DataHammer/scimrc huggingface dataset:
Question: Has Joshi's team reported that traditional NLP tools and techniques are not effective in the automatic detection of sarcasm?
Evidence: Sarcasm is an intensive, indirect and complex construct that is often intended to express contempt or ridicule . Sarcasm, in speech, is multi-modal, involving tone, body-language and gestures along with linguistic artifacts used in speech. Sarcasm in text, on the other hand, is more restrictive when it comes to such non-linguistic modalities. This makes recognizing textual sarcasm more challenging for both humans and machines.
Sarcasm detection plays an indispensable role in applications like online review summarizers, dialog systems, recommendation systems and sentiment analyzers. This makes automatic detection of sarcasm an important problem. However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques. This is apparent from the results reported by the survey from DBLP:journals/corr/JoshiBC16. The following discussion brings more insights into this.
Consider a scenario where an online reviewer gives a negative opinion about a movie through sarcasm: “This is the kind of movie you see because the theater has air conditioning”. It is difficult for an automatic sentiment analyzer to assign a rating to the movie and, in the absence of any other information, such a system may not be able to comprehend that prioritizing the air-conditioning facilities of the theater over the movie experience indicates a negative sentiment towards the movie. This gives an intuition to why, for sarcasm detection, it is necessary to go beyond textual analysis.
We aim to address this problem by exploiting the psycholinguistic side of sarcasm detection, using cognitive features extracted with the help of eye-tracking. A motivation to consider cognitive features comes from analyzing human eye-movement trajectories that supports the conjecture: Reading sarcastic texts induces distinctive eye movement patterns, compared to literal texts. The cognitive features, derived from human eye movement patterns observed during reading, include two primary feature types:
The cognitive features, along with textual features used in best available sarcasm detectors, are used to train binary classifiers against given sarcasm labels. Our experiments show significant improvement in classification accuracy over the state of the art, by performing such augmentation.
However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques. This is apparent from the results reported by the survey from Joshi et al. (2016).
Answer: Yes.
Question: Where is Stargate Command located in the Stargate universe.
Evidence: Stargate Command (abbreviated to SGC) is a top-secret military organization founded and led by the United States Air Force in conjunction with the International Oversight Advisory, tasked with operating the Stargate device and all matters pertaining to things offworld, such as threats to Earth or missions to procure new technology from extraterrestrial civilizations. It is based inside the Cheyenne Mountain Complex in Colorado, code named "Area 52". (SG1: "Politics")
Answer: The Stargate Command facility is located on planet Earth, in Colorado, United States, in the Cheyenne Mountain complex
Question: What are some things to consider when going to a theme park?
Answer: It is important to thing about what the weather may be like before going to a theme park.  If it is raining you might want to plan by wearing a rain jacket and also consider some of the roller coaster rides may not be operational during this kind of weather.  You also need to consider things like sun screen and hydration if the weather is going to be hot and sunny.  Bug spray for mosquitos is also another important item if the weather is humid.  Prepare a list of questions or things to bring before you travel to a theme park and you will be better prepared when you get there.
Question: When is the best time of year to visit the Smoky Mountains?
Answer: The Smoky Mountains are open and accessible year around.  One can visit the Smokies during each of the 4 different seasons and have a completely different experience in each.  Colder months, such as December through early March, are great if you want to explore when it's less crowded, but be prepared for certain higher elevation trails and roadways to be closed due to ice and snow.  Summer months are great for morning hikes and hikes at higher elevations as the temperatures are moderately warm and comfortable, but be prepared for larger crowds, as this is the busiest time of year around the park.  In October, the Smokies are full of red, orange, and yellow leaves changing colors before dropping for the Winter.  This is one of the more beautiful times of year in the Smokies, but also a very busy time as visitors flock to the park to see all the colors.  Spring break is also a great time of year to visit the Smoky Mountains, as the weather is cool for long hikes, the bears are just starting to come out with their cubs, and the crowds are less than what you'd see during the summer.
Question: How can I go from my couch to running a 5K in 3 months?
Answer: Basically, you are asking, how to go from not running at all to completing your first 5K.  It is best to first consult your doctor before starting any exercise program.  Make sure you invest in an excellent pair of running shoes, try a local running shoe store for advice.  Start off by measuring the time you are running.  Alternate days running and gradually increase the length of time being careful not to increase more than 10% per run.   Once you get up to 30 minutes of running every other day, start increasing the length of time running every fourth day by 10% again until you hit 45 minutes.  It is ok to walk if you feel you need to.  Then you will be 5K running race ready.
Question: How do they align the synthetic data?
Evidence: By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section. For automatic translation, we utilize a pre-trained and publicly released NMT model for En $\rightarrow $ De and train another NMT model for En $\rightarrow $ Fr using the WMT'15 En-Fr parallel corpus BIBREF19 . A beam of size 5 is used to generate synthetic sentences. Lastly, to match the size of the training data, PSEUDOmix is established by randomly sampling half of each Fr*-De and Fr-De* corpus and mixing them together.
By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section.
Answer: The answers are shown as follows:
* By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section.
Question: What was the textual source to which OpenIE was applied?
Evidence: We consider two knowledge sources. The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining. This corpus is used by the IR solver and also used to create the tuple KB T and on-the-fly tuples $T^{\prime }_{qa}$ . Additionally, TableILP uses $\sim $ 70 Curated tables (C) designed for 4th grade NY Regents exams.
We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. For each test set, we use the corresponding training questions $Q_\mathit {tr}$ to retrieve domain-relevant sentences from S. Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).
The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining.
We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB. 
We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T).
Answer: The answers are shown as follows:
* domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining
Question: which is the silicon valley of India.
Answer: Bangalore City in the state of Karnataka is very well known as the Information technology capital or silicon valley of India due to the presence of most of the tech giants and the start-up ecosystem. There is a lot of talent pool in the region which is also helping its booming ecosystems. In recent days, cities like Chennai, Pune, Hyderabad, and Noida are also helping many firms establish their centers.
Question: What did Gary C. Butler do?
Evidence: Gary C. Butler was the CEO and president of Automatic Data Processing. He held this position from August 2006 through November 2011, and had been with the company for 37 years. From 1990 to 1995, he served as Group President for ADP's Dealer Services Group; and from 1995 to 1998 he served in the same capacity for ADP's Employer Services Group. Prior to assuming his role as CEO in 2006, Butler was President and Chief Operating Officer of the company.
Answer: Gary C. Butler was the CEO and president of Automatic Data Processing.
Question: Is he still an active football player?
Evidence: Mariano Sánchez Martínez (born 28 January 1978) is a Spanish former professional footballer who played as a defensive midfielder.

He appeared in 108 Segunda División games over three seasons, scoring two goals for Cartagena.

Club career
Born in San Pedro del Pinatar, Region of Murcia, Sánchez did not reach the Segunda División B until he was 26, in 2004, arriving at CD Alcoyano from amateurs AD Mar Menor-San Javier. In the following year he moved to another club at that level, FC Cartagena, helping it promote to Segunda División in his fourth season.

Sánchez made his debut in the competition on 29 August 2009 at the age of 31 years and seven months, playing the full 90 minutes in a 1–0 away win against Girona FC. He scored his first league goal on 22 May 2010 in the 3–5 home loss to Levante UD, and never appeared in less than 34 league matches during his three seasons in that tier, suffering relegation in his last and renewing his contract for a further two years in June 2012.

On 14 May 2014, the 36-year-old Sánchez announced he would retire at the end of the campaign while hoping to help his team promote, which eventually did not befell.

Personal life
Sánchez rejected an offer to play youth football for Real Murcia when he was 18, after deciding to move to Madrid to study architecture. Not being able to enter Real Madrid's youth system, he chose to retire from football.

After his playing days, Sánchez continued to work as an architect. Still as an active player, he was the figurehead behind the creation of the sports complex Pinatar Arena, in his hometown.
Answer: No, he retired at the age of 36 on May 14, 2014. However he is still working as an architect which he studied during his graduation.
Question: For the list given, classify which items are considered vegan, vegitarian and non vegetarian: milk, tofu, curd, chicken, mutton, bacon, clam, tomato, potato, onion, egg, beef, pork, mango, apple, salmon, soy milk, paneer
Answer: Vegan : tofu, soy milk, tomato, potato, onion, mango, apple
Vegetarian: milk, curd, paneer, tofu, soy milk, tomato, potato, onion, mango, apple
Non vegetarian: chicken, mutton, bacon, clam,egg, beef, pork, salmon
Question: Do the authors report only on English language data?
Evidence: We segment a hashtag into meaningful English phrases. The `#' character is removed from the tweet text. As for example, #killthebill is transformed into kill the bill.
In order to achieve this, we use a dictionary of English words. We recursively break the hashtagged phrase into segments and match the segments in the dictionary until we get a complete set of meaningful words. This is important since many users tend to post tweets where the actual message of the tweet is expressed in form of terse hashtagged phrases.
The sentiment of url: Since almost all the articles are written in well-formatted english, we analyze the sentiment of the first paragraph of the article using Standford Sentiment Analysis tool BIBREF4 . It predicts sentiment for each sentence within the article. We calculate the fraction of sentences that are negative, positive, and neutral and use these three values as features.
We segment a hashtag into meaningful English phrases.
In order to achieve this, we use a dictionary of English words.
The sentiment of url: Since almost all the articles are written in well-formatted english, we analyze the sentiment of the first paragraph of the article using Standford Sentiment Analysis tool BIBREF4 . 
Answer: Yes.
Question: Which items from this list are types of poems and which are types of insurance: sonnet, disability, haiku, limerick, auto, umbrella, whole life, and homeowners.
Answer: The types of poems from this list include: sonnet, haiku, and limerick. The types of insurance include: disability, auto, umbrella, whole life, and homeowners.
Question: Vanessa and Lauren are both 32-year-old women with different levels of exercise experience. Vanessa, who weighs 124 lbs, has lifted weights before but Lauren, who weighs 166 lbs, has never participated in resistance training. Both women participate in initial fitness assessments and want your advice about starting a muscular fitness program at your exercise facility. Choose a different test to measure each woman's upper body muscular strength. Briefly explain the procedures for each assessment. What common errors should you watch for?
Answer: For Vanessa, we’ll choose for her to perform a one-rep max assessment, specifically with the bench press. This is because she has experience with lifting weights. To start, she should warm up by completing several submaximal repetitions. We’re going to select an initial weight of 50-70% of her capacity, and progressively increase resistance by 2.5 to 20 kg until the subject cannot complete the repetitions. The final weight lifted should be the absolute 1-RM. Some common error we want to watch for is to make sure that the repetitions are performed at the same speed of movement and range of motion to ensure consistency. Additionally, on the setup portion, we should check the surroundings for hazards, set the bar to the right height for Vanessa based on her arm length, remove safety bars from both sides of the rack, secure weights correctly with safety clips, and have a spotter present. We should also make sure there is a proper progression of weight lifted, she’s not bouncing the bar off her chest, that she’s maintaining a neutral spine, and that she is breathing during the lift. For Lauren, because she has never participated in resistance training, we will select the flexed arm support test, as she may not be able to perform the dynamic push-up assessment properly. To start, she should have her hands outside her shoulders, and keep her back and legs straight. She should lower her body until the upper arm is parallel to the floor or the elbow is flexed at 90 degrees. She should be supporting her body in a push-up position from the knees. Some common mistakes include not breathing during the assessment, having a too-wide or too-narrow width for her hands, not maintaining a straight back and legs, and not lowering her body enough for the assessment.
Question: What is the metric that is measures in this paper?
Evidence: To test if the learned representations can separate phonetic categories, we use a minimal pair ABX discrimination task BIBREF19 , BIBREF20 . It only requires to define a dissimilarity function $d$ between speech tokens, no external training algorithm is needed. We define the ABX-discriminability of category $x$ from category $y$ as the probability that $A$ and $X$ are further apart than $B$ and $X$ when $A$ and $X$ are from category $x$ and $x$0 is from category $x$1 , according to a dissimilarity function $x$2 . Here, we focus on phone triplet minimal pairs: sequences of 3 phonemes that differ only in the central one (“beg”-“bag”, “api”-“ati”, etc.). For the within-speaker task, all the phones triplets belong to the same speaker (e.g. $x$3 ) Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the across-speaker task, $x$4 and $x$5 belong to the same speaker, and $x$6 to a different one (e.g. $x$7 ). The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.
To test if the learned representations can separate phonetic categories, we use a minimal pair ABX discrimination task BIBREF19 , BIBREF20 .
For the within-speaker task, all the phones triplets belong to the same speaker (e.g. $x$3 ) Finally the scores for every pair of central phones are averaged and subtracted from 1 to yield the reported within-talker ABX error rate. For the across-speaker task, $x$4 and $x$5 belong to the same speaker, and $x$6 to a different one (e.g. $x$7 ). The scores for a given minimal pair are first averaged across all of the pairs of speakers for which this contrast can be made. As above, the resulting scores are averaged over all contexts over all pairs of central phones and converted to an error rate.
Answer: error rate in a minimal pair ABX discrimination task
Question: I want to write a some new articles for my data blog, can you give me some snappy ideas?
Answer: Lakehouse vs Warehouse - Hype vs Reality
ETL in the 2020s - What's in and what's out
Is the Data Warehouse dead - modern analytics for the modern era
Open data sharing - The whys, hows and gotchas
Question: How is electricity generated?
Evidence: Electricity generation is the process of generating electric power from sources of primary energy. For utilities in the electric power industry, it is the stage prior to its delivery (transmission, distribution, etc.) to end users or its storage (using, for example, the pumped-storage method).

The first power plants used water power or coal. Today, various energy sources are used, such as coal, nuclear, natural gas, hydroelectric, wind, and oil, as well as solar energy, tidal power, and geothermal sources.
Answer: Electricity generation is the process of generating electric power from sources of primary energy. For utilities in the electric power industry, it is the stage prior to its delivery (transmission, distribution, etc.) to end users or its storage (using, for example, the pumped-storage method).

Electricity is not freely available in nature, so it must be "produced" (transforming other forms of energy into electricity). Production is carried out in power stations (also called "power plants"). Electricity is most often generated at a power plant by electromechanical generators, primarily driven by heat engines fueled by combustion or nuclear fission but also by other means such as the kinetic energy of flowing water and wind. Other energy sources include solar photovoltaics and geothermal power. There are also exotic and speculative methods to recover energy, such as proposed fusion reactor designs which aim to directly extract energy from intense magnetic fields generated by fast-moving charged particles generated by the fusion reaction.
Question: Does their solution involve connecting images and text?
Evidence: Once the S-V-O is generated, Text2Visual provides users with visual components that convey the S-V-O text meanings.
Once the S-V-O is generated, Text2Visual provides users with visual components that convey the S-V-O text meanings.
Answer: Yes.
Question: Identify which instrument is string or percussion: Janggu, Chapman Stick
Answer: Chapman Stick is string, Janggu is percussion.
Question: By how much do they outpeform previous results on the word discrimination task?
Evidence: FLOAT SELECTED: Table 1: Final test set results in terms of average precision (AP). Dimensionalities marked with * refer to dimensionality per frame for DTW-based approaches. For CNN and LSTM models, results are given as means over several training runs (5 and 10, respectively) along with their standard deviations.
FLOAT SELECTED: Table 1: Final test set results in terms of average precision (AP). Dimensionalities marked with * refer to dimensionality per frame for DTW-based approaches. For CNN and LSTM models, results are given as means over several training runs (5 and 10, respectively) along with their standard deviations.
Answer: Their best average precision tops previous best result by 0.202
Question: Identify which instrument is string or percussion: Wind machine, Langeleik
Answer: Langeleik is string, Wind machine is percussion.
Question: From this passage, extract the names of Hawking's siblings, including both by birth and through adoption, and separate by bullets.
Evidence: Hawking was born on 8 January 1942 in Oxford to Frank and Isobel Eileen Hawking (née Walker). Hawking's mother was born into a family of doctors in Glasgow, Scotland. His wealthy paternal great-grandfather, from Yorkshire, over-extended himself buying farm land and then went bankrupt in the great agricultural depression during the early 20th century. His paternal great-grandmother saved the family from financial ruin by opening a school in their home. Despite their families' financial constraints, both parents attended the University of Oxford, where Frank read medicine and Isobel read Philosophy, Politics and Economics. Isobel worked as a secretary for a medical research institute, and Frank was a medical researcher. Hawking had two younger sisters, Philippa and Mary, and an adopted brother, Edward Frank David (1955–2003).
https://en.wikipedia.org/wiki/Stephen_Hawking
Answer: The names of Hawking's siblings are:
-Philippa
-Mary
-Edward Frank David
Question: Tell me about Indiana
Answer: Indiana is a state in the Midwest region of the United States and has the motto "Crossroads of America" due to the large number of interstates that pass through the state. The capitol is Indianapolis, which is home to over 800,000 people. Indiana is known for its agriculture and the popularity of basketball, particularly the Indiana University Hoosiers basketball team.