DataHammer/scimrc huggingface dataset:
Question: Why do people work out at the gym?
Answer: People work out at the gym for a number of reasons: to lose weight, stay healthy, body building competitions, learning about fitness, becoming a trainer, group fit classes, and fitness socializing / friendship.  The most common reason is most likely weight loss.  Getting in a gym for this reason is a great way to stay motivated because you see people around you who are also doing the same thing as you to lose weight.  This community of people will be able to give you the support system needed to be successful in a short or long term weight loss journey too.
Question: How does the SCAN dataset evaluate compositional generalization?
Evidence: A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb. Success on this difficult problem requires models to generalize knowledge gained about the other primitive verbs ("walk", "run" and "look") to the novel verb "jump," without having seen "jump" in any but the most basic context ("jump" $\rightarrow $ JUMP). It is trivial for human learners to generalize in this way (e.g. if I tell you that "dax" is a verb, you can generalize its usage to all kinds of constructions, like "dax twice and then dax again", without even knowing what the word means) BIBREF2 . However, standard recurrent seq2seq models fail miserably on this task, with the best-reported model (a gated recurrent unit augmented with an attention mechanism) achieving only 12.5% accuracy on the test set BIBREF2 , BIBREF4 . Recently, convolutional neural networks (CNN) were shown to perform better on this test, but still only achieved 69.2% accuracy on the test set.
A recently published dataset called SCAN BIBREF2 (Simplified version of the CommAI Navigation tasks), tests compositional generalization in a sequence-to-sequence (seq2seq) setting by systematically holding out of the training set all inputs containing a basic primitive verb ("jump"), and testing on sequences containing that verb.
Answer: it systematically holds out inputs in the training set containing basic primitive verb, "jump", and tests on sequences containing that verb.
Question: Who were the major players in the Watergate conspiracy?
Evidence: The Watergate scandal was a major political scandal in the United States involving the administration of President Richard Nixon from 1972 to 1974 that led to Nixon's resignation. The scandal stemmed from the Nixon administration's persistent attempts to cover up its involvement in the June 17, 1972 break-in of the Democratic National Committee headquarters at the Washington, D.C., Watergate Office Building.
Answer: The list begins with the President of the United States, Richard Nixon. John Dean served as the Counsel to the President, and was a lead witness whose testimony blew the case wide open, naming many top presidential aides as being closely involved. H.R. Haldeman, John Ehrlichman, and John Mitchell were all senior members of the administration who resigned in disgraced and were convicted. Howard Hunt and G. Gordon Liddy played major roles in the Watergate break-in. 

A major figure in the conspiracy was Daniel Ellsberg, but not for any wrongdoing. Rather, he was a target of the Nixon White House, and an attempted burglary of his psychiatrist's office was just one of the illegal acts that the White House covered up.
Question: What is the keto diet?   Is it a good way to lose weight?
Answer: The keto diet's central idea is to shift the body's energy source from carbohydrates to fat. 
 It centers around the restriction of carbohydrate intake between 30-50 grams per day.  The body's first choice is to burn carbohydrates for energy, but when carbohydrates are not present, it will revert to using fat as the energy source.  Fat from recent food intake, as well as from fat stores, is therefore used as the primary energy source.  This is a great way to lose excess stored fat.  But, you'll also need to have calorie deficit.
Question: Based on the following passage in which months of the year are storms most common in the North Indian basin?
Evidence: Summer is traditionally associated with hot or warm weather. In Mediterranean climates, it is also associated with dry weather, while in other places (particularly in Eastern Asia because of the monsoon) it is associated with rainy weather. The wet season is the main period of vegetation growth within the savanna climate regime. Where the wet season is associated with a seasonal shift in the prevailing winds, it is known as a monsoon.
In the northern Atlantic Ocean, a distinct tropical cyclone season occurs from 1 June to 30 November. The statistical peak of the Atlantic hurricane season is 10 September. The Northeast Pacific Ocean has a broader period of activity, but in a similar timeframe to the Atlantic. The Northwest Pacific sees tropical cyclones year-round, with a minimum in February and March and a peak in early September. In the North Indian basin, storms are most common from April to December, with peaks in May and November. In the Southern Hemisphere, the tropical cyclone season runs from the start of November until the end of April with peaks in mid-February to early March.
Thunderstorm season in the United States and Canada runs in the spring through summer but sometimes can run as late as October or even November in the fall. These storms can produce hail, strong winds and tornadoes, usually during the afternoon and evening.
Answer: In the North Indian basin, storms are most common from April to December, with peaks in May and November.
Question: Identify which instrument is string or percussion: Conga, Ninera
Answer: Ninera is string, Conga is percussion.
Question: How many times was Edinburgh Castle besieged in its long history?
Evidence: Edinburgh Castle is a historic castle in Edinburgh, Scotland. It stands on Castle Rock, which has been occupied by humans since at least the Iron Age, although the nature of the early settlement is unclear. There has been a royal castle on the rock since at least the reign of David I in the 12th century, and the site continued to be a royal residence until 1633. From the 15th century, the castle's residential role declined, and by the 17th century it was principally used as military barracks with a large garrison. Its importance as a part of Scotland's national heritage was recognised increasingly from the early 19th century onwards, and various restoration programmes have been carried out over the past century and a half.

As one of the most important strongholds in the Kingdom of Scotland, Edinburgh Castle was involved in many historical conflicts from the Wars of Scottish Independence in the 14th century to the Jacobite rising of 1745. Research undertaken in 2014 identified 26 sieges in its 1,100-year history, giving it a claim to having been "the most besieged place in Great Britain and one of the most attacked in the world".
Answer: Recent research - in the last ten years - has identified that the castle was attacked (besieged) 26 times in the course of its 1,100 year history. This meant it was almost constantly being attacked, destroyed and rebuilt. And at one point - in the 14th century - the castle was intentionally destroyed by Edward The Bruce after he had recaptured it, to prevent further attacks from happening on the same spot.
Question: Extract all words derived from Swahili language in the following paragraph:
Evidence: Shingopana is known from a partial jaw, represented by the angular bone. Shingopana is also known from four cervical vertebrae; with two of these vertebrae having preserved cervical ribs and another isolated cervical rib. Shingopana instead had remnants of a bulbous expansion on the incompletely preserved cervical vertebrae, which probably helped to strengthen its neck.
Answer: The following are words extracted from Swahili language in the above paragraph: Shingopana (which means wide neck).
Question: What are the three tasks proposed for selection-based QA?
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
Three tasks have been proposed for selection-based QA. Given a document, answer extraction (Shen and Klakow, 2006; Sultan et al., 2016) finds answer phrases whereas answer selection (Wang et al., 2007; Yih et al., 2013; Yu et al., 2014; Wang et al., 2016) and answer triggering (Yang et al., 2015; Jurczyk et al., 2016) find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks.
Answer: Three tasks have been proposed for selection-based QA. First is given a document, answer extraction. Second is finding answer phrases whereas answer selection. Third is answer triggering.
Question: Do all the corpora provide datasets for answer extraction and answer triggering?
Evidence: All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively. SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times. On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts. Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes.
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate. SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes. It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries. InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes. InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively. In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close.
Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons. Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on.
Fig. FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a. Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models.
All corpora provide datasets/splits for answer selection, whereas only (WIKIQA, SQUAD) and (WIKIQA, SELQA) provide datasets for answer extraction and answer triggering, respectively.
Answer: No, they don't.
Question: Come up with gift ideas for your girlfriend
Answer: Here are some possible gift ideas for girlfriend
* romantic date
* flowers
* plushies
* shoes
* clothing
To make a good gift, think of things that your girlfriend likes most. You should customize your gift based on her interests.
Question: Classify each word as a noun, verb, or adjective: playground, eating, skinny, chubby, tall, sleeping, bed
Answer: Playground: Noun
Eating: Verb
Skinny: Adjective
Chubby: Adjective
Tall: Adjective
Sleeping: Verb
Bed: Noun
Question: What automatic metrics are used to measure performance of the system?
Evidence: Following BIBREF3, BLEU scores and the slot error rate (ERR) are reported. BLEU score evaluates how natural the generated utterance is compared with human readers. ERR measures the exact matching of the slot tokens in the candidate utterances. $\text{ERR}=(p+q)/M$, where $M$ is the total number of slots in the dialog act, and $p$, $q$ is the number of missing and redundant slots in the given realisation. For each dialog act, we generate five utterances and select the top one with the lowest ERR as the final output.
Following BIBREF3, BLEU scores and the slot error rate (ERR) are reported. BLEU score evaluates how natural the generated utterance is compared with human readers. ERR measures the exact matching of the slot tokens in the candidate utterances. $\text{ERR}=(p+q)/M$, where $M$ is the total number of slots in the dialog act, and $p$, $q$ is the number of missing and redundant slots in the given realisation. For each dialog act, we generate five utterances and select the top one with the lowest ERR as the final output.
Answer: The answers are shown as follows:
* BLEU scores and the slot error rate (ERR)
Question: Who was the lead singer for the band Aerosmith?
Answer: Steven Tyler was the lead singer for Aerosmith
Question: Why do cloud service providers need to be FedRAMP authorized if they want to do business with U.S. federal agencies?
Answer: U.S. federal agencies must use FedRAMP-authorized service providers if the agencies want to use cloud computing services, as it is a federal government requirement.
Question: What is baseball?
Answer: Baseball is a sport.  It is played on a baseball field by 9 players on each team.  Each team takes turn fielding and pitching.  A normal game is nine innings.  An inning is when each team gets three outs while on defense.  Baseball is considered America's pastime due to its popularity.
Question: What is the source of external knowledge?
Evidence: The second type is the selectional preference (SP) knowledge. For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength. Specifically, we use the English Wikipedia as the base corpus for such counting. Then we parse the entire corpus through the Stanford parser and record all dependency edges in the format of (predicate, argument, relation, number), where predicate is the governor and argument the dependent in the original parsed dependency edge. Later for sentences in the training and test data, we firstly parse each sentence and find out the dependency edge linking $p$ and its corresponding predicate. Then for each candidate $n$ in a sentence, we check the previously created SP knowledge base and find out how many times it appears as the argument of different predicates with the same dependency relation (i.e., nsubj and dobj). The resulted frequency is grouped into the following buckets [1, 2, 3, 4, 5-7, 8-15, 16-31, 32-63, 64+] and we use the bucket id as the final SP knowledge. Thus in the previous example:
For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength. Specifically, we use the English Wikipedia as the base corpus for such counting.
Answer: counts of predicate-argument tuples from English Wikipedia
Question: What was state of the art on SemEval-2014 task4 Restaurant and Laptop dataset?
Evidence: BERT-ADA BIBREF33 is a domain-adapted BERT-based model proposed for the APC task, which fine-tuned the BERT-BASE model on task-related corpus. This model obtained state-of-the-art accuracy on the Laptops dataset.
We build a joint model for the multi-task of ATE and APC based on the BERT-BASE model. After optimizing the model parameters according to the empirical result, the joint model based on BERT-BASE achieved hopeful performance on all three datasets and even surpassed other proposed BERT based improved models on some datasets, such as BERT-PT, AEN-BERT, SDGCN-BERT, and so on. Meanwhile, we implement the joint-task model based on BERT-SPC. Compared with the BERT-BASE model, BERT-SPC significantly improves the accuracy and F1 score of aspect polarity classification. In addition, for the first time, BERT-SPC has increased the F1 score of ATE subtask on three datasets up to 99%.
BERT-ADA BIBREF33 is a domain-adapted BERT-based model proposed for the APC task, which fine-tuned the BERT-BASE model on task-related corpus. This model obtained state-of-the-art accuracy on the Laptops dataset.
We build a joint model for the multi-task of ATE and APC based on the BERT-BASE model. After optimizing the model parameters according to the empirical result, the joint model based on BERT-BASE achieved hopeful performance on all three datasets and even surpassed other proposed BERT based improved models on some datasets, such as BERT-PT, AEN-BERT, SDGCN-BERT, and so on.
Answer: The answers are shown as follows:
* BERT-ADA
* BERT-PT, AEN-BERT, SDGCN-BERT
Question: Why do Jewish holidays begin at sunset the night before?
Answer: In many modern cultures and calendars, days begin in the morning and end at night.  But what if the opposite were true?   In the opening lines of the Creation story in the book of Genesis, the foundational story for all Abrahamic cultures, we learn that all the world was darkness and void.  (Darkness came first.) And then God said "Let there be light."  (Light came second.)  "And there was evening, and there was morning—the first day." 

This last sentence is critical. The first day consists of evening followed by morning. The Jewish calendar abides by this tradition.  Its days are not morning to morning. Its days are night to night (as marked by sundown).  

As an example, the Jewish sabbath (Shabbat) is on the 7th day of the week. Shabbat = Shevat in Hebrew, which literally translates to the number seven.  The seventh day of the  week is is Saturday. However, Shabbat doesn't span Saturday morning to Sunday morning. It spans Friday night to Saturday night.  Many observant Jews will welcome the Shabbat with a family meal, candles, and prayer on Friday nights.
Question: What metric is used in their binary classification tasks?
Evidence: We present our results for the binary classification tasks in Tables TABREF23 – TABREF24 in terms of average precision, average recall and macro average F1 score. The results of the regression tasks are reported in Tables TABREF25 and TABREF29 in terms of the mean absolute error between the predicted and actual scores, as well as the Spearman INLINEFORM0 correlation between the rankings induced by both sets of scores. It can be clearly seen from the results that our proposed method (EGEL-All) can effectively integrate Flickr tags with the available structured information. It outperforms the baselines for all the considered tasks. Furthermore, note that the PPMI-based weighting in EGEL-Tags consistently outperforms GloVe and that both the addition of negative examples and term selection lead to further improvements. The use of term selection leads to particularly substantial improvements for the regression problems.
While our experimental results confirm the usefulness of embeddings for predicting environmental features, this is only consistently the case for the variants that use both the tags and the structured datasets. In particular, comparing BOW-Tags with EGEL-Tags, we sometimes see that the former achieves the best results. While this might seem surprising, it is in accordance with the findings in BIBREF54 , BIBREF38 , among others, where it was also found that bag-of-words representations can sometimes lead to surprisingly effective baselines. Interestingly, we note that in all cases where EGEL-KL(Tags+NS) performs worse than BOW-Tags, we also find that BOW-KL(Tags) performs worse than BOW-Tags. This suggests that for these tasks there is a very large variation in the kind of tags that can inform the prediction model, possibly including e.g. user-specific tags. Some of the information captured by such highly specific but rare tags is likely to be lost in the embedding.
To further analyze the difference in performance between BoW representations and embeddings, Figure TABREF29 compares the performance of the GloVe model with the bag-of-words model for predicting place scenicness, as a function of the number of tag occurrences at the considered locations. What is clearly noticeable in Figure TABREF29 is that GloVe performs better than the bag-of-words model for large corpora and worse for smaller corpora. This issue has been alleviated in our embedding method by the addition of negative examples.
We present our results for the binary classification tasks in Tables 1–3 in terms of average precision, average recall and macro average F1 score.
Answer: Average precision, average recall and macro average F1 score.
Question: Write a paragraph of a blog post describing the appeal of a weekend stay at Blue Ridge Parkway?
Answer: Blue Ridge Parkway is an incredible place to see the leaves change colors in Autumn. The magic happens during the month of October, usually towards late month depending on the altitude. The colors are so serene you would want to stay for longer than a weekend. For the best experience try driving some distance along the Blue Ridge Parkway, changing directions and elevations, instead of finding one spot to capture all. Plan your stay in Asheville, NC area which has great collection of hotels and restaurants and drive around the scenic roads to catch the colors.