DataHammer/scimrc huggingface dataset:
Question: What were the five English subtasks?
Evidence: Determining the sentiment polarity of tweets has become a landmark homework exercise in natural language processing (NLP) and data science classes. This is perhaps because the task is easy to understand and it is also easy to get good results with very simple methods (e.g. positive - negative words counting). The practical applications of this task are wide, from monitoring popular events (e.g. Presidential debates, Oscars, etc.) to extracting trading signals by monitoring tweets about public companies. These applications often benefit greatly from the best possible accuracy, which is why the SemEval-2017 Twitter competition promotes research in this area. The competition is divided into five subtasks which involve standard classification, ordinal classification and distributional estimation. For a more detailed description see BIBREF0 .
The competition is divided into five subtasks which involve standard classification, ordinal classification and distributional estimation. For a more detailed description see BIBREF0 .
Answer: The answers are shown as follows:
*  five subtasks which involve standard classification, ordinal classification and distributional estimation. For a more detailed description see BIBREF0
Question: What is the network architecture?
Evidence: FLOAT SELECTED: Figure 1: Our Hierarchical Network. In this network, junior tasks are supervised in lower layers, with an unsupervised task (Language Modeling) at the most senior layer.
In our model we represent linguistically motivated hierarchies in a multi-task Bi-Directional Recurrent Neural Network where junior tasks in the hierarchy are supervised at lower layers.This architecture builds upon sogaard2016deep, but is adapted in two ways: first, we add an unsupervised sequence labeling task (Language Modeling), second, we add a low-dimensional embedding layer between tasks in the hierarchy to learn dense representations of label tags. In addition to sogaard2016deep.
Our neural network has one hidden layer, after which each successive task is supervised on the next layer. In addition, we add skip connections from the hidden layer to the senior supervised layers to allow layers to ignore information from junior tasks.
FLOAT SELECTED: Figure 1: Our Hierarchical Network. In this network, junior tasks are supervised in lower layers, with an unsupervised task (Language Modeling) at the most senior layer.
In our model we represent linguistically motivated hierarchies in a multi-task Bi-Directional Recurrent Neural Network where junior tasks in the hierarchy are supervised at lower layers.This architecture builds upon sogaard2016deep, but is adapted in two ways: first, we add an unsupervised sequence labeling task (Language Modeling), second, we add a low-dimensional embedding layer between tasks in the hierarchy to learn dense representations of label tags. In addition to sogaard2016deep.
Our neural network has one hidden layer, after which each successive task is supervised on the next layer. In addition, we add skip connections from the hidden layer to the senior supervised layers to allow layers to ignore information from junior tasks.
Answer: The network architecture has a multi-task Bi-Directional Recurrent Neural Network, with an unsupervised sequence labeling task and a low-dimensional embedding layer between tasks. There is a hidden layer after each successive task with skip connections to the senior supervised layers.
Question: Is art useless?
Answer: Whether art possesses a purpose or not depends on perspective. 
From a practical perspective, art itself may not provide an instrumental value or necessity to humans. 

However, the continuous, innate appreciation of art remains visible in the human race, so although it has no use, humans continue to view its intrinsic value as transcendent to practicality. The millions of galleries and museums dedicated to preserving and enriching artistic culture are proof of this.

Many would argue that, through art, thoughts and emotions can be inspired, allowing people to connect on a deeper level to the beauty of the human experience. 

The use of art can also be analysed from a financial perspective. It not only allows for the artist to express their imagination and sell their paintings, but it also generates employment in the creative industry.

In conclusion, despite the lack of practicability of art, its intrinsic value is visible through the appreciation of beauty and talent.
Question: Do the tweets come from a specific region?
Evidence: Table TABREF14 presents the distribution of the tweets by country before and after the filtering process. A large portion of the samples is from India because the MeToo movement has peaked towards the end of 2018 in India. There are very few samples from Russia likely because of content moderation and regulations on social media usage in the country. Figure FIGREF15 gives a geographical distribution of the curated dataset.
Table TABREF14 presents the distribution of the tweets by country before and after the filtering process. A large portion of the samples is from India because the MeToo movement has peaked towards the end of 2018 in India. There are very few samples from Russia likely because of content moderation and regulations on social media usage in the country.
Answer: No.
Question: What activities can I use to entertain my ten year nephew this weekend?
Answer: If the weather is good, take your nephew to the beach or pool where you can swim. You can bring lunch with you and have a nice picnic. If the weather is bad, consider an indoor activity like going to a museum. Another bad weather option is playing fun board games at home.
Question: Name all members of The Beatles
Answer: The primary members of The Beatles were John Lennon, Paul McCartney, George Harrison, and Ringo Stars. John Lennon was a member from 1960-1969. The other three members remained from 1960-1970. Additional members include Pete Best, Stuart Sutcliffe, Chas Newby, Norman Chapman, Tommy Moore, and Jimmie Nicol.
Question: Who is Edgar Allen Poe?
Answer: Edgar Allen Poe is an American born poet and writer.  He has written many short stories, and is a renown figure of Romanticism in the US and US Literature.  High schools will teach about Edgar Allen Poe when teaching about American Literature and poetry.  One of his best quotes is the following: ""Words have no power to impress the mind without the exquisite horror of their reality."
Question: what are the advantages of having a vegetable garden at home on the owners health?
Answer: Vegetable garden allows you to think about the healthy factors of each vegetable. You can prioritize the health benefits of each vegetable and compare against how frequently you use that vegetable. You can plan on how many plants of each variety will be needed for making a dinner for your family. You will try to find vegetables that are easy to maintain and grow with minimal supervision. You will mix between vegetables that will grow in summer verses mild cold temperatures and choose your planting time. If you want year around produce of fresh vegetables you can even plan to have a green house with heating to keep your plants alive in all seasons. Making the soil ready and adding some plant food at regular intervals is required to keep the produce at good numbers. The owner will have health benefits of stress free time during the time they work on the garden and when they see the produce on the dinner table in the form of a delicious dish. Owners have a great time posting their produce pictures and dishes to social media to share with friends. Each year they plan 3 to 4 months in advance on the kind of plants that they would like to plant in the garden. They work with friends to find the seeds for various plants that they like to plant as well as share tips to keep the garden healthy. The key benefit is the owner will be able to get fresh produce that does not use any harsh chemicals or pesticides.
Question: Identify which car manufacturer is French or American: Aixam, Buick
Answer: Aixam is French, Buick is American
Question: Classify each of the following football player by goal scored : Romario, Messi, Ibrahimovic, Muller, Ronaldo
Answer: Ronaldo,Messi,Romario,Muller,Ibrahimovic
Question: What equations are used for the trainable gating network?
Evidence: A simple choice of non-sparse gating function BIBREF17 is to multiply the input by a trainable weight matrix INLINEFORM0 and then apply the INLINEFORM1 function. DISPLAYFORM0
We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to INLINEFORM0 (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix SECREF51 . The amount of noise per component is controlled by a second trainable weight matrix INLINEFORM1 . DISPLAYFORM0 DISPLAYFORM1
A simple choice of non-sparse gating function BIBREF17 is to multiply the input by a trainable weight matrix INLINEFORM0 and then apply the INLINEFORM1 function. DISPLAYFORM0

We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to INLINEFORM0 (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix SECREF51 . The amount of noise per component is controlled by a second trainable weight matrix INLINEFORM1 . DISPLAYFORM0 DISPLAYFORM1
A simple choice of non-sparse gating function BIBREF17 is to multiply the input by a trainable weight matrix INLINEFORM0 and then apply the INLINEFORM1 function. DISPLAYFORM0

We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to INLINEFORM0 (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix SECREF51 . The amount of noise per component is controlled by a second trainable weight matrix INLINEFORM1 . DISPLAYFORM0 DISPLAYFORM1
Answer: The answers are shown as follows:
* DISPLAYFORM0
* DISPLAYFORM0 DISPLAYFORM1
Question: What does question classification typically make use of?
Evidence: Question classification typically makes use of a combination of syntactic, semantic, surface, and embedding methods. Syntactic patterns BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 and syntactic dependencies BIBREF3 have been shown to improve performance, while syntactically or semantically important words are often expanding using Wordnet hypernyms or Unified Medical Language System categories (for the medical domain) to help mitigate sparsity BIBREF22 , BIBREF23 , BIBREF24 . Keyword identification helps identify specific terms useful for classification BIBREF25 , BIBREF3 , BIBREF26 . Similarly, named entity recognizers BIBREF6 , BIBREF27 or lists of semantically related words BIBREF6 , BIBREF24 can also be used to establish broad topics or entity categories and mitigate sparsity, as can word embeddings BIBREF28 , BIBREF29 . Here, we empirically demonstrate many of these existing methods do not transfer to the science domain.
The highest performing question classification systems tend to make use of customized rule-based pattern matching BIBREF30 , BIBREF7 , or a combination of rule-based and machine learning approaches BIBREF19 , at the expense of increased model construction time. A recent emphasis on learned methods has shown a large set of CNN BIBREF29 and LSTM BIBREF8 variants achieve similar accuracy on TREC question classification, with these models exhibiting at best small gains over simple term frequency models. These recent developments echo the observations of Roberts et al. BIBREF3 , who showed that existing methods beyond term frequency models failed to generalize to medical domain questions. Here we show that strong performance across multiple datasets is possible using a single learned model.
Due to the cost involved in their construction, question classification datasets and classification taxonomies tend to be small, which can create methodological challenges. Roberts et al. BIBREF3 generated the next-largest dataset from TREC, containing 2,936 consumer health questions classified into 13 question categories. More recently, Wasim et al. BIBREF4 generated a small corpus of 780 biomedical domain questions organized into 88 categories. In the education domain, Godea et al. BIBREF5 collected a set of 1,155 classroom questions and organized these into 16 categories. To enable a detailed study of science domain question classification, here we construct a large-scale challenge dataset that exceeds the size and classification specificity of other datasets, in many cases by nearly an order of magnitude.
Question classification typically makes use of a combination of syntactic, semantic, surface, and embedding methods.
Answer: A combination of syntactic, semantic, surface, and embedding methods.
Question: Should Elon Musk get into Formula 1?
Answer: Well, that depends on his personal ambitions and business goals. In my personal opinion, he should enter Formula 1 to further expand the brand value of Tesla and SpaceX. By entering Formula 1, he can show the world that not only can Tesla produce one of the safest cars, but it can also compete in one of the most prestigious, technologically advanced, and popular automotive competitions in the world. He can leverage the knowledge gained from his existing ventures, such as SpaceX and Tesla, to set up an extremely competitive Formula 1 team.
Question: What are the typical ingredient for Italian tomato sauce
Answer: While there are so many variations of making Italian tomato sauce, the typical ingredients are chopped garlic and or onion, olive oil, tomato paste, tomato purée, diced tomatoes and or crushed tomatoes.  You may use Basil and or oregano salt and pepper for seasoning.  It is also traditional to add pre boiled pork neck bones, sausage, meatballs or other meats to the sauce.  Sometimes other vegetables are added like mushrooms, green peppers etc.  Many recipes exist.  Buon Appetito!
Question: Which is a species of fish? Tiger barb or Sabretooth
Answer: Tiger barb
Question: Is golf an easy sport?
Answer: Golf can be a very difficult sport; in fact, most people will admit to the fact that golf is the hardest sport there is. If you have never played golf, the concept of how difficult golf is can be a hard one to grasp. Only a very small percentage of players will ever go on to being great players or a professional.
Question: What were the first ten states to enter the union of the United States?
Answer: In order of admission: Delaware, Pennsylvania, New Jersey, Georgia, Connecticut, Massachusetts, Maryland, South Carolina, New Hampshire, and Virginia.
Question: Classify each of the following cloud-based service as either AWS, Azure, or GCP: Redshift, Big Query, Lambda, Purview, Looker.
Answer: Redshift - AWS, Big Query - GCP, Lambda - AWS, Purview - Azure, Looker - GCP
Question: Why are Mothers good at multi-tasking?
Answer: Being a mother is demanding.  You are responsible for many things, including but not limited to the safety of your child.  An infant is solely dependent on his/her mother.  A mothers job is to feed, bathe and make sure that the child is well taken care of.  In what we could call a normal household, the mother is responsible for the well being of their child until they become an adult at the age of 18.  During those 18 years, they take them to the doctors, make sure they attend school, participate in extracurricular activities, and be an active member of the community.  They say it takes a village to raise a child, but raising that child starts with being a good mother.  While they are doing all of this for their own child(ren) they are also taking care of themselves, their household, their spouse and in a lot of cases their own career.  In order to manage all of these things, you must be good at multi-tasking and juggling a lot of responsibilities at the same time.  This is why I believe mothers are good at multi-tasking.
Question: How do the authors describe their proposed approach?
Evidence: In this section, we describe our proposed method in two subsections. The first subsection specifies the model architecture and the details of each module. Our first and second contributions, which are local and distant representations, are mainly described in this subsection. Meanwhile, the second subsection expounds on how the model is trained with unlabeled data through the modified CVT, which is our third contribution.
In this section, we describe our proposed method in two subsections. The first subsection specifies the model architecture and the details of each module. Our first and second contributions, which are local and distant representations, are mainly described in this subsection. Meanwhile, the second subsection expounds on how the model is trained with unlabeled data through the modified CVT, which is our third contribution.
Answer: They describe their proposed method in two subsections. The first subsection specifies the model architecture and the details of each module.Meanwhile, the second subsection expounds on how the model is trained with unlabeled data through the modified CVT.
Question: What is Attack on Titan?
Evidence: Attack on Titan (Japanese: 進撃の巨人, Hepburn: Shingeki no Kyojin, lit. 'The Advancing Giant') is a Japanese manga series written and illustrated by Hajime Isayama. It is set in a world where humanity is forced to live in cities surrounded by three enormous walls that protect them from gigantic man-eating humanoids referred to as Titans; the story follows Eren Yeager, who vows to exterminate the Titans after they bring about the destruction of his hometown and the death of his mother. It was serialized in Kodansha's monthly magazine Bessatsu Shōnen Magazine from September 2009 to April 2021, with its chapters collected in 34 tankōbon volumes.
Answer: Attack on Titan is a manga and anime written by Hajime Isayama. The story follows a protagonist Eren Yeager who lives with his friends Armin and Mikasa within the three walls of humanity. Outside of these walls exist ferocious beings called Titans that eat humans. Eren watches his mother get consumed by Titans and vows revenge which kicks off the main story. The story has deep messages about war and genocide, and takes many unexpected turns.
Question: How do people collaborate in a hybrid work environment?
Answer: People collaborate in a hybrid work environment by using tools that enable asynchronous communication such as email, instant messaging software, and cloud storage. People that operate in different time zones and schedules can work together on the same project through asynchronous communication. In a hybrid work environment, virtual communication tools such as video conferencing are also integral. Virtual communication tools allow people to communicate information without having to be in the same physical location.
Question: What Is Linux?
Answer: The name "Linux" is used to refer to three similar yet slightly different things, which can be confusing to all but the hardcore geek. The three usages vary by how much of a complete software system the speaker is talking about.

At the lowest level, every Linux system is based on the Linux kernel — the very low-level software that manages your computer hardware, multi-tasks the many programs that are running at any given time, and other such essential things. These low-level functions are used by other programs, so their authors can focus on the specific functionality they want to provide. Without the kernel, your computer is a very expensive doorstop. It has all of the features of a modern operating system: true multitasking, threads, virtual memory, shared libraries, demand loading, shared, copy-on-write executables, proper memory management, loadable device driver modules, video frame buffering, and TCP/IP networking.

Most often, the name "Linux" is used to refer to the Linux Operating System. An OS includes the kernel, but also adds various utilities — the kinds of programs you need to get anything done. For example, it includes a shell (the program that provides a command prompt and lets you run programs), a program to copy files, a program to delete files, and many other odds and ends. Some people honor the request of Richard Stallman and the GNU Project, and call the Linux OS GNU/Linux, because a good number of these utility programs were written by the GNU folks.
Question: What are some popular game shows that can be watched on TV?
Answer: Popular game shows that can be watched on TV are Jeopardy!, Family Feud, and The Price is Right.
Question: Do they fine-tune all parameters regardless of target language is the same as or different from the training language?
Evidence: We use a pre-trained Xnlg with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations BIBREF26. In the first pre-training stage, we directly use the 15-language pre-trained XLM BIBREF5 to initialize the parameters of our encoder and decoder. In the second stage, we use Wikipedia as the monolingual data for the DAE objective, and MultiUN BIBREF27 as the parallel data for the XAE objective. The DAE loss is trained with a weight of $0.5$. We train a two-language (English/Chinese) and a three-language (English/French/Chinese) Xnlg for two downstream NLG tasks, respectively. Following BIBREF5, we use the tokenizer provided by BIBREF28 for Chinese, and Moses for other languages, respectively. Then the words in all languages are split with a shared subword vocabulary learned by BPE BIBREF29. We use Adam optimizer with a linear warm-up over the first 4,000 steps and linear decay for later steps, and the learning rate is set to $10^{-4}$. The pre-training batch size is 64, and the sequence length is set to 256. It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
For fine-tuning on downstream NLG tasks, we use Adam optimizer with a learning rate of $5\times 10^{-6}$. We set the batch size as 16 and 32 for question generation and abstractive summarization, respectively. When the target language is the same as the language of training data, we fine-tune all parameters. When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder. We truncate the input sentences to the first 256 tokens. During decoding, we use beam search with beam size of 3, and limit the length of the target sequence to 80 tokens.
When the target language is the same as the language of training data, we fine-tune all parameters. When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder.
Answer: No, only when the target language is the same as the training language
Question: Classify each of the following as a state or a city: Florida, Texas, Alaska, Houston, Charlotte, Charleston, Savannah, Kansas, San Diego
Answer: Florida: State
Texas: State 
Alaska: State
Houston: City
Charlotte: City
Charleston: City
Savannah: City
Kansas: State
San Diego: City