DataHammer/scimrc huggingface dataset:
Question: What are five things I could do with a broken surfboard?
Answer: 1. You could repair it and keep using it as a surfboard
2. You could use the materials to make a new surfboard that is smaller than the original one
3. You could use it as a decoration
4. You could use it as a canvas for artwork
5. You could recycle the foam inside of it and use it for various purposes such as making concrete pavers or filling an item that needs foam
Question: Where is the system proposed by the author used?
Evidence: We described a method for monolingual and crosslingual clustering of an incoming stream of documents. The method works by maintaining centroids for the monolingual and crosslingual clusters, where a monolingual cluster groups a set of documents and a crosslingual cluster groups a set of monolingual clusters. We presented an online crosslingual clustering method which auto-corrects past decisions in an efficient way. We showed that our method gives state-of-the-art results on a multilingual news article dataset for English, Spanish and German. Finally, we discussed how to leverage different SVM training procedures for ranking and classification to improve monolingual and crosslingual clustering decisions. Our system is integrated in a larger media monitoring project BIBREF10 , BIBREF11 and solving the use-cases of monitors and journalists, having been validated with qualitative user testing.
Our system is integrated in a larger media monitoring project (Liepins et al., 2017; Germann et al., 2018) and solving the usecases of monitors and journalists, having been validated with qualitative user testing.
Answer: Their system is integrated in a larger media monitoring project and solving the usecases of monitors and journalists, having been validated with qualitative user testing.
Question: From the passage provided, extract the name of the first female recipient of Turing award
Evidence: The ACM A. M. Turing Award is an annual prize given by the Association for Computing Machinery (ACM) for contributions of lasting and major technical importance to computer science. It is generally recognized as the highest distinction in computer science and is colloquially known as or often referred to as the "Nobel Prize of Computing".

The award is named after Alan Turing, who was a British mathematician and reader in mathematics at the University of Manchester. Turing is often credited as being the key founder of theoretical computer science and artificial intelligence. From 2007 to 2013, the award was accompanied by an additional prize of US$250,000, with financial support provided by Intel and Google. Since 2014, the award has been accompanied by a prize of US$1 million, with financial support provided by Google.

The first recipient, in 1966, was Alan Perlis, of Carnegie Mellon University. The first female recipient was Frances E. Allen of IBM in 2006. The latest recipient, in 2022, is Robert Metcalfe for inventing Ethernet.
Answer: The first female recipient was Frances E. Allen of IBM in 2006
Question: What makes a good password?
Answer: Good passwords consist of a combination of letters, numbers, and symbols.  Good passwords should be long, easy for the user to remember, but hard to guess.  Most attempts to steal passwords use brute force, so the length of the password is more important in terms of the compute needed to discover it using brute force methods than would be special characters.  Special characters used in short passwords also prove ineffective because there are many well know leet dictionaries that provide common special character insertions.
Question: Why is the band Phish so popular?
Answer: Phish have taken influences from many genres of music including rock, metal, blues, jazz, country, latin, barbershop quartet, and americana and blended it into their own take on improvisational jam band rock.  Their music often contains sections that are composed interspersed with long improvisational passages.  Each time you hear one of their songs, in part, it has never been heard before.  The setlists of their live shows are never been repeated, each one is different.  Each of the musicians is virtuosic in their instrument - Trey Anastasio on guitar, Page McConnell on Keyboard, Mike Gordon on bass, and Jon Fishman on drums.  Their live shows are high energy and the music takes fans on a journey.  Their lighting director, Chris Kuroda, is known as the fifth member of the band as the lighting meshes seamlessly with the music.  Phish has been together since 1983 and over the course of the past 40 years they have hundreds of original songs as well as incorporating their own take on many cover songs.  The lyrics range from whimsical to melancholic but are more often geared towards the whimsical side.  Phish is regarded as being one of the most influential bands, along with the Grateful Dead, in the jam band music scene.  Like the Grateful Dead, their fans tend to be incredibly devoted, often traveling to see many shows in a single tour.
Question: How many total fingers do most humans have?
Answer: Most humans have 10 total fingers
Question: Are the pre-trained models and scripts running the system (or re-train it) publicly available?
Evidence: Current societies are exposed to a continuous flow of information that results in a large production of data (e.g. news articles, micro-blogs, social media posts, among others), at different moments in time. In addition to this, the consumption of information has dramatically changed: more and more people directly access information through social media platforms (e.g. Facebook and Twitter), and are less and less exposed to a diversity of perspectives and opinions. The combination of these factors may easily result in information overload and impenetrable “filter bubbles”. Events, i.e. things that happen or hold as true in the world, are the basic components of such data stream. Being able to correctly identify and classify them plays a major role to develop robust solutions to deal with the current stream of data (e.g. the storyline framework BIBREF0 ), as well to improve the performance of many Natural Language Processing (NLP) applications such as automatic summarization and question answering (Q.A.).
Event detection and classification has seen a growing interest in the NLP community thanks to the availability of annotated corpora BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 and evaluation campaigns BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . In the context of the 2014 EVALITA Workshop, the EVENTI evaluation exercise BIBREF11 was organized to promote research in Italian Temporal Processing, of which event detection and classification is a core subtask.
Since the EVENTI campaign, there has been a lack of further research, especially in the application of deep learning models to this task in Italian. The contributions of this paper are the followings: i.) the adaptation of a state-of-the-art sequence to sequence (seq2seq) neural system to event detection and classification for Italian in a single step approach; ii.) an investigation on the quality of existing Italian word embeddings for this task; iii.) a comparison against a state-of-the-art discrete classifier. The pre-trained models and scripts running the system (or re-train it) are publicly available. .
The pre-trained models and scripts running the system (or re-train it) are publicly available.
Answer: Yes, they are.
Question: Given this summary of The Eye of Heaven, what kind of ship did they find in the Arctic?
Evidence: The Eye of Heaven is a Fargo adventure novel. The two main characters of the Fargo novels are adventurers Sam Fargo and his wife, Remi. The Eye of Heaven is the sixth book of the Fargo series. The book's hardcover edition was first published September 2, 2014.

Plot
Baffin Island: Husband-and-wife team Sam and Remi Fargo are on a climate-control expedition in the Arctic, when to their astonishment they discover a Viking ship in the ice, perfectly preserved—and filled with pre–Columbian artifacts from Mexico.

How can that be? As they plunge into their research, tantalizing clues about a link between the Vikings and the legendary Toltec feathered serpent god Quetzalcoatl/Ce Acatl Topiltzin —and a fabled object known as the Eye of Heaven— begin to emerge. But so do many dangerous people. Soon the Fargos find themselves on the run through jungles, temples, and secret tombs, caught between treasure hunters, crime cartels, and those with a far more personal motivation for stopping them. At the end of the road will be the solution to a thousand-year-old mystery—or death.
Answer: A perfectly preserved viking ship, with pre-columbian artifacts from Mexico.
Question: how was annotation done?
Evidence: With the gathered comments, we reconstructed the original conversation trees, from the original post, the root, to the leaves, when they were available and selected a subset to annotated. For annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event. We added an extra constraint that the parent of the suspected trolling event should also be part of the direct responses, we hypothesize that if the suspected trolling event is indeed trolling, its parent should be the object of its trolling and would have a say about it. We recognize that this limited amount of information is not always sufficient to recover the original message conveyed by all of the participants in the snippet, and additional context would be beneficial. However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a “turker” to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described. Before annotating, we set up a qualification test along with borderline examples to guide them in process and align them with our criteria. The qualification test turned out to be very selective since only 5% of all of the turkers that attempted it passed the exam. Our dataset consists of 1000 conversations with 5868 sentences and 71033 tokens. The distribution over the classes per trolling aspect is shown in the table TABREF24 in the column “Size”.
For annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event.
However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a “turker” to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described.
Answer: Annotation was done with the help of annotators from Amazon Mechanical Turk on snippets of conversations
Question: What is the Gun Show in New Zealand
Answer: Welcome to the Gun Show is a New Zealand music programme which first went to air on New Zealand's Alt TV, Sky Digital 65. The show is produced by former Lynfield College student Johnathon Leyland.
Question: Who were the founding members of id Software?
Evidence: id Software LLC (/ɪd/) is an American video game developer based in Richardson, Texas. It was founded on February 1, 1991, by four members of the computer company Softdisk: programmers John Carmack and John Romero, game designer Tom Hall, and artist Adrian Carmack.
Answer: The founding members of id Software were John Carmack, John Romero, Tom Hall, and Adrian Carmack.
Question: Classify each of the following as an instrument or tool: screw driver, guitar, trumpet, chisel, tuba, wrench
Answer: Screw Driver: Tool
Guitar: Instrument
Trumpet: Instrument
Chisel: Tool
Tuba: Instrument
Wrench: Tool
Question: Who does the most relevant related work and where does it published?
Evidence: Propaganda has been tackled mostly at the article level. BIBREF3 created a corpus of news articles labelled as propaganda, trusted, hoax, or satire. BIBREF4 experimented with a binarized version of that corpus: propaganda vs. the other three categories. BIBREF5 annotated a large binary corpus of propagandist vs. non-propagandist articles and proposed a feature-based system for discriminating between them. In all these cases, the labels were obtained using distant supervision, assuming that all articles from a given news outlet share the label of that outlet, which inevitably introduces noise BIBREF6.
A related field is that of computational argumentation which, among others, deals with some logical fallacies related to propaganda. BIBREF7 presented a corpus of Web forum discussions with instances of ad hominem fallacy. BIBREF8, BIBREF9 introduced Argotario, a game to educate people to recognize and create fallacies, a by-product of which is a corpus with $1.3k$ arguments annotated with five fallacies such as ad hominem, red herring and irrelevant authority, which directly relate to propaganda.
Unlike BIBREF8, BIBREF9, BIBREF7, our corpus uses 18 techniques annotated on the same set of news articles. Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 BIBREF10 and describes a corpus that is a subset of the one used for this shared task.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019  and describes a corpus that is a subset of the one used for this shared task.
Answer: The most relevant related work is their own, which is published in parallel to this paper at EMNLP-IJCNLP 2019.
Question: Extract from the text the family of Parametrodes.
Evidence: Parametrodes is a genus of moths in the family Geometridae.
Answer: Geometridae
Question: Please, give me a short bullet list of main ingredients and procedure in order to cook risotto.
Evidence: There are many different risotto recipes with different ingredients, but they are all based on rice of an appropriate variety, cooked in a standard procedure. Risotto, unlike other rice dishes, requires constant care and attention. The rice is not to be pre-rinsed, boiled, or drained, as washing would remove much of the starch required for a creamy texture. 
 The rice is first cooked briefly in a soffritto of onion and butter or olive oil to coat each grain in a film of fat, called tostatura; white wine is added and must be absorbed by the grains. When it has been absorbed, the heat is raised to medium-high, and boiling stock is gradually added in small amounts while stirring constantly. The constant stirring, with only a small amount of liquid present, forces the grains to rub against each other and release the starch from the outside of the grains into the surrounding liquid, creating a smooth creamy-textured mass. When the rice is cooked the pot is taken off the heat for mantecatura, vigorously beating in refrigerated balls of grated parmesan cheese and butter, to make the texture as creamy and smooth as possible. It may be removed from the heat a few minutes earlier and left to cook with its residual heat.
Properly cooked risotto is rich and creamy even if no cream is added due to the starch in the grains. It has some resistance or bite (al dente) and separate grains. The traditional texture is fairly fluid, or all'onda ("wavy" or "flowing in waves"). It is served on flat dishes and should easily spread out but not have excess watery liquid around the perimeter. It must be eaten at once, as it continues to cook in its own heat, making the grains absorb all the liquid and become soft and dry
Answer: Risotto can be cooked with different recipes and ingredients, however the cooking procedure is standard and the basic ingredients are common in all recipes.

- First of all rice is briefly cooked in a soffritto made with onions and butter but also olive oil can be used
- This first phase is called tostatura and it’s important because it coats each rice grain with a film of fat
- Then you need to add white wine until is absorbed by the grains
- Raise then the heat to medium-high
- Now you need to start adding boiling stock in small amounts while constantly stirring
- When the rice is cooked take off the pot from the heat and vigorously beating in refrigerated balls of Parmesan cheese and butter
Question: Classify these as either states or cities: Illinois, California, San Francisco, Austin, Texas.
Answer: Illinois, California, and Texas are states.  San Francisco and Austin are cities.
Question: What are some ways I could cheer up my friend that just got dumped?
Answer: Im sorry your friend recently got dumped.  I would ask them if they want to talk about it, or be distracted from it.  If they want to talk about it, I would find a quiet place and a warm beverage and listen.  If they want to be distracted from it, I would find a local favorite bar or activity that your friend enjoys, and take them there.  Call as many of your joint friends as possible, and provide ample supply of distractions for your friend.
Question: What dierse domains and languages are present in new datasets?
Evidence: We provide three pairs of short/long datasets from different domains (movies and restaurants) and from different languages (English and Korean) suitable for the task: Mov_en, Res_en, and Mov_ko. Most of the datasets are from previous literature and are gathered differently The Mov_en datasets are gathered from different websites; the short dataset consists of hand-picked sentences by BIBREF19 from document-level reviews from the Rotten Tomatoes website, while the long dataset consists of reviews from the IMDB website obtained by BIBREF20. The Res_en dataset consists of reviews from Yelp, where the short dataset consists of reviews with character lengths less than 140 from BIBREF21, while reviews in the long dataset are gathered from BIBREF20. We also share new short/long datasets Mov_ko, which are gathered from two different channels, as shown in Figure FIGREF4, available in Naver Movies. Unlike previous datasets BIBREF9, BIBREF22 where they used polarity/binary (e.g., positive or negative) labels as classes, we also provide fine-grained classes, with five classes of different sentiment intensities (e.g., 1 is strong negative, 5 is strong positive), for Res_en and Mov_ko. Following the Cross Domain Transfer setting BIBREF9, BIBREF23, BIBREF24, we limit the size of the dataset to be small-scale to focus on the main task at hand. This ensures that models focus on the transfer task, and decrease the influence of other factors that can be found when using larger datasets. Finally, following BIBREF22, we provide additional unlabeled data for those models that need them BIBREF9, BIBREF23, except for the long dataset of Mov_ko, where the labeled reviews are very limited. We show the dataset statistics in Table TABREF9, and share the datasets here: https://github.com/rktamplayo/LeTraNets.
We provide three pairs of short/long datasets from different domains (movies and restaurants) and from different languages (English and Korean) suitable for the task: Mov_en, Res_en, and Mov_ko. Most of the datasets are from previous literature and are gathered differently The Mov_en datasets are gathered from different websites; the short dataset consists of hand-picked sentences by BIBREF19 from document-level reviews from the Rotten Tomatoes website, while the long dataset consists of reviews from the IMDB website obtained by BIBREF20.
Answer: The answers are shown as follows:
* movies 
* restaurants
* English 
* Korean
Question: How do the authors define or exemplify 'incorrect words'?
Evidence: Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. Panko BIBREF0 goes as far as claiming that human accuracy regarding research paper writing is none when considering the entire document. This has been aggravated with the advent of internet and social networks, which allowed language and modern communication to be been rapidly transformed BIBREF1, BIBREF2. Take Twitter for instance, where information is expected to be readily communicated in short and concise sentences with little to no regard to correct sentence grammar or word spelling BIBREF3.
Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. 
Answer: typos in spellings or ungrammatical words
Question: Write about a sport which is always interesting and can be enjoyed alone or with people.
Answer: Looking for the perfect sport can be difficult. Running or swimming can be an easy stress relieving work out but it can get tedious eventually. Team sports like basketball or soccer can be great to tire oneself out without the boredom but it requires a lot of coordination between groups of people to be able to play. Skiing however, is never boring and can be enjoyed both with friends or alone. Once you're getting off the lift, there is no going back, you have to go down the mountain. With the exhilaration of trying to hit 50 miles per hour on your skis or taking a break on a quiet part of the mountain in the middle of the trees, skiing allows one to experience nature, adventure, and excitement all in one sport.
Question: What do you think of chewing gums?
Answer: Chewing gums mostly is personal choices. There are both benefits and risks of doing so and it depends on individuals' opinions or preferences. Some of the benefits are improving oral health. Chewing gums help to promote saliva production and wash away bacteria and food particles. Saliva also contains enzymes that help to break down food and protect teeth from decay. Chewing gums can also help reduce stress and relieve anxiety. Chewing gums help fresh breath too which is the primary reason people do it. Some of the risks of chewing gums are putting excessive strains on the jaw which can be problem to some people. Chewing gums containing sugar also can contribute to tooth decay.
Question: Give me 5 ideas that can help me be more productive
Answer: Write a to-do list at the end of the day ready for the next day. 
Complete your most important task first. 
Time block your day to group tasks together.
Switch your phone on airplane mode for 30 minutes intervals to complete tasks without distractions.
Adopt "Inbox Zero" approach to your email management