DataHammer/scimrc huggingface dataset:
Question: Extract from the text what are the details needed for a  subledger, or subsidiary ledger. Separate them by a comma.
Evidence: The subledger, or subsidiary ledger, provides details behind entries in the general ledger used in accounting. The subledger shows detail for part of the accounting records such as property and equipment, prepaid expenses, etc. The detail would include such items as date the item was purchased or expense incurred, a description of the item, the original balance, and the net book value. The total of the subledger would match the line item amount on the general ledger. This corresponding line item in the general ledger is referred to as the controlling account. The subsidiary ledger balance is compared with its controlling account balance as part of the process of preparing a trial balance.

As part of an audit, a method of testing balances may include tracing individual acquisitions to the subsidiary ledger for amounts and descriptions. The objective of this test is to determine that the current-year acquisitions schedule agrees with related subledger amounts, and the total agrees with the general ledger.
Answer: date the item was purchased, expense incurred, description of the item, original balance, net book value
Question: What is their learning rate?
Evidence: We minimize the binary cross-entropy loss between the predicted outputs and the targets. We train our neural model with mini-batch size 8 using Adam BIBREF12 with learning rate INLINEFORM0 and decay the learning rate by multiplying INLINEFORM1 if the F1-Multi score on the validation set decreases during the validation. Gradient norms are clipped within 5 to prevent gradient explosion for feed-forward networks or recurrent neural networks. Since the training corpus is rather small, we use dropout BIBREF13 with probability INLINEFORM2 to prevent overfitting.
We train our neural model with mini-batch size 8 using Adam (Kingma and Ba, 2015) with learning rate 0.001 and decay the learning rate by multiplying 0.75 if the F1-Multi score on the validation set decreases during the validation.
Answer: 0.001.
Question: Do spotted hyenas actually laugh?
Answer: Spotted hyenas have two major vocalizations: a whoop and a cackle. The cackle is often thought to sound like the hyena is laughing, but is not really a true laugh.
Question: Given this paragraph about Jordan, tell me where it is located and which countries it borders?
Evidence: Jordan (Arabic: الأردن, tr. Al-ʾUrdunn [al.ʔur.dunː]), officially the Hashemite Kingdom of Jordan, is a country in Western Asia. It is situated at the crossroads of Asia, Africa, and Europe, within the Levant region, on the East Bank of the Jordan River. Jordan is bordered by Saudi Arabia to the south and east, Iraq to the northeast, Syria to the north, and the Palestinian West Bank, Israel, and the Dead Sea to the west. It has a 26 km (16 mi) coastline in its southwest on the Gulf of Aqaba's Red Sea, which separates Jordan from Egypt. Amman is Jordan's capital and largest city, as well as its economic, political, and cultural centre.
Answer: Jordan is a country in Western Asia and it is border by Saudi Arabia, Iraq, Syria, Palestine, and Israel.
Question: Should technology and software coding subjects be mandatory in the curriculum of high school education?
Answer: Acknowledging the wide spread influence, impact, and disruption that enabling technologies are having on society and business, strong consideration should be made to modernize educational curriculums to include technology subjects. Objectives would be to improve awareness, exposure, and creative thinking to ultimately inspire further experimentation and innovation.  Related to requiring students to learn language, science and math topics, strong consideration should me made to require students to gain base awareness, knowledge and coding skills. These foundational skills will become building blocks necessary to achieve life long successes and influence the pace of innovation across all industries and career paths.
Question: Categorize these super heroes as 'Marvel' or 'DC Comics'
Answer: Thor, Flash, Iron Man, Super Man, Wonder Woman, Captain America
Question: Do i must to hug my wife
Answer: Hugging Improves Relationships.
It's a deeper, more intimate level of connection that you don't get from talking. The second reason is that physical touch causes the release of the love hormone oxytocin, which improves bonding and trust and makes people feel closer to one another.
You should hug your spouse every day as an act of showing your love to them. Giving a hug is one of the simplest ways to show your love. Anytime, anywhere a hug can is a healthy reminder to your spouse of why you love them so much.
Question: What was used for estimating the correlation between the similarity measures and human judgments?
Evidence: We conducted our experiments using the freely available open source software package UMLS::Similarity BIBREF16 version 1.47. This package takes as input two terms (or UMLS concepts) and returns their similarity or relatedness using the measures discussed in Section SECREF2 .
Correlation between the similarity measures and human judgments were estimated using Spearman's Rank Correlation ( INLINEFORM0 ). Spearman's measures the statistical dependence between two variables to assess how well the relationship between the rankings of the variables can be described using a monotonic function. We used Fisher's r-to-z transformation BIBREF17 to calculate the significance between the correlation results.
Correlation between the similarity measures and human judgments were estimated using Spearman’s Rank Correlation (ρ).
Answer: Spearman’s Rank Correlation (ρ).
Question: By how much does their model outperform existing methods?
Evidence: We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction. The predictions confusion matrix is shown in table TABREF46 . The DDIs other than false being classified as false makes most of the classification error. It may perform better if a classifier which can tells true and false DDI apart is trained. We leave this two-stage classifier to our future work. Another phenomenon is that the “Int” type is often classified as “Effect”. The “Int” sentence describes there exists interaction between two drugs and this information implies the two drugs' combination will have good or bed effect. That's the reason why “Int” and “Effect” are often obfuscated.
We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction.
Answer: Answer with content missing: (Table II) Proposed model has F1 score of  0.7220 compared to 0.7148 best state-state-of-the-art result.
Question: Where did the random walk hypothesis come from?
Evidence: The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk (so price changes are random) and thus cannot be predicted.
The concept can be traced to French broker Jules Regnault who published a book in 1863, and then to French mathematician Louis Bachelier whose Ph.D. dissertation titled "The Theory of Speculation" (1900) included some remarkable insights and commentary. The same ideas were later developed by MIT Sloan School of Management professor Paul Cootner in his 1964 book The Random Character of Stock Market Prices. The term was popularized by the 1973 book A Random Walk Down Wall Street by Burton Malkiel, a professor of economics at Princeton University, and was used earlier in Eugene Fama's 1965 article "Random Walks In Stock Market Prices", which was a less technical version of his Ph.D. thesis. The theory that stock prices move randomly was earlier proposed by Maurice Kendall in his 1953 paper, The Analysis of Economic Time Series, Part 1: Prices.
Answer: It is said that the random walk hypothesis originated back in the mid 1800s from a French financier named Jules Regnault.  The random walk hypothesis notes that stock price movements are fully random and cannot be easily forecast.
Question: What is the difference between an asset and a liability.
Answer: The difference between an asset and a liability is that an asset generally provides value over the long term while a liability is an expense or debt owed.
Question: How do they evaluate their proposed metric?
Evidence: As shown in Table TABREF13, there is almost no discrimination among the last four methods under ROUGE-1 F1, and their rankings under ROUGE-1/2/L are quite different. In contrast, FAR shows that UnifiedSum(E) covers the most facets. Although FAR is supposed to be favored as FAMs are already manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference, we rank UnifiedSum(E), NeuSum, and Lead-3 in Table TABREF15. The order of the 1st rank in the human evaluation coincides with FAR. FAR also has higher Spearman's coefficient $\rho $ than ROUGE (0.457 vs. 0.44, n=30, threshold=0.362 at 95% significance).
Although FAR is supposed to be favored as FAMs are already manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference, we rank UnifiedSum(E), NeuSum, and Lead-3 in Table TABREF15. The order of the 1st rank in the human evaluation coincides with FAR. FAR also has higher Spearman's coefficient $\rho $ than ROUGE (0.457 vs. 0.44, n=30, threshold=0.362 at 95% significance).
Answer: The answers are shown as follows:
* manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference,
Question: What are symptoms of ‘Attention Deficit Hyperactivity Disorder (ADHD)’?
Answer: ADHD symptoms can be divided into three major categories: behavioral, cognitive, and mood. ADHD behavioral symptoms include aggression, excitability, fidgeting, hyperactivity, impulsivity, irritability, and repeated words or actions. Symptoms of cognitive impairment include absentmindedness, difficulty focusing, forgetfulness, attention problems, and short attention spans. There are many mood-related symptoms, such as anger, anxiety, boredom, excitement, and mood swings.
Question: What links Willie Brant, Lech Walesa, Yasser Arafat
Answer: Nobel Peace Prize
Question: What was the Long Night?
Answer: According to legends in Westeros, the Long Night was a darkness that fell over the known world and the Westeroi were subjected to not only a generation-long winter, but also the terror and destruction brought on by the Others. A similar telling comes from across the sea where the people of Essos experienced a winter that froze their waters and the Yi Ti described a generation where none saw the sun.
Question: Identify which instrument is string or woodwind: Stritch, Zhu
Answer: Zhu is string, Stritch is woodwind.
Question: What is CrossFit?
Answer: CrossFit is a workout program that was originally created by Greg Glassman in California. It focuses on high intensity constantly varied functional movements. People who do CrossFit go to CrossFit affiliated gyms to complete their workouts. The workouts are called WOD which stands for Workout Of the Day. These workouts vary in length, cardio intensity, and complexity of movements. CrossFit also combines elements of Olympic weightlifting, gymnastics, and traditional strength training. While CrossFit is an excellent way to get and stay in shape, it has received backlash from the media for the increased potential for injury that is also associated with the programming. Even with the media backlash, CrossFit has remained a popular syle of workout for those looking to get in shape.
Question: Identify which animal species is alive or extinct: Amargasaurus, Black Rhino
Answer: Black Rhino is alive, Amargasaurus is extinct.
Question: What are the performance results?
Evidence: To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively. On the restaurants dataset, this corresponds to an absolute improvement of $2.2\%$ compared to the previous state-of-the-art method BERT-PT. Language model finetuning produces a larger improvement on the restaurants dataset. We think that one reason for that might be that the restaurants domain is underrepresented in the pre-training corpora of BERTBASE. Generally, we find that language model finetuning helps even if the finetuning domain does not match the evaluation domain. We think the reason for this might be that the BERT-base model is pre-trained more on knowledge-based corpora like Wikipedia than on text containing opinions. Another finding is that BERT-ADA Joint performs better on the laptops dataset than BERT-ADA Rest, although the unique amount of laptop reviews are the same in laptops- and joint-corpora. We think that confusion can be created when mixing the domains, but this needs to be investigated further. We also find that the XLNet-base baseline performs generally stronger than BERT-base and even outperforms BERT-ADA Lapt with an accuracy of $79.89\%$ on the laptops dataset.
In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training. We think the reason for this might be that syntactical relationships between the aspect-target and the phrase expressing sentiment polarity as well as knowing the sentiment-polarity itself are sufficient to solve the ATSC task in many cases.
To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively.
In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training.
Answer: The answers are shown as follows:
* results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset
* new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively.
Question: Why might I want to stay at home on a Friday night?
Answer: - You're feeling under the weather
- The weather outside is stormy and dangerous
- All your friends are busy and can't hang out with you
- There's nothing fun going on
- It's too crowded out there
- You need to binge watch the latest show
Question: List all the years mentioned in the following passage
Evidence: During the Middle Ages, the Gran Tavola (Italian for "Great Table") was the largest Sienese bank; it was one of the most powerful banks in Europe from 1255 to 1298. The Gran Tavola has been called "the greatest bank of the thirteenth century" as well as "one of the largest commercial and banking enterprises in Europe".

The main branches of the Gran Tavola during the mid-thirteenth century were in Pisa, Bologna, Genoa, Marseille, and Paris.
Answer: 1255, 1298
Question: Given this reference text about the Grateful Dead, tell me who their keyboard players were over the years.
Evidence: The Grateful Dead was founded in the San Francisco Bay Area amid the rise of the counterculture of the 1960s. The founding members were Jerry Garcia (lead guitar, vocals), Bob Weir (rhythm guitar, vocals), Ron "Pigpen" McKernan (keyboards, harmonica, vocals), Phil Lesh (bass, vocals), and Bill Kreutzmann (drums). Members of the Grateful Dead, originally known as the Warlocks, had played together in various Bay Area ensembles, including the traditional jug band Mother McCree's Uptown Jug Champions. Lesh was the last member to join the Warlocks before they changed their name to the Grateful Dead; replacing Dana Morgan Jr., who had played bass for a few gigs. Drummer Mickey Hart and non-performing lyricist Robert Hunter joined in 1967. With the exception of McKernan, who died in 1973, and Hart, who took time off from 1971 to 1974, the core of the band stayed together for its entire 30-year history. The other official members of the band are Tom Constanten (keyboards; 1968–1970), John Perry Barlow (non-performing lyricist; 1971–1995), Keith Godchaux (keyboards, occasional vocals; 1971–1979), Donna Godchaux (vocals; 1972–1979), Brent Mydland (keyboards, vocals; 1979–1990), and Vince Welnick (keyboards, vocals; 1990–1995). Bruce Hornsby (accordion, piano, vocals) was a touring member from 1990 to 1992, as well as a guest with the band on occasion before and after the tours.
Answer: Ron "Pigpen" McKernan was the original keyboard player of the Grateful Dead. Others to play keyboards for the Grateful Dead include Tom Constanten, Keith Godchaux, Brent Mydland and Vince Welnick.
Question: Who won the best new artist as well as award for the Best Pop Vocal Album in 64th Annual Grammy Awards
Answer: Olivia Rodrigo
Question: Classify these vehicles by class. Mercedes A-Class, BMW M4, Toyota Camry, Ford F150, CyberTruck
Answer: Mercedes A-Class: Car
BMW M4: Car
Toyota Camry: Car
Ford F150: Truck
CyberTruck: Truck
Question: How do they generate the synthetic dataset?
Evidence: We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data
Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2
iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid).
We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data

Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2

iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid).
Answer: using generative process
Question: What does "digital nomads" mean?
Answer: Digital nomads are professionals who use technology and the internet to do their jobs while traveling and living a nomadic life. Digital nomads work remotely, instead of going to a company's office or headquarters.
Question: What would it be like to be a pirate?
Answer: If you were a pirate, your mission would be to find treasure. The question is between the vastness and depth of the sea, how could you ever find such a treasure? There are no treasure maps. You could look in history books to find coordinates of where they believe ships have crashed in the past, take those coordinates, and search for those sites. However, you may find the site only to realize the boat never carried any treasure that could stand the test of time under the water. The treasure may have wasted away.
Question: Where did the team collect the read speech datasets in their experiments?
Evidence: We evaluated the performance of Jasper on two read speech datasets: LibriSpeech and Wall Street Journal (WSJ). For LibriSpeech, we trained Jasper DR 10x5 using our NovoGrad optimizer for 400 epochs. We achieve SOTA performance on the test-clean subset and SOTA among end-to-end speech recognition models on test-other.
We trained a smaller Jasper 10x3 model with SGD with momentum optimizer for 400 epochs on a combined WSJ dataset (80 hours): LDC93S6A (WSJ0) and LDC94S13A (WSJ1). The results are provided in Table TABREF29 .
We evaluated the performance of Jasper on two read speech datasets: LibriSpeech and Wall Street Journal (WSJ).
Answer: From LibriSpeech and Wall Street Journal (WSJ).
Question: How are aspects identified in aspect extraction?
Evidence: Most of the previous works in aspect term extraction have either used conditional random fields (CRFs) BIBREF9 , BIBREF10 or linguistic patterns BIBREF7 , BIBREF11 . Both of these approaches have their own limitations: CRF is a linear model, so it needs a large number of features to work well; linguistic patterns need to be crafted by hand, and they crucially depend on the grammatical accuracy of the sentences. In this chapter, we apply an ensemble of deep learning and linguistics to tackle both the problem of aspect extraction and subjectivity detection.
In this chapter, we apply an ensemble of deep learning and linguistics to tackle both the problem of aspect extraction and subjectivity detection.
Answer: The answers are shown as follows:
* apply an ensemble of deep learning and linguistics t
Question: Why use this corpus as a basis?
Evidence: Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. In the Internet era, thanks to the mechanism of sharing in social networks, propaganda campaigns have the potential of reaching very large audiences BIBREF0, BIBREF1, BIBREF2.
Propagandist news articles use specific techniques to convey their message, such as whataboutism, red Herring, and name calling, among many others (cf. Section SECREF3). Whereas proving intent is not easy, we can analyse the language of a claim/article and look for the use of specific propaganda techniques. Going at this fine-grained level can yield more reliable systems and it also makes it possible to explain to the user why an article was judged as propagandist by an automatic system.
With this in mind, we organised the shared task on fine-grained propaganda detection at the NLP4IF@EMNLP-IJCNLP 2019 workshop. The task is based on a corpus of news articles annotated with an inventory of 18 propagandist techniques at the fragment level. We hope that the corpus would raise interest outside of the community of researchers studying propaganda. For example, the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.
We hope that the corpus would raise interest outside of the community of researchers studying propaganda.  For example, the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.
Answer: They hope that the corpus would raise interest outside of the community of researchers studying propaganda.