DataHammer/scimrc huggingface dataset:
Question: What are the features of the approach for the identification of complaint-based tweets?
Evidence: With the advent of social media platforms, increasing user base address their grievances over these platforms, in the form of complaints. According to BIBREF0, complaint is considered to be a basic speech act used to express negative mismatch between the expectation and reality. Transportation and its related logistics industries are the backbones of every economy. Many transport organizations rely on complaints gathered via these platforms to improve their services, hence understanding these are important for: (1) linguists to identify human expressions of criticism and (2) organizations to improve their query response time and address concerns effectively.
Presence of inevitable noise, sparse content along with rephrased and structurally morphed instances of posts, make the task at hand difficult BIBREF1. Previous works BIBREF2 in the domain of complaint extraction have focused on static datasets only. These are not robust to changes in the trends reflected, information flow and linguistic variations. We propose an iterative, semi-supervised approach for identification of complaint based tweets, having the ability to be replicated for stream of information flow. The preference of a semi-supervised approach over supervised ones is due to the stated reasons: (a) the task of isolating the training set, make supervised tasks less attractive and impractical and (b) imbalance between the subjective and objective classes lead to poor performance.
We propose an iterative, semi-supervised approach for identification of complaint based tweets, having the ability to be replicated for stream of information flow.
Answer: It is an iterative, semi-supervised for identification of complaint based tweets, which having the ability to be replicated for stream of information flow.
Question: Why is skiing a great sport?
Answer: Skiing is a fun and enjoyable sport or hobby for many reasons.  For many skiers their first memories of skiing are as a kid with their parents, and brings forward fond childhood memories of snow days, hot chocolate and being outdoors.  But what makes skiing so wonderful is that is blends both individual pursuits and as a group activity.  It is a fun activity to do with a group where all the participants are experiencing the outdoors and the trails together.  At the same time, each skier is dependent on their own skills to get down the trail, thereby providing the individual to have their own challenges and experience while spending time with friends or family.  

Skiing offers the opportunity to enjoy the natural wonders of the mountains and experience breathtaking views.  Every day brings different conditions the skier has to adjust to; visibility, temperature, snow conditions.  These all factor into how the skier is going to ski the terrain, which in-turn provides variety to skiing, even if you are visiting the same mountain and skiing the same trails.  

Skiing is known for having a communal type of culture.  Getting on a chair lift with a stranger can often times lead to great conversations, all be it short ones, the chair lift can be a time to connect with other skiers to learn what trails are offering the best conditions, or what areas of the mountain to avoid.  Skiers often look out for one another on the slopes to ensure others are safe if someone falls.

The exhilaration of speeding down snow packed trail, with views of the mountains and valleys around you, or through the quite of snow capped trees, enjoying time with friends or family, makes skiing one of the best sports people of all ages can enjoy; from little kids to people in their 60's, 70's and even 80's can still enjoy.
Question: Who was the first man to walk on the moon?
Answer: Neil Armstrong, 1969
Question: What are the key terms in probability
Evidence: Probability theory is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space. Any specified subset of the sample space is called an event. Central subjects in probability theory include discrete and continuous random variables, probability distributions, and stochastic processes (which provide mathematical abstractions of non-deterministic or uncertain processes or measured quantities that may either be single occurrences or evolve over time in a random fashion). Although it is not possible to perfectly predict random events, much can be said about their behavior. Two major results in probability theory describing such behaviour are the law of large numbers and the central limit theorem.
Answer: Probability
Random variable
Discrete random variable
Continuous random variable
Probability distributions 
Law of large numbers 
Central Limit theorem
Question: How far is Hornowek from Izabelin?
Evidence: Hornówek [xɔrˈnuvɛk] is a village in the administrative district of Gmina Izabelin, within Warsaw West County, Masovian Voivodeship, in east-central Poland. It lies approximately 2 kilometres (1 mi) south-west of Izabelin, 8 km (5 mi) north of Ożarów Mazowiecki, and 16 km (10 mi) north-west of Warsaw.
Answer: Hornowek is roughly 2 kilometers (1 mile) south west of Izabelin.
Question: Give me a list of the seven most polluted cities in America
Answer: According to the American Lung Association, the 7 most polluted cities include 5 cities from California.  The list is order is, Los Angeles-Long Beach, Bakersfield, Visalia, Fresno-Madera-Hanford, Phoenix-Mesa, San Diego-Chula Vista-Carlsbad, and Denver-Aurora
Question: What is the best programming language?
Answer: There is no single answer, as the specific use case, the nature of the project, and the skills of the programmer all play a role in determining the best programming language. Python may be generally easier to learn, while C++ may be faster because it is statically typed. Each language has its advantages and disadvantages.
Question: Which of the following were recurring characters on The Sopranos: Carmela, Tony, Christopher, Jody, Natasha, Silvio, Adriana, Tom & Jerry, Jerry Seinfeld, George Costanza
Answer: The following were recurring characters on The Sopranos: Carmela, Tony, Christopher, Silvio, Adriana.
Question: Give me a bulleted list of the cutest animals
Answer: Beauty is on the eye of the beholder. Having said that, here is a list of animals considered “cute” on average:
•	Dogs 
•	Cats
•	Bunnies
•	Birds
•	Pandas
•	Dolphins
Question: I am thinking of going to yoga. What type of class is good for me?
Answer: It depends upon many factors.  If you are a beginner there are low heat classes like yin or hatha or restore.  If you would like a good workout, you can go to a high heat vinyasa class.
Question: How do government control inflation?
Answer: The most common way to control inflation is to reduce the supply of money in the economy. This is achieved by increasing the interest rates.
Question: How big is improvement in performances of proposed model over state of the art?
Evidence: We consider three models as our baselines. SyntaxSQL-con and CD-Seq2Seq are two strong baselines introduced in the SParC dataset paper BIBREF2. SyntaxSQL-con employs a BiLSTM model to encode dialogue history upon the SyntaxSQLNet model (analogous to our Turn) BIBREF23, while CD-Seq2Seq is adapted from BIBREF4 for cross-domain settings (analogous to our Turn+Tree Copy). EditSQL BIBREF5 is a STOA baseline which mainly makes use of SQL attention and token-level copy (analogous to our Turn+SQL Attn+Action Copy).
Taking Concat as a representative, we compare the performance of our model with other models, as shown in Table TABREF34. As illustrated, our model outperforms baselines by a large margin with or without BERT, achieving new SOTA performances on both datasets. Compared with the previous SOTA without BERT on SParC, our model improves Ques.Match and Int.Match by $10.6$ and $5.4$ points, respectively.
FLOAT SELECTED: Table 1: We report the best performance observed in 5 runs on the development sets of both SPARC and COSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the results show the improvements of our model are significant with p < 0.005.
EditSQL BIBREF5 is a STOA baseline which mainly makes use of SQL attention and token-level copy (analogous to our Turn+SQL Attn+Action Copy).
Taking Concat as a representative, we compare the performance of our model with other models, as shown in Table TABREF34. As illustrated, our model outperforms baselines by a large margin with or without BERT, achieving new SOTA performances on both datasets. Compared with the previous SOTA without BERT on SParC, our model improves Ques.Match and Int.Match by $10.6$ and $5.4$ points, respectively.
FLOAT SELECTED: Table 1: We report the best performance observed in 5 runs on the development sets of both SPARC and COSQL, since their test sets are not public. We also conduct Wilcoxon signed-rank tests between our method and the baselines, and the results show the improvements of our model are significant with p < 0.005.
Answer: The answers are shown as follows:
* Compared with the previous SOTA without BERT on SParC, our model improves Ques.Match and Int.Match by $10.6$ and $5.4$ points, respectively.
Question: What caused the Global Financial Crises?
Evidence: The preconditioning for the financial crisis were complex and multi-causal. Almost two decades prior, the U.S. Congress had passed legislation encouraging financing for affordable housing. However, in 1999, parts of the Glass-Steagall legislation, which had been adopted in 1933, were repealed, permitting financial institutions to commingle their commercial (risk-averse) and proprietary trading (risk-taking) operations. Arguably the largest contributor to the conditions necessary for financial collapse was the rapid development in predatory financial products which targeted low-income, low-information homebuyers who largely belonged to racial minorities. This market development went unattended by regulators and thus caught the U.S. government by surprise.
Answer: There were multiple factors that caused the financial crises.  Two decades before the crisis, the U.S Government implemented multiple new laws to help encourage financing for affordable housing.  However, in 1999, portions of the Glass-Steagall act, which was passed by congress in 1933, was repealed and as a result, created more overall risk in the financial system as it allowed institutions to commingle low risk real estate investments with riskier investment operations.  The final major factor was a large increase in predatory home loan lending practices which targeted low-income, low-credit, uninformed homebuyers (mostly racial minorities).  The severity of these factors went unnoticed by U.S financial regulators and by the time the effects surfaced, the government was caught by surprise.
Question: Write a linkedIn Message to ask a higher officer in company before joining asking about what all I have to prepare any tips or advice on what I should focus on or study for so that i can be well accustomed with the required skill sets for the job.
Answer: Hi [Manager] Sir,

I hope you're doing well!
Myself [My Name] from ITER, SOA University.

I just wanted to reach out to you before I start at [Company] in a month. I'm really excited to join the team and get started on my role as Intern Trainee Engineer- Platform. I was wondering if you had any tips or advice on what I should focus on or study before I start to make sure I'm as prepared as possible? I want to make sure I hit the ground running and make a good impression on the team.
Any suggestions would be greatly appreciated. Thank you for your time I'm looking forward to joining the team and working together!

Thanks,
[My Name]
Question: What are the specific NLP tasks?
Evidence: Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask. Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks. Low-resource languages still lag behind.
Endangered languages pose an additional challenge. The process of documenting an endangered language typically includes the creation of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored in large online linguistics archives. This process is often hindered by the Transcription Bottleneck: the linguistic fieldworker and the language community may not have time to transcribe all of the recordings and may only transcribe segments that are linguistically salient for publication or culturally significant for the creation of community resources.
With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina. We hope to ameliorate the resource gap and the transcription bottleneck in two ways. First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation). In providing baselines and datasets splits, we hope to further facilitate research on low-resource NLP for this language through our data set. Research on low-resource speech recognition is particularly important in relieving the transcription bottleneck, while tackling the research challenges that speech synthesis and machine translation pose for such languages could lead to such systems being deployed to serve more under-represented communities.
First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation).
Answer: Speech recognition, speech synthesis, and machine translation
Question: What is the training and test data used?
Evidence: We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Then the collected tweets were preprocessed as mentioned in the preprocessing section.
Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. The tweets before the attack day and on the attack day were used to train the two LDA models mentioned in the approach section.
In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack. There are 590 tweets in $D_a$ and 5229 tweets in $D_b$. In this experiment, we want to find out whether a model trained on Bank of America data can make good classification on PNC and Wells Fargo data.
We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset.
Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment.
In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack.
Answer: Tweets related to a Bank of America DDos attack were used as training data. The test datasets contain tweets related to attacks to Bank of America, PNC and Wells Fargo.
Question: In autosports, what is the apex and racing line?
Answer: The racing line in autosports is an imaginary line in which when followed by the vehicle will provide the most efficient and quickest way through a turn or series of turns.  The apex of the racing line is the ideal point in which the car should be positioned closest to the inside of a curve.  The term, 'hitting the apex' is used when the ideal racing line is followed.
Question: Tell me if these animals are mammals or birds: Emu, Gorilla, Platypus, Kangaroo, Ostrich, Robin, Raven
Answer: Emu: Bird, Gorilla: Mammal, Platypus: Mammal, Kangaroo: Mammal, Ostrich: Bird, Robin: Bird, Raven: Bird
Question: How do the authors compute forget gate values?
Evidence: We evaluate our method on natural language inference (NLI), paraphrase identification (PI), and sentiment classification. We also conduct analysis on gate values and experiments on model variants. For detailed experimental settings, we refer readers to the supplemental material.
For the NLI and PI tasks, there exists recent work specializing in sentence pair classification. However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the models in extracting semantics. But note that the applicability of CAS-LSTM is not limited to sentence encoding based approaches.
To inspect the effect of the additional forget gate, we investigate how the values of vertical forget gates are distributed. We sample 1,000 random sentences from the development set of the SNLI dataset, and use the 3-layer CAS-LSTM model trained on the SNLI dataset to compute gate values
Answer: They sample 1,000 random sentences from the development set of the SNLI dataset, and use the 3-layer CAS-LSTM model trained on the SNLI dataset to compute gate values
Question: Which national parks should I visit in Utah?
Answer: Utah has what is called the Mighty 5. This list includes Arches, Bryce Canyon, Canyonlands, Capitol Reef and Zion. With so many choices to choose from, it can be difficult to decide. Arches is known for it's famous "Delicate Arch" which you have seen on postcards and the like. Bryce Canyon has countless hoodoos, perfect for your picturesque moment. Canyonlands, may even remind you of the Grand Canyon, so if you've enjoyed that in the past, you might want to start here (it's close by to Arches). Capitol reef has a wide variety of landscapes including arches, cliffs, canyons, etc. Last but most certainly not least, Zion. This park may be the most well-traveled on the list. Zion is especially known for it's big wall climbs.
Question: To focus on the effectiveness of the architectures in extracting semantics, what method do the authors take?
Evidence: We evaluate our method on natural language inference (NLI), paraphrase identification (PI), and sentiment classification. We also conduct analysis on gate values and experiments on model variants. For detailed experimental settings, we refer readers to the supplemental material.
For the NLI and PI tasks, there exists recent work specializing in sentence pair classification. However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the models in extracting semantics. But note that the applicability of CAS-LSTM is not limited to sentence encoding based approaches.
However in this work we confine our model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction, in order to focus on the effectiveness of the architectures in extracting semantics.
Answer: Confine their model to the architecture that encodes each sentence using a shared encoder without any inter-sentence interaction
Question: Who did Stafford throw touchdown passes to in Super Bowl LV?
Evidence: Against the Cincinnati Bengals in Super Bowl LVI, Stafford and the Rams came away victorious on their home field with a final score of 23–20. Scoring started early in the first quarter thanks to a long drive that culminated in a Odell Beckham Jr. touchdown reception. The Bengals managed a field goal to make the score 7–3. Stafford then tossed another touchdown to wide receiver Cooper Kupp on the next possession to extend the lead to 13–3. On the first play of the second half, however, the Bengals retook the lead, 17–13. After field goals by both teams, the Rams trailed by a score of 20–16 in the fourth quarter. Stafford orchestrated a drive in the final minutes that ended with another touchdown pass to Kupp to regain the lead at 23–20. Overall, Stafford completed 26 of his 40 pass attempts for 283 yards, three passing touchdowns, and two interceptions as he earned his first championship ring in his first season with the Rams.
Answer: The text provided does not mention information about Super Bowl LV. However, it does provide information regarding Super Bowl LVI.

In Super Bowl LVI, Stafford threw 3 touchdown passes, two to Cooper Kupp and one to Odell Beckham Jr.

In the first quarter, Stafford led a long drive that culminated in a Odell Beckham Jr. touchdown reception. Stafford later tossed a touchdown to wide receiver Cooper Kupp. In the fourth quarter, Stafford orchestrated a drive in the final minutes that ended with another touchdown pass to Kupp.
Question: If you are traveling outside the country, categorize each of the following as either: ‘Mandatory’, ‘Good to have’, ‘Least important’. Passport, Cash of the country visiting, Power bank, Book, Pen, Laptop
Answer: Mandatory: Passport
Good to have: Cash of the country visiting, Powerbank
Least important: Book, Pen, Laptop
Question: What are the merits of skipping breakfast? Can you target your answer to a typical American and describe in layman's terms rather than detailed scientific jargon?
Answer: A truism instilled in youths is "Breakfast is the most important meal of the day". A well-rounded breakfast may indeed lead to a productive and energetic day. However, advertisements often position breakfast as a dessert rather than a well-rounded meal. For example, many eat sugary cereal with large servings of milk to kickstart their day. This is ill-advised. One should consider a glass of water or cup of coffee in place of breakfast. Skipping breakfast creates a "fast" in the body, which allows restoration of vital biological functions without spending cycles digesting new nutrients. In short, if one skips breakfast, additional time and focused energy may be awarded, especially if the breakfast has a poor nutrient profile.
Question: Whats the name of the person who wrote  the US declaration of independence?
Answer: Thomas Jefferson