DataHammer/scimrc huggingface dataset:
Question: Is the explanation from saliency map correct?
Evidence: We showed that in some cases the saliency maps are truthful to the network's computation, meaning that they do highlight the input features that the network focused on. But we also showed that in some cases the saliency maps seem to not capture the important input features. This brought us to discuss the fact that these attributions are not sufficient by themselves, and that we need to define the counter-factual case and test it to measure how truthful the saliency maps are.
The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates. The saliency maps on Figure 3 correspond to the summary from Figure 1 , and we don't see the word “video" highlighted in the input text, which seems to be important for the output.
But we also showed that in some cases the saliency maps seem to not capture the important input features. 
The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates
Answer: No.
Question: What is the origin of shortbread cookies?
Evidence: A cookie (American English), or a biscuit (British English), is a baked or cooked snack or dessert that is typically small, flat and sweet. It usually contains flour, sugar, egg, and some type of oil, fat, or butter. It may include other ingredients such as raisins, oats, chocolate chips, nuts, etc.

Most English-speaking countries call crunchy cookies "biscuits", except for the United States and Canada, where "biscuit" refers to a type of quick bread. Chewier biscuits are sometimes called "cookies" even in the United Kingdom. Some cookies may also be named by their shape, such as date squares or bars.

Biscuit or cookie variants include sandwich biscuits, such as custard creams, Jammie Dodgers, Bourbons and Oreos, with marshmallow or jam filling and sometimes dipped in chocolate or another sweet coating. Cookies are often served with beverages such as milk, coffee or tea and sometimes dunked, an approach which releases more flavour from confections by dissolving the sugars, while also softening their texture. Factory-made cookies are sold in grocery stores, convenience stores and vending machines. Fresh-baked cookies are sold at bakeries and coffeehouses.
Answer: A cookie (American English), or a biscuit (British English), is a baked or cooked snack or dessert that is typically small, flat and sweet. It usually contains flour, sugar, egg, and some type of oil, fat, or butter. It may include other ingredients such as raisins, oats, chocolate chips, nuts, etc.

Most English-speaking countries call crunchy cookies "biscuits", except for the United States and Canada, where "biscuit" refers to a type of quick bread. Chewier biscuits are sometimes called "cookies" even in the United Kingdom. Some cookies may also be named by their shape, such as date squares or bars.

Biscuit or cookie variants include sandwich biscuits, such as custard creams, Jammie Dodgers, Bourbons and Oreos, with marshmallow or jam filling and sometimes dipped in chocolate or another sweet coating. Cookies are often served with beverages such as milk, coffee or tea and sometimes dunked, an approach which releases more flavour from confections by dissolving the sugars, while also softening their texture. Factory-made cookies are sold in grocery stores, convenience stores and vending machines. Fresh-baked cookies are sold at bakeries and coffeehouses.
Question: What are the four major tournaments for the Professional Golfers Association?
Answer: The four major tournaments for the Professional Golfers' Association (PGA) Tour are the Masters, the PGA Championship, the US Open, and the British Open.  The Masters is the first tournament of the calendar year and is held in April in Augusta, Georgia, USA.  The PGA Championship comes second and is held in May followed by the US Open in June and the British Open in July.  The PGA Championship, US Open, and British Open are held at different venues each year whereas the Masters tournament is always held at Augusta National Golf Club.  The Masters, US Open, and British Open all welcome qualifying amateurs and professionals alike whereas the PGA Championship is limited to professionals only.
Question: What would you do to improve the rules of Tennis, to make it a better TV viewing experience?
Answer: I would recommend the following things be changed in the rules of tennis to make it more interesting. (1) Reduce the length of a 'set' to be 4 games long, and the first person to 4 wins the set, with no requirement to lead by 2 clear games over their opponent. (2) I would only allow one serve - instead of two - per player when starting each point. (3) I would stop players from wasting time between points by limiting their towel breaks to 23 seconds long. (4) If a player's service hits the net and goes over, they win the point (this means no replaying of points due to hitting the netcord and flopping over the net). (5) I would declare a rally null and void if it goes over 20 shots; it would count for nothing and both players would have wasted their efforts without any positive outcome. (6) I would not allow players to take a break, between games, until 4 games had been consecutively played, regardless of their physical capabilities. (7) The maximum number of tennis sets that can be played in major Tennis tournaments be limited to three. (8) If a tennis match exceeds three hours in duration it gets called a "tie" - no winner, and rewards are halved.
Question: What other tasks do they test their approaches on?
Evidence: Sentiments of words differ from one corpus to another. Inducing general sentiment lexicons for languages and using them cannot, in general, produce meaningful results for different domains. In this paper, we combine contextual and supervised information with the general semantic representations of words occurring in the dictionary. Contexts of words help us capture the domain-specific information and supervised scores of words are indicative of the polarities of those words. When we combine supervised features of words with the features extracted from their dictionary definitions, we observe an increase in the success rates. We try out the combinations of contextual, supervised, and dictionary-based approaches, and generate original vectors. We also combine the word2vec approach with hand-crafted features. We induce domain-specific sentimental vectors for two corpora, which are the movie domain and the Twitter datasets in Turkish. When we thereafter generate document vectors and employ the support vector machines method utilising those vectors, our approaches perform better than the baseline studies for Turkish with a significant margin. We evaluated our models on two English corpora as well and these also outperformed the word2vec approach. It shows that our approaches are cross-lingual and cross-domain.
We evaluated our models on two English corpora as well and these also outperformed the word2vec approach.
Answer: They tested their models on two other English corpora as well.
Question: Given a reference text about the American Society for Clinical Investigation (ASCI), what research does the society support?
Evidence: The American Society for Clinical Investigation (ASCI), established in 1908, is one of the oldest and most respected medical honor societies in the United States.

Organization and purpose
The ASCI is an honorary society to which more than 2,800 physician-scientists from all medical specialties belong. Prospective members are proposed by standing members of the society and elected to ASCI on the basis of an outstanding record of scholarly achievement in biomedical research. The ASCI includes physician-scientists who are active clinically, in basic research, or in teaching. Many of its senior members are widely recognized leaders in academic medicine. As of 2015 the membership of ASCI has included 417 members of the National Academy of Medicine, 191 members of the National Academies of Sciences, Engineering, and Medicine, 40 Lasker Award winners, and 19 Nobel laureates.

The ASCI supports the research into basic mechanisms and/or treatment of human diseases, and to the education of future generations of physician-scientists. The ASCI considers the nominations of several hundred physician-scientists from the United States and abroad each year and elects up to 80 new members each year for their significant research accomplishments relatively early in their careers.

The ASCI is a member of the Federation of American Societies for Experimental Biology (FASEB), which includes numerous biomedical research societies. The ASCI publishes the peer-reviewed biomedical research journals Journal of Clinical Investigation and JCI Insight.

A collection of the society's papers is held at the National Library of Medicine.
Answer: The ASCI supports research into basic mechanisms and treatment of human diseases and to the education of the future generations of physician-scientists.
Question: What is HDFS?
Answer: HDFS (Hadoop Distributed File System) is a distributed file system that handles large data sets running on commodity hardware. It is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. HDFS is one of the major components of Apache Hadoop, the others being MapReduce and YARN.
Question: What is data visualisation in the context of business intelligence?
Answer: Data visualisation in any context is the art and science of organising data in such a way that makes it easily consumable by a human audience. Specifically, in the context of business intelligence, you are typically presenting facts and insights about a business to stakeholders in order for them to take action and make better decisions to run that business. 

Good data visualisation combines user experience design with an understanding of how people interpret data to provide something intuitive and compelling for the intended audience to use. 

Here are some examples of design and interpretation choices you might make:

In terms of design, many cultures write on a page from top to bottom, left to right. This means that the first place on any visualisation that a person from any of these cultures will look is top left. Consequently, the content you put in the top left space of your dashboard should be a critically important part of the story you are trying to tell. In a culture that writes right to left you might put higher-value content on the right-hand side of the visualisation. 

In terms of in terms interpretation you want to key into things that people are naturally good at interpreting. For example, if you are presenting sales by product category and you have 10 product categories you could choose to leverage a bar chart where the length of the bar represents sales or a pie chart where the angle of the slice represents sales. A bar chart would be a better choice because people find it easy to compare the length of a bar. We cannot distinguish between the sizes of a slice with anywhere near the same level of accuracy. Choosing a pie chart would leave your users spending more time and effort interpreting your data visualisation than if you had chosen a bar chart to present the information to them.
Question: What are the state of the art measures?
Evidence: As Garimella et al. BIBREF23 have made their code public , we reproduced their best method Randomwalk on our datasets and measured the AUC ROC, obtaining a score of 0.935. An interesting finding was that their method had a poor performance over their own datasets. This was due to the fact (already explained in Section SECREF4) that it was not possible to retrieve the complete discussions, moreover, in no case could we restore more than 50% of the tweets. So we decided to remove these discussions and measure again the AUC ROC of this method, obtaining a 0.99 value. Our hypothesis is that the performance of that method was seriously hurt by the incompleteness of the data. We also tested our method on these datasets, obtaining a 0.99 AUC ROC with Walktrap and 0.989 with Louvain clustering.
As Garimella et al. BIBREF23 have made their code public , we reproduced their best method Randomwalk on our datasets and measured the AUC ROC, obtaining a score of 0.935. An interesting finding was that their method had a poor performance over their own datasets. This was due to the fact (already explained in Section SECREF4) that it was not possible to retrieve the complete discussions, moreover, in no case could we restore more than 50% of the tweets. So we decided to remove these discussions and measure again the AUC ROC of this method, obtaining a 0.99 value. Our hypothesis is that the performance of that method was seriously hurt by the incompleteness of the data. We also tested our method on these datasets, obtaining a 0.99 AUC ROC with Walktrap and 0.989 with Louvain clustering.
Answer: The answers are shown as follows:
* Randomwalk
* Walktrap
* Louvain clustering
Question: What datasets are used?
Evidence: Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.).
The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
In this section, we evaluate the performance of the proposed cross-lingual RE approach on both in-house dataset and the ACE (Automatic Content Extraction) 2005 multilingual dataset BIBREF11.
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.).
The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
the ACE (Automatic Content Extraction) 2005 multilingual dataset BIBREF11.
Answer: The answers are shown as follows:
* in-house dataset
* ACE05 dataset
Question: Classify the following as either an insect or a dog breed: mosquito, Labrador retriever, German shepherd, ant, Siberian husky, poodle, and ladybug.
Answer: The insects are the mosquito, ant, and ladybug. The dog breeds are the Labrador retriever, German shepherd, Siberian husky, and poodle.
Question: Is three body problem solved?
Answer: there is no generic closed form solution to three body problem. There are some special case solutions, more can be found at https://en.wikipedia.org/wiki/Three-body_problem
Question: What are the advantages of the author's model over other sequence annotation models?
Evidence: For the Thai sentence segmentation task, our model is superior to all the baselines on both Thai sentence segmentation datasets, as shown in Table TABREF45 . On the Orchid dataset, the supervised model that includes both local and distant representation was adopted for comparison to the baseline model. Our model improves the F1 score achieved by CRF-ngram, which is the state-of-the-art model for Thai sentence segmentation in Orchid, from 91.9% (row (d)) to 92.5% (row (g)). Meanwhile, in the UGWC dataset, our CVT model (row (h)) achieves an F1 score of 88.9%, which is higher than the F1 score of both the baselines (CRF-ngram and Bi-LSTM-CRF (rows d and e, respectively)). Thus, our model is now the state-of-the-art model for Thai sentence segmentation on both the Orchid and UGWC datasets.
Our model outperforms all the sequence tagging models. T-BRNN-pre (row (c)) is the current state-of-the-art model, as shown in Table TABREF47 . The CVT model improves the overall F1 score from the 64.4% of T-BRNN-pre to 65.3% (row (h)), despite the fact that T-BRNN-pre integrates a pretrained word vector. Moreover, our model also achieves a 2-class F1 score 1.3% higher than that of Bi-LSTM-CRF (row (e)).
Our model outperforms all the sequence tagging models. T-BRNN-pre (row (c)) is the current state-of-the-art model, as shown in Table 4. The CVT model improves the overall F1 score from the 64.4% of T-BRNN-pre to 65.3% (row (h)), despite the fact that T-BRNN-pre integrates a pretrained word vector. Moreover, our model also achieves a 2-class F1 score 1.3% higher than that of Bi-LSTM-CRF (row (e)).
Answer: The CVT model improves the overall F1 score from the 64.4% of T-BRNN-pre to 65.3%, despite the fact that T-BRNN-pre integrates a pretrained word vector. Moreover, their model also achieves a 2-class F1 score 1.3% higher than that of Bi-LSTM-CRF.
Question: List down the key takeaways from the below paragraph on how Singapore growth is derived from its monetary policy
Evidence: Unlike many other central banks such as Federal Reserve System, European Central Bank or Bank of England, MAS does not regulate the monetary system via interest rates to influence the liquidity in the system. Instead, it chooses to do it via the foreign exchange mechanism, which it has been doing since 1981. In doing so it manages the Singapore dollar versus a number of currencies that they do not reveal publicly – a Singapore dollar nominal effective exchange rate (S$ NEER). It carries this out by intervening in the SGD market as well as other operations in the money market. The MAS reviews its policy stance less frequently than most central banks, in a cycle that is around every 6 months. In some circumstances, such as during the COVID-19 pandemic MAS can change the date of its twice yearly meeting.
Answer: Policy to adopt foreign exchange mechanism instead of interest rates to regulate monetary system
It manages Singapore dollar against multiple currency.
it policy review/change is less than other central banks
Singapore dollar nominal effective exchange rate (S$ NEER).
Question: What exactly was Apollo 11?
Answer: Apollo 11 was the fifth manned mission of the United States Apollo Program and the first human landing on the Moon in history. Neil Armstrong, Edwin Aldrin, and Michael Collins were the astronauts on this mission. On July 21, 1969, astronaut Neil Armstrong, soon followed by his colleague Edwin "Buzz" Aldrin, left the first human footprint on the lunar surface, while the third member of the mission, Michael Collins, circled the Moon in the command module for nearly two hours. "One small step for a man, one giant leap for mankind," Armstrong said as he set foot on the moon, became one of history's most famous quotes.
Question: Why are some people vegetarian?
Answer: There are many reasons for being vegetarian. Some religions prohibit eating meat. Other people prefer not eat meat because they consider it immoral. The environmental impact of the meat industry has convinced other groups of people to stop eating meat to reduce their carbon footprint. Lastly, some people simply do not enjoy the taste of meat, or find it too expensive to purchase.
Question: Which English football club has won the most FA Cup trophies?
Evidence: They hold the highest number of FA Cup trophies, with 14. The club is one of only six clubs to have won the FA Cup twice in succession, in 2002 and 2003, and 2014 and 2015. Arsenal have achieved three League and FA Cup "Doubles" (in 1971, 1998 and 2002), a feat only previously achieved by Manchester United (in 1994, 1996 and 1999). They were the first side in English football to complete the FA Cup and League Cup double, in 1993. Arsenal were also the first London club to reach the final of the UEFA Champions League, in 2006, losing the final 2–1 to Barcelona.
Answer: Arsenal - 14
Question: How did Constantine die?
Evidence: Constantine (Greek: Κωνσταντῖνος, 820s or 830s – before 836) was an infant prince of the Amorian dynasty who briefly ruled as co-emperor of the Byzantine Empire sometime in the 830s, alongside his father Theophilos. Most information about Constantine's short life and titular reign is unclear, although it is known that he was born sometime in the 820s or 830s and was installed as co-emperor soon after his birth. He died sometime before 836, possibly after falling into a palace cistern.
Answer: It is believed that Constantine died after falling into a palace cistern circa 836.
Question: Which of the tree species mentioned in the text are used as a source of food for animals?
Evidence: Multipurpose trees or multifunctional trees are trees that are deliberately grown and managed for more than one output. They may supply food in the form of fruit, nuts, or leaves that can be used as a vegetable; while at the same time supplying firewood, adding nitrogen to the soil, or supplying some other combination of multiple outputs. "Multipurpose tree" is a term common to agroforestry, particularly when speaking of tropical agroforestry where the tree owner is a subsistence farmer.

While all trees can be said to serve several purposes, such as providing habitat, shade, or soil improvement; multipurpose trees have a greater impact on a farmer's well-being because they fulfill more than one basic human need. In most cases multipurpose trees have a primary role; such as being part of a living fence, or a windbreak, or used in an ally cropping system. In addition to this they will have one or more secondary roles, most often supplying a family with food or firewood, or both.

When a multipurpose tree is planted, a number of needs and functions can be fulfilled at once. They may be used as a windbreak, while also supplying a staple food for the owner. They may be used as fencepost in a living fence, while also being the main source of firewood for the owner. They may be intercropped into existing fields, to supply nitrogen to the soil, and at the same time serve as a source of both food and firewood.

Common multipurpose trees of the tropics include:

Gliricidia (Gliricidia sepium) – the most common tree used for living fences in Central America, firewood, fodder, fixing nitrogen into the soil.
Moringa (Moringa oleifera) – edible leaves, pods and beans, commonly used for animal forage and shade (it does not fix nitrogen as is commonly believed)
Coconut palm (Cocos nucifera) – used for food, purified water (juice from inside the coconut), roof thatching, firewood, shade.
Neem (Azadirachta indica) – limited use as insect repellent, antibiotic, adding nitrogen to the soil, windbreaks, biomass production for use as mulch, firewood.
Ideally most trees found on tropical farms should be multipurpose, and provide more to the farmer than simply shade and firewood. In most cases they should be nitrogen fixing legumes, or trees that greatly increase the farmer's food security.
Answer: Gliricidia (Gliricidia sepium) and Moringa (Moringa oleifera) are used as a source of food for humans.
Question: What is a Penalty in Football?
Answer: In FIFA Football, a penalty kick is a unique version of the direct free kick. It takes place during normal play (as opposed to a shootout). A penalty kicker shoots the ball from the penalty mark which is a spot located 12 yards (11 metres) from the goal line.