training process of the sentiment classifier for irony, we first implement a one-layer LSTM network trained with the dataset for the sentiment analysis of common twitters to classify the non-ironies into positive and negative non-ironies. In this section, we describe some additional experiments on the transformation from ironic sentences to non-ironic sentences. 
 Question: What experiments are conducted?

Answer: Extractive


Question: The contributions of this work are: 
 Question: Do they look for inconsistencies between different UD treebanks?

Answer:
Yes-no