DataHammer/scimrc huggingface dataset:
Question: Jill, a 60-year-old personal training client, wants to improve her hip flexibility. Jill is concerned about her balance and the risk of falling. Recommend two exercises she can do to improve balance.
Answer: She can perform weight shifts, which is an easy exercise for someone of her age to perform to improve their balance. She should begin with feet hip-width apart, and then shift her weight to her right side, lifting her left foot off the floor. She can maintain this position for 30 seconds, return to the starting position, and repeat for her other side. Besides weight shifts, she can focus on exercises that target auxiliary muscles in her lower back and core, which help with balance. For example, she can consider doing partial sit-ups, which strengthen abdominal muscles, or if she finds that too difficult, she can perform cat and camel, which is where she arches her back, and then lets it slowly sag toward the floor while in modified pushup position (knees on the ground).
Question: On which data set was the Thai sentence segmentation experiment conducted?
Evidence: To identify the improvement from CVT, we compared the models that use different training processes: standard supervised training ( INLINEFORM0 ) and CVT ( INLINEFORM1 ). The model trained with CVT improves the accuracy in terms of the F1 score on both Thai and English datasets, as shown in tab:thairesult,tab:engresult (row (g) vs row (h)).
This experiment was conducted only on the UGWC dataset because no unlabeled data are available in the Orchid dataset, as mentioned in Section UID33 . The model improves the F1 score slightly, from 88.8% (row (g)) to 88.9% (row (h)) on the UGWC dataset. This result occurs because both the labeled and unlabeled data in the UGWC dataset are drawn from the same finance domain. The average number of new words found in a new unlabeled data passage is only 0.650, as shown in Table TABREF32 . Therefore, there is little additional information to be learned from unlabeled data.
CVT also improved the model on the IWSLT dataset, from an overall F1 score of 64.5% (row (g)) to 65.3% (row (h)) and from a 2-class F1 score of 81.7% to 82.7%. Because both the labeled and unlabeled data were collected from TED talks, the number of vocabulary words grows substantially more than in the UGWC dataset because the talks cover various topics. In this dataset, average 1.225 new words found in each new unlabeled data passage, as shown in Table TABREF32 ; consequently the model representation learns new information from these new words effectively.
This experiment was conducted only on the UGWC dataset because no unlabeled data are available in the Orchid dataset,
Answer: UGWC dataset.
Question: What is the 24 hour equivalent to 2PM?
Answer: 2PM in 24 hour time is 14:00.
Question: What time of year does school normally start in the United States?
Answer: School normally starts in early Fall, in either August or September
Question: Should I buy a house or invest in a stock market?
Answer: It depends on your goal. If your goal is to have your own place, then you should buy a house. If you are looking for a liquid assets that has a good potential upside, you should look into stocks. A diversified portfolio that includes stocks, real estate, gold, silver, bond etc. is preferred to non-diversified portfolios. Please consult with a financial advisor before making any financial decisions.
Question: Is the dataset built by authors the largest Arabic offensive language dataset to date?
Evidence: Disclaimer: Due to the nature of the paper, some examples contain highly offensive language and hate speech. They don't reflect the views of the authors in any way, and the point of the paper is to help fight such speech. Much recent interest has focused on the detection of offensive language and hate speech in online social media. Such language is often associated with undesirable online behaviors such as trolling, cyberbullying, online extremism, political polarization, and propaganda. Thus, offensive language detection is instrumental for a variety of application such as: quantifying polarization BIBREF0, BIBREF1, trolls and propaganda account detection BIBREF2, detecting the likelihood of hate crimes BIBREF3; and predicting conflict BIBREF4. In this paper, we describe our methodology for building a large dataset of Arabic offensive tweets. Given that roughly 1-2% of all Arabic tweets are offensive BIBREF5, targeted annotation is essential for efficiently building a large dataset. Since our methodology does not use a seed list of offensive words, it is not biased by topic, target, or dialect. Using our methodology, we tagged 10,000 Arabic tweet dataset for offensiveness, where offensive tweets account for roughly 19% of the tweets. Further, we labeled tweets as vulgar or hate speech. To date, this is the largest available dataset, which we plan to make publicly available along with annotation guidelines. We use this dataset to characterize Arabic offensive language to ascertain the topics, dialects, and users' gender that are most associated with the use of offensive language. Though we suspect that there are common features that span different languages and cultures, some characteristics of Arabic offensive language is language and culture specific. Thus, we conduct a thorough analysis of how Arabic users use offensive language. Next, we use the dataset to train strong Arabic offensive language classifiers using state-of-the-art representations and classification techniques. Specifically, we experiment with static and contextualized embeddings for representation along with a variety of classifiers such as a deep neural network classifier and Support Vector Machine (SVM).
The contributions of this paper are as follows:
We built the largest Arabic offensive language dataset to date that includes special tags for vulgar language and hate speech. We describe the methodology for building it along with annotation guidelines.
We performed thorough analysis of the dataset and described the peculiarities of Arabic offensive language.
We experimented with Support Vector Machine classifiers on character and word ngrams classification techniques to provide strong results on Arabic offensive language classification.
We built the largest Arabic offensive language dataset to date that is also labeled for vulgar language and hate speech and is not biased by topic or dialect. We describe the methodology for building it along with annotation guidelines.
Answer: Yes，it is.
Question: what datasets did they use?
Evidence: Single-Turn: This dataset consists of single-turn instances of statements and responses from the MiM chatbot developed at Constellation AI BIBREF5. The responses provided were then evaluated using Amazon Mechanical Turk (AMT) workers. A total of five AMT workers evaluated each of these pairs. The mean of the five evaluations is then used as the target variable. A sample can be seen in Table TABREF3. This dataset was used during experiments with results published in the Results section.
Multi-Turn: This dataset is taken from the ConvAI2 challenge and consists of various types of dialogue that have been generated by human-computer conversations. At the end of each dialogue, an evaluation score has been given, for each dialogue, between 1–4.
Single-Turn: This dataset consists of single-turn instances of statements and responses from the MiM chatbot developed at Constellation AI BIBREF5.
Multi-Turn: This dataset is taken from the ConvAI2 challenge and consists of various types of dialogue that have been generated by human-computer conversations.
Answer: The answers are shown as follows:
* Single-Turn
* Multi-Turn
Question: what are the most popular sports in america
Answer: The most popular sports in america are football, baseball, basketball, hockey
Question: Identify which instrument is string or percussion: Turkish crescent, Banjo
Answer: Banjo is string, Turkish crescent is percussion.
Question: Eggs are expensive these days. Should I rear my own chickens instead?
Answer: Chickens typically take around 18 weeks to start laying eggs. Of course, this depends on the age and the breed of the chickens as well. You wouldn’t expect a newborn to start producing eggs. A hen does not lay eggs in cold seasons. So, if you can wait for your chickens to lay eggs and don’t mind spending on raising chickens, buying a flock of chickens at $30 to start might not be a bad idea.
Question: Name some masterpieces of Russian Literature
Answer: * War and Peace, Leo Tolstoy.
* Crime and Punishment, Fyodor Dostoevsky.
* Evgenyi Onegin, Alexander Pushkin.
* Dead Souls, Nikolay Gogol.
* Woe from Wit,Alexander Griboedov.
* The Master and Margarita, Mikhail Bulgakov.
* Oblomov, Ivan Goncharov.
Question: Given this reference about the Battle of Okinawa during WW2, what was the difference in between the US (77th Infantry Division) and the Japanese casualties and wounded?
Evidence: The land battle took place over about 81 days beginning on 1 April 1945. The first Americans ashore were soldiers of the 77th Infantry Division who landed in the Kerama Islands, 15 mi (24 km) west of Okinawa on 26 March. Subsidiary landings followed, and the Kerama group was secured over the next five days. In these preliminary operations, the 77th Infantry Division suffered 27 dead and 81 wounded, while the Japanese dead and captured numbered over 650. The operation provided a protected anchorage for the fleet and eliminated the threat from suicide boats.
Answer: The difference was about 542
Question: How many domains of ontologies do they gather data from?
Evidence: The Software Ontology (SWO) BIBREF5 is included because its set of CQs is of substantial size and it was part of Ren et al.'s set of analysed CQs. The CQ sets of Dem@Care BIBREF8 and OntoDT BIBREF9 were included because they were available. CQs for the Stuff BIBREF6 and African Wildlife (AWO) BIBREF7 ontologies were added to the set, because the ontologies were developed by one of the authors (therewith facilitating in-depth domain analysis, if needed), they cover other topics, and are of a different `type' (a tutorial ontology (AWO) and a core ontology (Stuff)), thus contributing to maximising diversity in source selection.
The Software Ontology (SWO) BIBREF5 is included because its set of CQs is of substantial size and it was part of Ren et al.'s set of analysed CQs. The CQ sets of Dem@Care BIBREF8 and OntoDT BIBREF9 were included because they were available. CQs for the Stuff BIBREF6 and African Wildlife (AWO) BIBREF7 ontologies were added to the set, because the ontologies were developed by one of the authors (therewith facilitating in-depth domain analysis, if needed), they cover other topics, and are of a different `type' (a tutorial ontology (AWO) and a core ontology (Stuff)), thus contributing to maximising diversity in source selection.
Answer: 5 domains: software, stuff, african wildlife, healthcare, datatypes
Question: How many years did the Pony Express mail delivery service last?
Answer: The Pony Express was in operation for just around one-and-a-half years, from April 1860 to October 1861.
Question: Tell whether these are countries, cities, continents or neither: Australia, Mexico, Mexico city, Antartica, Singapore, India, Gulf, Beijing, Jakarta, Luxembourg, Vatican City
Answer: Australia is a country as well as a continent.
Mexico is a country.
Mexico City is a city.
Antarctica is a continent.
Singapore is a country as well as a city.
India is a country.
Gulf is neither.
Beijing is a city.
Jakarta is a city.
Luxembourg is a country as well as a city.
Vatican City is a country as well as a city.
Question: List terrible things to step on with bare feet.
Answer: - Lego
- Dead mouse
- Slug
- Dog poop
- Matchbox cars
- Glass
- Rusty Nail
- Thumbtack
- Ice
- Plastic green army men
Question: Please give me details about the French submarine Requin
Answer: The French submarine Requin was the lead ship of the Requin-class submarines built for the French Navy in the mid-1920s. Laid down in June 1922, it was launched in July 1924 and commissioned in May 1926. It was captured by Italian forces at Bizerte, Tunisia on 8 December 1942 and renamed FR 113. On 9 September 1943, it was recaptured by German forces. It was sold for scrap in Genes, Italy in 1944.

Design
78 m (255 ft 11 in) long, with a beam of 6.8 m (22 ft 4 in) and a draught of 5.1 m (16 ft 9 in), Requin-class submarines could dive up to 80 m (260 ft). The submarine had a surfaced displacement of 1,150 tonnes (1,132 long tons) and a submerged displacement of 1,441 tonnes (1,418 long tons). Propulsion while surfaced was provided by two 2,900 hp (2,163 kW) diesel motors and two 1,800 hp (1,342 kW) electric motors. The submarines' electrical propulsion allowed it to attain speeds of 9 knots (17 km/h; 10 mph) while submerged and 15 knots (28 km/h; 17 mph) on the surface. Their surfaced range was 7,700 nautical miles (14,300 km) at 9 knots (17 km/h), and 4,000 nautical miles (7,400 km) at 12 knots (22 km/h), with a submerged range of 70 nautical miles (130 km) at 5 knots (9.3 km/h).
Question: Why playing Phase 10 make people so angry?
Answer: Unlike a similar card game, Uno, players can target one another in a more direct way.  This can cause players to feel singled out and targeted which may feel unfair.  Skip cards can be given to anyone which means that everyone can skip the same person if they wanted to or make someone draw card after card until they are so far behind there is no reason for them to continue to play the game.  This can be frustrating and cause players to lash out.
Question: Can airplanes land in windy conditions?
Answer: Yes, airplanes can generally land in windy conditions, but there are limits. Strong, stable winds along the runway direction (or close to it) is generally not a problem. The most challenging are gusty winds and crosswinds. Gusty winds make a stabilized approach to the runway more difficult, while crosswinds make it harder to stay aligned with the runway.
Question: Name some famous breweries in Seattle
Answer: Reuben's, Georgetown, Stoup, Machine House, Future Primitive, Red Hook, Bale Breaker
Question: Classify the following based on the category of signals they use.
radio waves, television waves, sound waves, Computers, Digital Phones, Digital pens
Answer: Analog signal- radio waves, television waves, sound waves
Digital signal- Computers, Digital Phones, Digital pens
Question: What Indian sport is like baseball?
Answer: Baseball is bat-ball game played in field and involved scoring runs, Baseball game is played with 9 players per team and played with two teams in a game. The fielding team’s positions are made up of a pitcher, catcher, first baseman, second baseman, shortstop, third baseman, and three outfielders at left field, centre field and right field. While the rules of playing an Indian sport like baseball may be different, the intent of the game is still about scoring runs and involve fielders with similar responsibilities which is called CRICKET.
Question: what bicycle should I buy?
Answer: Buying the right bicycle depends on how much money you want to spend and what you want to use the bicycle for.  So first decide on a budget, then go to a local bike shop and talk to them about what kinds of bicycles they sell for the needs that you have.
Question: Please fix the typos and grammar issues in the following text and reply with the updated text:

I wuld love to go te cynema but I dont like the corrent movies.
Answer: I would love to go to the cinema but I don't like the current movies.
Question: What American city is referred to as The City of Roses?
Answer: Portland, Oregon is referred to as The City of Roses, even though roses are not native to the region.
Question: I'm considering buying football boots. How do I know which one to buy?
Answer: Choosing the correct football boot depends on several factors. Of primary importance would be the surface you are playing on. Certain soleplate patterns are optimized for Hard Ground, Firm Ground, Soft Ground and Artificial Grass. Another important factor is the position you play. Midfielders prefer boots with more control, forwards prefer boots with a better striking face, while wingers prefer lightweight boots that are optimized for speed.
Question: Who are the experts?
Evidence: The prediction of outcomes of debates is very interesting in our case. Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post. This implies that certain rules that were used to score the candidates in the debates by said-experts were in fact reflected by reading peoples' sentiments expressed over social media. This opens up a wide variety of learning possibilities from users' sentiments on social media, which is sometimes referred to as the wisdom of crowd.
Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post.
Answer: The answers are shown as follows:
* political pundits of the Washington Post
Question: What does their analysis indicate?
Evidence: The NLP community is revisiting the role of linguistic structure in applications with the advent of contextual word representations (cwrs) derived from pretraining language models on large corpora BIBREF2, BIBREF3, BIBREF4, BIBREF5. Recent work has shown that downstream task performance may benefit from explicitly injecting a syntactic inductive bias into model architectures BIBREF6, even when cwrs are also used BIBREF7. However, high quality linguistic structure annotation at a large scale remains expensive—a trade-off needs to be made between the quality of the annotations and the computational expense of obtaining them. Shallow syntactic structures (BIBREF8; also called chunk sequences) offer a viable middle ground, by providing a flat, non-hierarchical approximation to phrase-syntactic trees (see Fig. FIGREF1 for an example). These structures can be obtained efficiently, and with high accuracy, using sequence labelers. In this paper we consider shallow syntax to be a proxy for linguistic structure.
While shallow syntactic chunks are almost as ubiquitous as part-of-speech tags in standard NLP pipelines BIBREF9, their relative merits in the presence of cwrs remain unclear. We investigate the role of these structures using two methods. First, we enhance the ELMo architecture BIBREF0 to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context (§SECREF2). Our second method involves classical addition of chunk features to cwr-infused architectures for four different downstream tasks (§SECREF3). Shallow syntactic information is obtained automatically using a highly accurate model (97% $F_1$ on standard benchmarks). In both settings, we observe only modest gains on three of the four downstream tasks relative to ELMo-only baselines (§SECREF4).
Recent work has probed the knowledge encoded in cwrs and found they capture a surprisingly large amount of syntax BIBREF10, BIBREF1, BIBREF11. We further examine the contextual embeddings obtained from the enhanced architecture and a shallow syntactic context, using black-box probes from BIBREF1. Our analysis indicates that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings (§SECREF18).
Overall, our findings show that while shallow syntax can be somewhat useful, ELMo-style pretraining discovers representations which make additional awareness of shallow syntax largely redundant.
Our analysis indicates that our shallowsyntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings.
Answer: Their shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings.
Question: Im going to the beach, which of these things should I avoid bringing?  Sunglasses, towel, beachball, kite, refrigerator, cooler, and portable chairs.
Answer: why would you bring a refrigerator to the beach?
Question: what is the name of the podcast hosted by luke burbank?
Answer: TBTL
Question: How much more water does a bath use than a shower?
Answer: If taking a 5 minute shower, a bath uses 75% more water.
Question: What kind of swimming pool was used in the 1908 Summer Olympics in London?
Evidence: At the 1908 Summer Olympics in London, six swimming events were contested. These were the first Olympic Games in which a 100-metre pool had been especially constructed (inside the main stadium's track and field oval). Previous Olympic events were swum in open water (1896: The Mediterranean Sea, 1900: The Seine River, 1904: an artificial lake). Only men participated in the swimming events. The competitions were held from Monday, July 13, 1908, to Saturday, July 25, 1908.

All six of the 1908 events became staples of the Olympic swimming programme, and have been contested at every edition of the Games since. The 50 m, 200 m, and 800 m freestyle events were dropped from the previous edition (though the 50 m and 200 m would later return), the relay was lengthened from 4×50 yards to 4×200 m, and the 400 m breaststroke was shortened to 200 metres.
Answer: The 1908 Summer Olympics swimming competition was held in a specially constructed 100 meter swimming pool.
Question: Which is a species of fish? Bream or Cream
Answer: Bream
Question: What is used to define new cross-lingual document classification tasks for eight very different languages?
Evidence: There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions.
The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification.
We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese.
Answer: The authors use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages.