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.

Example Input: The task presented here was easy and simple to analyze, however, future work may be done on natural language tasks. If these properties hold it might indicate that a new evaluation paradigm for NLP should be pushed; one that emphasizes performance on uncharacteristic (but structurally sound) inputs in addition to the data typically seen in training. 
 Question: Can the findings of this paper be generalized to a general-purpose task?
Example Output: Yes-no

Example Input: Contextualized word embeddings, sentence embeddings, such as deep contextualized word representations BIBREF20 , BERT BIBREF22 , encode the complex characteristics and meanings of words in various context by jointly training a bidirectional language model.  The second method is to use sentence embeddings, BERT. It is used to a generate single 786-dimensional sentence embedding from 10k-dimensional one-hot vector or distribution over previous words and then merge into a single context vector with two different merging methods. Using this approach, we can obtain a more dense, informative, fixed-length vectors to encode conversational-context information, $e^k_{context}$ to be used in next $k$ -th utterance prediction. We use contextual gating mechanism in our decoder network to combine the conversational-context embeddings with speech and word embeddings effectively. Our gating is contextual in the sense that multiple embeddings compute a gate value that is dependent on the context of multiple utterances that occur in a conversation.  Let $e_w = e_w(y_{u-1})$ be our previous word embedding for a word $y_{u-1}$ , and let $e_s = e_s(x^k_{1:T})$ be a speech embedding for the acoustic features of current $k$ -th utterance $x^k_{1:T}$ and $e_c = e_c(s_{k-1-n:k-1})$ be our conversational-context embedding for $n$ -number of preceding utterances ${s_{k-1-n:k-1}}$ . Then using a gating mechanism:

$$g = \sigma (e_c, e_w, e_s)$$ (Eq. 15)

where $\sigma $ is a 1 hidden layer DNN with $\texttt {sigmoid}$ activation, the gated embedding $e$ is calcuated as

$$e = g \odot (e_c, e_w, e_s) \\ h = \text{LSTM}(e)$$ (Eq. 16)

and fed into the LSTM decoder hidden layer.  The output of the decoder $h$ is then combined with conversational-context embedding $e_c$ again with a gating mechanism,

$$g = \sigma (e_C, h) \\ \hat{h} = g \odot (e_c, h)$$ (Eq. 17)

Then the next hidden layer takes these gated activations, $\hat{h}$ , and so on. 
 Question: How are sentence embeddings incorporated into the speech recognition system?
Example Output: Abstractive

Example Input: This work also contributes a new dataset of INLINEFORM0 pairs of free-form natural language instructions and high-level navigation plans. This dataset was collected through Mechanical Turk using 100 simulated environments with a corresponding topological map and, to the best of our knowledge, it is the first of its kind for behavioral navigation. The dataset opens up opportunities to explore data-driven methods for grounding navigation commands into high-level motion plans. While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort. 
 Question: Did the collection process use a WoZ method?
Example Output:
Yes-no