Definition: 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.

Positive Example 1 -
Input: We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. On the other hand, for the factual accounts, we use a list with another 32 Twitter accounts from BIBREF19 that are considered trustworthy by independent third parties. 
 Question: How did they obtain the dataset?
Output: Extractive

Positive Example 2 -
Input: We evaluate our pointer-generator performance using BLEU score. The baseline language model is trained using RNNLM BIBREF23 . Perplexity measure is used in the evaluation.

 
 Question: Did they use other evaluation metrics?
Output: Yes-no

Negative Example 1 -
Input: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress. 
 Question: What are the corpora used for the task?
Output: Yes-no

Negative Example 2 -
Input: For Turkish, as the first dataset, we utilised the movie reviews which are collected from a popular website. The number of reviews in this movie corpus is 20,244 and the average number of words in reviews is 39. Each of these reviews has a star-rating score which is indicative of sentiment. These polarity scores are between the values 0.5 and 5, at intervals of 0.5. We consider a review to be negative it the score is equal to or lower than 2.5. On the other hand, if it is equal to or higher than 4, it is assumed to be positive. We have randomly selected 7,020 negative and 7,020 positive reviews and processed only them. 
 Question: What details are given about the movie domain dataset?
Output: Extractive

Now complete the following example -
Input: Our video question answering task is novel and to our knowledge, no model has been designed specifically for this task. As a first step towards solving this problem, we evaluated the performance of state-of-the-art models developed for other QA tasks, including a sentence-level prediction task and two segment retrieval tasks. Baselines ::: Baseline1: Sentence-level prediction
Given a transcript (a sequence of sentences) and a question, Baseline1 predicts (starting sentence index, ending sentence index). The model is based on RaSor BIBREF13, which has been developed for the SQuAD QA task BIBREF6. RaSor concatenates the embedding vectors of the starting and the ending words to represent a span. Following this idea, Baseline1 represents a span of sentences by concatenating the vectors of the starting and ending sentences. The left diagram in Fig. FIGREF15 illustrates the Baseline1 model.

Model. The model takes two inputs, a transcript, $\lbrace s_1, s_2, ... s_n\rbrace $ where $s_i$ are individual sentences and a question, $q$. The output is the span scores, $y$, the scores over all possible spans. GLoVe BIBREF14 is used for the word representations in the transcript and the questions. We use two bi-LSTMs BIBREF15 to encode the transcript.

where n is the number of sentences . The output of Passage-level Encoding, $p$, is a sequence of vector, $p_i$, which represents the latent meaning of each sentence. Then, the model combines each pair of sentence embeddings ($p_i$, $p_j$) to generate a span embedding.

where [$\cdot $,$\cdot $] indicates the concatenation. Finally, we use a one-layer feed forward network to compute a score between each span and a question.

In training, we use cross-entropy as an objective function. In testing, the span with the highest score is picked as an answer. Baselines ::: Baseline2: Segment retrieval
We also considered a simpler task by casting our problem as a retrieval task. Specifically, in addition to a plain transcript, we also provided the model with the segmentation information which was created during the data collection phrase (See Section. SECREF3). Note that each segments corresponds to a candidate answer. Then, the task is to pick the best segment for given a query. This task is easier than Baseline1's task in that the segmentation information is provided to the model. Unlike Baseline1, however, it is unable to return an answer span at various granularities. Baseline2 is based on the attentive LSTM BIBREF17, which has been developed for the InsuranceQA task. The right diagram in Fig. FIGREF15 illustrates the Baseline2 model.

Model. The two inputs, $s$ and $q$ represent the segment text and a question. The model first encodes the two inputs.

$h^s$ is then re-weighted using attention weights.

where $\odot $ denotes the element-wise multiplication operation. The final score is computed using a one-layer feed-forward network.

During training, the model requires negative samples. For each positive example, (question, ground-truth segment), all the other segments in the same transcript are used as negative samples. Cross entropy is used as an objective function.

 Baselines ::: Baseline3: Pipeline Segment retrieval
We construct a pipelined approach through another segment retrieval task, calculating the cosine similarities between the segment and question embeddings. In this task however, we want to test the accuracy of retrieving the segments given that we first retrieve the correct video from our 76 videos. First, we generate the TF-IDF embeddings for the whole video transcripts and questions. The next step involves retrieving the videos which have the lowest cosine distance between the video transcripts and question. We then filter and store the top ten videos, reducing the number of computations required in the next step. Finally, we calculate the cosine distances between the question and the segments which belong to the filtered top 10 videos, marking it as correct if found in these videos. While the task is less computationally expensive than the previous baseline, we do not learn the segment representations, as this task is a simple retrieval task based on TF-IDF embeddings.

Model. The first two inputs are are the question, q, and video transcript, v, encoded by their TF-IDF vectors: BIBREF18:

We then filter the top 10 video transcripts(out of 76) with the minimum cosine distance, and further compute the TF-IDF vectors for their segments, Stop10n, where n = 10. We repeat the process for the corresponding segments:

selecting the segment with the minimal cosine distance distance to the query. 
 Question: What baseline algorithms were presented?
Output: 


Answer: Abstractive