DataHammer/scimrc huggingface dataset:
Question: What dataset do they introduce in their paper?
Evidence: Understanding and applying qualitative knowledge is a fundamental facet of intelligence. For example, we may read that exercise improves health, and thus decide to spend more time at the gym; or that larger cars cause more pollution, and thus decide to buy a smaller car to be environmentally sensitive. These skills require understanding the underlying qualitative relationships, and being able to apply them in specific contexts.
To promote research in this direction, we present the first open-domain dataset of qualitative relationship questions, called QuaRTz (“Qualitative Relationship Test set”). Unlike earlier work in qualitative reasoning, e.g., BIBREF0, the dataset is not restricted to a small, fixed set of relationships. Each question $Q_i$ (2-way multiple choice) is grounded in a particular situation, and is paired with a sentence $K_i$ expressing the general qualitative knowledge needed to answer it. $Q_i$ and $K_i$ are also annotated with the properties being compared (Figure FIGREF1). The property annotations serve as supervision for a potential semantic parsing based approach. The overall task is to answer the $Q_i$ given the corpus $K = \lbrace K_i\rbrace $.
We test several state-of-the-art (BERT-based) models and find that they are still substantially (20%) below human performance. Our contributions are thus (1) the dataset, containing 3864 richly annotated questions plus a background corpus of 400 qualitative knowledge sentences; and (2) an analysis of the dataset, performance of BERT-based models, and a catalog of the challenges it poses, pointing the way towards solutions.
To promote research in this direction, we present the first open-domain dataset of qualitative relationship questions, called QUARTZ (“Qualitative Relationship Test set”)1 .
Answer: QuaRTz ("Qualitative Relationship Test set”).
Question: How does their model differ from BERT?
Evidence: In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning. Specifically, we use the PyTorch implementation of pre-trained $bert-base-uncased$ models supplied by Google. This model has 12 layers (i.e., Transformer blocks), a hidden size of 768, and 12 self-attention heads. In all cases we set the feed-forward/filter size to be 3072 for the hidden size of 768. The total number of parameters of the model is 110M.
In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning.
Answer: Their model does not differ from BERT.
Question: Did they experiment with the dataset?
Evidence: In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized “SARS-CoV-2" as the “CORONAVIRUS" type, “bat" and “pangolins" as the “WILDLIFE" type and “Van der Waals forces" as the “PHYSICAL_SCIENCE" type. This NER annotation results help downstream text mining tasks in discovering the origin and the physical nature of the virus. Our NER methods are domain-independent that can be applied to corpus in different domains. In addition, we show another example of NER annotation on New York Times with our system in Figure FIGREF29.
In Figure FIGREF30, we show the comparison of our annotation results with existing NER/BioNER systems. In Figure FIGREF30, we can see that only our method can identify “SARS-CoV-2" as a coronavirus. In Figure FIGREF30, we can see that our method can identify many more entities such as “pylogenetic" as a evolution term and “bat" as a wildlife. In Figure FIGREF30, we can also see that our method can identify many more entities such as “racism" as a social behavior. In summary, our distantly- and weakly-supervised NER methods are reliable for high-quality entity recognition without requiring human effort for training data annotation.
In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized “SARS-CoV-2" as the “CORONAVIRUS" type, “bat" and “pangolins" as the “WILDLIFE" type and “Van der Waals forces" as the “PHYSICAL_SCIENCE" type. This NER annotation results help downstream text mining tasks in discovering the origin and the physical nature of the virus. Our NER methods are domain-independent that can be applied to corpus in different domains. In addition, we show another example of NER annotation on New York Times with our system in Figure FIGREF29.

In Figure FIGREF30, we show the comparison of our annotation results with existing NER/BioNER systems. In Figure FIGREF30, we can see that only our method can identify “SARS-CoV-2" as a coronavirus. In Figure FIGREF30, we can see that our method can identify many more entities such as “pylogenetic" as a evolution term and “bat" as a wildlife. In Figure FIGREF30, we can also see that our method can identify many more entities such as “racism" as a social behavior. In summary, our distantly- and weakly-supervised NER methods are reliable for high-quality entity recognition without requiring human effort for training data annotation.
Answer: Yes.
Question: Where are the oldest oak trees in Central America?
Evidence: The Cerro Arenal Natural Reserve was declared a protected area on November 4, 1991, it compromises one of 78 protected areas of Nicaragua and is managed by Ministry of the Environment and Natural Resources (MARENA).

The highest point in the reserve is located inside a tropical cloud forest 1,570 meters above sea level, rain occurs 8 months annually, between May and December. Cerro Arenal Natural Reserve is one of the smallest protected areas of Nicaragua and is abundant with flora and fauna.  Many animals are present throughout the reserve, animals such as monkeys, and armadillos. The reserve also contains the oldest oak trees in Central America.
Answer: The oldest oak trees reside in the Cerro Arenal Natural Reserve, which was officially declared a protected area on November 4, 1991.
Question: Where do chia seeds come from?
Answer: Chia seeds actually come from a flowering plant in the mint family. This plant is native to Central America.
Question: Identify which car manufacturer is French or American: Bugatti, GMC, Dodge
Answer: Bugatti is French, GMC and Dodge is American
Question: Tell me which of these characters are from the Mario universe: Yoshi, Zelda, King Kong, Luigi, Princess Peach, Link, Bowser, Alex Rodriguez.
Answer: Yoshi, Luigi, Princess Peach, and Bowser are from the Mario universe.
Question: Tell me which of these cities are located in Latvia
Answer: Riga, Tallin, Jurmala,  Pinki, Munich,  Daugavpils, Klaipeda, Ventspils, Tartu
Question: How did the select the 300 Reddit communities for comparison?
Evidence: Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 ).
Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 ).
Answer: They collect subreddits from January 2013 to December 2014,2 for which there are at
least 500 words in the vocabulary used to estimate the measures,
in at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language.
Question: Given the following paragraph about politicians, who was the last surviving person to have been a member of the Irish House of Commons?
Evidence: Sir Thomas Staples, 9th Baronet (31 July 1775 – 14 May 1865) was an Anglo-Irish politician and lawyer. He was the last surviving person to have been a member of the Irish House of Commons, albeit only having been in the House for a short time.
Answer: Sir Thomas Staples, 9th Baronet
Question: The following are some reasons to start a restaurant. the reasons should be categorized as 'great', 'neutral', 'terrible' - 

It will provide food to people
Potential to become a millionaire
Help you follow your passion
All your savings will go into opening it
You dont know how to cook
The world needs more farm to table services
You may be able to build a career out of it
You will enjoy it
You will lose your job
You dont have money to open a restaurant
Answer: Great:
Help you follow your passion
The world needs more farm to table services
Potential to become a millionaire

Neutral:
You will enjoy it
It will provide food to people
You may be able to build a career out of it

Terrible:
All your savings will go into opening it
You will lose your job
You dont know how to cook
You dont have money to open a restaurant
Question: Tell me whether the following are "useful" or "not useful" when working remote: Internet connection, screaming babies, home office, second monitor, power outage
Answer: Internet Connection: Useful
Screaming Babies: Not Useful
Home Office: Useful
Second Monitor: Useful
Power Outage: Not Useful
Question: What are the sports that Kraft sponsors?
Evidence: Kraft is an official partner and sponsor of both Major League Soccer and the National Hockey League. Since 2006, Kraft Foods has sponsored Kraft Hockeyville, a reality television series produced by CBC/SRC Sports, in which communities demonstrate their commitment to the sport of ice hockey in a contest revolving around the theme of community spirit. The winning community gets a cash prize dedicated to upgrading their hometown arena, as well as the opportunity to host an NHL preseason game. In 2007, it was then relegated to segments aired during Hockey Night in Canada. In 2015, Kraft Hockeyville was expanded to the United States with a separate competition for communities there.

From 2002 to 2014, Kraft sponsored the Kraft Nabisco Championship, one of the four "majors" on the LPGA tour. The company also sponsored the Kraft Fight Hunger Bowl, a post-season college football bowl game, from 2010 to 2012.
Answer: Soccer, hockey, golf, football
Question: What is the difference between its and it's?
Answer: Its (without an apostrophe) indicates possession. Contrary to common possession rules in English, the apostrophe is dropped in its. 

It's is a contraction, a shortened form of it is or it has.
Question: How long did Air Finland operate?
Evidence: Oy Air Finland was an airline with its head office and base at Helsinki Airport in Vantaa, Finland, which was operating chartered and scheduled flights to holiday destinations, as well as aircraft lease services. The company was founded in 2002 and filed for bankruptcy in 2012.

History

Boeing 757-200 in the old livery at Kangerlussuaq Airport (2010).

Boeing 757-200 in the latest livery at Málaga Airport (2012).

An ex-Air Finland Boeing 757-200 in basic Jet2 Holidays livery at Leeds Bradford International Airport (2011)
The airline was established in January 2002 and started flight operations on 3 April 2003. It was owned by three individuals from the aviation, finance and travel marketing industries: Harri Naivo (Chairman and Chief Financial Officer), Mika Helenius (Chief Executive Officer), and Lauri Komi. At March 2007, the company had 210 employees. On 26 June 2012, Air Finland announced that it would immediately cancel all flights and filed for bankruptcy.
Answer: They operated as a business for 10 years, between 2002 and 2012.
Question: Where is the annual indoor rowing World Championship?
Evidence: Ergometer rowing machines (colloquially ergs or ergo) simulate the rowing action and provide a means of training on land when waterborne training is restricted, and of measuring rowing fitness. Ergometers do not simulate the lateral balance challenges, the exact resistance of water, or the exact motions of true rowing including the sweep of the oar handles. For that reason ergometer scores are generally not used as the sole selection criterion for crews (colloquially "ergs don't float"), and technique training is limited to the basic body position and movements. However, this action can still allow a workout comparable to those experienced on the water. Indoor rowing has become popular as a sport in its own right with numerous indoor competitions (and the annual World Championship CRASH-B Sprints in Boston) during the winter off-season.
Answer: The indoor rowing world championship is held annually in Boston during the winter off-season.
Question: Who won the Man Booker International Prize for  2019?
Answer: Jokha Alharthi (Oman Author)
Question: How many systems participated in the release of this dataset?
Evidence: The proliferation of the online social media has lately resulted in the democratization of online content sharing. Among other media, Twitter is very popular for research and application purposes due to its scale, representativeness and ease of public access to its content. However, tweets, that are short messages of up to 140 characters, pose several challenges to traditional Natural Language Processing (NLP) systems due to the creative use of characters and punctuation symbols, abbreviations ans slung language.
Named Entity Recognition (NER) is a fundamental step for most of the information extraction pipelines. Importantly, the terse and difficult text style of tweets presents serious challenges to NER systems, which are usually trained using more formal text sources such as newswire articles or Wikipedia entries that follow particular morpho-syntactic rules. As a result, off-the-self tools trained on such data perform poorly BIBREF0 . The problem becomes more intense as the number of entities to be identified increases, moving from the traditional setting of very few entities (persons, organization, time, location) to problems with more. Furthermore, most of the resources (e.g., software tools) and benchmarks for NER are for text written in English. As the multilingual content online increases, and English may not be anymore the lingua franca of the Web. Therefore, having resources and benchmarks in other languages is crucial for enabling information access worldwide.
In this paper, we propose a new benchmark for the problem of NER for tweets written in French. The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities. The annotators were native speakers of French and had previous experience in the task of NER. Overall, the generated datasets consists of INLINEFORM0 tweets, split in training and test parts.
The paper is organized in two parts. In the first, we discuss the data preparation steps (collection, annotation) and we describe the proposed dataset. The dataset was first released in the framework of the CAp 2017 challenge, where 8 systems participated. Following, the second part of the paper presents an overview of baseline systems and the approaches employed by the systems that participated. We conclude with a discussion of the performance of Twitter NER systems and remarks for future work.
The dataset was first released in the framework of the CAp 2017 challenge, where 8 systems participated.
Answer: 8.
Question: What is the difference between a simile and a metaphor?
Answer: Similes describe similarities using words such as "like" and "as" which make explicit comparisons whereas metaphors compare things using implicit comparisons.
Question: What's an unpopular opinion you have?
Answer: An unpopular opinion I have is that 5:00 pm in the evening is the best time to eat dinner. Many cultures enjoy eating dinner late at night. Many young people prefer to eat at night so that they can enjoy the company of friends and a drink at night. Typically, only elderly people and small children eat dinner at an earlier time. I prefer to eat dinner early because most of the best restaurants have open reservations at 5:00 pm, so I can almost always get a good table. I'm not particularly eager to eat late in the evening because then I have a hard time sleeping at night. I prefer to have dinner at 5:00 pm because it starts the evening earlier, forces you to leave work and allows you to spend the whole evening with your friends and family. It leaves space for the evening to unfold.
Question: Why do companies play dividends?
Answer: Companies will pay dividends out of their after tax earnings. It's generally done to signal to shareholders that the balance sheet is strong and that growth is expected without the need to reinvest all free cashflow back into the business.
Question: Do they explain model predictions solely on attention weights?
Evidence: Our application is the escalation of Internet chats. To maintain quality of service, users are transferred to human representatives when their conversations with an intelligent virtual assistant (IVA) fail to progress. These transfers are known as escalations. We apply Han to such conversations in a sequential manner by feeding each user turn to Han as they occur, to determine if the conversation should escalate. If so, the user will be transferred to a live chat representative to continue the conversation. To help the human representative quickly determine the cause of the escalation, we generate a visualization of the user's turns using the attention weights to highlight the turns influential in the escalation decision. This helps the representative quickly scan the conversation history and determine the best course of action based on problematic turns.
To help the human representative quickly determine the cause of the escalation, we generate a visualization of the user's turns using the attention weights to highlight the turns influential in the escalation decision.
Answer: Yes.
Question: What is the TREC-CAR dataset?
Evidence: Introduced by BIBREF16 , in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section. The corpus consists of all of the English Wikipedia paragraphs, except the abstracts. The released dataset has five predefined folds, and we use the first four as a training set (approximately 3M queries), and the remaining as a validation set (approximately 700k queries). The test set is the same one used to evaluate the submissions to TREC-CAR 2017 (approx. 1,800 queries).
Introduced by BIBREF16 , in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section. The corpus consists of all of the English Wikipedia paragraphs, except the abstracts. The released dataset has five predefined folds, and we use the first four as a training set (approximately 3M queries), and the remaining as a validation set (approximately 700k queries). The test set is the same one used to evaluate the submissions to TREC-CAR 2017 (approx. 1,800 queries).
Answer: The answers are shown as follows:
* in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section
Question: What problem do the authors show that transfer solves, an overfitting problem or a search problem?
Evidence: In the neural encoder-decoder framework for MT BIBREF3 , BIBREF4 , BIBREF0 , BIBREF5 , BIBREF6 , we use a recurrent neural network (encoder) to convert a source sentence into a dense, fixed-length vector. We then use another recurrent network (decoder) to convert that vector to a target sentence. In this paper, we use a two-layer encoder-decoder system (Figure 1 ) with long short-term memory (LSTM) units BIBREF7 trained to optimize maximum likelihood (via a softmax layer) with back-propagation through time BIBREF8 . Additionally, we use an attention mechanism that allows the target decoder to look back at the source encoder, specifically the local attention plus feed-input model from luong2015effective.
We compare the learning curves of transfer and no-transfer models showing that transfer solves an overfitting problem, not a search problem.
Answer: An overfitting problem.
Question: What are their proposed features?
Evidence: Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list):
[itemsep=0pt,leftmargin=*,topsep=0pt]
Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.
Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively.
How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task.
General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing.
Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations:
Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list):

[itemsep=0pt,leftmargin=*,topsep=0pt]

Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.

Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively.

How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task.

General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing.

Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations:

[itemsep=0pt,leftmargin=*,topsep=0pt]
Answer: The answers are shown as follows:
* Non-contextual properties of a word
* Word usage in an OP or PC (two groups)
* How a word connects an OP and PC.
* General OP/PC properties