DataHammer/scimrc huggingface dataset:
Question: What unimodal detection models were used?
Evidence: We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word.
We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations.
Answer: The answers are shown as follows:
*  single layer LSTM with a 150-dimensional hidden state for hate / not hate classification
Question: What happens when you meditate?
Answer: The art of meditation has a long prolonged positive effect on human behaviour and thinking. The mind is equated to a jumping monkey that doesn't remain quiet. The restlessness that it builds can lead to health issues and sometimes uncontrollable behaviour.  In general, meditation is to calm the mind and becoming the spectator or observer of the thoughts that rise in your consciousness. 

Now, let's break it down further ...

It first starts with understanding of the 'self' which can be equated to mind in loose terms. Experiences happen to you in your waking state and the mind first starts labelling things. Negative experiences are profoundly deep rooted then positive ones. All these experiences gradually defines how you react to situations that shapes your individual personality. Do you know that there is a spectator within your mind more like a dual personality? it is the subject - object thing. The spectator within you is the observer of things and is often neutral.

Idea of meditation is to remain a spectator for long periods of time and let fewer and fewer thoughts arise into your conscious state. At the end it leaves you a with a sense of accomplishment because as a spectator you're waiting and this deliberate attempt makes the thought (referred as devil) disappear.
Question: What is the difference between the Masters and the U.S. Open in golf?
Answer: The Masters is one of the four majors that is played every year in golf and is the only major that is played at the same course each year.  Every year the Masters is played in April at Augusta National golf course and it is the first major of the year.  The U.S. Open is another one of the golf majors and is played at a different course every year, although some courses are repeated once every 8-10 years.  The tournament is normally played in the summer and gives the opportunity for amateur golfers all over the U.S. to qualify and play with the professionals.
Question: How did Legos get their name?
Answer: Legos was made by a Danish company and the danish phrase was 'leg godt' which translates as 'play well'
Question: how are multiple answers from multiple reformulated questions aggregated?
Evidence: During training, we have access to the reward for the answer returned for each reformulation $q_i$ . However, at test time we must predict the best answer $a^*$ . The selection model selects the best answer from the set $\lbrace a_i\rbrace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants. We use pre-trained embeddings for the tokens of query, rewrite, and answer. For each, we add a 1-dimensional CNN followed by max-pooling. The three resulting vectors are then concatenated and passed through a feed-forward network which produces the output.
Unlike the reformulation policy, we train the answer with either beam search or sampling. We can produce many rewrites of a single question from our reformulation system. We issue each rewrite to the QA environment, yielding a set of (query, rewrite, answer) tuples from which we need to pick the best instance. We train another neural network to pick the best answer from the candidates. We frame the task as binary classification, distinguishing between above and below average performance. In training, we compute the F1 score of the answer for every instance. If the rewrite produces an answer with an F1 score greater than the average score of the other rewrites the instance is assigned a positive label. We ignore questions where all rewrites yield equally good/bad answers. We evaluated FFNNs, LSTMs, and CNNs and found that the performance of all systems was comparable. We choose a CNN which offers good computational efficiency and accuracy (cf. "Training" ).
 The selection model selects the best answer from the set $\lbrace a_i\rbrace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants.
We train another neural network to pick the best answer from the candidates. We frame the task as binary classification, distinguishing between above and below average performance. In training, we compute the F1 score of the answer for every instance. If the rewrite produces an answer with an F1 score greater than the average score of the other rewrites the instance is assigned a positive label.
Answer: The answers are shown as follows:
* The selection model selects the best answer from the set $\lbrace a_i\rbrace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants.
Question: What is Puha, New Zealand
Answer: Puha or Puhatikotiko is a locality between the Waipaoa River and the Waikohu River in the Gisborne District of New Zealand's North Island. It is located close to the confluence of the two rivers on State Highway 2 close to Te Karaka, inland from the city of Gisborne.

A notable resident was Te Kani Te Ua.
Question: What does it take to become an airplane pilot in the USA?
Answer: To become a pilot (airplane-single-engine-land) you must complete a number of milestones and training objectives as outlined in Part 61 or Part 141 in the Federal Aviation Regulations (FAR). This includes, but is not limited to: flying with a certified instructor and performing several different maneuvers demonstrating flight proficiency (taxing, takeoff, cruise, stall recovery, turns, emergency procedures, and landing), accruing the minimum hours of applicable aeronautical experience as outlined in Part 61.109, taking a written examination issued by the FAA, as well as performing an Oral and Practical examination with a Designated Pilot Examiner (DPE). Upon successful completion of each of these areas, in addition to meeting all of the eligibility requires as outlined in Part 61.103, a student pilot can receive their private pilot certificate affording them to fly passengers (not for hire), day or night, in visual meteorological conditions in the USA.
Question: When did Arsenal first reach the first division? How many league titles did they win between 1989 and 2005 and how many FA cups? How many trophies in total for that period?
Evidence: Arsenal was the first club from the South of England to join the Football League in 1893, and they reached the First Division in 1904. Relegated only once, in 1913, they continue the longest streak in the top division, and have won the second-most top-flight matches in English football history. In the 1930s, Arsenal won five League Championships and two FA Cups, and another FA Cup and two Championships after the war. In 1970–71, they won their first League and FA Cup Double. Between 1989 and 2005, they won five League titles and five FA Cups, including two more Doubles. They completed the 20th century with the highest average league position. Between 1998 and 2017, Arsenal qualified for the UEFA Champions League for nineteen consecutive seasons.
Answer: Arsenal reached the first division in 1904. Between 1989 and 2005, Arsenal won 5 league titles and 5 FA cups. That’s a total of 10 trophies.
Question: What is the effect of the personalized approach to understanding privacy policies in this paper?
Evidence: Privacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data. As legal documents, they function using the principle of notice and choice BIBREF0, where companies post their policies, and theoretically, users read the policies and decide to use a company's products or services only if they find the conditions outlined in its privacy policy acceptable. Many legal jurisdictions around the world accept this framework, including the United States and the European Union BIBREF1, BIBREF2. However, the legitimacy of this framework depends upon users actually reading and understanding privacy policies to determine whether company practices are acceptable to them BIBREF3. In practice this is seldom the case BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10. This is further complicated by the highly individual and nuanced compromises that users are willing to make with their data BIBREF11, discouraging a `one-size-fits-all' approach to notice of data practices in privacy documents.
With devices constantly monitoring our environment, including our personal space and our bodies, lack of awareness of how our data is being used easily leads to problematic situations where users are outraged by information misuse, but companies insist that users have consented. The discovery of increasingly egregious uses of data by companies, such as the scandals involving Facebook and Cambridge Analytica BIBREF12, have further brought public attention to the privacy concerns of the internet and ubiquitous computing. This makes privacy a well-motivated application domain for NLP researchers, where advances in enabling users to quickly identify the privacy issues most salient to them can potentially have large real-world impact.
[1]https://play.google.com/store/apps/details?id=com.gotokeep.keep.intl [2]https://play.google.com/store/apps/details?id=com.viber.voip [3]A question might not have any supporting evidence for an answer within the privacy policy.
Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations. The goal of this effort is to kickstart the development of question-answering methods for this domain, to address the (unrealistic) expectation that a large population should be reading many policies per day. In doing so, we identify several understudied challenges to our ability to answer these questions, with broad implications for systems seeking to serve users' information-seeking intent. By releasing this resource, we hope to provide an impetus to develop systems capable of language understanding in this increasingly important domain.
We adopt a personalized approach to understanding privacy policies, that allows users to query a document and selectively explore content salient to them.
Answer: The personalized approach allows users to query a document and selectively explore content salient to them.
Question: By how much of MGNC-CNN out perform the baselines?
Evidence: FLOAT SELECTED: Table 1: Results mean (min, max) achieved with each method. w2v:word2vec. Glv:GloVe. Syn: Syntactic embedding. Note that we experiment with using two and three sets of embeddings jointly, e.g., w2v+Syn+Glv indicates that we use all three of these.
We repeated each experiment 10 times and report the mean and ranges across these. This replication is important because training is stochastic and thus introduces variance in performance BIBREF4 . Results are shown in Table TABREF2 , and the corresponding best norm constraint value is shown in Table TABREF2 . We also show results on Subj, SST-1 and SST-2 achieved by the more complex model of BIBREF11 for comparison; this represents the state-of-the-art on the three datasets other than TREC.
FLOAT SELECTED: Table 1: Results mean (min, max) achieved with each method. w2v:word2vec. Glv:GloVe. Syn: Syntactic embedding. Note that we experiment with using two and three sets of embeddings jointly, e.g., w2v+Syn+Glv indicates that we use all three of these.
We repeated each experiment 10 times and report the mean and ranges across these. This replication is important because training is stochastic and thus introduces variance in performance BIBREF4 . Results are shown in Table TABREF2 , and the corresponding best norm constraint value is shown in Table TABREF2 . 
Answer: In terms of Subj the Average MGNC-CNN is better than the average score of baselines by 0.5.  Similarly, Scores of SST-1, SST-2, and TREC where MGNC-CNN has similar improvements. 
In case of Irony the difference is about 2.0.
Question: How do you make pizza?
Answer: Pizza comes in many styles. I make Neapolitan pizza, which generally requires a high hydration dough and a very high oven temperatures. The dough is made with four cups of flour, three tablespoons olive oil, a cup and a half of water, one package of yeast, a tablespoon of sugar, and a teaspoon and a half of salt. I typically activate the yeast in lukewarm water and sugar first, then mix all the ingredients in a stand mixer with a dough attachment. I'll add additional water until the dough is very wet, let it rise once in the mixing bowl, then turn the dough out on a surface coated generously with flour. Then I quarter the dough, fold a few times, and let rise again. 

Forming the pies is the tricky part. I've found pushing the dough into a rough circle with my fingers as a first step works well, but he dough will tend to snap back. I'll pick up the small dough circle and stretch it out on top of my fists to start to break the gluten and form a larger pie with a thinner base. I use an Ooni oven pre-heated on high, but turn the heat down when I'm ready to put the pizza in. The peel needs to be generously coated with flour to get a good launch into the oven. After a few seconds, the dough on the bottom will cook enough that manipulating the pizza becomes much easier. I keep the toppings simple, mozzarella (low moisture), tomato sauce, basil. 

Enjoy!
Question: Could you describe who is BNP Paribas (BNPP)?
Evidence: BNP Paribas is a French international banking group, founded in 2000 from the merger between Banque Nationale de Paris (BNP, "National Bank of Paris") and Paribas, formerly known as the Banque de Paris et des Pays-Bas. The full name of the group's parent entity is BNP Paribas S.A.

With 190,000 employees as of February 2021, the bank is organized into three major business areas: Commercial, Personal Banking & Services (CPBS), Investment & Protection Services (IPS) and Corporate & Institutional Banking (CIB). The group is listed on the first market of Euronext Paris and a component of the Euro Stoxx 50 stock market index, while it also included in the French CAC 40 index.

BNP Paribas is the second largest banking group in Europe, after HSBC, and ninth largest banking group in the world (by assets, December 2021). It became one of the five largest banks in the world following the 2008 financial crisis. Despite some legal difficulties in 2014, including being fined the largest ever sum as reparation for violating U.S. sanctions, it remains one of the ten largest banks worldwide. It is considered a systemically important bank by the Financial Stability Board.
Answer: BNP Paribas alias BNPP is a French Banking group. In 2022 they made 50.4 billion euros of revenue and 10.2 billion euros of net income.
Question: How many people attended the Summer Breeze Open Air?
Evidence: Summer Breeze Open Air is an annual German heavy metal music festival. It was first held in 1997. The festival had been held in Abtsgmünd until 2006 when it was moved to its new location of Dinkelsbühl, Bavaria. The festival draws around 40,000 attendees annually.
Answer: The German heavy metal music festival Summer Breeze Open Air drew 40,000 attendees annually.
Question: What technique do they want to use to control for hyperparameter effects?
Evidence: The scientific process by which the deep learning research community operates is guided by empirical studies that evaluate the relative quality of models. Complicating matters, the measured performance of a model depends not only on its architecture (and data), but it can strongly depend on hyperparameter values that affect learning, regularisation, and capacity. This hyperparameter dependence is an often inadequately controlled source of variation in experiments, which creates a risk that empirically unsound claims will be reported.
In this paper, we use a black-box hyperparameter optimisation technique to control for hyperparameter effects while comparing the relative performance of language modelling architectures based on LSTMs, Recurrent Highway Networks BIBREF0 and NAS BIBREF1 . We specify flexible, parameterised model families with the ability to adjust embedding and recurrent cell sizes for a given parameter budget and with fine grain control over regularisation and learning hyperparameters.
Once hyperparameters have been properly controlled for, we find that LSTMs outperform the more recent models, contra the published claims. Our result is therefore a demonstration that replication failures can happen due to poorly controlled hyperparameter variation, and this paper joins other recent papers in warning of the under-acknowledged existence of replication failure in deep learning BIBREF2 , BIBREF3 . However, we do show that careful controls are possible, albeit at considerable computational cost.
Several remarks can be made in light of these results. First, as (conditional) language models serve as the central building block of many tasks, including machine translation, there is little reason to expect that the problem of unreliable evaluation is unique to the tasks discussed here. However, in machine translation, carefully controlling for hyperparameter effects would be substantially more expensive because standard datasets are much larger. Second, the research community should strive for more consensus about appropriate experimental methodology that balances costs of careful experimentation with the risks associated with false claims. Finally, more attention should be paid to hyperparameter sensitivity. Models that introduce many new hyperparameters or which perform well only in narrow ranges of hyperparameter settings should be identified as such as part of standard publication practice.
In this paper, we use a black-box hyperparameter optimisation technique to control for hyperparameter effects while comparing the relative performance of language modelling architectures based on LSTMs, Recurrent Highway Networks and NAS.
Answer: A black-box hyperparameter optimisation technique.
Question: How often are the Olympics held?
Answer: The Olympics occur every 4 years.
Question: What are the top 10 television dramas of all time
Answer: 1. The Wire
2. The Sopranos
3. Breaking Bad
4. Friday Night Lights
5. NYPD Blue
6. Deadwood
7. Succession
8. The OC
9. Mad Men
10. 90210
Question: Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}
Evidence: The 56.5 acres (22.9 ha) park is an example of a Depression Era public works project that was constructed between 1936 and 1938 by the Works Progress Administration (WPA). James Depew Edmundson, an Iowa attorney who grew up in Oskaloosa, gave a $20,000 bequest to the city to establish a park. City engineer Don Russell saw the New Deal program as an opportunity to add value to the project. The park is the work of landscape architect Ray F. Wyrick. Its structures were designed in the rustic style that was promoted by the National Park Service between 1916 and 1942 for parks supported by all levels of government.
Answer: • James Depew Edmundson
• Don Russell
• Ray F. Wyrick
Question: Was the creator of Beavis and Butthead and King of the Hill the son of an archaeologist?
Answer: Yes, Mike Judge was the son of Jim Judge, an archaeologist in the American Southwest who helped record Chaco Canyon and was an accomplished professor.
Question: Given the reference text below, what is Safer Internet Day?
Evidence: Safer Internet Day is celebrated worldwide to raise awareness about online safety. Many people take safety on the internet for granted and for that reason it is important to educate people about possible data breaches. Childnet, as part of the UK Safer Internet Centre, coordinate Safer Internet Day in the UK. Safer Internet Day 2015 saw over  800 organizations in the UK taking part to help promote the safe, responsible and positive use of digital technology for children and young people. For the day the UK Safer Internet Centre launched a new study Friendship in a Digital Age and launched the #Up2Us film, which was made by 150 young people to inspire others to do something kind online.
Answer: Safer Internet Day raises awareness about internet safety for children and young people.
Question: Who won man booker International prize 2018?
Answer: Polish author Olga Tokarczuk
Question: how to use toothpaste creatively
Answer: Use it in insect bites
Use it to make art
Use it as bleaching
Use it for cleaning stains off Tiles
Use it for cleaning pores
Question: Give me a list of words that are associated with Easter.
Answer: Words associated with Easter may include chocolate, rabbits, eggs, basket, Spring and and rebirth.
Question: What are rare-earth materials
Evidence: A rare-earth mineral contains one or more rare-earth elements as major metal constituents. Rare-earth minerals are usually found in association with alkaline to peralkaline igneous complexes, in pegmatites associated with alkaline magmas and in or associated with carbonatite intrusives. Perovskite mineral phases are common hosts to rare-earth elements within the alkaline complexes. Mantle-derived carbonate melts are also carriers of the rare earths. Hydrothermal deposits associated with alkaline magmatism contain a variety of rare-earth minerals.
Answer: Although originally thought to be rare, many of the minerals are actually common in the Earth's crust. However, due to the difficulties in extracting the metal from the ore, rare is a fitting term.
Question: How can I quickly treat a headache?
Answer: Occasional headache is something that is more annoying and causes discomfort than concern. To quickly relieve a headache, make sure you are in a well ventilated space, drink a couple glasses of cold water, take some caffeine like coffee or cola, stare at something green like a plant for ten minutes, and close your eyes or lie down for about ten minutes. You can also twist your head left to right, right to left, then stare up, until you get a sigh or a yawn.
Question: what are their evaluation metrics?
Evidence: Evaluating the final model, we set as a baseline the prediction of the majority class, i.e., the fake news class. This baseline has an F1 of 41.59% and accuracy of 71.22%. The performance of the built models can be seen in Table TABREF19 . Another stable baseline, apart from just taking the majority class, is the TF.IDF bag-of-words approach, which sets a high bar for the general model score. We then observe how much the attention mechanism embeddings improve the score (AttNN). Finally, we add the hand-crafted features (Feats), which further improve the performance. From the results, we can conclude that both the attention-based task-specific embeddings and the manual features are important for the task of finding fake news.
 This baseline has an F1 of 41.59% and accuracy of 71.22%.
Answer: The answers are shown as follows:
* F1
* accuracy
Question: Stan laurel, Mickey Rooney, Lana Turner what in common
Answer: 8 marriages