DataHammer/scimrc huggingface dataset:
Question: What models do they use to instantiate their approach?
Evidence: Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so long as the prior is well-behaved. In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction. Similarly, we find that our tree-structured model achieves state-of-the-art performance on unsupervised dependency parsing for the difficult training condition where neither gold POS annotation nor punctuation-based constraints are available.
In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction.
Answer: Markov and tree-structured.
Question: Write a story with the theme "Be wise when you speak."
Answer: Judge Matt had it all, a successful career, a loving family, and the respect of his peers. His quick wit and tongue made him a well-known person within the courtroom. But outside work, he was reckless and impulsive. While at a public event, he made a careless remark which was caught on camera, spreading like wildfire across the internet. He lost his family's trust, his job, and his reputation. He was left with nothing in his darkest moment. Judge Matt realized the truth of the adage, "Be wise when you speak," and committed to change for the better. Seeing his efforts, his former colleague reached out with an opportunity to return to the bench. Judge Matt embraced the chance and transformed himself, earning the trust of his family and friends. He had gained something more valuable than he had lost—the power of his words, wisdom, and compassion.
Question: Given a reference text about refugees, tell me about refugee camps.
Evidence: A refugee camp is a place built by governments or NGOs (such as the Red Cross) to receive refugees, internally displaced persons or sometimes also other migrants. It is usually designed to offer acute and temporary accommodation and services and any more permanent facilities and structures often banned. People may stay in these camps for many years, receiving emergency food, education and medical aid until it is safe enough to return to their country of origin. There, refugees are at risk of disease, child soldier and terrorist recruitment, and physical and sexual violence. There are estimated to be 700 refugee camp locations worldwide.
Answer: A refugee camps are generally built by governments or non-government organizations, such as the Red Cross. They are built to receive displaced individuals.
Question: Classify each of the following as either a dog or cat: Golden Retriever, Domestic Short Hair, Black Labrador Retriever, Siamese, Dachshund, Ragdoll
Answer: Golden Retriever: Dog
Domestic Short Hair: Cat
Black Labrador Retriever: Dog
Siamese: Cat
Dachshund: Dog
Ragdoll: Cat
Question: Why is Citizen Kane considered the Best Film?
Answer: The American Film Institute and the British Film Institute have Citizen Kane listed as the number one movie of all time.  The British Film Institute has had this rating of Citizen Kane for 50 years.  There are multitudes of reasons why this is a deserving title with a few examples below.

1. Citizen Kane was a thinly veiled movie about a powerful person.  Even before the movie was released newspaper magnate William R Hearst became aware that Kane had many similarities to Hearst.  Both controlled media empires, ran for office, and had giant mansions filled with odds and ends.  Where it allegedly became personal was when scenes showing Kane's wife put through an humiliating run in showbiz propped up by Kane's largesse mirrored Hearst a bit too closely to real life.  Hearst reportedly offered to buy all copies of the film and told newspapers to not print anything or even mention its name.

2. It heralded a new age of cinematography in 1941.  Citizen Kane pioneered in the mainstream many types of compositions and angles we now find quite common.  For a low angle shot in an office to show Kane's towering presence film cameras were too heavy and bulky to sit on the floor.  Cinematographer Gregg Toland solved this by having a hole dug deep enough for camera and tripod to achieve the desired effect.  Also the concept of "deep focus" or achieving a large depth of field to make characters near and far appear sharp is difficult to achieve.  These scenes were used to great effect to portray Kane as being surrounded and alone with his wealth later in the movie.  They also provided a literal window into a young innocent Kane during a tense scene inside the house, to which Kane was blissfully unaware but the audience experienced both to jarring effect.

3. Orson Welles was famous and an excellent promoter.  Up to this point Welles most famous exploit had been his radio re-enactment of H.G. Wells "War of the Worlds."  Choosing to follow a newsreel style format and not having an intro saying this is a work of fiction many listeners at home were swept up in the drama and thought an alien invasion was happening.  His popularity from this event carried a great deal of interest over to his first film.  For his first film to come together with a compelling story, technical wizardry, and cultural relevancy truly made it the standard for what a "Best Film..." should be.

As a sidenote in 1921 Citizen Kane won 9 Academy awards including Best Picture, Director, Editing, and Cinematography. Orson Welles would never have a similar level of success with his later films.
Question: Extract the theories for why the Mary Celeste was abandoned.
Evidence: Mary Celeste (/səˈlɛst/; often erroneously referred to as Marie Celeste) was an American-registered merchant brigantine, best known for being discovered adrift and deserted in the Atlantic Ocean off the Azores Islands on December 4, 1872. The Canadian brigantine Dei Gratia found her in a dishevelled but seaworthy condition under partial sail and with her lifeboat missing. The last entry in her log was dated ten days earlier. She had left New York City for Genoa on November 7 and was still amply provisioned when found. Her cargo of alcohol was intact, and the captain's and crew's personal belongings were undisturbed. None of those who had been on board were ever seen or heard from again.

Mary Celeste was built in Spencer's Island, Nova Scotia, and launched under British registration as Amazon in 1861. She was transferred to American ownership and registration in 1868, when she acquired her new name. Thereafter she sailed uneventfully until her 1872 voyage. At the salvage hearings in Gibraltar following her recovery, the court's officers considered various possibilities of foul play, including mutiny by Mary Celeste's crew, piracy by the Dei Gratia crew or others, and conspiracy to carry out insurance or salvage fraud. No convincing evidence supported these theories, but unresolved suspicions led to a relatively low salvage award.

The inconclusive nature of the hearings fostered continued speculation as to the nature of the mystery, and the story has repeatedly been complicated by false detail and fantasy. Hypotheses that have been advanced include the effects on the crew of alcohol fumes rising from the cargo, submarine earthquakes, waterspouts, attack by a giant squid, and paranormal intervention.

After the Gibraltar hearings, Mary Celeste continued in service under new owners. In 1885, her captain deliberately wrecked her off the coast of Haiti as part of an attempted insurance fraud. The story of her 1872 abandonment has been recounted and dramatized many times in documentaries, novels, plays, and films, and the name of the ship has become a byword for unexplained desertion. In 1884, Arthur Conan Doyle wrote "J. Habakuk Jephson's Statement", a short story based on the mystery, but spelled the vessel's name as Marie Celeste. The story's popularity led to the spelling becoming more common than the original in everyday use.
Answer: The theories for why the Mary Celeste was abandoned include mutiny by Mary Celeste's crew; piracy; conspiracy to carry out insurance or salvage fraud; and abandonment by the crew due to alcohol fumes rising from the cargo, submarine earthquakes, waterspouts, attack by a giant squid, and paranormal intervention.
Question: What is future work planned to improve the performance?
Evidence: The model implemented in this paper only includes source-side morphological segmentation and does not include a target side morphological agglutination to give back the output in words rather than morphemes. In order to implement an end-to-end translation system for morphologically rich languages, a morphological generator is essential because the output units of the translation cannot be morphemes.
The same model implemented can be further enhanced by means of a better corpus that can generalize over more than just domain specific source sentences. Also, the use of a better GPU would result in a better allocation of the hidden layer sizes and the batch sizes thereby possibly increasing the scope and accuracy of learning of the translation model.
Although not directly related to Machine Translation, the novel encoder– decoder architecture proposed in by Rocktaschel et al. rocktaschel2015reasoning for Natural Language Inference (NLI) can be used for the same. Their model fuses inferences from each and every individual word, summarizing information at each step, thereby linking the hidden state of the encoder with that of the decoder by means of a weighted sum, trained for optimization.
In order to implement an end-to-end translation system for morphologically rich languages, a morphological generator is essential because the output units of the translation cannot be morphemes. The same model implemented can be further enhanced by means of a better corpus that can generalize over more than just domain specific source sentences. Also, the use of a better GPU would result in a better allocation of the hidden layer sizes and the batch sizes thereby possibly increasing the scope and accuracy of learning of the translation model.
Answer: In order to implement an end-to-end translation system for morphologically rich languages, a morphological generator is essential because the output units of the translation cannot be morphemes. The same model implemented can be further enhanced by means of a better corpus that can generalize over more than just domain specific source sentences. Also, the use of a better GPU would result in a better allocation of the hidden layer sizes and the batch sizes thereby possibly increasing the scope and accuracy of learning of the translation model.
Question: Write an email to my landlord explaining why I missed rent payment this month.
Answer: Hi Landlord, I wanted to send this message to apologize for missing my rent payment deadline this month. When rent was due, I went to the online portal to transfer you the money from my bank funds as I do each month. After logging in I saw that my balance was $0, and that the money had been transferred to a different account. I immediately filed a fraudulent transaction report with my bank, and they noted that my bank account was insured and the money will be returned. I am still waiting for the bank to return the funds which were stolen, but have confirmation that they should be returned shortly. Once the funds have been returned I will pay you in full, but ask that you accept this explanation while I await my bank account being restored, and apologize for any inconvenience this causes you.
Question: What conclusions do the authors draw from their detailed analyses?
Evidence: We established strong baselines for two story narrative understanding datasets: CaTeRS and RED. We have shown that neural network-based models can outperform feature-based models with wide margins, and we conducted an ablation study to show that contextualized representation learning can boost performance of NN models. Further research can focus on more systematic study or build stronger NN models over the same datasets used in this work. Exploring possibilities to directly apply temporal relation extraction to enhance performance of story generation systems is another promising research direction.
We have shown that neural network-based models can outperform feature-based models with wide margins, and we conducted an ablation study to show that contextualized representation learning can boost performance of NN models.
Answer: The answers are shown as follows:
* neural network-based models can outperform feature-based models with wide margins
* contextualized representation learning can boost performance of NN models
Question: Describe a tense scene of a dystopian future where a human in a laboratory is attempting to transform her body into a cyborg.
Answer: Quo stands in the center of her laboratory, surrounded by a maze of high-tech machinery and computer screens. The walls are lined with mechanical arms and robotic limbs, each designed to assist her in her work. In the center of the room, a large table holds a variety of instruments and tools, all of which Quo wields with practiced precision. She's plugged into a complex system of wires and cables, her body convulsing as she screams in agony. The air is thick with the sound of machines whirring and gears grinding as Quo works feverishly to transform herself. 

In the background, a large screen displays a series of intricate diagrams and schematics, each one representing a different aspect of her transformation. Despite the pain and suffering, Quo's eyes burn with fierce determination as she pushes herself to the brink of what is possible.
Question: Whether BRLM can better relieve the burden of the domain shift problem?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
BRLM fully utilizes the alignment information to obtain better word-level representation alignment between different languages, which better relieves the burden of the domain shift problem.
Answer: Yes.
Question: Name some of the most popular rides at Disney World's Magic Kingdom theme park.
Answer: Magic Kingdom has a beautiful selection of rides and attractions. Some of the most popular rides include Space Mountain, Tron Lightcycle / Run, Seven Dwarfs Mine Train, The Haunted Mansion, Peter Pan's Flight, Pirates of the Caribbean, and It's a small world.
Question: What is the size of ACL-ARC datasets?
Evidence: ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field. The data was split into three standard stratified sets of train, validation, and test with 85% of data used for training and remaining 15% divided equally for validation and test. Each citation unit includes information about the immediate citation context, surrounding context, as well as information about the citing and cited paper. The data includes six intent categories outlined in Table 2 .
ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field.
Answer: The answers are shown as follows:
* includes 1,941 citation instances from 186 papers
Question: What is their dataset?
Evidence: Our datasets are scientific articles that were published on arXiv. The sets contain articles (in LaTeX format) from four computer science domains: NLP, IR, AI, and ML. They were created by filtering arXiv articles based on their primary and secondary categories. We used the following categories for the four collections: cs.cl for NLP; cs.ir for IR; cs.ai for AI and stat.ml, stat.co, stat.me or cs.lg for ML.
Table TABREF22 shows the number of documents along with the number of unique words, equations and equation units for each collection. The equations are display equations that were enumerated in the LaTeX version of the articles. Unlike inline equations, which in many instances represent variables with general meaning (e.g. INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , etc.) and even numerical values, display equations typically represent mathematical concepts with more specific semantics. For the empirical study we used a random subset of 2k singletons from the total collection, along with all equations that occur more than once. For the qualitative analysis, we used all equations.
We extracted words by tokenizing articles using the NLTK package BIBREF12 and restricted the vocabulary to noun phrases and adjectives. The vocabulary was selected by:
removing common stopwords
treating the top 25 most frequent words as stop words and removing them
including words whose term frequency is greater than or equal to 10 and whose character length is greater than or equal to 4
including the top 50 most frequent abbreviations whose character length is 3 (an exception to our previous rule)
When tokenizing equations, we first create an effective vocabulary of equation units. We convert equations into SLT format and collect collection wide frequency statistics over the equation units. The vocabulary contains all equation units whose frequency count is greater than INLINEFORM0 .
Our datasets are scientific articles that were published on arXiv. The sets contain articles (in LaTeX format) from four computer science domains: NLP, IR, AI, and ML. They were created by filtering arXiv articles based on their primary and secondary categories. We used the following categories for the four collections: cs.cl for NLP; cs.ir for IR; cs.ai for AI and stat.ml, stat.co, stat.me or cs.lg for ML.
Answer: Scientific articles that were published on arXiv.
Question: What is a jet engine?
Answer: A jet engine is a reaction engine used for propulsion, most commonly in airplanes. It works by compressing and discharging heated gas, and follows Newton's third law of motion. Most jet engine used in airplanes are breathing axial flow gas turbines. These gas turbines are powered using aviation turbine fuel (avtur) or more commonly called jet fuel. The gas turbines have at least three stages: a compressor, a combustor, and a turbine.
Question: What tweet representation models did the authors use?
Evidence: We conducted an extensive battery of experiments on the dataset to establish strong Arabic offensive language classification results. Though the offensive tweets have finer-grained labels where offensive tweet could also be vulgar or constitute hate speech, we conducted coarser-grained classification to determine if a tweet was offensive or not. For classification, we experimented with several tweet representation and classification models. For tweet representations, we used: the count of positive and negative terms, based on a polarity lexicon; static embeddings, namely fastText and Skip-Gram; and deep contextual embeddings, namely BERTbase-multilingual.
For tweet representations, we used: the count of positive and negative terms, based on a polarity lexicon; static embeddings, namely fastText and Skip_x0002_Gram; and deep contextual embeddings, namely BERTbase-multilingual and AraBERT (Antoun et al., 2020).
Answer: For tweet representations, the authors used: the count of positive and negative terms, based on a polarity lexicon; static embeddings, namely fastText and SkipGram; and deep contextual embeddings, namely BERTbase-multilingual and AraBERT.
Question: I want to watch a movie that won the Oscar for best picture. Here are my choices: Moonlight, Wizard of Oz, Arrival, Elvis, Everything Everywhere All at Once, Chicago, Gone with the Wind, Top Gun. What should I choose?
Answer: These movies are Oscar winners for Best Picture: Moonlight, Everything Everywhere All at Once, Gone with the Wind and Chicago.
Question: List the trim levels available for the F-150 Lightning.  Separate them with a comma.
Evidence: For the 2022 model year, the F-150 Lightning is available in four trim levels: Pro, XLT, Lariat, and Platinum. The Pro trim is only available with the smaller-capacity battery, and the Platinum only offers the higher-capacity battery, although all other trims include the smaller-capacity battery as standard equipment, but also offer the higher-capacity battery as an option.
Answer: The F-150 Lightning is available in four trim levels: Pro, XLT, Lariat, and Platinum.
Question: Tell me whether these clothes are formal or informal: suit, tuxedo, bathing suit, gym shorts, slacks, tank top
Answer: Suit: Formal
Tuxedo: Formal
Bathing Suit: Informal
Gym Shorts: Informal
Slacks: Formal
Tank Top: Informal
Question: Name some famous shows with Jennifer Anniston.
Answer: Some famous shows with Jennifer Anniston are Friends and The Morning Show.
Question: Which colors are in a rainbow?
Answer: A rainbow consists of the following seven colors: red, orange, yellow, green, blue, indigo, and violet.
Question: What type of brushes are typically used to create an acrylic painting?
Answer: Paintbrushes are available in a range of widths and may have stiff (bristle) or soft (synthetic or sable) hairs — and short or long handles. Synthetic brushes are less expensive.

Here are some common brush types:
1. Round brushes — A small, round brush (such as a size 3 or smaller) has a rounded, fine tip excellent for precise, detailed work. A large round brush (such as a size 8) carries more paint and is excellent for heavy strokes. A round bristle brush can be used to add texture to a painting through thick, impasto strokes.
2. Flat brushes — A flat brush can make thin lines or wide rectangular strokes.
3. Wash brushes — A wide wash brush (such as a 1 inch) can be used to apply thin washes of color to a painting surface.
4. Fan brush — A fan brush can be used to blend adjacent colors — though acrylics dry quickly and this type of blending brushwork will need to be done fast while the paint is still wet.