DataHammer/scimrc huggingface dataset:
Question: When was the 68th Division of People's Republic of China deactivated?
Evidence: The 68th Division (Chinese: 第68师) was created in February 1949 under the Regulation of the Redesignations of All Organizations and Units of the Army, issued by Central Military Commission on November 1, 1948, basing on the 11th Division, 4th Column of the Huadong Field Army. Its history can be traced to the 2nd Brigade, 1st Division of Huadong Field Army, formed in October 1946.

The division was a part with 23rd Corps. It took part in several major battles during the Chinese Civil War, including the Lunan Campaign, the Laiwu Campaign, the Menglianggu Campaign, the Yudong Campaign, the Huaihai Campaign and the Shanghai Campaign. During the last phase of Huaihai campaign the division captured General Du Yuming, the deputy commander-in-chief of Suppression General Headquarters of Xuzhou Garrison.

The division was composed of 202nd, 203rd and 204th Infantry Regiments.

In August 1952 the division was inactivated:

Headquarters, 68th Division and Headquarters, 202nd Infantry Regiment were transferred to the People's Liberation Army Air Force;
202nd Infantry Regiment was transferred to 67th Division as 199th Infantry Regiment;
Headquarters, 203rd Infantry Regiment was transferred to the People's Liberation Army Navy.
Answer: The 68th Division of People's Republic of China was deactivated in 1952.
Question: What is Apu's wife's name on the Simpsons?
Answer: Manjula
Question: what sentiment sources do they compare with?
Evidence: As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 . These lexica contain general domain words (as opposed to Twitter or Bible). As gold standard for twitter domain we use emoticon dataset and perform emoticon sentiment prediction BIBREF16 , BIBREF17 .
As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 .
Answer: The answers are shown as follows:
* manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15
Question: Tell me which of the following are MLB teams: A's, Washing Machines, Ringed Tails, Giants, Yankees, Astros, Phillies, Cherries, Wild.
Answer: A's, Giants, Yankees, Astros, Phillies.
Question: Who was found guilty for the murder of Nualchawee Petchrung?
Evidence: Nualchawee Petchrung (Thai: นวลฉวี เพชรรุ่ง, also spelled Nuanchawee) was a Thai nurse who was murdered by her medical doctor husband, Athip Suyansethakarn, on 10 September 1959. The investigation and trial received sensational coverage in the media—Siang Ang Thong newspaper, which later became the country's top circulating daily Thai Rath, gained popularity from its coverage of the case, which regularly filled the front page—and the case became one of the best known murders in Thailand. Athip was found guilty and sentenced to death, but was later pardoned. Nonthaburi Bridge, where her body was disposed into the Chao Phraya River, is still commonly known as Nualchawee Bridge, and evidence from the case is on display at the Songkran Niyomsane Forensic Medicine Museum.
Answer: Athip Suyansethakarn was found guilty of murdering his wife, Nualchawee Petchrung.
Question: Which is a species of fish? Lizard or Lizardfish
Answer: Lizardfish
Question: Given a reference text about Shivaji, tell me when they were born, when they died, what their most notable accomplishment and what people called them post-death.
Evidence: Shivaji I (Shivaji Bhonsle; Marathi pronunciation: c.19 February 1630 – 3 April 1680), also referred to as Chhatrapati Shivaji Maharaj, was an Indian ruler and a member of the Bhonsle Maratha clan. Eventually, Shivaji carved out his own independent kingdom from the declining Adilshahi sultanate of Bijapur which formed the genesis of the Maratha Empire. In 1674, he was formally crowned the Chhatrapati of his realm at Raigad Fort.

Over the course of his life, Shivaji engaged in both alliances and hostilities with the Mughal Empire, the Sultanate of Golkonda, Sultanate of Bijapur and the European colonial powers. Shivaji's military forces expanded the Maratha sphere of influence, capturing and building forts, and forming a Maratha navy. Shivaji established a competent and progressive civil rule with well-structured administrative organisations. He revived ancient Hindu political traditions, court conventions and promoted the usage of the Marathi and Sanskrit languages, replacing Persian in court and administration.

Shivaji's legacy was to vary by observer and time, but nearly two centuries after his death, he began to take on increased importance with the emergence of the Indian independence movement, as many Indian nationalists elevated him as a proto-nationalist and hero of the Hindus.
Answer: Shivaji was born on 19th February 1630 and died on 3rd April 1680. Shivaji accomplished to  out his own independent kingdom from the declining Adilshahi sultanate of Bijapur which formed the genesis of the Maratha Empire. He is called as proto-nationalist and hero of the Hindus.
Question: Which model do they use to generate key messages?
Evidence: In order to find the object type, SimplerVoice, first, builds an ontology-based knowledge tree. Then, the system maps the object with a tree's leaf node based on the object's title. For instance, given the object's title as “Thomas' Plain Mini Bagels", SimplerVoice automatically defines that the object category is “bagel". Note that both the knowledge tree, and the mapping between object and object category are obtained based on text-based searching / crawling web, or through semantic webs' content. Figure FIGREF6 shows an example of the sub-tree for object category "bagel". While the mapped leaf node is the O in our S-V-O model, the parents nodes describe the more general object categories, and the neighbors indicate other objects' types which are similar to the input object. All the input object's type, the direct parents category, and the neighbors' are, then, put in the next step: generating verbs (V).
We propose to use 2 methods to generate the suitable verbs for the target object: heuristics-based, and n-grams model. In detail, SimplerVoice has a set of rule-based heuristics for the objects. For instance, if the object belongs to a "food | drink" category, the verb is generated as "eat | drink". Another example is the retrieved "play" verb if input object falls into "toy" category. However, due to the complexity of object's type, heuristics-based approach might not cover all the contexts of object. As to solve this, an n-grams model is applied to generate a set of verbs for the target object. An n-gram is a contiguous sequence of n items from a given speech, or text string. N-grams model has been extensively used for various tasks in text mining, and natural language processing field BIBREF14 , BIBREF15 . Here, we use the Google Books n-grams database BIBREF16 , BIBREF17 to generate a set of verbs corresponding to the input object's usage. Given a noun, n-grams model can provide a set of words that have the highest frequency of appearance followed by the noun in the database of Google Books. For an example, "eaten", "toasted", "are", etc. are the words which are usually used with "bagel". To get the right verb form, after retrieving the words from n-grams model, SimplerVoice performs word stemming BIBREF18 on the n-grams' output.
In order to find the object type, SimplerVoice, first, builds an ontology-based knowledge tree.
We propose to use 2 methods to generate the suitable verbs for the target object: heuristics-based, and n-grams model.
Answer: The answers are shown as follows:
* ontology-based knowledge tree
* heuristics-based
* n-grams model
Question: What are the number of provinces in Belgium?
Answer: There are ten provinces in Belgium. The provinces are West-Flanders, East-Flanders, Antwerp, Halnaut, Flemish-Brabant, Wallonia-Braband, Liege, Namur, Luxenbourg,
Question: What are the main contributions of this paper?
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 includes special tags for vulgar language and hate speech.
Answer: The authors built the largest Arabic offensive language dataset to date that includes special tags for vulgar language and hate speech.
Question: What is the significance of the moon's hidden face?
Answer: The Moon's hidden face is the hemisphere of this satellite that is not visible from Earth because the Moon always shows the same face. This is because the moon takes the same amount of time to rotate on its own axis (rotation) as it does to orbit the Earth (translation). Tidal coupling is the term used to describe this phenomenon. The first images of the Moon's far side were sent by the Soviet spacecraft Luna 3, the third spacecraft launched to the Moon, on October 7, 1959.
Question: Who is the best basketball team in the NBA?
Answer: The Los Angeles Lakers are undoubtedly the best basketball team in the history of the NBA.
Question: Who is this article most useful for?
Evidence: The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases.
The main problems in BioIE are similar to those in Information Extraction:
This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain.
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers.
Answer: For current NLP researchers.
Question: What datasets are used to evaluate the model?
Evidence: As we show below, STransE performs better than the SE and TransE models and other state-of-the-art link prediction models on two standard link prediction datasets WN18 and FB15k, so it can serve as a new baseline for KB completion. We expect that the STransE will also be able to serve as the basis for extended models that exploit a wider variety of information sources, just as TransE does.
As we show below, STransE performs better than the SE and TransE models and other state-of-the-art link prediction models on two standard link prediction datasets WN18 and FB15k, so it can serve as a new baseline for KB completion.
Answer: WN18, FB15k
Question: What are the pros and cons of living in Seattle?
Answer: Seattle is famous for its fusion of natural scenes ands city vibes. There are a lot of hiking spots and lakes in the Seattle area which would be a pro for people who enjoy nature. Seattle is also an important city for rock and roll music and pop culture. People widely consider the cons of living in Seattle to be the weather. One should expect seeing small rain and gloomy weather during the winter in Seattle.
Question: Extract from the text the main characteristics of the city of São Paulo. Separate them with a comma.
Evidence: São Paulo is the most populous city in Brazil, and is the capital of the state of São Paulo, the most populous and wealthiest Brazilian state, located in the country's Southeast Region. Listed by the GaWC as an alpha global city, São Paulo is the most populous city proper in the Americas, the Western Hemisphere, and the Southern Hemisphere, as well as the world's 4th largest city proper by population. Additionally, São Paulo is the largest Portuguese-speaking city in the world. It exerts strong international influences in commerce, finance, arts and entertainment. The city's name honors the Apostle, Saint Paul of Tarsus. The city's metropolitan area, the Greater São Paulo, ranks as the most populous in Brazil and the 12th most populous on Earth. The process of conurbation between the metropolitan areas around the Greater São Paulo (Campinas, Santos, Jundiaí, Sorocaba and São José dos Campos) created the São Paulo Macrometropolis, a megalopolis with more than 30 million inhabitants, one of the most populous urban agglomerations in the world.
Answer: most populous city in Brazil, most populous and wealthiest Brazilian state, located in the country's Southeast Region, Listed by the GaWC as an alpha global city, the most populous city proper in the Americas, the world's 4th largest city proper by population, São Paulo is the largest Portuguese-speaking city in the world, city's name honors the Apostle, Saint Paul of Tarsus, ranks as the most populous in Brazil and the 12th most populous on Earth
Question: Classify each of the following as a city, state, or country: Dallas, Bozeman, Kentucky, Ohio, USA, Canada
Answer: Dallas: City
Bozeman: City
Kentucky: State
Ohio: State
USA: Country
Canada: Country
Question: What are the main components of a drum set?
Answer: Drum sets, as the name would suggest, are comprised of a set of drums, cymbals, and other percussion instruments played together by the musician, usually known as a drummer. While drum sets vary widely in size depending on drummer preference and musical style, there are a number of key components included in almost every drum set:

Kick drum: this is a large bass drum played with a mallet driven by a foot pedal, often used as the base on top of which other drums and/or cymbals are attached. 

Snare drum: this is a high pitched drum with springs stretched over the bottom head, creating a unique clap noise.

Toms: these are drums attached to the kick drum or sitting on stands on the floor that are often used in drum fills.

Hi-hat cymbal: this is a set of two cymbals that can be controlled using a foot pedal, opening or closing to create a different sound when struck. Hi-hats are used to keep the beat.

Ride cymbal: this is a large, heavy cymbal used as an alternative to hi-hats, creating a splashier sound.

Crash cymbal: this is a medium or large cymbal often struck to make a loud, climactic crashing noise.
Question: How to overcome data sparsity between the source and the target languages?
Evidence: Consider the task of translating from an extremely low-resource language (source) to a target language. The parallel corpus between the two languages if available may be too small to train a NMT model. Similar to existing works BIBREF3 , BIBREF10 , BIBREF12 , we use transfer learning to overcome data sparsity and train a NMT model between the source and the target languages. Specifically, the NMT model (parent model) is trained on the assisting language and target language pairs. We choose English as the assisting language in all our experiments. In our resource-scarce scenario, we have no parallel corpus for the child task. Hence, at test time, the source language sentence is translated using the parent model after performing a word-by-word translation into the assisting language.
Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the contextual representations generated by the encoder for the two languages. In this paper, we propose to pre-order English sentences (assisting language sentences) to match the word-order of the source language and train the parent model on this pre-ordered corpus. In our experiments, we look at scenarios where the assisting language has SVO word order and the source language has SOV word order.
For instance, consider the English sentence Anurag will meet Thakur. One of the pre-ordering rule swaps the position of the noun phrase followed by a transitive verb with the transitive verb. The original and the resulting re-ordered parse tree will be as shown in the Table TABREF5 . Applying this reordering rule to the above sentence Anurag will meet Thakur will yield the reordered sentence Anurag Thakur will meet. Additionally, the Table TABREF5 shows the parse trees for the above sentence with and without pre-ordering.
Pre-ordering should also be beneficial for other word order divergence scenarios (e.g., SOV to SVO), but we leave verification of these additional scenarios for future work.
Consider the task of translating for an extremely low-resource language pair. The parallel corpus between the two languages, if available may be too small to train an NMT model. Similar to Zoph et al. (2016), we use transfer learning to overcome data sparsity between the source and the target languages.
Answer: The authors use transfer learning to overcome data sparsity between the source and the target languages.
Question: Identify different ways to achieve safe atmospheric entry for space shuttles and provide a bullet list.
Evidence: To return to Earth, the escape velocity of the Moon must be overcome for the spacecraft to escape the gravity well of the Moon. Rockets must be used to leave the Moon and return to space. Upon reaching Earth, atmospheric entry techniques are used to absorb the kinetic energy of a returning spacecraft and reduce its speed for safe landing. These functions greatly complicate a moon landing mission and lead to many additional operational considerations. Any moon departure rocket must first be carried to the Moon's surface by a moon landing rocket, increasing the latter's required size. The Moon departure rocket, larger moon landing rocket and any Earth atmosphere entry equipment such as heat shields and parachutes must in turn be lifted by the original launch vehicle, greatly increasing its size by a significant and almost prohibitive degree.
Answer: - Reduce speed and absorb kinetic energy on reentry.
- Use heat shields
- Use parachutes
Question: Give me some suggestions to stop my kids from whining.
Answer: 1. Explain that there are other ways to communicate.
2. Ignore your child's whining, step away if you can't.
3. Enforce discipline.
Question: Based on this paragraph, please summarize Cormac McCarthy's use of punctuation.
Evidence: McCarthy uses punctuation sparsely, even replacing most commas with "and" to create polysyndetons; it has been called "the most important word in McCarthy's lexicon". He told Oprah Winfrey that he prefers "simple declarative sentences" and that he uses capital letters, periods, an occasional comma, or a colon for setting off a list, but never semicolons, which he has labelled as "idiocy". He does not use quotation marks for dialogue and believes there is no reason to "blot the page up with weird little marks". Erik Hage notes that McCarthy's dialogue often lacks attribution, but that "Somehow ... the reader remains oriented as to who is speaking." His attitude to punctuation dates to some editing work he did for a professor of English while enrolled at the University of Tennessee, when he stripped out much of the punctuation in the book being edited, which pleased the professor. McCarthy edited fellow Santa Fe Institute Fellow W. Brian Arthur's influential article "Increasing Returns and the New World of Business", published in the Harvard Business Review in 1996, removing commas from the text. He has also done copy-editing work for physicists Lawrence M. Krauss and Lisa Randall.
Answer: Cormac McCarthy is known for his preference to avoid punctuation in situations when it is commonly used. Two typical examples of replacing commas with the word "and" and avoiding quotation marks when characters in his novels are speaking.
Question: List several ways to take ice cubes out of a glass?
Answer: - Pick each ice cube out of the glass
- Drop all ice cubes out of the glass by facing it down
Question: What kind of bears can you see in the Katmai National Park and Preserve?
Answer: Brown bears are a main attraction at the Katmai Park.
Question: Generally in socer games, which position scores goals more often: the strikers or the backs?
Answer: The strikers.