DataHammer/scimrc huggingface dataset:
Question: What do the authors predict about the sentences?
Evidence: We created a new dataset called CW-USPD-2016 (check-worthiness in the US presidential debates 2016) for finding check-worthy claims in context. In particular, we used four transcripts of the 2016 US election: one vice-presidential and three presidential debates. For each debate, we used the publicly-available manual analysis about it from nine reputable fact-checking sources, as shown in Table TABREF7. This could include not just a statement about factuality, but any free text that journalists decided to add, e.g., links to biographies or behavioral analysis of the opponents and moderators. We converted this to binary annotation about whether a particular sentence was annotated for factuality by a given source. Whenever one or more annotations were about part of a sentence, we selected the entire sentence, and when an annotation spanned over multiple sentences, we selected each of them.
Ultimately, we ended up with a dataset of four debates, with a total of 5,415 sentences. The agreement between the sources was low as Table TABREF8 shows: only one sentence was selected by all nine sources, 57 sentences by at least five, 197 by at least three, 388 by at least two, and 880 by at least one. The reason for this is that the different media aimed at annotating sentences according to their own editorial line, rather than trying to be exhaustive in any way. This suggests that the task of predicting which sentence would contain check-worthy claims will be challenging. Thus, below we focus on a ranking task rather than on absolute predictions. Moreover, we predict which sentence would be selected (i) by at least one of the media, or (ii) by a specific medium.
Note that the investigative journalists did not select the check-worthy claims in isolation. Our analysis shows that these include claims that were highly disputed during the debate, that were relevant to the topic introduced by the moderator, etc. We will make use of these contextual dependencies below, which is something that was not previously tried in related work.
Moreover, we predict which sentence would be selected (i) by at least one of the media, or (ii) by a specific medium.
Answer: The authors predict which sentence would be selected (i) by at least one of the media, or (ii) by a specific medium.
Question: What plant do cats like to chew or sniff, which may give them a "high"?
Answer: Many cats enjoy chewing or sniffing fresh and dried catnip.
Question: What semantic features help in detecting whether a piece of text is genuine or generated? of 
Evidence: Many practical fake news detection algorithms use a kind of semantic side information, such as whether the generated text is factually correct, in addition to its statistical properties. Although statistical side information would be straightforward to incorporate in the hypothesis testing framework, it remains to understand how to cast such semantic knowledge in a statistical decision theory framework.
Many practical fake news detection algorithms use a kind of semantic side information, such as whether the generated text is factually correct, in addition to its statistical properties. Although statistical side information would be straightforward to incorporate in the hypothesis testing framework, it remains to understand how to cast such semantic knowledge in a statistical decision theory framework.
Answer: No feature is given, only discussion that semantic features are use in practice and yet to be discovered how to embed that knowledge into statistical decision theory framework.
Question: Classify the cities based on whether they are located in Chile or Antigua and Barbuda.
Concepción, Saint John’s, Santiago, Puente Alto
Answer: Chile - Santiago, Concepción, Puente Alto
Antigua and Barbuda - Saint John’s
Question: What are the main sources of recall errors in the mapping?
Evidence: We present the intrinsic task's recall scores in tab:recall. Bear in mind that due to annotation errors in the original corpora (like the vas example from sec:resources), the optimal score is not always $100\%$ . Some shortcomings of recall come from irremediable annotation discrepancies. Largely, we are hamstrung by differences in choice of attributes to annotate. When one resource marks gender and the other marks case, we can't infer the gender of the word purely from its surface form. The resources themselves would need updating to encode the relevant morphosyntactic information. Some languages had a very low number of overlapping forms, and no tag matches or near-matches between them: Arabic, Hindi, Lithuanian, Persian, and Russian. A full list of observed, irremediable discrepancies is presented alongside the codebase.
Because our conversion rules are interpretable, we identify shortcomings in both resources, using each as validation for the other. We were able to find specific instances of incorrectly applied UniMorph annotation, as well as specific instances of cross-lingual inconsistency in both resources. These findings will harden both resources and better align them with their goal of universal, cross-lingual annotation.
irremediable annotation discrepancies
Some shortcomings of recall come from irremediable annotation discrepancies. Largely, we are hamstrung by differences in choice of attributes to annotate. When one resource marks gender and the other marks case, we can't infer the gender of the word purely from its surface form. The resources themselves would need updating to encode the relevant morphosyntactic information. Some languages had a very low number of overlapping forms, and no tag matches or near-matches between them: Arabic, Hindi, Lithuanian, Persian, and Russian. A full list of observed, irremediable discrepancies is presented alongside the codebase.
We were able to find specific instances of incorrectly applied UniMorph annotation, as well as specific instances of cross-lingual inconsistency in both resources. 
Answer: The answers are shown as follows:
* irremediable annotation discrepancies
* differences in choice of attributes to annotate
* The resources themselves would need updating to encode the relevant morphosyntactic information. Some languages had a very low number of overlapping forms, and no tag matches or near-matches between them
* the two annotations encode distinct information
* incorrectly applied UniMorph annotation
* cross-lingual inconsistency in both resources
Question: what is the size of the dataset?
Evidence: In particular, the training dataset contains 434 unique articles with duplicates. These articles have three reposts each on average, with the most reposted article appearing 45 times. If we take into account the labels of the reposted articles, we can see that if an article is reposted, it is more likely to be fake news. The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322. We detect the duplicates based on their titles as far as they are distinctive enough and the content is sometimes slightly modified when reposted.
The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322.
Answer: The answers are shown as follows:
* The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322.
Question: How long did dinosaurs rule earth?
Evidence: Dinosaurs are a diverse group of reptiles[note 1] of the clade Dinosauria. They first appeared during the Triassic period, between 245 and 233.23 million years ago (mya), although the exact origin and timing of the evolution of dinosaurs is a subject of active research. They became the dominant terrestrial vertebrates after the Triassic–Jurassic extinction event 201.3 mya and their dominance continued throughout the Jurassic and Cretaceous periods. The fossil record shows that birds are feathered dinosaurs, having evolved from earlier theropods during the Late Jurassic epoch, and are the only dinosaur lineage known to have survived the Cretaceous–Paleogene extinction event approximately 66 mya. Dinosaurs can therefore be divided into avian dinosaurs—birds—and the extinct non-avian dinosaurs, which are all dinosaurs other than birds.
Answer: Dinosaurs were the dominant terrestrial species on earth for about 135.3 million years.
Question: Are the intent labels imbalanced in the dataset?
Evidence: For in-cabin intent understanding, we described 4 groups of usages to support various natural commands for interacting with the vehicle: (1) Set/Change Destination/Route (including turn-by-turn instructions), (2) Set/Change Driving Behavior/Speed, (3) Finishing the Trip Use-cases, and (4) Others (open/close door/window/trunk, turn music/radio on/off, change AC/temperature, show map, etc.). According to those scenarios, 10 types of passenger intents are identified and annotated as follows: SetDestination, SetRoute, GoFaster, GoSlower, Stop, Park, PullOver, DropOff, OpenDoor, and Other. For slot filling task, relevant slots are identified and annotated as: Location, Position/Direction, Object, Time Guidance, Person, Gesture/Gaze (e.g., `this', `that', `over there', etc.), and None/O. In addition to utterance-level intents and slots, word-level intent related keywords are annotated as Intent. We obtained 1331 utterances having commands to AMIE agent from our in-cabin dataset. We expanded this dataset via the creation of similar tasks on Amazon Mechanical Turk BIBREF21 and reached 3418 utterances with intents in total. Intent and slot annotations are obtained on the transcribed utterances by majority voting of 3 annotators. Those annotation results for utterance-level intent types, slots and intent keywords can be found in Table TABREF7 and Table TABREF8 as a summary of dataset statistics.
FLOAT SELECTED: Table 2: AMIE Dataset Statistics: Slots and Intent Keywords
 Those annotation results for utterance-level intent types, slots and intent keywords can be found in Table TABREF7 and Table TABREF8 as a summary of dataset statistics.
FLOAT SELECTED: Table 2: AMIE Dataset Statistics: Slots and Intent Keywords
Answer: Yes.
Question: Who is the rbr formula 1 team principal?
Answer: The Red Bull Racing team principal is Christian Horner
Question: What is the most dangerous spider?
Answer: It is hard to pinpoint the most dangerous spider because such a characterization involves not just how dangerous the spider's venom is, but also how aggressive the spider is, and how likely it is to bite someone.

The Brazillian Wandering Spider and Sydney Funnel Web spider both have very dangerous venom that can be lethal to humans.

Although the Brazillian Wandering Spider's venom is more dangerous, it often produces a "dry bite" where it doesn't inject venom.

The Sydney Funnel Web spider, on the other hand, is known to be aggressive and doesn't have the ability to perform a "dry bite".
Question: Which of the following are breeds of dogs:
Dachshund, Tabby, Russian Blue, Airedale Terrier, Papillon, Bengal, Greyhound
Answer: Dachshund, Airedale Terrier, Papillon and Greyhound are all breeds of dogs.
Tabby, Russian Blue and Bengal are breeds of cats.
Question: Does proposed end-to-end approach learn in reinforcement or supervised learning manner?
Evidence: To train our model, we generated a dataset of 20,000 demonstrated 7 DOF trajectories (6 robot joints and 1 gripper dimension) in our simulated environment together with a sentence generator capable of creating natural task descriptions for each scenario. In order to create the language generator, we conducted an human-subject study to collect sentence templates of a placement task as well as common words and synonyms for each of the used features. By utilising these data, we are able to generate over 180,000 unique sentences, depending on the generated scenario.
To test our model, we generated 500 new scenario testing each of the three features to identify the correct target among other bowls. A task is considered to be successfully completed when the cube is withing the boundaries of the targeted bowl. Bowls have a bounding box of 12.5 and 17.5cm edge length for the small and large variant, respectively. Our experiments showed that using the objects color or shape to uniquely identify an object allows the robot successfully complete the binning task in 97.6% and 96.0% of the cases. However, using the shape alone as a unique identifier, the task could only be completed in 79.0% of the cases. We suspect that the loss of accuracy is due to the low image resolution of the input image, preventing the network from reliably distinguishing the object shapes. In general, our approach is able to actuate the robot with an target error well below 5cm, given the target was correctly identified.
To train our model, we generated a dataset of 20,000 demonstrated 7 DOF trajectories (6 robot joints and 1 gripper dimension) in our simulated environment together with a sentence generator capable of creating natural task descriptions for each scenario. In order to create the language generator, we conducted an human-subject study to collect sentence templates of a placement task as well as common words and synonyms for each of the used features. By utilising these data, we are able to generate over 180,000 unique sentences, depending on the generated scenario.
To test our model, we generated 500 new scenario testing each of the three features to identify the correct target among other bowls. 
Answer: supervised learning
Question: Let's think of a two different methods to learn how to skateboard
Answer: One way to learn how to skateboard is to purchase a skateboard, new or used, and practice riding it. Once you feel comfortable riding, you can begin practicing tricks. This would be considered a "self-taught" method of learning how to ride a skateboard. Another method would be "instructional." Instructional method of learning to skateboard would include formal training from an expert or someone proficient in skateboarding. This person or people would help accelerate your knowledge by giving you tips about how to execute riding a skateboard and doing tricks.
Question: What are some common ways that adults break bones?
Answer: - Falling off of a ladder
- Falling down the stairs
- Playing recreational sports
- Being in a car crash
Question: Five reasons to watch good doctor
Answer: It has balance between the medicine and the human connection
There are no overly dramatic stories the episodes are good point and it stick around it
The series has connection to current affairs .
The stories shows doctor side and renews our faith in the health care system .
Though the story moves around Murphy but still all characters are equal
Question: What is the best way to answer an interview question?
Answer: The first recommended step is to ask clarifying questions to ensure you understand the question correctly and gather the requirements needed to answer effectively. If it is a mathematical or computer science question, go ahead and work your way through the problem while you verbalize your thought process along the way. If it is a situation question, you should use the STAR method. 1. Situation - set the scene and provide context, 2. Task - describe what was needed to be done and why, 3. Action - explain what action you took, and 4. Result - describe the result of your actions and what you learned from the experience
Question: Create a workout to strengthen my upper body
Answer: If you have access to weights, a workout might start with four sets of bench press and four sets of upright row at 6 to 10 reps each to strengthen your chest and back. If you don’t have access to weights you could replace those exercises with some combination of push ups, pull ups, and chin ups. Next move into three sets of shoulder press and rear delt raises for ten reps each. Finally, do three sets of bicep curls and three sets of tricep dips for ten and thirty reps respectively. The weights and repetitions for each exercise should scale with your comfort and ability doing that exercise. 

When working out, it’s very important to use good form and not overextend yourself. When lifting heavy weights it’s a good idea to get help from a spotter, and in particular you should never do bench press alone. Don’t fall into the trap of pushing too hard and risk your personal safety - you’ll get a lot stronger if you avoid injuries!
Question: What stylistic features are used to detect drunk texts?
Evidence: FLOAT SELECTED: Table 1: Our Feature Set for Drunk-texting Prediction
FLOAT SELECTED: Table 1: Our Feature Set for Drunk-texting Prediction
Answer: LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors,  Spelling errors, Repeated characters, Capitalisation, Length,  Emoticon (Presence/Count ) 
 and Sentiment Ratio
Question: What does WHO refer to?
Answer: WHO is an acronym for the World Health Organization.
Question: Tell me whether these are the names of presidents or movie stars: George Washington, Barack Obama, Brad Pitt, George Clooney, Matt Damon
Answer: George Washington was the first U.S. President
Barack Obama was the 44th U.S. President
Brad Pitt is a movie star known for starring in Fight Club, Once Upon a Time in Hollywood and Troy
George Clooney is a movie star known for starring in Ocean's Eleven, Batman & Robin and ER
Matt Damon is a movie star known for starring in Good Will Hunting, The Bourne Identity and Ford v Ferrari
Question: From the text, provide me with the number of medals Michael Phelps won in the 2016 olympics. Separate them in bullet points by number of gold, silver and bronze medals won.
Evidence: Michael Fred Phelps II (born June 30, 1985) is an American former competitive swimmer. He is the most successful and most decorated Olympian of all time with a total of 28 medals. Phelps also holds the all-time records for Olympic gold medals (23), Olympic gold medals in individual events (13), and Olympic medals in individual events (16). At the 2004 Summer Olympics in Athens, Phelps tied the record of eight medals of any color at a single Games, held by gymnast Alexander Dityatin, by winning six gold and two bronze medals. Four years later, when he won eight gold medals at the 2008 Beijing Games, he broke fellow American swimmer Mark Spitz's 1972 record of seven first-place finishes at any single Olympic Games. At the 2012 Summer Olympics in London, Phelps won four gold and two silver medals, and at the 2016 Summer Olympics in Rio de Janeiro, he won five gold medals and one silver. This made him the most successful athlete of the Games for the fourth Olympics in a row.
Answer: Michael Phelps won 6 medals in the 2016 olympic games.

Gold medals: 5
Silver medals: 1
Bronze medals: 0
Question: What is a digital camera?
Answer: A digital camera is a hand held device that captures images with a push of a button.  These images are usually stored on a memory disk or card which can be downloaded to a computer at a later time.  Digital cameras were also part of a revolution that replaced photographic film.  Digital cameras use flashes when no natural light is available.  They produce better picture quality than traditional photographic film cameras and there is no cost to develop pictures and print them in order to enjoy them.
Question: In one sentence, what is this paragraph conveying?
Evidence: A person's last words, their final articulated words stated prior to death or as death approaches, are often recorded because of the decedent's fame, but sometimes because of interest in the statement itself. (People dying of illness are frequently inarticulate at the end, and in such cases their actual last utterances may not be recorded or considered very important.) Last words may be recorded accurately, or, for a variety of reasons, may not. Reasons can include simple error or deliberate intent. Even if reported wrongly, putative last words can constitute an important part of the perceived historical records or demonstration of cultural attitudes toward death at the time.
Answer: The paragraph talk about the finals recorded words a person says at the time of death, and how they could be historically or culturally significance.
Question: Which of the following cast members have names that start with the letters A, B or P?
Evidence: Balraj Sahni as Abdul Rehman Khan
Sonu as Mini
Usha Kiran as Rama, Mini's mother
Padma
Laxmi
Sarita Devi
Anwari Bai
Leela Agha
Baby Farida
Asit Sen as Bhola
Answer: Balraj Sahni, Padma, Anwari Bai, Baby Farida, Asit Sen
Question: Give me a list of the ten best romance novels of all time
Answer: Outlander, The Notebook, Wuthering Heights, Jane Eyre, Pride and Prejudice, The Time Travelers Wife, The Thorn Birds, Gone With the Wind, The Proposal, Anna Karenina
Question: What did Bobby Moore die from?
Evidence: Moore's first cancer was in 1964, two years before England's first World Cup win — a diagnosis of testicular cancer, treated by orchidectomy of one; it had not spread. In April 1991, Moore underwent an operation for suspected colorectal cancer. At the time it was reported as an "emergency stomach operation".

On 14 February 1993, he announced he was suffering from colorectal cancer and liver cancer; by this time it had spread. Three days later he commentated on an England match against San Marino at Wembley, alongside his friend Jonathan Pearce. Moore attended a dinner after the match and made a presentation. That was to be his final public act; seven days later on 24 February, at 6:36 am, he died at the age of 51.
Answer: Booby Moore died aged 51 from colorectal and liver cancer.
Question: What do the authors find?
Evidence: We find that exposing cwr-based models to shallow syntax, either through new cwr learning architectures or explicit pipelined features, has little effect on their performance, across several tasks. Linguistic probing also shows that cwrs aware of such structures do not improve task transferability. Our architecture and methods are general enough to be adapted for richer inductive biases, such as those given by full syntactic trees (RNNGs; BIBREF32), or to different pretraining objectives, such as masked language modeling (BERT; BIBREF5); we leave this pursuit to future work.
We find that exposing CWR-based models to shallow syntax, either through new CWR learning architectures or explicit pipelined features, has little effect on their performance, across several tasks.
Answer: Exposing cwr-based models to shallow syntax, either through new cwr learning architectures or explicit pipelined features, has little effect on their performance, across several tasks.
Question: Identify which car manufacturer is Italian or American: Pagani, Panoz
Answer: Pagani is Italian, Panoz is American
Question: Who is Antonio Jose de Sucre?
Evidence: Antonio José de Sucre y Alcalá (Spanish pronunciation: [anˈtonjo xoˈse ðe ˈsukɾe j alkaˈla] (listen); 3 February 1795 – 4 June 1830), known as the "Gran Mariscal de Ayacucho" (English: "Grand Marshal of Ayacucho"), was a Venezuelan independence leader who served as the president of Peru and as the second president of Bolivia. Sucre was one of Simón Bolívar's closest friends, generals and statesmen.
Answer: Antonio José de Sucre y Alcalá (Spanish pronunciation: [anˈtonjo xoˈse ðe ˈsukɾe j alkaˈla] (listen); 3 February 1795 – 4 June 1830), known as the "Gran Mariscal de Ayacucho" (English: "Grand Marshal of Ayacucho"), was a Venezuelan independence leader who served as the president of Peru and as the second president of Bolivia. Sucre was one of Simón Bolívar's closest friends, generals and statesmen.

Due to his influence on geopolitical affairs of Latin America, a number of notable localities on the continent now bear Sucre's name. These include the eponymous capital of Bolivia, the Venezuelan state, the department of Colombia and both the old and new airports of Ecuador's capital Quito. Additionally, many schools, streets and districts across the region bear his name as well.