DataHammer/scimrc huggingface dataset:
Question: Why do authors freeze several layers of the pre-trained universal encoder during the training process?
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.
Train a pivot→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 is sue (Howard and Ruder 2018).
Answer: To avoid the degeneracy is sue.
Question: Tell me whether these foods are high or low in carbs: rice, spaghetti, chicken, pork, quinoa, salami
Answer: Rice: High in Carbs
Spaghetti: High in Carbs
Chicken: Low in Carbs
Pork: Low in Carbs
Quinoa: High in Carbs
Salami: Low in Carbs
Question: How much better performance of proposed model compared to answer-selection models?
Evidence: As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute).
As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute).
Answer: The answers are shown as follows:
* significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)
Question: Which is a species of fish? Nurseryfish or Nursery
Answer: Nurseryfish
Question: How many times Max Verstappen won the F1 Championship ?
Answer: 2 times
Question: How many types of entities does the dataset have?
Evidence: The proliferation of the online social media has lately resulted in the democratization of online content sharing. Among other media, Twitter is very popular for research and application purposes due to its scale, representativeness and ease of public access to its content. However, tweets, that are short messages of up to 140 characters, pose several challenges to traditional Natural Language Processing (NLP) systems due to the creative use of characters and punctuation symbols, abbreviations ans slung language.
Named Entity Recognition (NER) is a fundamental step for most of the information extraction pipelines. Importantly, the terse and difficult text style of tweets presents serious challenges to NER systems, which are usually trained using more formal text sources such as newswire articles or Wikipedia entries that follow particular morpho-syntactic rules. As a result, off-the-self tools trained on such data perform poorly BIBREF0 . The problem becomes more intense as the number of entities to be identified increases, moving from the traditional setting of very few entities (persons, organization, time, location) to problems with more. Furthermore, most of the resources (e.g., software tools) and benchmarks for NER are for text written in English. As the multilingual content online increases, and English may not be anymore the lingua franca of the Web. Therefore, having resources and benchmarks in other languages is crucial for enabling information access worldwide.
In this paper, we propose a new benchmark for the problem of NER for tweets written in French. The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities. The annotators were native speakers of French and had previous experience in the task of NER. Overall, the generated datasets consists of INLINEFORM0 tweets, split in training and test parts.
The paper is organized in two parts. In the first, we discuss the data preparation steps (collection, annotation) and we describe the proposed dataset. The dataset was first released in the framework of the CAp 2017 challenge, where 8 systems participated. Following, the second part of the paper presents an overview of baseline systems and the approaches employed by the systems that participated. We conclude with a discussion of the performance of Twitter NER systems and remarks for future work.
The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities.
Answer: 13.
Question: Where does the word aurora come from?
Evidence: The word "aurora" is derived from the name of the Roman goddess of the dawn, Aurora, who travelled from east to west announcing the coming of the sun. Ancient Greek poets used the corresponding name Eos metaphorically to refer to dawn, often mentioning its play of colors across the otherwise dark sky (e.g., "rosy-fingered dawn").
Answer: The word "aurora" is derived from the name of the Roman goddess of the dawn,
Question: Which astronomer is name-dropped in "Bohemian Rhapsody?"
Answer: Galileo.
Question: What is barycentric Newton diagram?
Evidence: By using the probability values that emerge from the activation function in the neural network, rather than just the final classification, we can draw a barycentric Newton diagram, as shown in Figure 4 . The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates. This visualization allows an intuitive interpretation of which country a recipe belongs to. If the probability of Japanese is high, the recipe is mapped near the Japanese. The countries on the Newton diagram are placed by spectral graph drawing BIBREF9 , so that similar countries are placed nearby on the circle. The calculation is as follows. First we define the adjacency matrix $W$ as the similarity between two countries. The similarity between country $i$ and $j$ is calculated by cosine similarity of county $i$ vector and $j$ vector. These vector are defined in next section. $W_{ij} = sim(vec_i, vec_j)$ . The degree matrix $D$ is a diagonal matrix where $D_{ii} = \sum _{j} W_{ij}$ . Next we calculate the eigendecomposition of $D^{-1}W$ . The second and third smallest eingenvalues and corresponded eingevectors are used for placing the countries. Eigenvectors are normalized so as to place the countries on the circle.
The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates. This visualization allows an intuitive interpretation of which country a recipe belongs to. If the probability of Japanese is high, the recipe is mapped near the Japanese. The countries on the Newton diagram are placed by spectral graph drawing BIBREF9 , so that similar countries are placed nearby on the circle. 
Answer: The answers are shown as follows:
*  The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates.
Question: Think of different types of pies that can be served at dinner.
Answer: Many different types of pies can be served with dinner. Some pies that go well with dinner include apple pie, pecan pie, pumpkin pie, key lime pie and coconut pie. There are many types of pies depending on what you are looking for.
Question: What is SQL?
Answer: SQL is short for Structured Query Language, which is a declarative programming language typically used for querying, exploring, and interacting with structured data.
Question: What is the size of the Quora dataset?
Evidence: We obtained our Quora dataset BIBREF7 through web-based crawls between June 2014 to August 2014. This crawling exercise has resulted in the accumulation of a massive Q&A dataset spanning over a period of over four years starting from January 2010 to May 2014. We initiated crawling with 100 questions randomly selected from different topics so that different genre of questions can be covered. The crawling of the questions follow a BFS pattern through the related question links. We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions. For each question, we separately crawl their revision logs that contain different types of edit information for the question and the activity log of the question asker.
We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions.
Answer: It contains 822,040 unique questions with a total of 1,833,125 answers.
Question: Give me a bulleted list of hot dog toppings.
Answer: - Mustard
- Ketchup
- Relish
- Chopped onion
- Chopped tomato
- Chili
- Saukraut
- Coleslaw
- Shredded cheese
Question: What models did they compare to?
Evidence: Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models) . For many of the tasks we model, there have not been standard benchmarks for comparisons across models. This makes it difficult to measure progress and identify areas worthy of allocating efforts and budgets. As such, by publishing our toolkit models, we believe model-based comparisons will be one way to relieve this bottleneck. For these reasons, we also package models from our recent works on dialect BIBREF12 and irony BIBREF14 as part of AraNet .
Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models) . For many of the tasks we model, there have not been standard benchmarks for comparisons across models. This makes it difficult to measure progress and identify areas worthy of allocating efforts and budgets.
Answer: The answers are shown as follows:
*  we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models)
Question: How do you charge a phone
Answer: This depends on the phone brand. Most modern phones have wireless charging that allows phones to be placed on a charging mat or pad. Some phones require you to plug them in. Plugs for phones vary by manufacturer. Apple for instance has used a proprietary charging system called lightning for years but recently was sued and now will have to comply with an industry standard of using USB-C to charge their phones.
Question: Who is William Alfred Bayly?
Evidence: William Alfred Bayly (15 July 1906 – 20 July 1934) was a New Zealand farmer and convicted murderer. He was born in Auckland, New Zealand, on 15 July 1906.

In October 1928, Bayly's cousin, 17-year-old Elsie Walker found dead in some scrub in a scoria pit in Panmure, Auckland, with a bruise on her head. Bayly was suspected of killing her, but he was never charged.

Bill Bayly and his wife Phyllis had been dairy farming at Ruawaro, near Huntly, since November 1928. Their neighbours were Samuel and Christobel Lakey. Relations between Bill Bayly and the Lakeys were initially friendly, but soured when Mrs Lakey was said to have accused Bayly of having murdered his niece.

On 16 October 1933 neighbours were concerned as to why the Lakey's cows hadn't been milked that morning. Christobel was discovered lying face down in her pond next to the farmhouse. She had been struck a blow to the face then held down underwater until she drowned. Her husband was missing. Some speculated that her husband had killed her and fled, but it soon became evident that both Lakeys had been the victims of foul play. Two days later bloodstains were found on a wheeled frame near the boundary between the Lakey and Bayly farms, bloodstains were discovered on Bill's sledge, Lakey's guns were found buried in Bayly's swamp, and chemical tests revealed charred bone fragments on a shovel taken from his dairy.

In December Bayly, who had been under surveillance by the police, disappeared, leaving a suicide note. He soon surfaced in Auckland, and was arrested for the murder of Christobel Lakey. More evidence was found in Bayly's garden. Bayly had attempted to incinerate all traces of Lakey. On 10 January 1934 Bayly, was charged with Samuel Lakey's murder.

Bayly was hanged in Mount Eden Prison, Auckland, on 20 July 1934. He was survived by his wife and two young sons.

Police finally buried the remains of Samuel Lakey alongside those of Christobel in Huntly cemetery in 2015. His remains had been used for forensic training.
Answer: William Alfred Bayly was a New Zealand farmer, who killed his neighbor, Christobel Lakey. He was also suspected of killing others, but was not convicted. He was arrested in Auckland, and was hanged.
Question: What are the best camps ideas for kids?
Answer: Kids camps cover many different activities, depending on the season, and the age of the children attending. Here are some fun camp ideas, that most kids will enjoy:
1. Sports camp - kids play different sports every day
2. Art camp - kids get a chance to use different materials to be creative
3. Sailing camp - if the child loves the water, sailing is a great activity to learn
4. Math & Reading camp - this might sound like school, but reading and math can be fun too, and specialized camps try to gamify these foundational academic skills
5. Adventure camp - give kids some freedom and a taste of the outdoors, adventure camps tend to involve hiking, kayaking or canoeing, exploring, and learning about the natural environment
Question: Given this paragraph about David Bowie, in which decade might you describe his music as having peaked?
Evidence: David Robert Jones (8 January 1947 – 10 January 2016), known professionally as David Bowie (/ˈboʊi/ BOH-ee), was an English singer-songwriter and actor. A leading figure in the music industry, he is regarded as one of the most influential musicians of the 20th century. Bowie was acclaimed by critics and musicians, particularly for his innovative work during the 1970s. His career was marked by reinvention and visual presentation, and his music and stagecraft had a significant impact on popular music.
Answer: 1970s
Question: Is an entity linking process used?
Evidence: Given a multiple-choice question $qa$ with question text $q$ and answer choices A= $\lbrace a_i\rbrace $ , we select the most relevant tuples from $T$ and $S$ as follows.
Selecting from Tuple KB: We use an inverted index to find the 1,000 tuples that have the most overlapping tokens with question tokens $tok(qa).$ . We also filter out any tuples that overlap only with $tok(q)$ as they do not support any answer. We compute the normalized TF-IDF score treating the question, $q$ as a query and each tuple, $t$ as a document: $ &\textit {tf}(x, q)=1\; \textmd {if x} \in q ; \textit {idf}(x) = log(1 + N/n_x) \\ &\textit {tf-idf}(t, q)=\sum _{x \in t\cap q} idf(x) $
Given a multiple-choice question $qa$ with question text $q$ and answer choices A= $\lbrace a_i\rbrace $ , we select the most relevant tuples from $T$ and $S$ as follows.
Selecting from Tuple KB: We use an inverted index to find the 1,000 tuples that have the most overlapping tokens with question tokens $tok(qa).$ .
Answer: No.
Question: What is the best tv series in the world
Answer: Dexter- The Dexter is so exciting to watch that it should be the best TV series in the world
Question: What evaluation metrics were used?
Evidence: A two-step validation was conducted for English-speaking customers. An initial A/B testing for the LR model in a production setting was performed to compare the labelling strategies. A second offline comparison of the models was conducted on historical data and a selected labelling strategy. One month of data and a subset of the customers was used (approx. eighty thousand). The sampled dataset presents a fraction of positive labels of approximately 0.5 for reuse and 0.2 for one-day return. Importantly, since this evaluation is done on a subset of users, the dataset characteristic's do not necessarily represent real production traffic. The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc). The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set. As a benchmark, we also consider two additional methods: a non-personalized popularity model and one that follows BIBREF16, replacing the transformer joke encoder with a CNN network (the specialized loss and other characteristics of the DL model are kept).
 The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set. 
Answer: The answers are shown as follows:
* AUC-ROC
Question: What is a landlord?
Answer: A landlord is a person, business, or other entity that owns some form of real-estate that they rent out for an amount of money paid yearly, monthly, semi-monthly or weekly.  The type of real estate owned can be land, commercial real-estate, or home real-estate.  A landlord evaluates different tenant applications submitted to rent or use their owned real-estate.  Once a match is found an agreement or contract is signed and executed between both parties.  The tenant pays the landlord an agreed amount of money during some finite time to be able to use the landlord's owned real estate for a specific purpose outlined in the original signed contract.
Question: Think up of a bunch of different holidays celebrated in the US
Answer: Some US holidays include 4th of July, Memorial Day, Labor Day, Juneteenth and Thanksgiving
Question: Why was Bacardi 151 discontinued in the US?
Evidence: Bacardi 151 is a discontinued brand of highly alcoholic rum made by Bacardi Limited of Hamilton, Bermuda. It is named for its alcohol concentration level of 151 U.S. proof, that is, 75.5% alcohol by volume. This is much higher than typical rum, which averages around 35%–40% alcohol by volume. Bacardi 151 was sold in the United States and Canada from at least 1963 until 2016, when its production was discontinued.

Safety and lawsuits
Because of its high alcohol content, Bacardi 151 was particularly flammable among liquors. For this reason it was often used as the ignition source for "flaming shots" and even for at-home pyrotechnic stunts. The bottle carried a warning label advising consumers, among other things, "do not use this product for flaming dishes or other drinks." Bottles were also equipped with a stainless steel flame arrester crimped onto the neck of the bottle to prevent the rum inside the bottle from igniting. Nevertheless, Bacardi faced at least two lawsuits from consumers who claimed to have been badly injured by the product's flames. While the company never issued a formal statement regarding the product's discontinuation, it has been speculated that "it's a pretty good guess that Bacardi got sick of being sued."

Mixed drinks
Bacardi 151 was typically used in sweet drinks, such as the Hurricane and Caribou Lou, which combine rum and fruit juices.
Answer: Because of its high alcohol content, Bacardi 151 was particularly flammable among liquors. Bacardi faced at least two lawsuits from consumers who claimed to have been badly injured by the product's flames. It has been speculated that "it's a pretty good guess that Bacardi got sick of being sued."
Question: What is stream processing ?
Answer: Stream processing refers to techniques used to process a continuous stream of data. In batch processing, the amount of data is known up front when the processing starts. However, in stream processing, the computation (or query) runs ‘forever’ and as new data continues to get added to the input the computation updates its response.  To handle an infinite stream of data without needing to maintain infinite history stream processing queries often include the idea of windows which limits how much history is needed to be kept.
Question: What evaluation metrics did they use?
Evidence: We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
Answer: The answers are shown as follows:
* BLEU and TER scores
Question: Extract the names of the islands located on the Central Group of the Azores plateau from the text. Separate them with a comma.
Evidence: These islands can be divided into three recognizable groups located on the Azores Plateau:
The Eastern Group (Grupo Oriental) of São Miguel, Santa Maria and Formigas Islets
The Central Group (Grupo Central) of Terceira, Graciosa, São Jorge, Pico and Faial
The Western Group (Grupo Ocidental) of Flores and Corvo.
Answer: Terceira, Graciosa, São Jorge, Pico and Faial