DataHammer/scimrc huggingface dataset:
Question: Why is the ice smooth during ice hockey games?
Answer: The ice hockey smoothing machine was invented in 1940 and is used to keep the ice rink smooth periodically between periods in the game.
Question: Give me ideas how I could become more productive.
Answer: Here are a few things you can try to become more productive:
1. Limit multitasking: While it may feel more productive, you can easily get distracted and spend more time on those tasks overall. Instead, try to complete one task to a higher standard in less time.
2. Set small goals: Large tasks can feel overwhelming which can in turn lead to procrastination. Try to break them down into smaller, manageable chunks and complete them one by one.
3. Take a break: It is scientifically proven that brain productivity increases after having a short break. Consider scheduling frequent short breaks of 10-15 minutes. Make sure you step away from your work environment. This will help your mind to have a rest and come up with new ideas after the break.
4. Complete the hardest task first: The most complex or hardest task can be intimidating to start with and we tend to avoid them therefore. Try to start with one of these tasks. It can energize and motivate you throughout the day, helping you accomplish a lot of smaller tasks.
5. Delegate: If you are part of a team, consider collaborating with team members more closely. Delegation is not about offloading work. Instead, it is about better task allocation ensuring each team member works on a task that is best suited to their skills and availability.
Question: How many episodes in season one of "Game of Thrones" did Brian Kirk direct?
Answer: Brian Kirk directed three episodes in the first season of Game of Thrones: Lord Snow, Cripples, Bastards, and Broken Things, and The Wolf and the Lion
Question: When was "I'm Just a Singer (In a Rock and Roll Band)" released?
Evidence: "I'm Just a Singer (In a Rock and Roll Band)" is a 1973 hit single by the English progressive rock band the Moody Blues, written by the band's bassist, John Lodge. It was first released in 1972 as the final track on the album Seventh Sojourn and was later released as a single in 1973, with "For My Lady" as its B-side. It was the second single released from Seventh Sojourn, with the first being "Isn't Life Strange", which was also written by Lodge.

The song reached number 12 on the Billboard Hot 100 in the US, becoming one of their highest-charting hits in that country, but fared less well in their native UK, where it managed number 36. It was also the final single released by the Moody Blues prior to their five-year hiatus, which was agreed upon so each of the band members could pursue their own solo careers. Their next single would not be until 1978, with "Steppin' in a Slide Zone".
Answer: "I'm Just a Singer (In a Rock and Roll Band)" was originally released in 1972 as the final track on the album Seventh Sojourn and was later released as a single in 1973.
Question: How do you make wine?
Answer: Wine is made from grapes.  You first need to harvest or collect the grapes and then take them off the stem.  Next you need to crush the grapes and get as much juice out of them as possible.  Then you let the grapes ferment for many weeks or months and during the fermentation process alcohol will be released.  Then you can siphon the wine in the barrels or let the wine age longer and then get it ready for bottling or corking / capping.
Question: How does one get accepted into a United States federal academy like West Point.
Answer: In order to get excepted into a United States Federal Academy, one has to receive a nomination either from a congressman, senator, or president. The process is long and requires patience, but can be rewarding.
Question: Which NFL franchise was ripped away from hits hometown and renamed the Baltimore Ravens?
Answer: Cleveland Browns
Question: What are the advantages and disadvantages of the SummaRuNNer classifier?
Evidence: Traditional extractive summarization methods are mostly based on explicit surface features BIBREF10 , relying on graph-based methods BIBREF11 , or on submodular maximization BIBREF12 . Benefiting from the success of neural sequence models in other NLP tasks, chenglapata propose a novel approach to extractive summarization based on neural networks and continuous sentence features, which outperforms traditional methods on the DailyMail dataset. In particular, they develop a general encoder-decoder architecture, where a CNN is used as sentence encoder, a uni-directional LSTM as document encoder, with another uni-directional LSTM as decoder. To decrease the number of parameters while maintaining the accuracy, summarunner present SummaRuNNer, a simple RNN-based sequence classifier without decoder, outperforming or matching the model of BIBREF2 . They take content, salience, novelty, and position of each sentence into consideration when deciding if a sentence should be included in the extractive summary. Yet, they do not capture any aspect of the topical structure, as we do in this paper. So their approach would arguably suffer when applied to long documents, likely containing multiple and diverse topics.
While SummaRuNNer was tested only on news, EMNLP2018 carry out a comprehensive set of experiments with deep learning models of extractive summarization across different domains, i.e. news, personal stories, meetings, and medical articles, as well as across different neural architectures, in order to better understand the general pros and cons of different design choices. They find that non auto-regressive sentence extraction performs as well or better than auto-regressive extraction in all domains, where by auto-regressive sentence extraction they mean using previous predictions to inform future predictions. Furthermore, they find that the Average Word Embedding sentence encoder works at least as well as encoders based on CNN and RNN. In light of these findings, our model is not auto-regressive and uses the Average Word Embedding encoder.
They take content, salience, novelty, and position of each sentence into consideration when deciding if a sentence should be included in the extractive summary. Yet, they do not capture any aspect of the topical structure, as we do in this paper. So their approach would arguably suffer when applied to long documents, likely containing multiple and diverse topics.
Answer: They take content, salience, novelty, and position of each sentence into consideration when deciding if a sentence should be included in the extractive summary. Yet, they do not capture any aspect of the topical structure, as they do in this paper. So their approach would arguably suffer when applied to long documents, likely containing multiple and diverse topics.
Question: What will be in focus for future work?
Evidence: However, we also showed difference in produced errors for each method and different impact at word-level depending of the approach or units. Thus, future work will focus on analysing the orthographic output of these systems in two ways: 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French and 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words.
However, we also showed difference in produced errors for each method and different impact at word-level depending of the approach or units. Thus, future work will focus on analysing the orthographic output of these systems in two ways: 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French and 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words.
Answer: The answers are shown as follows:
* 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French
* 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words
Question: What is the corpus used in the study?
Evidence: The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3. It was mined from http://zeno.org and covers a time period from the mid 16th century up to the first decades of the 20th century. It contains many important texts that can be considered as part of the literary canon, even though it is far from complete (e.g. it contains only half of Rilke’s work). We find that around 51k texts are annotated with the label ’verse’ (TGRID-V), not distinguishing between ’lyric verse’ and ’epic verse’. However, the average length of these texts is around 150 token, dismissing most epic verse tales. Also, the poems are distributed over 229 authors, where the average author contributed 240 poems (median 131 poems). A drawback of TGRID-V is the circumstance that it contains a noticeable amount of French, Dutch and Latin (over 400 texts). To constrain our dataset to German, we filter foreign language material with a stopword list, as training a dedicated language identification classifier is far beyond the scope of this work.
The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3.
Answer: The answers are shown as follows:
* TextGrid Repository
Question: Which is a species of fish? Herring or Hearing
Answer: Herring
Question: What is the contribution of the extrinsic analysis in this paper?
Evidence: We present a comprehensive comparison study of the existing corpora for selection-based question answering. Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora. Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning. Additionally, we create a silver-standard dataset for answer retrieval and triggering, which will be publicly available. In the future, we will explore different ways of improving the quality of our silver-standard datasets by fine-tuning the hyper-parameters.
Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning.
Answer: The extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning.
Question: Please give me a comma separated list of the countries that signed the North Atlantic Treaty in April 1949 given the text below
Evidence: The Treaty of Dunkirk was signed by France and the United Kingdom on 4 March 1947, during the aftermath of World War II and the start of the Cold War, as a Treaty of Alliance and Mutual Assistance in the event of possible attacks by Germany or the Soviet Union. In March 1948, this alliance was expanded in the Treaty of Brussels to include the Benelux countries, forming the Brussels Treaty Organization, commonly known as the Western Union. Talks for a wider military alliance, which could include North America, also began that month in the United States, where their foreign policy under the Truman Doctrine promoted international solidarity against actions they saw as communist aggression, such as the February 1948 coup d'état in Czechoslovakia. These talks resulted in the signature of the North Atlantic Treaty on 4 April 1949 by the member states of the Western Union plus the United States, Canada, Portugal, Italy, Norway, Denmark, and Iceland. Canadian diplomat Lester B. Pearson was a key author and drafter of the treaty.
Answer: France, United Kingdom, Belgium, Luxembourg , Netherlands, United States, Canada, Portugal, Italy, Norway, Denmark, Iceland
Question: what datasets did they use?
Evidence: Argus Dataset
AI2-8grade/CK12 Dataset
We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0). However, we released it to the public and still included it in the comparison as these qualities reflect many realistic datasets of unknown qualities, so we find relative performances of models on such datasets instructive.
MCTest Dataset
The Machine Comprehension Test BIBREF8 dataset has been introduced to provide a challenge for researchers to come up with models that approach human-level reading comprehension, and serve as a higher-level alternative to semantic parsing tasks that enforce a specific knowledge representation. The dataset consists of a set of 660 stories spanning multiple sentences, written in simple and clean language (but with less restricted vocabulary than e.g. the bAbI dataset BIBREF9 ). Each story is accompanied by four questions and each of these lists four possible answers; the questions are tagged as based on just one in-story sentence, or requiring multiple sentence inference. We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers.
Argus Dataset
AI2-8grade/CK12 Dataset
We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0). 
MCTest Dataset
We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers.
Answer: The answers are shown as follows:
* Argus Dataset
* AI2-8grade/CK12 Dataset
* MCTest Dataset
Question: What are some common mushrooms used in cooking?
Answer: Common mushrooms used in cooking include white button, cremini, portobello, shiitake, oyster, porcini, morel, and enoki.
Question: What should I eat when I visit South India ?
Answer: South India is known for vegetarian delicacies. Here is a list of some foods that you should try : 

1. Masala Dosa
2. Filter Coffee
3. Idly
4. Vada
5. Pongal
Question: Why the discriminator can learn to provide more proper rewards as the training proceeds?
Evidence: The discriminator INLINEFORM0 aims to distinguish real data and generated data like other GANs. Inspired by Least-Square GAN BIBREF25 , we devise the loss function as follows: DISPLAYFORM0 
This loss function forces the discriminator to assign higher rewards to real data than to generated data, so the discriminator can learn to provide more proper rewards as the training proceeds.
This loss function forces the discriminator to assign higher rewards to real data than to generated data, so the discriminator can learn to provide more proper rewards as the training proceeds.
Answer: Because the loss function forces the discriminator to assign higher rewards to real data than to generated data.
Question: Who is responsible for deportation of Ukrainian children to Russia?
Evidence: On 17 March 2023, the International Criminal Court (ICC) issued arrest warrants for Vladimir Putin, the President of Russia, and Maria Lvova-Belova, Russian Commissioner for Children's Rights, alleging responsibility for the unlawful deportation and transfer of children during the Russian invasion of Ukraine. The warrant against Putin is the first against the leader of a permanent member of the United Nations Security Council.

The 123 member states of the ICC are obliged to detain and transfer Putin and Lvova-Belova if either sets foot on their territory.
Answer: President of Russian Federation Vladimir Putin and Maria Lvova-Belova, Russian Commissioner for Children's Rights are directly responsible the unlawful deportation of Ukrainian children to Russia. This crime against humanity along with many other atrocities (like Bucha massacre) has been committed by Russians during the Russian invasion of Ukraine that started on February 24, 2022.
Question: Are experiments performed with any other pair of languages, how did proposed method perform compared to other models?
Evidence: We evaluate the proposed transfer learning techniques in two non-English language pairs of WMT 2019 news translation tasks: French$\rightarrow $German and German$\rightarrow $Czech.
We evaluate the proposed transfer learning techniques in two non-English language pairs of WMT 2019 news translation tasks: French$\rightarrow $German and German$\rightarrow $Czech.
Answer: No.
Question: What problem do the authors encounter when testing systems?
Evidence: Reasoning is part of our every day routine: we hear Natural Language (NL) sentences, we participate in dialogues, we read books or legal documents. Successfully understanding, participating or communicating with others in these situations presupposes some form of reasoning: about individual sentences, whole paragraphs of legal documents, small or bigger pieces of dialogue and so on. The human reasoning performed in these different situations cannot be explained by a single rigid system of reasoning, plainly because reasoning is performed in different ways in each one of them. Consider the following example:
Three representatives are needed.
If a human reasoner with expert knowledge was to interpret the above utterance in a legal context, s/he will most probably judge that a situation where more than three references are provided could be compatible with the semantics of the utterance. To the contrary, if the same reasoner was to interpret the above as part of a casual, everyday conversation, then three would most likely be interpreted as exactly three, making the same situation incompatible with the utterance. In this paper, we want to focus on precise inference, inference which is either performed by experts or normal people after taking some time to consider the inferences that follow or not from a set of premises. The problem one encounters in testing systems of this type of reasoning is the fact that no large scale datasets of this sort of inference exist. The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
The problem one encounters in testing systems of this type of reasoning is the fact that no large scale datasets of this sort of inference exist.
Answer: There is no large scale datasets of this sort of inference.
Question: What were five of the best english language situation comedies from the 1990s?
Answer: 1. Seinfeld
2. Friends
3. Cheers
4. Frazier
5. The Simpsons
Question: Who was the first women to do the spacewalk?
Answer: Svetlana Savitskaya in the year 1984
Question: What is Zenthoefer Furs
Evidence: Zenthoefer Furs were an amateur U.S. soccer club which played in St. Louis, Missouri during the late 1940s and early 1950s.

They had played as a junior squad named Schumachers in the 1947-48 season where they fell short of national honors when they lost in the final round of the National Junior Cup to Lighthouse Boys of Philadelphia. The Zenthoefers turned senior the next season. Under the guidance of manager George E. McGann they made their way to the 1949 National Amateur Cup final losing to SC Elizabeth by a 6-1 score. They also lost to the Chicago Polish-Americans 5-1 in the 1949 National Challenge Cup quarterfinals. In 1951, they won the St. Louis Major League by ten points over St. Louis Simpkins-Ford. In 1952, they entered the American Soccer League of St. Louis.
Answer: Zenthoefer Furs were an amateur U.S. soccer club which played in St. Louis, Missouri during the late 1940s and early 1950s.
Question: Who do you think is the most powerful Mandalorian in Star Wars history?
Answer: It would have be Te Ani'la Mand'alor, or also called Mandalore the Ultimate.
Question: What is Stiftung Lesen ?
Evidence: Stiftung Lesen (Reading Foundation) is a non-profit organization based in Mainz, Germany under the patronage of Joachim Gauck. Stiftung Lesen acts as a stakeholder for reading promotion on a national and international level. It contributes to reading promotion and reading education through programmes, scientific research, and political recommendations. To create a basis for the development of reading skills, and to enhance the overall level of literacy, Stiftung Lesen established adequate and accessible programmes for every member of society – regardless of financial, cultural or social background. In 2006 Stiftung Lesen established its own Institute for Research on Reading and Media in order to align its projects with the latest scientific findings. The institute is tasked with conducting scientific research on the use of media, reading and reading socialisation, the supervision and evaluation of the foundation’s projects and the organisation of conferences on topics concerning literacy and media research (including reading in the digital age), publishing some of its reports for the German government.
Answer: Stiftung Lesen or Reading Foundation is a non-profit organization based in Germany that acts as a stakeholder for reading promotion on a national and international level
Question: List 10 highest-grossing actors in the world.
Evidence: The following are non-definitive lists of the actors with the highest career-film grosses. There are box-office gross revenue lists, which are not adjusted for inflation, as well as lists for the number of box-office ticket sales. Because of the long-term effects of inflation on movie theater ticket prices, a list unadjusted for inflation gives far more weight to later actors. Therefore, the gross revenue lists are largely meaningless for comparing acting careers widely separated in time, as many actors from earlier eras will never appear on a modern unadjusted list, despite their films' achieving higher commercial success when adjusted for price increases.
Answer: The following are non-definitive lists of the actors with the highest career-film grosses. There are box-office gross revenue lists, which are not adjusted for inflation, as well as lists for the number of box-office ticket sales. Because of the long-term effects of inflation on movie theater ticket prices, a list unadjusted for inflation gives far more weight to later actors. Therefore, the gross revenue lists are largely meaningless for comparing acting careers widely separated in time, as many actors from earlier eras will never appear on a modern unadjusted list, despite their films' achieving higher commercial success when adjusted for price increases.

Lead roles
This list only includes leading roles and lead ensemble roles, including voice acting. The list is updated as of 12 January 2023.

Rank	Actor	Worldwide total	Top-grossing film	Films	Average
1	Scarlett Johansson	$14,519,031,650	Avengers: Endgame ($2,794,731,755)	33	$439,970,656
2	Robert Downey Jr.	$14,393,065,668	Avengers: Endgame ($2,794,731,755)	43	$334,722,457
3	Samuel L. Jackson	$14,376,505,937	Avengers: Endgame ($2,794,731,755)	64	$224,632,905
4	Zoe Saldaña	$12,805,874,340	Avatar ($2,899,384,102)	30	$426,862,478
5	Chris Hemsworth	$11,958,567,765	Avengers: Endgame ($2,794,731,755)	25	$478,342,711
6	Chris Pratt	$11,742,796,476	Avengers: Endgame ($2,794,731,755)	23	$510,556,369
7	Tom Cruise	$11,547,725,996	Top Gun: Maverick ($1,487,575,965)	43	$268,551,767
8	Chris Evans	$11,277,890,989	Avengers: Endgame ($2,794,731,755)	29	$388,892,793
9	Tom Hanks	$10,704,310,520	Toy Story 4 ($1,073,064,540)	55	$194,623,828
10	Vin Diesel	$10,537,096,327	Avengers: Infinity War ($2,048,359,754)	27	$390,262,827