DataHammer/scimrc huggingface dataset:
Question: What was the Underground Railroad?
Evidence: The Underground Railroad was a network of clandestine routes and safe houses established in the United States during the early- to the mid-19th century. It was used by enslaved African Americans primarily to escape into free states and Canada. The network was assisted by abolitionists and others sympathetic to the cause of the escapees. The enslaved persons who risked escape and those who aided them are also collectively referred to as the "Underground Railroad". Various other routes led to Mexico, where slavery had been abolished, and to islands in the Caribbean that were not part of the slave trade. An earlier escape route running south toward Florida, then a Spanish possession (except 1763–1783), existed from the late 17th century until approximately 1790. However, the network now generally known as the Underground Railroad began in the late 18th century. It ran north and grew steadily until the Emancipation Proclamation was signed by President Abraham Lincoln. One estimate suggests that by 1850, approximately 100,000 enslaved people had escaped to freedom via the network
Answer: The Underground Railroad was a secret network of routes and safe houses in the United States established in the early 19th century that led to free states, Canada, Mexico and other overseas areas. Black slaves used the Underground Railroad to escape from slavery. It is estimated that by 1850, about 100,000 slaves had escaped to freedom by the "Railroad".
Question: In what aspects the shallow discourse parser is very useful?
Evidence: Automated deriving discourse relation from free text is a challenging but im-portant problem. The shallow discourse parsing is very useful in the text summariza-tion BIBREF0 , opinion analysis BIBREF1 and natural language generation. Shallow discourse parser is the system of parsing raw text into a set of discourse relations between two adjacent or non-adjacent text spans. Discourse relation is composed of a discourse connective, two arguments of the discourse connective and the sense of the discourse connective. Discourse connective signals the explicit dis-course relation, but in non-explicit discourse relation, a discourse connective is omit-ted. Two arguments of the discourse connective, Arg1 and Arg2, which are the two adjacent or non-adjacent text spans connecting in the discourse relation. The sense of the discourse connective characterizes the nature of the discourse relations. The following discourse relation annotation is taken from the document in the PDTB. Arg1 is shown in italicized, and Arg2 is shown in bold. The discourse connective is underlined.
The connective identifier finds the connective word, “unless”. The arguments identifier locates the two arguments of “unless”. The sense classifier labels the dis-course relation. The non-explicit identifier checks all the pair of adjacent sentences. If the non-explicit identifier indentifies the pair of sentences as non-explicit relation, it will label it the relation sense. Though many research work BIBREF2 , BIBREF3 , BIBREF4 are committed to the shallow discourse parsing field, all of them are focus on the subtask of parsing only rather than the whole parsing process. Given all that, a full shallow discourse parser framework is proposed in our paper to turn the free text into discourse relations set. The parser includes connective identifier, arguments identifier, sense classifier and non-explicit identifier, which connects with each other in pipeline. In order to enhance the performance of the parser, the feature-based maximum entropy model approach is adopted in the experiment. Maximum entropy model offers a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context in a simple and accessible manner. The three main contributions of the paper are:
The rest of this paper is organized as follows. Section 2 reviews related work in discourse parsing. Section 3 describes the experimental corpus–PDTB. Section 4 de-scribes the framework and the components of the parser. Section 5 presents experi-ments and evaluations. Conclusions are presented in the Section 6.
The shallow discourse parsing is very useful in the text summariza-tion (Marcu , 2000), opinion analysis (Somasundaran et al., 2009) and natural language generation.
Answer: Text summariza-tion, opinion analysis and natural language generation.
Question: What is a Keystone?
Answer: A keystone is the top block of an arch. It is an essential part of an arch because it keeps other stones from falling.  A keystone is shaped like a trapezoid, the widest part at the top, so it doesn't slide out of the arch.  Humans have built remarkable buildings with heavy ceilings on arches supported by a keystone.  Many architects and artisans have been known to sculpt family emblems, symbols and designs on keystones.
Question: What are previous state-of-the-art document summarization methods used?
Evidence: To demonstrate SPNet's effectiveness, we compare it with two state-of-the-art methods, Pointer-Generator BIBREF5 and Transformer BIBREF6. Pointer-Generator is the state-of-the-art method in abstractive document summarization. In inference, we use length penalty and coverage penalty mentioned in BIBREF36. The hyperparameters in the original implementation BIBREF5 were used. Transformer uses attention mechanisms to replace recurrence for sequence transduction. Transformer generalizes well to many sequence-to-sequence problems, so we adapt it to our task, following the implementation in the official OpenNMT-py documentation.
To demonstrate SPNet's effectiveness, we compare it with two state-of-the-art methods, Pointer-Generator BIBREF5 and Transformer BIBREF6.
Answer: The answers are shown as follows:
* Pointer-Generator
* Transformer
Question: What is Passover?
Answer: Passover is a major Jewish holiday that celebrates the Biblical story of the Israelites' escape from slavery in Egypt.
Question: What state makes the best BBQ?
Answer: There is no one answer, but I believe Texas for beef and North Carolina for Pork.
Question: Tell me if these are body parts or places: nose, beach, supermarket, feet, eyes, hand, mall, school, restaurant, heart
Answer: The body parts are: nose, feet, eyes, hand, heart
The places are: beach, supermarket, mall, school, restaurant
Question: What is Inflation?
Answer: Inflation is the rate of increase in the prices of goods and services over a specified period of time. A high inflation value would represent greater increase in the prices of goods and commodities leading to lower value of the currency over-time.
Question: Why do the Dutch wear Orange?
Answer: The Dutch wear orange NOT because their flag is orange (the Dutch flag is actually red, white and blue).

However, the first Kind of the Netherlands was William of Orange (Willem van Oranje), who come from the House of Orange-Naussau; one of the most influential royal house in Europe in the 16th and 17th century.  Originally this house started in the Principality of Orange (France).

This principality was named after a previous Roman-Celtic Settlement (Arausio) - but it was also know for being on the route where oranges came into the country.

The family of the Prince of Orange officially adopted the name and the colour orange in 1570. After the Eighty Years' War (1568-1648) against the French, the Dutch became independent first as a republic. In 1815, the Netherlands became a monarchy under the House of Orange-Naussau.
Question: Tell me who are the top 5 leading scorers of all time in the NBA
Answer: The all time leading scorers in the NBA are Lebron James, Kareem Abdul-Jabbar, Karl Malone, Kobe Bryant and Michael Jordan
Question: What are some different types of desserts?
Answer: Some different types of desserts are: cupcakes, cookies, cake, chocolate fondue, pudding, and lemon tart.
Question: What to do if you drop your iPhone into toilet?
Answer: I'm a bit embarrassed to admit this, but I once dropped my new iPhone into the toilet by accident. It's a humbling experience that can happen to anyone. Fortunately, the water was clean and I was able to quickly retrieve the phone. However, the screen was dark, so I used a towel to remove the water and a dryer to dry it out. I then placed the phone in a container of rice and left it overnight. The next day, when I tried turning it on, it worked! I was relieved that I didn't have to buy a new one. The lesson I learned is to be more careful and not put my iPhone near the toilet.
Question: What US state has the most coastline?
Answer: Alaska is the state with the largest coastline at approximately 66,000 miles.
Question: Based on the reference text, what does the champion of the Masters Tournament receive?
Evidence: The Masters Tournament
The tournament has a number of traditions. Since 1949, a green jacket has been awarded to the champion, who must return it to the clubhouse one year after his victory, although it remains his personal property and is stored with other champions' jackets in a specially designated cloakroom. In most instances, only a first-time and currently reigning champion may remove his jacket from the club grounds. A golfer who wins the event multiple times uses the same green jacket awarded upon his initial win (unless they need to be re-fitted with a new jacket). The Champions Dinner, inaugurated by Ben Hogan in 1952, is held on the Tuesday before each tournament, and is open only to past champions and certain board members of the Augusta National Golf Club. Beginning in 1963, legendary golfers, usually past champions, have hit an honorary tee shot on the morning of the first round to commence play. These have included Fred McLeod, Jock Hutchinson, Gene Sarazen, Sam Snead, Byron Nelson, Arnold Palmer, Jack Nicklaus, Gary Player, Lee Elder, and Tom Watson. Since 1960, a semi-social contest at the par-3 course has been played on Wednesday, the day before the first round.
Answer: The champion of the Masters Tournament receives a green jacket. However, it must be returned to the clubhouse the following year to be stored in a designated cloakroom.
Question: What are some popular social media applications?
Answer: Popular social media applications are Facebook, Instagram, TikTok, Twitter, Reddit, and Youtube
Question: What is a cashless option exercise?
Answer: A cashless option exercise is a method of exercising a stock option without using cash to pay the exercise price.  Instead of cash, a portion of the shares are used to pay the exercise price.  

There are two methods of a cashless exercise.  The first method is a net settlement, where the company withholds a sufficient number of shares with a fair market value equal to the aggregate exercise price, and such withheld shares constitute payment of the exercise price.  The second method is a sell-to-cover (or same-day sale), where sufficient shares equal to the aggregate exercise price are sold and the proceeds are used to pay the exercise price.  Generally, the second method is available only for companies whose shares are publicly traded, as there is greater complexity to use this method for privately held companies.
Question: Is it safe to look at the sun directly?
Answer: No, it is not safe to look directly at the sun as it can cause damage to the eyes
Question: What does each posting contain?
Evidence: We study human-human negotiations in the buyer-seller bargaining scenario, which has been a key research area in the literature BIBREF0. In this section, we first describe our problem setup and key terminologies by discussing the dataset used. Later, we formalize our problem definition.
Dataset: For our explorations, we use the Craigslist Bargaining dataset (CB) introduced by BIBREF4. Instead of focusing on the previously studied game environments BIBREF5, BIBREF6, the dataset considers a more realistic setup: negotiating the price of products listed on Craigslist. The dataset consists of 6682 dialogues between a buyer and a seller who converse in natural language to negotiate the price of a given product (sample in Table TABREF1). In total, 1402 product ad postings were scraped from Craigslist, belonging to six categories: phones, bikes, housing, furniture, car and electronics. Each ad posting contains details such as Product Title, Category Type and a Listing Price. Moreover, a secret target price is also pre-decided for the buyer. The final price after the agreement is called the Agreed Price, which we aim to predict.
Defining the problem: Say we are provided with a product scenario $S$, a tuple: (Category, Title, Listing Price, Target Price). Define the interactions between a buyer and seller using a sequence of $n$ events $E_n:<e_{1}, e_{2}, ..., e_{n}>$, where $e_{i}$ occurs before $e_{j}$ iff $i<j$. Event $e_{i}$ is also a tuple: (Initiator, Type, Data). Initiator is either the Buyer or Seller, Type can be one of (message, offer, accept, reject or quit) and Data consists of either the corresponding natural language dialogue, offer price or can be empty. Nearly $80\%$ of events in CB dataset are of type `message', each consisting a textual message as Data. An offer is usually made and accepted at the end of each negotiation. Since the offers directly contain the agreed price (which we want to predict), we only consider `message' events in our models. Given the scenario $S$ and first $n$ events $E_n$, our problem is then to learn the function $f_{n}$: $A = f_{n}(S, E_n)$ where $A$ refers to the final agreed price between the two negotiating parties.

Each posting contains the title, category, a target for the seller (called listing price), and a secret target for the buyer (called target price). The final price after the agreement is called the agreed price, which we aim to predict.
Answer: Each posting contains the title, category, a target for the seller (called listing price), and a secret target for the buyer (called target price). The final price after the agreement is called the agreed price, which they aim to predict.
Question: Does the HP U32 4K HDR Monitor have built-in speakers?
Answer: No. Built-in speakers are not listed in the manufacturers specification page for this monitor. "HP U32 4K HDR Monitor Product Specifications" https://support.hp.com/us-en/document/c07721979.

A review of this monitor by PCMag lists "Lacks built-in speakers" as a Con against the monitor. https://www.pcmag.com/reviews/hp-u28-4k-hdr-monitor

The monitor does have a 3.5 mm Audio Jack (Audio Out) which can be used to connect external speakers.
Question: Classify each of the following objects as either heavy or lightweight: bubble, aircraft, bear, mouse, bus, raisin, strawberry, popcorn, elephant, piano, plastic cup, train, butterfly, whale, refrigerator, feather, pencil, ship, tree, flower.
Answer: Heavy weight objects include: aircraft, bear, bus, elephant, piano, refrigerator, ship, tree, train, whale.
Lightweight objects include: bubble, butterfly, feather, flower, mouse, pencil, plastic cup, popcorn, raisin, strawberry.
Question: What is retail banking?
Answer: Retail banking, also known as consumer banking or personal banking, is the provision of services by a bank to the general public, rather than to companies, corporations or other banks, which are often described as wholesale banking. Banking services which are regarded as retail include provision of savings and transactional accounts, mortgages, personal loans, debit cards, and credit cards. Retail banking is also distinguished from investment banking or commercial banking. It may also refer to a division or department of a bank which deals with individual customers.

In the U.S., the term commercial bank is used for a normal bank to distinguish it from an investment bank. After the Great Depression, the Glass–Steagall Act restricted normal banks to banking activities, and investment banks to capital market activities. That distinction was repealed in the 1990s. Commercial bank can also refer to a bank or a division of a bank that deals mostly with deposits and loans from corporations or large businesses, as opposed to individual members of the public (retail banking).
Question: What is the WordNet counterpart for Persian?
Evidence: FarsNet [20] [21] is the first WordNet for Persian, developed by the NLP Lab at Shahid Beheshti University and it follows the same structure as the original WordNet. The first version of FarsNet contained more than 10,000 synsets while version 2.0 and 2.5 contained 20,000 synsets. Currently, FarsNet version 3 is under release and contains more than 40,000 synsets [7].
FarsNet [20] [21] is the first WordNet for Persian, developed by the NLP Lab at Shahid Beheshti University and it follows the same structure as the original WordNet.
Answer: The answers are shown as follows:
* FarsNet
Question: How do they measure the correlation between manual groundings and model generated ones?
Evidence: We evaluate the performance of our proposed method using two criteria: i) rank-correlation BIBREF25 to evaluate visual grounding and ii) accuracy to evaluate question answering. Intuitively, rank-correlation measures the similarity between human and model attention maps under a rank-based metric. A high rank-correlation means that the model is `looking at' image areas that agree to the visual information used by a human to answer the same question. In terms of accuracy of a predicted answer INLINEFORM0 is evaluated by: DISPLAYFORM0
We evaluate the performance of our proposed method using two criteria: i) rank-correlation BIBREF25 to evaluate visual grounding and ii) accuracy to evaluate question answering. Intuitively, rank-correlation measures the similarity between human and model attention maps under a rank-based metric. A high rank-correlation means that the model is `looking at' image areas that agree to the visual information used by a human to answer the same question. In terms of accuracy of a predicted answer INLINEFORM0 is evaluated by: DISPLAYFORM0


Answer: The answers are shown as follows:
* rank-correlation BIBREF25
Question: What is skydiving?
Answer: Skydiving is considering a thrill seeking opportunity.  It can be classified as a risky sport because every safety check and setup has to be 100% perfect in order to execute the skydiving perfectly each time.  If something does go wrong or you miss a safety check injury or death can occur.  That is why its important to triple check all your gear and equipment before skydiving or have an expert who has 1000's of jumps check your gear for you.  Skydiving is considered fun for some and scare for others.  If you are risk-averse you can consider this sport as a new opportunity for fun and enjoyment
Question: What is the last festival celebrated in Tihar and how long does the festival last?
Evidence: Lakshmi Puja is celebrated as a part of Tihar, a second national festival of Nepal after Dashain. In Nepal, it is celebrated for five days, which include Kag (crow) Tihar; Kukur (dog) Tihar; Gai (cow) Tihar in the morning and Lakshmi Puja at night; Maha puja (self puja); Goru (Ox and Bull) Tihar and Gobardhan puja; and finally, Bhai Tika (Bhai dhooj)—respectively the first, second, third, fourth and fifth days.
Answer: The festival lasts for 5 days and Bhai Tika is the last festival.
Question: How do you cook ribs?
Answer: 1.       Spread French's mustard all over the ribs
2.       Season ribs top and bottom liberally with the rub. Seriously don't be shy.
3.       Place ribs on a smoker at 225 for 2-3 hours (oak).
4.       In a bowl mix one cup of honey, brown sugar, a table spoon of apple cider vinegar, Mix together and Drizzle in the middle of the foil, place ribs top side down in the foil. Wrap tight.
5.       Return to the smoke for another 1.5-2 hours or until tender but not 'falling off the bone'
6.       In a sauce pan add a bottle of your favorite BBQ sauce, one cup of apple jelly, one finely shredded apple, and one tsp of rib rub.
7.       Pull them out of the foil, season again with rub.
8.       Top with BBQ sauce with apple and apple jelly.
9.       Put the ribs back on the pit for 30 minutes for the sauce to set-up.
 
Ingredients:
 
·         Three racks of pork ribs
·         Rib rub
·         French’s mustard
·         Honey
·         Brown sugar
·         Apple cider vinegar
·         KC Masterpiece BBQ sauce
·         Apple jelly
·         One finely shredded apple
Question: 'A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem is linearly separable.''-Is the term ``linearly separable'' correct?
Evidence: A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function. Next we give the formal definition of kernel machines discussed in this paper. Given a training data set {X, y} of n training instances where {X ∈ R n×d , y ∈ R n } = {(x 1 , y 1 ), (x 2 , y 2 ), ..., (x n , y n )}, and (x i , y i ) denotes the instance x i ∈ R d with its label y i , the objective of the kernel machine training is to find an optimal ω * which minimizes the structural risk as follows.
min
where λ denotes the regularization constant and f (ω, x i ) = ω, φ(x i ) . The variable ω is defined on the reproducing kernel Hilbert space (RKHS) and •, • is the inner product on the RKHS. The function φ(•) maps the instances from their original data space to a higher dimensional feature space induced by the kernel function. Assume the loss l(•, •) is an affine function of ω. The representer theorem shows that a minimizer of the optimization problem ()
where k(x i , x j ) denotes a positive definite kernel function and k(x i , x j ) = φ(x i ), φ(x j ) . By substituting the expressions of f (ω, x i ) and ω into the Equation (1), we have the objective with respect to α below.
where α = [α 1 . . . α n ] T is an n-dimension vector, each dimension of which corresponds to the contribution of a training instance to the kernel machine.
Then, we can derive that the Hessian matrix H = [H ij ] n×n of Problem () is equal to the kernel matrix. The element in the i-th row and j-th column of the matrix H is H ij = k(x i , x j ). The derivations of the first and second derivatives can be found in the supplementary material. For clarity, we use kernel matrix H to denote the Hessian matrix of the kernel machine in the rest of the paper.
A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function (Keerthi & Lin, 2003; Hofmann et al., 2008).
Answer: It should be ''may be linearly separable''.
Question: Give me a few ways that I can get stronger and in better shape before the next ski season.
Answer: Skiing requires both muscular strength and endurance in your legs and core, and it is important that as you exercise these muscles your knees and other joints are strengthened as well. Some good leg and knee exercises to prepare for skiing include both normal lunges and reverse lunges, lateral jumps, and both normal planks and side planks. Improving your cardiovascular health is also important, although this can be done in any way that keeps your heart rate in Zone 2 or Zone 3 for an extended duration of time.
Question: What domains are present in the data?
Evidence: FLOAT SELECTED: Table 2: The total number of intents (services in parentheses) and dialogues for each domain across train1, dev2 and test3 sets. Superscript indicates the datasets in which dialogues from the domain are present. Multi-domain dialogues contribute to counts of each domain. The domain Services includes salons, dentists, doctors, etc.
FLOAT SELECTED: Table 2: The total number of intents (services in parentheses) and dialogues for each domain across train1, dev2 and test3 sets. Superscript indicates the datasets in which dialogues from the domain are present. Multi-domain dialogues contribute to counts of each domain. The domain Services includes salons, dentists, doctors, etc.
Answer: Alarm, Banks, Buses, Calendar, Events, Flights, Homes, Hotels, Media, Messaging, Movies, Music, Payment, Rental Cars, Restaurants, Ride Sharing, Services, Train, Travel, Weather