DataHammer/scimrc huggingface dataset:
Question: here is a blurb about the 98th Illinois General Assembly, what were the notable laws that were passed?
Evidence: The 98th General Assembly enacted a total of 1,175 bills into law. Notable among these was the Illinois Religious Freedom and Marriage Fairness Act, which removed legal barriers to same-sex marriage in Illinois. The House of Representatives passed the bill by a 61–54 vote on November 5, 2013, and the Senate passed it by a 32–21 vote. The governor signed the bill into law on November 20, and the law took effect on June 1, 2014.

The General Assembly also passed the Illinois Bill of Rights for the Homeless, which made Illinois the second state to ban discrimination against the homeless.

The General Assembly also passed the Firearm Concealed Carry Act (FCCA), a change to Illinois gun law that made Illinois the last state in the country to enact concealed carry. Passed over the governor's veto, the FCCA took effect "mere hours before the seventh circuit's deadline" in Moore v. Madigan.

Late in the legislative session, the General Assembly passed a bill that changes the original Illinois wiretapping law adding that in order to commit a criminal offense, a person must be recording "in a surreptitious manner". On December 30, 2014, Governor Quinn signed the bill into law as Public Act 098-1142. The bill's sponsors, Elaine Nekritz and Kwame Raoul, claimed the law upholds the rights of citizens to record in public.

The General Assembly approved five ballot measures for the 2014 Illinois general election, an unusually large number for the state, including three advisory referendums and two constitutional amendments. The constitutional amendments, Marsy's Law and the Illinois Right to Vote Amendment, both passed with the required 60% majority of the popular vote.
Answer: removing the legal barriers to same-sex marriage, banning discrimination against homeless people, conceal and carry firearms, and revising the wiretapping laws so that citizens can record in public.
Question: What basic tools should I keep around the house?
Answer: Keeping tools around the house to make simple fixes is super important as a homeowner and a renter. Furthermore, being self sufficient and not relying on others to do basic work is very empowering. Some basic tools that everyone should keep around the house are a  screwdriver with various different heads (eg. phillips, flat head, allen wrench, etc), needle nose pliers, channel lock pliers, slip joint pliers an electric drill, drill bit set, and a hammer. You can accomplish a lot of small fixes and have on hand when that emergency situation arises.
Question: What teams did Tom Brady play for when he won the Super Bowl?
Answer: Tom Brady won the Super Bowl while playing for the New England Patriots and Tampa Bay Buccaneers.
Question: What hyperparameters have been tuned?
Evidence: We first focused on directed coattention via context to query and query to context attention as discussed in BIDAF BIBREF9. We then implemented localized feature extraction by 1D convolutions to add local information to coattention based on the QANET architecture BIBREF10. Subsequently, we experimented with different types of skip connections to inject BERT embedding information back into our modified network. We then applied what we learned using the base BERT model to the large BERT model. Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks. Each part of the project is discussed further in the subsections below.
Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks.
Answer: The answers are shown as follows:
* number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks
Question: Which is a species of fish? Hoki or Loki
Answer: Hoki
Question: What is Christmas?
Answer: Christmas is a Christian holiday that represents when Jesus was born.  This holiday is celebrated on December 25 every year.  Church's will have a manger setup with figures showing what the birth of Christ would have looked like thousands of years ago.  The figures represented would be the wise men, mary, joseph, and Jesus in the manger.  The wise men were led to the manger of Jesus by following a star in the sky.  Billions of people all over the world celebrate this holiday every year.
Question: What is binary variational dropout?
Evidence: We use the dropout technique of Gal & Ghahramani gal as a baseline because it is the most similar dropout technique to our approach and denote it VBD (variational binary dropout).
We use the dropout technique of Gal & Ghahramani gal as a baseline because it is the most similar dropout technique to our approach and denote it VBD (variational binary dropout).
Answer: The answers are shown as follows:
* the dropout technique of Gal & Ghahramani gal
Question: What new economies developed because of the anti-slave treaties?
Evidence: In West Africa, the decline of the Atlantic slave trade in the 1820s caused dramatic economic shifts in local polities. The gradual decline of slave-trading, prompted by a lack of demand for slaves in the New World, increasing anti-slavery legislation in Europe and America, and the British Royal Navy's increasing presence off the West African coast, obliged African states to adopt new economies. Between 1808 and 1860, the British West Africa Squadron seized approximately 1,600 slave ships and freed 150,000 Africans who were aboard.

Action was also taken against African leaders who refused to agree to British treaties to outlaw the trade, for example against "the usurping King of Lagos", deposed in 1851. Anti-slavery treaties were signed with over 50 African rulers. The largest powers of West Africa (the Asante Confederacy, the Kingdom of Dahomey, and the Oyo Empire) adopted different ways of adapting to the shift. Asante and Dahomey concentrated on the development of "legitimate commerce" in the form of palm oil, cocoa, timber and gold, forming the bedrock of West Africa's modern export trade. The Oyo Empire, unable to adapt, collapsed into civil wars.
Answer: Instead of exporting slaves, Africa began exporting palm oil, cocoa, timber, and gold.
Question: What baselines do they use in the paper?
Evidence: SVM A Support Vector Machines model in BIBREF36 detects misinformation relying on manually extracted features.
CNN A Convolutional Neural Network model BIBREF37 employs pre-trained word embeddings based on Word2Vec as input embeddings to capture features similar to n-grams.
TE Tensor Embeddings BIBREF38 leverages tensor decomposition to derive concise claim embeddings, which are used to create a claim-by-claim graph for label propagation.
DeClarE Evidence-Aware Deep Learning BIBREF39 encodes claims and articles by Bi-LSTM and focuses on each other based on attention mechanism, and then concatenates claim source and article source information.
MTL-LSTM A multi-task learning model based on LSTM networks BIBREF14 trains jointly the tasks of veracity classification, rumor detection, and stance detection.
TRNN Tree-structured RNN BIBREF40 is a bottom-up and a top-down tree-structured model based on recursive neural networks.
Bayesian-DL Bayesian Deep Learning model BIBREF41 first adopts Bayesian to represent both the prediction and uncertainty of claim and then encodes replies based on LSTM to update and generate a posterior representations.
SVM A Support Vector Machines model in (Derczynski et al., 2017) detects misinformation relying on manually extracted features. CNN A Convolutional Neural Network model (Chen et al., 2017) employs pre-trained word embeddings based on Word2Vec as input embeddings to capture features similar to n-grams. TE Tensor Embeddings (Guacho et al., 2018) leverages tensor decomposition to derive concise claim embeddings, which are used to create a claimby-claim graph for label propagation. DeClarE Evidence-Aware Deep Learning (Popat et al., 2018) encodes claims and articles by Bi-LSTM and focuses on each other based on attention mechanism, and then concatenates claim source and article source information. MTL-LSTM A multi-task learning model based on LSTM networks (Kochkina et al., 2018) trains jointly the tasks of veracity classification, rumor detection, and stance detection. TRNN Tree-structured RNN (Ma et al., 2018b) is a bottom-up and a top-down tree-structured model based on recursive neural networks. Bayesian-DL Bayesian Deep Learning model (Zhang et al., 2019) first adopts Bayesian to represent both the prediction and uncertainty of claim and then encodes replies based on LSTM to update and generate a posterior representations.
Answer: SVM, CNN, TE, DeClarE, MTL-LSTM, TRNN, and Bayesian-DL.
Question: Is negative sampling used in the experiment？
Evidence: The word embeddings of the source and target language sentences are used as initial vectors of the model to improve contextualization. The skip gram model of the word2vec algorithm optimizes the vectors by accounting for the average log probability of context words given a source word. DISPLAYFORM0 
where k is the context window taken for the vectorization, INLINEFORM0 refers to the INLINEFORM1 word of the corpus and INLINEFORM2 is the size of the training corpus in terms of the number of words. Here, the probabily INLINEFORM3 is computed as a hierarchical softmax of the product of the transpose of the output vector of INLINEFORM4 and the input vector of INLINEFORM5 for each and every pair over the entire vocabulary. The processes of negative sampling and subsampling of frequent words that were used in the original model aren't used in this experiment BIBREF3 .
For the process of creating semantically meaningful word embeddings, a monolingual corpus of 569,772 Tamil sentences was used. This gave the vectors more contextual richness due to the increased size of the corpus as opposed to using just the bilingual corpus' target side sentences BIBREF3 .
In the experiment, the word2vec model was trained using a vector size of 100 to ensure that the bulk of the limited memory of the GPU will be used for the neural attention translation model. It has been shown that any size over that of 150 used for word vectorization gives similar results and that a size of 100 performs close to the model with 150-sized word vectors BIBREF7 . A standard size of 5 was used as window size and the model was trained over 7 worker threads simultaneously. A batch size of 50 words was used for training. The negative sampling was set at 1 as it is the nature of morphologically rich languages to have a lot of important words that don't occur more than once in the corpus. The gensim word2vec toolkit was used to implement this word embedding process BIBREF8 .
The processes of negative sampling and subsampling of frequent words that were used in the original model aren't used in this experiment (Mikolov and Dean 2013).
Answer: No.
Question: What kind of task was organised?
Evidence: Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. In the Internet era, thanks to the mechanism of sharing in social networks, propaganda campaigns have the potential of reaching very large audiences BIBREF0, BIBREF1, BIBREF2.
Propagandist news articles use specific techniques to convey their message, such as whataboutism, red Herring, and name calling, among many others (cf. Section SECREF3). Whereas proving intent is not easy, we can analyse the language of a claim/article and look for the use of specific propaganda techniques. Going at this fine-grained level can yield more reliable systems and it also makes it possible to explain to the user why an article was judged as propagandist by an automatic system.
With this in mind, we organised the shared task on fine-grained propaganda detection at the NLP4IF@EMNLP-IJCNLP 2019 workshop. The task is based on a corpus of news articles annotated with an inventory of 18 propagandist techniques at the fragment level. We hope that the corpus would raise interest outside of the community of researchers studying propaganda. For example, the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.
With this in mind, we organised the shared task on fine-grained propaganda detection at the NLP4IF@EMNLP-IJCNLP 2019 workshop.
Answer: The shared task on fine-grained propaganda detection.
Question: Fix the typos and grammar below
Evidence: Bernd Rosemeyer (14 October 1909 – 28 January 1938) was a german racing driver andd speed record holder. He is considered on of the greatest racing driver of all time. Tough he was no a member of the Nazi party, she was made a member of the SS for propaganda purposes and hold the rank off Hauptsturmführer.
Answer: Bernd Rosemeyer (14 October 1909 – 28 January 1938) was a German racing driver and speed record holder. He is considered one of the greatest racing drivers of all time. Though he was not a member of the Nazi party, he was made a member of the SS for propaganda purposes and held the rank of Hauptsturmführer.
Question: What is System Thinking
Answer: Systems thinking is a way of making sense of complexity by looking at it in terms of wholes and relationships rather than splitting it into parts.  It is based on the idea that all critical processes in an organization are interrelated and work together to achieve a common goal.
Question: What is the dataset used in the paper?
Evidence: The data for this project are two parts, the first part is the historical S&P 500 component stocks, which are downloaded from the Yahoo Finance. We use the data over the period of from 12/07/2017 to 06/01/2018. The second part is the news article from financial domain are collected with the same time period as stock data. Since our paper illustrates the relationship between the sentiment of the news articles and stocks' price. Hence, only news article from financial domain are collected. The data is mainly taken from Webhose archived data, which consists of 306242 news articles present in JSON format, dating from December 2017 up to end of June 2018. The former 85% of the dataset is used as the training data and the remainder 15% is used as the testing data. The News publishers for this data are CNBC.com, Reuters.com, WSJ.com, Fortune.com. The Wall Street Journal is one of the largest newspapers in the United States, which coverage of breaking news and current headlines from the US and around the world include top stories, photos, videos, detailed analysis and in-depth thoughts; CNBC primarily carries business day coverage of U.S. and international financial markets, which following the end of the business day and on non-trading days; Fortune is an American multinational business magazine; Reuters is an international news organization. We preprocess the raw article body and use NLTK sentiment package alence Aware Dictionary and Sentiment Reasoner (VADER) to extract sentiment scores.
The data for this project are two parts, the first part is the historical S&P 500 component stocks, which are downloaded from the Yahoo Finance. We use the data over the period of from 12/07/2017 to 06/01/2018. The second part is the news article from financial domain are collected with the same time period as stock data.
Hence, only news article from financial domain are collected. The data is mainly taken from Webhose archived data, which consists of 306242 news articles present in JSON format, dating from December 2017 up to end of June 2018.
Answer: historical S&P 500 component stocks
 306242 news articles
Question: Are leafcutters solitary or social insects?
Evidence: Solitary bees, such as leafcutters, do not form colonies. Unlike social insects (ants, yellow jackets, honeybees), leafcutters work alone building isolated nests. Similar to honeybees, female bees perform nearly all essential tasks of brood rearing. These native insects perform essential tasks, pollinating wild plants. The alfalfa leaf cutter bee (Megachile rotundata), native to Europe, has been semi-domesticated for crop pollination. In North America, the species was deliberately imported to assist in the pollination of food crops, but has now become feral and widespread.
Answer: solitary
Question: Few creative ways we can market products and services in modern era?
Answer: 1. Influencer Partnership
2. Social Media Campaign
3. Events and experiential marketing
Question: How can I improve my sleep?
Answer: There are a number of variables that can help you improve your sleep.  It is important to try different recommendations and see which ones will have the biggest impact for you.  Sleep is the foundation of our mental and physical health and performance in all endeavors. 

1) View sunlight by going outside within 30-60 minutes of waking. Do that again in the late afternoon, prior to sunset. If you wake up before the sun is out and you want to be awake, turn on artificial lights and then go outside once the sun rises.
On bright cloudless days: view morning and afternoon sun for 10 min; cloudy days: 20 min; very overcast days 30-60 min. If you live someplace with very minimal light, consider an artificial daytime simulator source.
Don’t wear sunglasses for this practice if you safely can, but contact lenses and eyeglasses are fine.
No, you don’t have to look directly at the sun, and never look at ANY light so bright it is painful to view! That said, you can’t wear a brimmed hat, sunglasses and remain in the shade and expect to “wake up” your circadian clock.

2) Wake up at the same time each day and go to sleep when you first start to feel sleepy. Pushing through the sleepy late evening feeling and going to sleep too late is one reason people wake at 3 am and can’t fall back asleep.

3) Avoid caffeine within 8-10 hours of bedtime. 

4) Avoid viewing bright lights—especially bright overhead lights between 10 pm and 4 am. Here is a simple rule: only use as much artificial lighting as is necessary for you to remain and move about safely at night. Blue blockers can help a bit at night but still dim the lights. Viewing bright lights of all colors are a problem for your circadian system. Candlelight and moonlight are fine. 

5) Limit daytime naps to less than 90 min, or don’t nap at all. 

6) If you wake up in the middle of the night (which, by the way, is normal to do once or so each night) but you can’t fall back asleep, consider doing an NSDR protocol when you wake up. 

7) Keep the room you sleep in cool and dark and layer on blankets that you can remove.
Your body needs to drop in temperature by 1-3 degrees to fall and stay asleep effectively. Body temperature increases are one reason you wake up. Thus, keep your room cool and remove blankets as needed. If it’s too hot you would have to use a cooling device and that’s harder than simply tossing off blankets if you get too warm.

8) Drinking alcohol messes up your sleep. As do most sleep medications.

9) Kids have changing sleep needs over time. Adjust accordingly.
Teens might be night owls at 15 but become “morning people” as they age or need 6 hours a night in summer and 7-8 in winter. It will vary.
Question: What is the best electrical system for RVs?
Answer: There have been recent improvements in RV electrical systems. At the core, RVs run off “house batteries”, or large batteries that power the RV portion of the vehicle. There are several types of batteries available, and several ways of powering these batteries. The two main types of batteries are lead acid, and lithium. Lithium batteries are newer technology and will not be found in older RVs unless retrofitted. Lithium batteries have a larger charge capacity and can discharge safely further than lead acid batteries. However, lead acid batteries are cheaper. There are several ways of charging these batteries. The most common is a plug that provides either 30A or 50A of power, depending on the RV size. Most RVs also have generators, but generators are noisy and require either gas or diesel to run. Some RVs with lithium batteries (or more rarely, lead acid), have a second alternator installed that charges the batteries while driving. Finally, solar power is a popular option. Solar power is completely quiet and renewable, but can be expensive to install and is slower to charge batteries. Most RVs have a combination of the above, and consumers choose the system that best fits their lifestyle.
Question: If I give you a list of weapons, can you break them into melee and ranged. The list is knife, bow, axe, shotgun, mace, whip, rifle and cannon.
Answer: Melee weapons include the knife, axe, mace and whip. Ranged weapons include the bow, shotgun, rifle and cannon.
Question: What's the utilization of fast text?
Evidence: We utilize fasttext BIBREF18 to generate the embeddings of all words from both Text-SQuAD and Spoken-SQuAD. We adopt the phoneme sequence embeddings to replace the original character sequence embeddings using the method proposed by Li et al. BIBREF5 . The source domain model and the target domain model share the same set of word embedding matrix to improve the alignment between these two domains.
W-GAN is adopted for our domain discriminator BIBREF19 , which stacks 5 residual blocks of 1D convolutional layers with 96 filters and filter size 5 followed by one linear layer to convert each input vector sequence into one scalar value.
All models used in the experiments are trained with batch size 20, using adam with learning rate INLINEFORM0 and the early stop strategy. The dimension of the hidden state is set to 96 for all layers, and the number of self-attention heads is set to 2. The setup is slightly different but better than the setting suggested by the original QAnet.
We utilize fasttext [19] to generate the embeddings of all words from both Text-SQuAD and Spoken-SQuAD.
Answer: To generate the embeddings of all words from both Text-SQuAD and Spoken-SQuAD
Question: What is the size of the dataset?
Evidence: Using our platform, we extracted anonymized user interaction data in the manner of real quizzes generated for a collection of several input video sources. We obtained a corpus of approximately 300,000 sentences, from which roughly 1.5 million single-quiz question training examples were derived. We split this dataset using the regular 70/10/20 partition for training, validation and testing.
We obtained a corpus of approximately 300,000 sentences, from which roughly 1.5 million single-quiz question training examples were derived. 
Answer: 300,000 sentences with 1.5 million single-quiz questions
Question: Write a friendly email to my teacher asking her to postpone our quiz schedule for tomorrow to next week because our basketball team is in the championship game tonight and all students will be out late celebrating our schools victory with pride!
Answer: Dear Mrs. Johnson, 

I'm writing to request that you move tomorrows chemistry quiz to next week's class. As you know, our school basketball team is in the conference championship tonight. Most of the student body will either be in attendance or watching on TV (until late into the night.) We expect to win and to be celebrating our wonderful schools accomplishments. Given that this is such a great opportunity for our school to celebrate together, I think more students would attend and also do better on the quiz if it was postponed.  

Respectfully,
Your best student Joe