DataHammer/scimrc huggingface dataset:
Question: Given this paragraph about Twitch Interactive, in what year did Amazon acquire them?
Evidence: Twitch is an American video live streaming service that focuses on video game live streaming, including broadcasts of esports competitions, in addition to offering music broadcasts, creative content, and "in real life" streams. Twitch is operated by Twitch Interactive, a subsidiary of Amazon.com, Inc. It was introduced in June 2011 as a spin-off of the general-interest streaming platform Justin.tv. Content on the site can be viewed either live or via video on demand. The games shown on Twitch's current homepage are listed according to audience preference and include genres such as real-time strategy games (RTS), fighting games, racing games, and first-person shooters.

The popularity of Twitch eclipsed that of its general-interest counterpart. In October 2013, the website had 45 million unique viewers, and by February 2014, it was considered the fourth-largest source of peak Internet traffic in the United States. At the same time, Justin.tv's parent company was re-branded as Twitch Interactive to represent the shift in focus – Justin.tv was shut down in August 2014. That month, the service was acquired by Amazon for US$970 million, which later led to the introduction of synergies with the company's subscription service Amazon Prime. Twitch acquired Curse LLC in 2016, an operator of online video gaming communities and introduced means to purchase games through links on streams along with a program allowing streamers to receive commissions on the sales of games that they play.

By 2015, Twitch had more than 100 million viewers per month. In 2017, Twitch remained the leading live streaming video service for video games in the US, and had an advantage over YouTube Gaming, which shut down its standalone app in May 2019. As of February 2020, it had 3 million broadcasters monthly and 15 million daily active users, with 1.4 million average concurrent users. As of May 2018, Twitch had over 27,000 partner channels.[1
Answer: Amazon acquired Twitch in the year 2014.
Question: How do banks make money?
Answer: Traditionally, banks accept deposits and loan that money to borrowers providing a time arbitrage on the velocity of money which regulates in profit. In more recent years, banks have diversified their profit centers by investing those deposits in fixed income securities. There has even been a proliferation of into multiple types of banks. Some of these banks include investment banks which transact in the capital markets pairing investors with capital seeking counterparties. The most innovative banks are starting to see how data can become a profit center as well as allow them to provide a better customer experience.
Question: What is the difference between Montessori and Regular schooling?
Answer: A Montessori format of schooling is based on an individual's learning pace. In a Montessori classroom, there are a plethora of activities laid out at the eye level of the child. Each child can pick and choose an activity that interests him or her. The learning progresses based on the child's interests. Children often observe other children practicing other activities, which fosters curiosity and motivates them to try different activities in the Montessori setting.

In a regular schooling environment, the curriculum for the year is pre-decided and the children are supposed to follow the same, hence everyone is expected to learn at the same pace. It is structured around student-teacher interaction and adheres to a time-table.
Question: How far did Oskar Dillon kick in the winning goal at the NPL Queensland championship?
Evidence: Oskar Dillon (born 10 February 1999), is an Australian professional footballer who plays as a centre back for Oakleigh Cannons in NPL Victoria.

Club career
Gold Coast Knights
Dillon was part of the 2019 NPL Queensland championship winning Gold Coast Knights team. He played the full game and scored the winning goal, a free-kick from 25 yards out, as the Knights' defeated Olympic FC 2-1 in the 2019 NPL Queensland Grand Final on 14 September 2019, claiming their first piece of top-flight silverware. After an impressive campaign, Dillon was awarded the NPL Queensland Young Player of the Year at the inaugural Football in Queensland Awards Night.

Western United
On 2 January 2020, Dillon signed his first professional contract with Western United, penning a one-year deal for the 2019-20 season. He made his debut in a Round 15 clash against Central Coast Mariners, playing the full game as United ran out 3-0 winners at GMHBA Stadium. Dillon was released by the club at the end of the 2019–20 A-League.
Answer: Oskar Dillon, the Australian professional footballer did a free kick from 25 yards out defeating the Knights in the 2019 NPL Queensland Grand final.
Question: What is the source of the "control" corpus?
Evidence: Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Twitter activity consists of short posts called tweets which are limited to 140 characters. Retweets, where users repost a tweet to spread its content, were not considered. (The spread of causal statements will be considered in future work.) We considered only English-language tweets for this study. To avoid cross-language effects, we kept only tweets with a user-reported language of `English' and, as a second constraint, individual tweets needed to match more English stopwords than any other language's set of stopwords. Stopwords considered for each language were determined using NLTK's database BIBREF29 . A tweet will be referred to as a `document' for the rest of this work.
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not included due to its use as a popular contraction for `because'. One `cause-word' per document restricted the analysis to single relationships between two relata. Documents that contain bidirectional words (`associate', `relate', `connect', `correlate', and any of their stems) were also not selected for analysis. This is because our focus is on causality, an inherently one-sided relationship between two objects. We also did not consider additional synonyms of these cause words, although that could be pursued for future work. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. The end result of this procedure identified 965,560 causal and 965,560 control documents. Each of the three “cause-words”, `caused', `causes', and `causing' appeared in 38.2%, 35.0%, and 26.8% of causal documents, respectively.
Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API.
Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present.
Answer: Randomly selected from a Twitter dump, temporally matched to causal documents
Question: What additional techniques could be incorporated to further improve accuracy?
Evidence: The main advantage of translating to a programming language is - it has a concrete and strict lexical and grammatical structure which human languages lack. The aim of this paper was to make the text-to-code framework work for general purpose programming language, primarily Python. In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.
In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.
Answer: The answers are shown as follows:
* phrase-based word embedding
* Abstract Syntax Tree(AST)
Question: How do they filter the datasets in their experiments?
Evidence: This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.
Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.
We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user", “URL" and “hashtag" references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user”, “URL” and “hashtag” references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
Answer: To use efficient regular expressions to delete three kinds of references and NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons.
Question: How spicy is the hottest peppers
Answer: The hottest pepper is the ghost pepper with a measure of a million scoville units.
Question: Which language is divided into six dialects in the task mentioned in the paper?
Evidence: The focus of the aforementioned language and dialect identification competitions was diatopic variation and thus the data made available in these competitions was synchronic contemporary corpora. In the 2019 edition of the workshop, for the first time, a task including historical languages was organized. The CLI shared task provided participants with a dataset containing languages and dialects written in cuneiform script: Sumerian and Akkadian. Akkadian is divided into six dialects in the dataset: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian BIBREF14 .
Akkadian is divided into six dialects in the dataset: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian BIBREF14 .
Answer: The answers are shown as follows:
* Akkadian.
Question: What web and user-generated NER datasets are used for the analysis?
Evidence: Since the goal of this study is to compare NER performance on corpora from diverse domains and genres, seven benchmark NER corpora are included, spanning newswire, broadcast conversation, Web content, and social media (see Table 1 for details). These datasets were chosen such that they have been annotated with the same or very similar entity classes, in particular, names of people, locations, and organisations. Thus corpora including only domain-specific entities (e.g. biomedical corpora) were excluded. The choice of corpora was also motivated by their chronological age; we wanted to ensure a good temporal spread, in order to study possible effects of entity drift over time.
FLOAT SELECTED: Table 1 Corpora genres and number of NEs of different classes.
Since the goal of this study is to compare NER performance on corpora from diverse domains and genres, seven benchmark NER corpora are included, spanning newswire, broadcast conversation, Web content, and social media (see Table 1 for details).
FLOAT SELECTED: Table 1 Corpora genres and number of NEs of different classes.
Answer: MUC, CoNLL, ACE, OntoNotes, MSM, Ritter, UMBC
Question: What could you do on a Sunday?
Answer: Most people do not work on Sundays, so they are free to pursue leisure activities to relax and recharge. To some people this may mean pursuing a hobby or a sport. Others may see friends or family. Unfortunately, Sundays may not be entirely free of unenjoyable chores such doing the laundry or cleaning the house.
Question: Who founded the city of Detroit?
Answer: The site that was to become the city of Detroit was established on July 24, 1701, by Antoine de la Mothe Cadillac, a French military leader and trader.
Question: I'm going to give you a passage from Marty Essen's book "Cool Creatures, Hot Planet: Exploring the Seven Continents" and want you to answer the following question: Where are western Hermann's tortoises found?

After spending the night at a large chain hotel, Deb and I traveled southeast with tortoises on our mind. Our plan for the day was to infiltrate the Massif des Maures area and search for one of France's most endangered reptiles-- the western Hermann's tortoise.  The first stop of our quest was Le Village de Tortues (the Tortoise Village), a tortoise rehabilitation center near the town of Gonfaron. Our mission was to befriend a staff member, learn as much as we could, and get detailed information on tracking down the wily Hermann's tortoise in the wild. When we couldn't find an English-speaking staff member, our mission was nearly thwarted. Only through quick thinking and improvisation were we able to acquire the vital background information we needed. Okay - we utilized the signs provided for British visitors. For many people, the word tortoise invokes thoughts of giant four-foot-long Galapagos tortoises. Western Hermann's tortoises, on the other hand, are small, reaching a maximum length of eight inches. While both reptiles make the IUCN Red List of Threatened Species, the Galapagos tortoise is listed as "vulnerable" and the western Hermann's is listed as "endangered." In France, habitat destruction, egg pilfering, and fire are the primary factors for the tortoises decline.
Answer: Western Hermann's tortoises are found in France, in a turtle rehabilitation village, Le Village de Tortues, near Gonfaron.
Question: Identify which instrument is string or percussion: Octoban, Socavon
Answer: Socavon is string, Octoban is percussion.
Question: Given the following summary of the book "Human Compatible" by Stuart Russell, what is the author's proposal to overcome the limitations of the standard approach for developing AI?
Evidence: Russell begins by asserting that the standard model of AI research, in which the primary definition of success is getting better and better at achieving rigid human-specified goals, is dangerously misguided. Such goals may not actually reflect what human designers intend, such as by failing to take into account any human values not included in the goals. If an AI developed according to the standard model were to become superintelligent, it would likely not fully reflect human values and could be catastrophic to humanity. Russell asserts that precisely because the timeline for developing human-level or superintelligent AI is highly uncertain, safety research should be begun as soon as possible, as it is also highly uncertain how long it would take to complete such research.

Russell argues that continuing progress in AI capability is inevitable because of economic pressures. Such pressures can already be seen in the development of existing AI technologies such as self-driving cars and personal assistant software. Moreover, human-level AI could be worth many trillions of dollars. Russell then examines the current debate surrounding AI risk. He offers refutations to a number of common arguments dismissing AI risk and attributes much of their persistence to tribalism—AI researchers may see AI risk concerns as an "attack" on their field. Russell reiterates that there are legitimate reasons to take AI risk concerns seriously and that economic pressures make continued innovation in AI inevitable.

Russell then proposes an approach to developing provably beneficial machines that focuses on deference to humans. Unlike in the standard model of AI, where the objective is rigid and certain, this approach would have the AI's true objective remain uncertain, with the AI only approaching certainty about it as it gains more information about humans and the world. This uncertainty would, ideally, prevent catastrophic misunderstandings of human preferences and encourage cooperation and communication with humans. Russell concludes by calling for tighter governance of AI research and development as well as cultural introspection about the appropriate amount of autonomy to retain in an AI-dominated world.
Answer: Russell suggests that the AIs should be trained in a way that makes them gradually gain knowledge of what is truly beneficial to humans. AIs developed in this fashion would cooperate with humans while leaving them with a high enough level of autonomy.
Question: What geographic area do Sephardic Jews originate from?
Answer: Sephardic Judaism is rooted in the Iberian peninsula
Question: Given a reference text about Mikel Artetxe Guezuraga, tell me when he turned professional and what his biggest wins are.
Evidence: Mikel Artetxe Guezuraga (born 24 September 1976) is a Spanish former road bicycle racer, who rode professionally between 1999 and 2007 for the Euskaltel–Euskadi, 3 Molinos Resort and Fuerteventura–Canarias teams.

Career
He turned professional in 1999 with Euskaltel–Euskadi, where he stayed for seven seasons. In his time with Euskaltel–Euskadi, Artetxe took two stages and the overall victory in the GP Jornal de Noticias in 2000, a stage victory in the 2001 Vuelta a Andalucía and a stage victory in the 2002 Troféu Joaquim Agostinho.

At the start of the 2006 season, Artetxe moved a division down to the newly formed 3 Molinos Resort team. Here he had the biggest wins of his career, with a stage win in the Vuelta a Asturias and the sprints classification in the Troféu Joaquim Agostinho. With the rapid demise of 3 Molinos Resort, Artetxe moved to the newly formed Fuerteventura–Canarias team for 2007.
Answer: Mikel Artetxe Guezuraga turned professional in 1999 and had the biggest wins of his career in 2006 with a stage win in the Vuelta a Asturias and the sprints classification in the Troféu Joaquim Agostinho.
Question: How do they get the formal languages?
Evidence: BIBREF7 investigated the learning capabilities of simple RNNs to process and formalize a context-free grammar containing hierarchical (recursively embedded) dependencies: He observed that distinct parts of the networks were able to learn some complex representations to encode certain grammatical structures and dependencies of the context-free grammar. Later, BIBREF8 introduced an RNN with an external stack memory to learn simple context-free languages, such as $a^n b^m$ , $a^nb^ncb^ma^m$ , and $a^{n+m} b^n c^m$ . Similar studies BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 have explored the existence of stable counting mechanisms in simple RNNs, which would enable them to learn various context-free and context-sensitive languages, but none of the RNN architectures proposed in the early days were able to generalize the training set to longer (or more complex) test samples with substantially high accuracy.
BIBREF9 , on the other hand, proposed a variant of Long Short-Term Memory (LSTM) networks to learn two context-free languages, $a^n b^n$ , $a^n b^m B^m A^n$ , and one strictly context-sensitive language, $a^n b^n c^n$ . Given only a small fraction of samples in a formal language, with values of $n$ (and $m$ ) ranging from 1 to a certain training threshold $N$ , they trained an LSTM model until its full convergence on the training set and then tested it on a more generalized set. They showed that their LSTM model outperformed the previous approaches in capturing and generalizing the aforementioned formal languages. By analyzing the cell states and the activations of the gates in their LSTM model, they further demonstrated that the network learns how to count up and down at certain places in the sample sequences to encode information about the underlying structure of each of these formal languages.
Later, BIBREF8 introduced an RNN with an external stack memory to learn simple context-free languages, such as $a^n b^m$ , $a^nb^ncb^ma^m$ , and $a^{n+m} b^n c^m$ . Similar studies BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 have explored the existence of stable counting mechanisms in simple RNNs, which would enable them to learn various context-free and context-sensitive languages
BIBREF9 , on the other hand, proposed a variant of Long Short-Term Memory (LSTM) networks to learn two context-free languages, $a^n b^n$ , $a^n b^m B^m A^n$ , and one strictly context-sensitive language, $a^n b^n c^n$ .
Answer: These are well-known formal languages some of which was used in the literature to evaluate the learning capabilities of RNNs.
Question: which country that has the most pyramids in the world?
Answer: Sudan has more pyramids than any country in the world
Question: Why was the model T only produced in black?
Answer: Henry Ford produced the model T only in black because the black paint available at the time was the fastest to dry.
Question: Do the classifiers based on the MultiCCA embeddings perform very well on the development corpus?
Evidence: The classification accuracy for zero-shot transfer on the test set of our Multilingual Document Classification Corpus are summarized in Table 4 . The classifiers based on the MultiCCA embeddings perform very well on the development corpus (accuracies close or exceeding 90%). The system trained on English also achieves excellent results when transfered to a different languages, it scores best for three out of seven languages (DE, IT and ZH). However, the transfer accuracies are quite low when training the classifiers on other languages than English, in particular for Russian, Chinese and Japanese.
The systems using multilingual sentence embeddings seem to be overall more robust and less language specific. They score best for four out of seven languages (EN, ES, FR and RU). Training on German or French actually leads to better transfer performance than training on English. Cross-lingual transfer between very different languages like Chinese and Russian also achieves remarkable results.
The classifiers based on the MultiCCA embeddings perform very well on the development corpus (accuracies close or exceeding 90%).
Answer: Yes, they do.