DataHammer/scimrc huggingface dataset:
Question: What different languages do the authors define new cross-lingual document classification tasks for?
Evidence: There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions.
The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification.
We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese.
Answer: English, French, Spanish, Italian, German, Russian, Chinese and Japanese.
Question: Summarize what and when the quiet periods occur during an IPO.
Evidence: Under American securities law, there are two-time windows commonly referred to as "quiet periods" during an IPO's history. The first and the one linked above is the period of time following the filing of the company's S-1 but before SEC staff declare the registration statement effective. During this time, issuers, company insiders, analysts, and other parties are legally restricted in their ability to discuss or promote the upcoming IPO (U.S. Securities and Exchange Commission, 2005).

The other "quiet period" refers to a period of 10 calendar days following an IPO's first day of public trading. During this time, insiders and any underwriters involved in the IPO are restricted from issuing any earnings forecasts or research reports for the company. When the quiet period is over, generally the underwriters will initiate research coverage on the firm. A three-day waiting period exists for any member that has acted as a manager or co-manager in a secondary offering.
Answer: There are two quiet periods during an IPO during which company insiders cannot promote the IPO or issue information that might affect it. One is after the Form S-1 is filed but before the SEC approves it, and one is the 10 calendar days after an IPO's first day of trading.
Question: What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?
Evidence: Pre-processing. Tweets are subject to standard preprocessing steps for text such as tokenization, stemming, lemmatization, stop-word removal, and part-of-speech tagging. Moreover, due to their noisy nature, they are also processed using some Twitter-specific techniques such as substitution/removal of URLs, of user mentions, of hashtags, and of emoticons, spelling correction, elongation normalization, abbreviation lookup, punctuation removal, detection of amplifiers and diminishers, negation scope detection, etc. For this, one typically uses Twitter-specific NLP tools such as part-of-speech and named entity taggers, syntactic parsers, etc. BIBREF47 , BIBREF48 , BIBREF49 .
Despite all these opportunities, the rise of social media has also presented new challenges for natural language processing (NLP) applications, which had largely relied on NLP tools tuned for formal text genres such as newswire, and thus were not readily applicable to the informal language and style of social media. That language proved to be quite challenging with its use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, e.g., RT for re-tweet and #hashtags. In addition to the genre difference, there is also a difference in length: social media messages are generally short, often length-limited by design as in Twitter, i.e., a sentence or a headline rather than a full document. How to handle such challenges has only recently been the subject of thorough research BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 .
 Moreover, due to their noisy nature, they are also processed using some Twitter-specific techniques such as substitution/removal of URLs, of user mentions, of hashtags, and of emoticons, spelling correction, elongation normalization, abbreviation lookup, punctuation removal, detection of amplifiers and diminishers, negation scope detection, etc.
That language proved to be quite challenging with its use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, e.g., RT for re-tweet and #hashtags. In addition to the genre difference, there is also a difference in length: social media messages are generally short, often length-limited by design as in Twitter, i.e., a sentence or a headline rather than a full document
Answer: Tweets noisy nature, use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, short (length limited) text
Question: Where do they employ feature-wise sigmoid gating?
Evidence: The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism.
The vector gate is inspired by BIBREF11 and BIBREF12 , but is different to the former in that the gating mechanism acts upon each dimension of the word and character-level vectors, and different to the latter in that it does not rely on external sources of information for calculating the gating mechanism.
Answer: The answers are shown as follows:
* gating mechanism acts upon each dimension of the word and character-level vectors
Question: What other datasets are used?
Evidence: To pretrain BERT and GPT-2 language models, as well as an AWD-LSTM language model for use in ULMFiT, a large unlabeled training corpora is needed. For this purpose, we construct a corpus of 172,815 articles from Tagalog Wikipedia which we call WikiText-TL-39 BIBREF18. We form training-validation-test splits of 70%-15%-15% from this corpora.
For this purpose, we construct a corpus of 172,815 articles from Tagalog Wikipedia which we call WikiText-TL-39 BIBREF18.
Answer: The answers are shown as follows:
* WikiText-TL-39
Question: Give me ideas of ways to add a spicy kick to my pho.
Answer: You can spice up a bowl of pho by adding some sriracha sauce, fresh jalapenos, pickled jalapenos, or chili oil.
Question: Classify each of the following as either a brand of kombucha or a soda brand: Humm, Brew Doctor, Pepsi, 7Up, Mountain Dew, Coca-Cola, Sprite, Fanta, GT's, Remedy, Health-Ade
Answer: Kombucha: Humm, Brew Doctor, GT's, Remedy, Health-Ade 
Soda: Pepsi, 7Up, Mountain Dew, Coca-Cola, Sprite, Fanta
Question: Give me a list of certain insurances that I should take when moving to Germany.
Answer: 1. Health Insurance (Krankenversicherung): It is mandatory to register to in Germany. Many people prefer Public but you can also enroll for private insurance.
2. Private Liability Insurance (Haftpflichtversicherung): For accidental damage coverages. Although optional but highly recommended by residents.
3. Legal Protection Insurance (Rechtsschutzversicherung): This is totally optional but might come handy to deal and send notices for legal or public disorder cases e.g. problems with landlord, etc.
4. Household Insurance (Hausratversicherung): This is another good to have insurance to cover the damages related to house specially for rented homes.
Question: How many national championships had Youngstown State Penguins football team won prior to 1991?
Evidence: The 1991 Youngstown State Penguins football team represented Youngstown State University in the 1991 NCAA Division I-AA football season. The Penguins were led by sixth-year head coach Jim Tressel and played their home games at Stambaugh Stadium. They finished the season 12–3. They received an at-large bid to the I-AA playoffs, where they defeated Villanova, Nevada, and Samford to advance to the National Championship Game, where they defeated Marshall. This was their first national championship in school history.
Answer: None. 1991 was their first national championship in school history so there weren't any prior national championships for Youngstown State Penguins football team.
Question: How many member states does the European Union have?
Evidence: The European Union (EU) is a supranational political and economic union of 27 member states that are located primarily in Europe. The union has a total area of 4,233,255.3 km2 (1,634,469.0 sq mi) and an estimated total population of nearly 447 million. The EU has often been described as a sui generis political entity (without precedent or comparison) combining the characteristics of both a federation and a confederation.
Answer: 27
Question: Tell me some children's songs that I might use for a class.
Answer: There are a number of popular and well-known children's song for classroom activities, such as 'I'm a Little Teapot', 'Three Blind Mice', 'The Grand Old Duke of York', 'Head, Shoulders, Knees and Toes' and ' Old Macdonald Had a Farm'.
Question: What is First-stage boot loader
Evidence: Boot loaders may face peculiar constraints, especially in size; for instance, on the earlier IBM PC and compatibles, a boot sector should typically work in only 32 KiB (later relaxed to 64 KiB) of system memory and only use instructions supported by the original 8088/8086 processors. The first stage of PC boot loaders (FSBL, first-stage boot loader) located on fixed disks and removable drives must fit into the first 446 bytes of the Master boot record in order to leave room for the default 64-byte partition table with four partition entries and the two-byte boot signature, which the BIOS requires for a proper boot loader — or even less, when additional features like more than four partition entries (up to 16 with 16 bytes each), a disk signature (6 bytes), a disk timestamp (6 bytes), an Advanced Active Partition (18 bytes) or special multi-boot loaders have to be supported as well in some environments. In floppy and superfloppy volume boot records, up to 59 bytes are occupied for the extended BIOS parameter block on FAT12 and FAT16 volumes since DOS 4.0, whereas the FAT32 EBPB introduced with DOS 7.1 requires even 87 bytes, leaving only 423 bytes for the boot loader when assuming a sector size of 512 bytes. Microsoft boot sectors therefore traditionally imposed certain restrictions on the boot process, for example, the boot file had to be located at a fixed position in the root directory of the file system and stored as consecutive sectors,conditions taken care of by the SYS command and slightly relaxed in later versions of DOS.The boot loader was then able to load the first three sectors of the file into memory, which happened to contain another embedded boot loader able to load the remainder of the file into memory.When Microsoft added LBA and FAT32 support, they even switched to a boot loader reaching over two physical sectors and using 386 instructions for size reasons. At the same time other vendors managed to squeeze much more functionality into a single boot sector without relaxing the original constraints on only minimal available memory (32 KiB) and processor support (8088/8086). For example, DR-DOS boot sectors are able to locate the boot file in the FAT12, FAT16 and FAT32 file system, and load it into memory as a whole via CHS or LBA, even if the file is not stored in a fixed location and in consecutive sectors.

BIOS and UEFI not only load the operating system from a non-volatile device, they also initialize system hardware for operating system.

Examples of first-stage bootloaders include BIOS, coreboot, Libreboot and Das U-Boot.
Answer: The first-stage bootloader (FSBL) is the first boot stage for the HPS. In FPGA Configuration First mode, the SDM extracts and loads the FSBL into the on-chip RAM of the HPS. The SDM releases the HPS from reset after the FPGA has entered user mode. After the HPS exits reset, it uses the FSBL hardware handoff file to setup the clocks, HPS dedicated I/Os, and peripherals. Typically, the FSBL then loads the SSBL into HPS SDRAM and passes the control to the SSBL.
Question: How many news articles has the author annotated for the shared task?
Evidence: The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators. More details about the data collection and the annotation, as well as statistics about the corpus can be found in BIBREF10, where an earlier version of the corpus is described, which includes 450 news articles. We further annotated 47 additional articles for the purpose of the shared task using the same protocol and the same annotators.
The training, the development, and the test partitions of the corpus used for the shared task consist of 350, 61, and 86 articles and of 16,965, 2,235, and 3,526 sentences, respectively. Figure FIGREF15 shows an annotated example, which contains several propaganda techniques. For example, the fragment babies on line 1 is an instance of both Name_Calling and Labeling. Note that the fragment not looking as though Trump killed his grandma on line 4 is an instance of Exaggeration_or_Minimisation and it overlaps with the fragment killed his grandma, which is an instance of Loaded_Language.
Table TABREF23 reports the total number of instances per technique and the percentage with respect to the total number of annotations, for the training and for the development sets.
More details about the data collection and the annotation, as well as statistics about the corpus can be found in (Da San Martino et al., 2019), where an earlier version of the corpus is described, which includes 450 news articles. We further annotated 47 additional articles for the purpose of the shared task using the same protocol and the same annotators.
Answer: 497.
Question: What are all 15 types of modifications ilustrated in the dataset?
Evidence: We selected 15 modifications types to collect COSTRA 1.0. They are presented in annotationinstructions.
FLOAT SELECTED: Table 2: Sentences transformations requested in the second round of annotation with the instructions to the annotators. The annotators were given no examples (with the exception of nonsense) not to be influenced as much as in the first round.
We selected 15 modifications types to collect COSTRA 1.0. They are presented in annotationinstructions.
FLOAT SELECTED: Table 2: Sentences transformations requested in the second round of annotation with the instructions to the annotators. The annotators were given no examples (with the exception of nonsense) not to be influenced as much as in the first round.
Answer: - paraphrase 1
- paraphrase 2
- different meaning
- opposite meaning
- nonsense
- minimal change
- generalization
- gossip
- formal sentence
- non-standard sentence
- simple sentence
- possibility
- ban
- future
- past
Question: is diners drive-ins and dives the best food network show?
Answer: It depends what you are looking for! If you are interested in learning about new foods in predominantly US locations that are generally affordable without a competitive element then diners drive-ins and dives, or triple D, may be great for you. Given how many years the show has been on air it is almost impossible to watch a repeat and you usually find something interesting to pay attention to. While it's not competitive, you can definitely see which restaurants make the host super excited versus not. It is a great show to decompress from work or stress before going to bed.
Question: What is the best way to eat a steak?
Answer: Medium-Rare is the best way to eat a steak where the internal temperature is about 130-135 degrees F. The outside carmelizes, the fat renders, and the juice remains within the meat for the most enjoyable steak eating experience. Cooking the meat past 135 degrees F will cause moisture to be released rapidly, drying out the piece of meat quickly.
Question: Tell me whether each of these are flat or pointed: mountain, plateau, table, knife, arrow, road
Answer: Mountain: Pointed
Plateau: Flat
Table: Flat
Knife: Pointed
Arrow: Pointed
Road: Flat
Question: What would be some useful random items to help you on an island if you were stranded
Answer: A hockey skate would be great because of the blades and laces

Glasses could maybe be used to help you start a fire if you used the glasses to reflect the sunlight

The cord from a blow dryer could be quite useful
Question: Classify each of the shapes as having 3, 4, or 5 sides: square, rectangle, pentagon, rhombus, triangle
Answer: Square: 4 Sides
Rectangle: 4 Sides
Pentagon: 5 Sides
Rhombus: 4 Sides
Triangle: 3 Sides
Question: How do the authors employ the gradient-based approach?
Evidence: Neural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, such as English-French BIBREF0, BIBREF1 and Chinese-English BIBREF2. However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
The understanding of NMT models has been approached primarily from two complementary perspectives. The first thread of work aims to understand the importance of representations by analyzing the linguistic information embedded in representation vectors BIBREF3, BIBREF4 or hidden units BIBREF5, BIBREF6. Another direction focuses on understanding the importance of input words by interpreting the input-output behavior of NMT models. Previous work BIBREF7 treats NMT models as black-boxes and provides explanations that closely resemble the attention scores in NMT models. However, recent studies reveal that attention does not provide meaningful explanations since the relationship between attention scores and model output is unclear BIBREF8.
In this paper, we focus on the second thread and try to open the black-box by exploiting the gradients in NMT generation, which aims to estimate the word importance better. Specifically, we employ the integrated gradients method BIBREF9 to attribute the output to the input words with the integration of first-order derivatives. We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
We analyze the linguistic behaviors of words with the importance and show its potential to improve NMT models. First, we leverage the word importance to identify input words that are under-translated by NMT models. Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods. Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language. For example, nouns are more important for Chinese$\Rightarrow $English translation, while prepositions are more important for English-French and -Japanese translation. This finding can inspire better design principles of NMT architectures for different language pairs. For instance, a better architecture for a given language pair should consider its own language characteristics.
We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
Answer: Justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures.
Question: What are the different types of stances in tennis?
Evidence: Stance refers to the way a player prepares themselves in order to best be able to return a shot. Essentially, it enables them to move quickly in order to achieve a particular stroke. There are four main stances in modern tennis: open, semi-open, closed, and neutral. All four stances involve the player crouching in some manner: as well as being a more efficient striking posture, it allows them to isometrically preload their muscles in order to play the stroke more dynamically. What stance is selected is strongly influenced by shot selection. A player may quickly alter their stance depending on the circumstances and the type of shot they intend to play. Any given stance also alters dramatically based upon the actual playing of the shot with dynamic movements and shifts of body weight occurring.
Answer: There are four main stances in tennis. These are; open, semi-open, closed and neutral. While open stance is the most common stance, closed stance is least common one.
Question: Identify which animal is domesticated or wild: Koi, Common Carp
Answer: Common Carp is wild, Koi is domesticated.
Question: What is the launch date of Artemis mission?
Evidence: During preliminary reviews in 2011, the launch date of Artemis was placed somewhere between 2019 and 2021, but afterwards the launch date was delayed to 2023 on a Space Launch System (SLS) launch vehicle. Although, as of March 2023, the mission is pegged for a November 2024 launch, the need to recycle and refurbish components from Orion that flew on Artemis 1 may push this back to later in 2024 or the first quarter of 2025.
Answer: Artemis launch date is November 2024 as of now but it’s likely to change.
Question: What is Palmitic acid?
Answer: Palmitic acid (hexadecanoic acid in IUPAC nomenclature) is a fatty acid with a 16-carbon chain. It is the most common saturated fatty acid found in animals, plants and microorganisms. Its chemical formula is CH3(CH2)14COOH, and its C:D (the total number of carbon atoms to the number of carbon–carbon double bonds) is 16:0. It is a major component of the oil from the fruit of oil palms (palm oil), making up to 44% of total fats. Meats, cheeses, butter, and other dairy products also contain palmitic acid, amounting to 50–60% of total fats. Palmitates are the salts and esters of palmitic acid. The palmitate anion is the observed form of palmitic acid at physiologic pH (7.4).