DataHammer/scimrc huggingface dataset:
Question: How large was the dataset of Danish comments?
Evidence: We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark. In light of the General Data Protection Regulations in Europe (GDPR) and the increased concern for online privacy, we applied some necessary pre-processing steps on our dataset to ensure the privacy of the authors of the comments that were used. Personally identifying content (such as the names of individuals, not including celebrity names) was removed. This was handled by replacing each name of an individual (i.e. author or subject) with @USER, as presented in both BIBREF0 and BIBREF2 . All comments containing any sensitive information were removed. We classify sensitive information as any information that can be used to uniquely identify someone by the following characteristics; racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic data, and bio-metric data.
We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark.
Answer: The answers are shown as follows:
* 3600 user-generated comments
Question: What is Thomas Anderson's hacker name?
Answer: Thomas Anderson, in The Matrix, called himself "Neo."
Question: Identify which instrument is string or percussion: Nagara, Sape
Answer: Sape is string, Nagara is percussion
Question: In a website browser address bar, what does “www” stand for?
Answer: World Wide Web
Question: Is there any work done before on handling the shortcomings of MLE?
Evidence: Language models can be optimized to recognize syntax and semantics with great accuracy BIBREF0. However, the output generated can be repetitive and generic leading to monotonous or uninteresting responses (e.g “I don't know”) regardless of the input BIBREF1. While application of attention BIBREF2, BIBREF3 and advanced decoding mechanisms like beam search and variation sampling BIBREF4 have shown improvements, it does not solve the underlying problem. In creative text generation, the objective is not strongly bound to the ground truth—instead the objective is to generate diverse, unique or original samples. We attempt to do this through a discriminator which can give feedback to the generative model through a cost function that encourages sampling of creative tokens. The contributions of this paper are in the usage of a GAN framework to generate creative pieces of writing. Our experiments suggest that generative text models, while very good at encapsulating semantic, syntactic and domain information, perform better with external feedback from a discriminator for fine-tuning objectiveless decoding tasks like that of creative text. We show this by evaluating our model on three very different creative datasets containing poetry, metaphors and lyrics.
Previous work on handling the shortcomings of MLE include length-normalizing sentence probability BIBREF5, future cost estimation BIBREF6, diversity-boosting objective function BIBREF7, BIBREF1 or penalizing repeating tokens BIBREF8. When it comes to poetry generation using generative text models, Zhang and Lapata BIBREF9, Yi et al. BIBREF10 and Wang et al. BIBREF11 use language modeling to generate Chinese poems. However, none of these methods provide feedback on the quality of the generated sample and hence, do not address the qualitative objective required for creative decoding. For the task of text generation, MaskGAN BIBREF12 uses a Reinforcement Learning signal from the discriminator, FMD-GAN BIBREF13 uses an optimal transport mechanism as an objective function. GumbelGAN BIBREF14 uses Gumbel-Softmax distribution that replaces the non-differentiable sample from a categorical distribution with a differentiable sample to propagate stronger gradients. Li et al. BIBREF1 use a discriminator for a diversity promoting objective. Yu et al. BIBREF15 use SeqGAN to generate poetry and comment on the performance of SeqGAN over MLE in human evaluations, encouraging our study of GANs for creative text generation. However, these studies do not focus solely on creative text.
Previous work on handling the shortcomings of MLE include length-normalizing sentence probability [6], future cost estimation [7], diversity-boosting objective function [8, 2] or penalizing repeating tokens [9].
Answer: Yes. It includes length-normalizing sentence probability, future cost estimation, diversity-boosting objective function or penalizing repeating tokens .
Question: What result from experiments suggest that natural language based agents are more robust?
Evidence: Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representations methods. It can be seen that the NLP representation outperforms the other methods. This is contrary to the fact that it contains the same information as the semantic segmentation maps. More interestingly, comparing the vision-based and feature-based representations render inconsistent conclusions with respect to their relative performance. NLP representations remain robust to changes in the environment as well as task-nuisances in the state. As depicted in fig: nuisance scenarios, inflating the state space with task-nuisances impairs the performance of all representations. There, a large amount of unnecessary objects were spawned in the level, increasing the state's description length to over 250 words, whilst retaining the same amount of useful information. Nevertheless, the NLP representation outperformed the vision and feature based representations, with high robustness to the applied noise.
Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representations methods. It can be seen that the NLP representation outperforms the other methods. 
NLP representations remain robust to changes in the environment as well as task-nuisances in the state. 
Answer: Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances
Question: What are the official forms of written Norwegian?
Evidence: As established by law and government policy, the two official forms of written Norwegian are Bokmål (literally 'book tongue') and Nynorsk ('new Norwegian'). The official Norwegian Language Council (Språkrådet) is responsible for regulating the two forms, and recommends the terms Norwegian Bokmål and Norwegian Nynorsk in English.[citation needed] Two other written forms without official status also exist. One, called Riksmål ('national language'), is today to a large extent the same language as Bokmål though somewhat closer to the Danish language. It is regulated by the unofficial Norwegian Academy, which translates the name as 'Standard Norwegian'. The other is Høgnorsk ('High Norwegian'), a more purist form of Nynorsk, which maintains the language in an original form as given by Ivar Aasen and rejects most of the reforms from the 20th century; this form has limited use.
Answer: The two official forms of written Norwegian are Bokmål and Nynorsk.
Question: What are the best things to do when visiting Los Angeles?
Answer: Some popular tourist attractions in Los Angeles are:
- Eating out in K-Town
- Visiting the Huntington Library
- Riding rollercoasters at Universal Studios
- Finding your favorite celebrities at the Hollywood Walk of Fame
- Getting a cool selfie in front of the Hollywood sign
Question: Given this paragraph on Mercury-Redstone 1, how many people were on the MR-1 when the launched failed, and when was the launch?
Evidence: Mercury-Redstone 1 (MR-1) was the first Mercury-Redstone uncrewed flight test in Project Mercury and the first attempt to launch a Mercury spacecraft with the Mercury-Redstone Launch Vehicle. Intended to be an uncrewed sub-orbital spaceflight, it was launched on November 21, 1960 from Cape Canaveral Air Force Station, Florida. The launch failed in abnormal fashion: immediately after the Mercury-Redstone rocket started to move, it shut itself down and settled back on the pad, after which the capsule jettisoned its escape rocket and deployed its recovery parachutes. The failure has been referred to as the "four-inch flight", for the approximate distance traveled by the launch vehicle.
Answer: MR-1 was launched on November 21, 1960, and MR-1 was an uncrewed flight, so there were no people on the spacecraft.
Question: what is the supervised model they developed?
Evidence: Support Vector Machine (SVM) ( BIBREF25 ) is a commonly used supervised classification algorithm that has shown good performance over a range of tasks. SVM can be thought of as a binary linear classifier where the goal is to maximize the size of the gap between the class-separating line and the points on either side of the line. This helps avoid over-fitting on the training data. SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below. SVMRank has previously been used in the task of document retrieval in ( BIBREF27 ) for a more traditional short query task and has been shown to be a top-performing system for ranking.
SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below. 
Answer: The answers are shown as follows:
* SVMRank
Question: What did Apple co-founder, Steve Jobs, die from?
Answer: Steve Jobs died in 2011 from pancreatic cancer.
Question: How does the scoring model work?
Evidence: To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. 
Answer: The answers are shown as follows:
*  the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history
Question: What made C. Shannon Bacon famous
Evidence: C. Shannon Bacon (born 1971/1972) is an American attorney, legal scholar, and jurist serving as the chief justice of the New Mexico Supreme Court.

Early life and education
Bacon was raised in North Valley, New Mexico. She earned a Bachelor of Arts from Creighton University in Omaha and a Juris Doctor from the Creighton University School of Law.

Career
After graduating from law school, Bacon returned to Albuquerque, where she was admitted to the State Bar of New Mexico in 1997. She was formerly partner at a pair of Albuquerque law firms and clerked under Judge A. Joseph Alarid at the New Mexico Court of Appeals. Bacon is an adjunct professor at the University of New Mexico School of Law, where she teaches courses on evidence and trial practice.

Bacon identifies as a lesbian. She is the first openly LGBT member of the New Mexico Supreme Court.[citation needed]
Answer: C. Shannon Bacon (born 1971/1972) is an American attorney, legal scholar, and jurist serving as the chief justice of the New Mexico Supreme Court. Bacon identifies as a lesbian. She is the first openly LGBT member of the New Mexico Supreme Court.

Bacon was raised in North Valley, New Mexico. She earned a Bachelor of Arts from Creighton University in Omaha and a Juris Doctor from the Creighton University School of Law.

After graduating from law school, Bacon returned to Albuquerque, where she was admitted to the State Bar of New Mexico in 1997. She was formerly partner at a pair of Albuquerque law firms and clerked under Judge A. Joseph Alarid at the New Mexico Court of Appeals. Bacon is an adjunct professor at the University of New Mexico School of Law, where she teaches courses on evidence and trial practice.
Question: How many keys are in a standard piano?
Answer: There are 88 keys in a standard piano. It has been the standard since Steinway built their pianos in the 1880s. There are 52 white keys and 36 black keys.
Question: Give me a bulleted list of ways to protect a house from hurricane damage
Answer: The following are ways to protect a house from hurricane damage:
1. Ensure all exterior windows are impact windows
2. Build the house elevated above ground level
3. Use sandbags to reduce water entering exterior doors
4. Cover windows with plywood or hurricane shutters
5. Don't build a house in hurricane and flood prone areas
Question: Tell me whether these are TV shows or movies
Answer: Game of Thrones, Titanic, Smallville, Ozark, Peaky Blinders
Question: Without quoting directly from the text give me a summary of what a restricted Boltzmann machine is?
Evidence: A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986, and rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000. RBMs have found applications in dimensionality reduction, classification, collaborative filtering, feature learning, topic modelling and even many body quantum mechanics. They can be trained in either supervised or unsupervised ways, depending on the task.

As their name implies, RBMs are a variant of Boltzmann machines, with the restriction that their neurons must form a bipartite graph: a pair of nodes from each of the two groups of units (commonly referred to as the "visible" and "hidden" units respectively) may have a symmetric connection between them; and there are no connections between nodes within a group. By contrast, "unrestricted" Boltzmann machines may have connections between hidden units. This restriction allows for more efficient training algorithms than are available for the general class of Boltzmann machines, in particular the gradient-based contrastive divergence algorithm.

Restricted Boltzmann machines can also be used in deep learning networks. In particular, deep belief networks can be formed by "stacking" RBMs and optionally fine-tuning the resulting deep network with gradient descent and backpropagation.
Answer: A restricted Boltzmann machine (RBM) is a category of an artificial neural network invented by Paul Smolensky and popularized by Geoffrey Hinton. RBM's have been useful in various applications within the field of machine learning and can be trained in supervised and unsupervised ways.

RBMS is a restricted variant of Boltzmann machines that constrains their neurons; a pair of nodes from each of the two groups of neurons, visible and hidden forming a bipartite graph. Connection can only be between two groups and not within groups (visible and hidden). This constrained formulation results in the ability to more efficiently train algorithms. 

RBM's can also be integrated into deep learning networks such as deep belief networks with options to fine tune those networks using gradient descent and backpropagation.
Question: How can I choose a favorite professional basketball team?
Answer: There is no wrong answer, but here are a few things you can consider in making that choice. You can determine which team is the closest to where you live and become a fan of the local team. Alternatively, you can determine which player or players are your favorites to watch and cheer for the teams that they play for. Another option is to cheer for a team that wins often, as it is more fun to root for a winning team.
Question: What is the nature of god, from a logical perspective?
Answer: Assuming god exists, one can think of the nature of god in two dimensions: is god active or passive, and is god benevolent or indifferent? Therefore, god is either active and benevolent, active and indifferent, passive and benevolent, or passive and indifferent. If god is active and benevolent, then all the bad things that happen would not happen, so this combination is unlikely. If god is active, god would likely be benevolent, so an indifferent active god seems a low probability. If god is benevolent, one would think they would be active, so a benevolent passive god seems a remote possibility. That leaves as the likeliest scenario a god that is passive and indifferent.
Question: Which of these forms of Bay Area transit are AC Transit bus lines and which are trains: Caltrain, BART, 51B, 36, 51A, 79, VTA Light Rail.
Answer: The trains are the Caltrain, BART, and VTA Light Rail, and the bus lines are the 51B, 36, 51A, and 79.
Question: What is the oldest film set in ancient Egypt?
Answer: The oldest film set in ancient Egypt is from 1899! Cléopâtre from director Georges Méliès.
Question: Given this paragraph about material, what is lithophane?
Evidence: A lithophane is a thin plaque of translucent material, normally porcelain, which has been moulded to varying thickness, such that when lit from behind the different thicknesses show as different shades, forming an image. Only when lit from behind does the image display properly. They were invented in the 19th century and became very popular, typically for lampshades, nightlights, or to be hung on windows. They could also be given stands, to be placed in front of a light source. The longest side of a lithophane is typically between 6 and 10 inches.
Answer: A lithophane is a thin plaque of translucent material, normally porcelain, which has been moulded to varying thickness, such that when lit from behind the different thicknesses show as different shades, forming an image.
Question: Out of these animals, categorize them as either 'small' or 'large: A horse, a mouse, an elephant, a snake, and a whale.
Answer: The horse, elephant, and whale would all be categorized as large animals in this example. A snake and a mouse are much smaller by comparison, so these would be considered small animals.
Question: Are the hyperparameters for other methods tuned using the validation set?
Evidence: Hyperparameter tuning for firefly: Since the online optimization algorithms we run have access to querying the simulator over the course of training, we can simply utilize the value of the latest proposed design as a way to perform early stopping and hyperparameter tuning. A naïve way to perform hyperparameter tuning for such evolutionary methods is to run the algorithm for multiple rounds with multiple hyperparameters, however this is compute and time intensive. Therefore, we adopted a dynamic hyperparameter tuning strategy. Our implementation of the firefly optimizer tunes hyperparameters by scoring a set of hyperparameters based on its best performance over a sliding window of T data points. This allows us to adapt to the best hyperparameters on the fly, within the course of optimization, effectively balancing the number of runs that need to be run in the simulator and hyperparameter tuning. This dynamic hyperparameter tuning strategy requires some initial coverage of the hyperparameter space before hyperparameter tuning begins, and therefore, this tuning begins only after 750 datapoints. After this initial phase, every T = 50 iterations, the parameters γ and β0 are updated via an evolutionary scoring strategy towards their best value.
Answer: For the online methods we study, we directly utilize the online performance evaluated against the simulator for cross-validation and choosing hyperparameters, as it is the groundtruth performance metric that we wish to optimize. We have now added the details of tuning the Firefly optimizer in Appendix B.1.1. (paragraph titled “Hyperparameter tuning for firefly”). Briefly, we adaptively adjust hyperparameters for firefly over the course of online optimization, which gives us a favorable balance between time and compute requirements for running firefly against the simulator and tuning hyperparameters. For COMs baseline in Appendix A, which is offline, we use the validation set + Kendall’s rank correlation metric as well for tuning.
Question: What are the primary causes of inflation?
Answer: Inflation is caused by imbalance of supply and demand.
Question: Where is Hesse's Demi Bastion
Answer: Hesse's Demi Bastion is a demi-bastion in the British Overseas Territory of Gibraltar. It is part of the Northern Defences of Gibraltar. The bastion forms a link in a chain of fortifications which ascend the lower north-west slopes of the Rock of Gibraltar, below the King's Lines Battery and Bombproof Battery. The Moorish Castle's Tower of Homage is at the top of the same incline.

History
It was originally the Baluado de S. Pedro (St. Peter's Bastion) during the Spanish period prior to the Capture of Gibraltar in 1704 under Prince George of Hesse-Darmstadt, after whom the bastion is named. The old Spanish bastion was rebuilt and renamed by the British in 1730. In 1762, during the Seven Years' War, it was armed with two 18-pdrs and four 4-pdrs to flank the ditch in front of the Landport Gate. By 1885 the bastion's guns were all 32-pdrs.
Question: What is your favorite teaching from Stoic philosophy?
Answer: The Discipline of Ascent trains you to pay attention to your thought process and cultivate a healthy mind.  You must pause and think about your responses to life, rather than allowing instincts, habits, and default behavior to run your life.  Preventing negative thinking is an active process, you will improve with more practice. Most importantly, learning to guide your thought processes will allow you to put more energy into what you control.