DataHammer/scimrc huggingface dataset:
Question: Do they report results only on English data?
Evidence: We henceforth refer to a tweet affirming climate change as a “positive" sample (labeled as 1 in the data), and a tweet denying climate change as a “negative" sample (labeled as -1 in the data). All data were downloaded from Twitter in two separate batches using the “twint" scraping tool BIBREF5 to sample historical tweets for several different search terms; queries always included either “climate change" or “global warming", and further included disaster-specific search terms (e.g., “bomb cyclone," “blizzard," “snowstorm," etc.). We refer to the first data batch as “influential" tweets, and the second data batch as “event-related" tweets.
All data were downloaded from Twitter in two separate batches using the “twint" scraping tool BIBREF5 to sample historical tweets for several different search terms; queries always included either “climate change" or “global warming", and further included disaster-specific search terms (e.g., “bomb cyclone," “blizzard," “snowstorm," etc.). 
Answer: Yes.
Question: In the discussion the authors claim the proposed approach improves the explainability of the model, but I don't see how that is the case.
Evidence: The D and W selected by the assistant module in AdaGCCN compared to the original depth and width in CapsGNN on different datasets are displayed in Table 5. Note that the optimal D and W determined by AM are different on the 10 splittings. We take the D and W on the splitting with the best test accuracy for the comparison against CapsGNN
Answer: We agree with your suggestion. We added the comparison between the D and W selected by the assistant module and the original depth and width in CapsGNN on each dataset in Table 5 in the appendix.
Question: Classify each of the following animal as a carnivore, omnivore or herbivore: tiger, bear, giraffe, wolverine, elephant, monkey
Answer: Carnivores: Tiger, wolverine
Omnivore: Bear, monkey
Herbivore: Elephant, giraffe
Question: What is an expert system, and when did they first emerge?
Evidence: Boom 1980–1987
In the 1980s a form of AI program called "expert systems" was adopted by corporations around the world and knowledge became the focus of mainstream AI research. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project. Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart. Once again, AI had achieved success.

The rise of expert systems
An expert system is a program that answers questions or solves problems about a specific domain of knowledge, using logical rules that are derived from the knowledge of experts. The earliest examples were developed by Edward Feigenbaum and his students. Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach.

Expert systems restricted themselves to a small domain of specific knowledge (thus avoiding the commonsense knowledge problem) and their simple design made it relatively easy for programs to be built and then modified once they were in place. All in all, the programs proved to be useful: something that AI had not been able to achieve up to this point.

In 1980, an expert system called XCON was completed at CMU for the Digital Equipment Corporation. It was an enormous success: it was saving the company 40 million dollars annually by 1986. Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments. An industry grew up to support them, including hardware companies like Symbolics and Lisp Machines and software companies such as IntelliCorp and Aion.
Answer: An expert system is a program that uses logical rules derived from expert knowledge to answer questions or solve problems in a specific domain of knowledge. The first expert system, called Dendral, was developed in 1965 by Edward Feigenbaum and his students and identified compounds from spectrometer readings.
Question: What is the capital of Spain and what is it's population?
Answer: The capital of Spain is Madrid and it has approximately 3.4 million people
Question: Which rock band from the 2000s is the most influential?
Answer: The most influential rock band from the 2000s is The Strokes. They are often credited with spearheading a revival of rock. They have influenced and inspired extremely popular bands such as The Arctic Monkeys, The Killers, and Franz Ferdinand.
Question: What is the primary advantage of a hydrogen fuel cell powered vehicle?
Answer: No tailpipe emissions, emit only water vapour and warm air, fastest charging technology, Refilling of hydrogen very fast, Environment friendly
Question: Given this paragraph, what is an important function of the London Assembly?
Evidence: The administration of London is formed of two tiers: a citywide, strategic tier and a local tier. Citywide administration is coordinated by the Greater London Authority (GLA), while local administration is carried out by 33 smaller authorities. The GLA consists of two elected components: the mayor of London, who has executive powers, and the London Assembly, which scrutinises the mayor's decisions and can accept or reject the mayor's budget proposals each year.
Answer: A important function of the London Assembly is to scrutinise the mayor's decisions and accept or reject the mayor's budget proposals each year.
Question: What does RAND Corporation do?
Evidence: The RAND Corporation (from the phrase "research and development") is a non-partisan American nonprofit global policy think tank and research institute that conducts research and development (R&D) in multiple fields and industries including national security, military-defense, aerospace, education, public health, energy, the environment (environmental science, studies), economics, political science and international relations, infrastructure, law and criminology (criminal justice), anthropology, sociology, social policy, the social sciences, natural sciences, technology, engineering, mathematics, and their application on public policy, public administration, and business administration issues.
Answer: RAND is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous.
Question: Are you ever too old for anything?
Answer: The one thing i will always fiercely & unabashedly resent is the stance that someone is “too old for something” past a certain age. Telling someone they’re “too old” to enjoy a show or practice a hobby or have a personality or do anything that’s not considered “mature” is one of the ugliest standards society has managed to ingrain into people’s brains. Please pursue what makes you happy without shame. if it doesn’t hurt you or anyone else, then it’s no one’s business but yours. You owe no one an explanation. but you do owe yourself authenticity & happiness. And no one can decide how that looks like but you
Question: Do they give generalizations of patterns?
Evidence: Twitter, a micro-blogging and social networking site has emerged as a platform where people express themselves and react to events in real-time. It is estimated that nearly 500 million tweets are sent per day . Twitter data is particularly interesting because of its peculiar nature where people convey messages in short sentences using hashtags, emoticons, emojis etc. In addition, each tweet has meta data like location and language used by the sender. It's challenging to analyze this data because the tweets might not be grammatically correct and the users tend to use informal and slang words all the time. Hence, this poses an interesting problem for NLP researchers. Any advances in using this abundant and diverse data can help understand and analyze information about a person, an event, a product, an organization or a country as a whole. Many notable use cases of the twitter can be found here.
Along the similar lines, The Task 1 of WASSA-2017 BIBREF0 poses a problem of finding emotion intensity of four emotions namely anger, fear, joy, sadness from tweets. In this paper, we describe our approach and experiments to solve this problem. The rest of the paper is laid out as follows: Section 2 describes the system architecture, Section 3 reports results and inference from different experiments, while Section 4 points to ways that the problem can be further explored.
The rest of the paper is laid out as follows: Section 2 describes the system architecture, Section 3 reports results and inference from different experiments, while Section 4 points to ways that the problem can be further explored.
Answer: Yes.
Question: What neural network did they build?
Evidence: Using our cleaned data set, we now built a recurrent neural network (RNN) with long short-term memory gates (LSTM). Our RNN/LSTM is trained to predict, given a description, whether a home corresponds to a high/medium/low popularity listing. The architecture of the RNN/LSTM employs Tensorflow’s Dynamic RNN package. Each sentence input is first fed into an embedding layer, where the input’s text is converted to a GloVe vector. These GloVe vectors are learned via a global word-word co-occurrence matrix using our corpus of Airbnb listing descriptions [8]. At each time step, the GloVe vectors are then fed into an LSTM layer. For each layer, the model forward propagates the output of the LSTM layer to the next time-step’s LSTM layer via a rectified linear unit (RLU) activation function. Each layer also pipes the output of the LSTM through a cross-entropy operation, to predict, for each time-step, the category of the input sequence. We finally ensemble these predictions, to create the model’s complete output prediction.
Using our cleaned data set, we now built a recurrent neural network (RNN) with long short-term memory gates (LSTM). Our RNN/LSTM is trained to predict, given a description, whether a home corresponds to a high/medium/low popularity listing.
Answer: A recurrent neural network (RNN) with long short-term memory gates (LSTM).
Question: What are the 13 departments and main communities of France's administrative region of Occitania?
Evidence: Geography
Occitania is the second-largest region in mainland France, covering an area of 72,724 km2 (28,079 sq mi); it has a population of 5,845,102 (2017). It has a Mediterranean coast on the southeast and is neighboured by Provence-Alpes-Côte d'Azur on the east, Auvergne-Rhône-Alpes on the northeast and Nouvelle-Aquitaine on the west and northwest, as well as foreign borders of Andorra (Canillo, Encamp, La Massana, Ordino) and Spain (Aragon and Catalonia) on the south.

Departments
Code	Arms 1	Department	Prefecture	Named after	Population (2017)
09	Coat of arms of department 09	Ariège	Foix	Ariège River	153,153
11	Coat of arms of department 11	Aude	Carcassonne	Aude River	370,260
12	Coat of arms of department 12	Aveyron	Rodez	Aveyron River	279,206
30	Coat of arms of department 30	Gard	Nîmes	Gardon River	744,178
31	Coat of arms of department 31	Haute-Garonne	Toulouse	Garonne River	1,362,672
32	Coat of arms of department 32	Gers	Auch	Gers River	191,091
34	Coat of arms of department 34	Hérault	Montpellier	Hérault River	1,144,892
46	Coat of arms of department 46	Lot	Cahors	Lot River	173,828
48	Coat of arms of department 48	Lozère	Mende	Mont Lozère	76,601
65	Coat of arms of department 65	Hautes-Pyrénées	Tarbes	Pyrenees	228,530
66	Coat of arms of department 66	Pyrénées-Orientales	Perpignan	Pyrenees	474,452
81	Coat of arms of department 81	Tarn	Albi	Tarn River	387,890
82	Coat of arms of department 82	Tarn-et-Garonne	Montauban	Tarn River and Garonne River	258,349
Major communities

Toulouse, Occitania's prefecture and largest city

The Pont du Gard from the Roman Era, one of Occitanie's main landmarks and tourist attractions
The largest communes of Occitania are (population as of 2017:

Toulouse (479,553)
Montpellier (285,121)
Nîmes (150,610)
Perpignan (120,158)
Béziers (77,177)
Montauban (60,810)
Narbonne (54,700)
Albi (50,759)
Carcassonne (47,365)
Answer: The 13 Departments of Occitania are:
- Ariège
- Aude
- Aveyron
- Gard
- Haute-Garonne
- Gers
- Hérault
- Lot
- Lozère
- Hautes-Pyrénées
- Pyrénées-Orientales
- Tarn
- Tarn-et-Garonne

The main communities located within Occitania are:
- Toulouse (population: 479,553)
- Montpellier (population: 285,121)
- Nîmes (population: 150,610)
- Perpignan (population: 120,158)
- Béziers (population: 77,177)
- Montauban (population: 60,810)
- Narbonne (population: 54,700)
- Albi (population: 50,759)
- Carcassonne (population: 47,365)
Question: Give me a bulleted list of Aaron Fenster's accomplishments.
Evidence: Aaron Fenster is a medical physicist at the University of Western Ontario Robarts Research Institute in London, Ontario, Canada. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013 for his contributions to medical imaging and three-dimensional ultrasound-guided interventions. He is also a fellow of the Canadian Academy of Health Sciences and co-program director of the Ontario Institute for Cancer Research Imaging Program. He holds Ph.D. from the University of Toronto and received further training at the Ontario Cancer Institute.
Answer: - Medical physicist at the University of Western Ontario Robarts Research Institute.
- Fellow of the Institute of Electrical and Electronics Engineers (IEEE).
- Fellow of the Canadian Academy of Health Sciences. 
- Co-program director of the Ontario Institute for Cancer Research Imaging Program. 
- Completed a Ph.D. from the University of Toronto.
Question: Classify the following as either red wines or white wines: Cabernet Sauvignon, Pinot Blanc, Riesling, Chardonnay, Pinot Grigio, Sauvignon Blanc, Merlot, Syrah, Shiraz
Answer: Red wines: Cabernet Sauvignon, Merlot, Syrah, Shiraz
White wines: Pinot Blanc, Riesling, Chardonnay, Pinot Grigio, Sauvignon Blanc
Question: What is the Philly Special and when was it used ?
Answer: The Philly Special is a trick play in American football that was used successfully by the Philadelphia Eagles against the New England Patriots in Super Bowl LXXII.  Instead of snapping the ball directly to the quarterback, the ball was snapped to the running back, who lateraled it to the tight end who passed it to a wide open quarterback for a touchdown.
Question: What is a noun?
Answer: A noun is a word that is used to identify a person, place, or thing.
Question: Write a brief passage arguing for wearing fanny packs when walking around town.
Answer: A fanny pack, otherwise known as a belt bag or waist pack, is a functional and fashionable item to wear while going around town. It can hold any items essential to make your day comfortable, such as keys, a wallet, sunglasses, snacks, lip balm, or others. It's especially useful for when your clothes either don't have pockets, or don't have pockets large enough to fit everything you'd like to bring. In the past, fanny packs were considered ugly and bulky, but in recent times, fanny packs have become quite fashionable; luxury brands have started selling fanny packs and fashion magazine outfits often incorporate a fanny pack slung around a shoulder or around the waist. Overall, fanny packs are a capable item that is sure to improve your experience when talking a walk.
Question: Is the RNN model evaluated against any baseline?
Evidence: For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8. The probability of a sequence of words is the product of the probabilities of each word, given the word’s context: the preceding word. As in the following equation:
For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8.
Answer: Yes.
Question: In the targeted data collection approach, what type of data is targetted?
Evidence: There are a larger number of male-gendered character personas than female-gendered character personas (see Section SECREF2), so we balance existing personas using gender-swapping. For every gendered character in the dataset, we ask annotators to create a new character with a persona of the opposite gender that is otherwise identical except for referring nouns or pronouns. Additionally, we ask annotators to swap the gender of any characters that are referred to in the persona text for a given character.
For every gendered character in the dataset, we ask annotators to create a new character with a persona of the opposite gender that is otherwise identical except for referring nouns or pronouns.
Answer: Gendered characters in the dataset
Question: What do the Olympic rings mean?
Answer: The Olympic rings represent the five inhabited continents of the world that participate in the Olympic Games: Africa, America, Asia, Europe and Oceania. The colors of the five Olympic rings (blue, yellow, black, green and red) are the colors that are part of almost every national flag.
Question: Classify each of the following as either caloric or low-fat products: butter, turnips, oils, salad dressings, spinach, sugar, water, nuts, seeds, dry bread, zucchini, strawberries, dry cereal, crackers, tomatoes, egg yolk, avocado, dried fruit, ananas, red meat, pizza, salmon, ketchup, watermelon
Answer: yes, no, yes, yes, no, yes, no, yes, yes, yes, no, no, yes, yes, no, yes, yes, yes, no, yes, yes, yes, yes, no
Question: What is the research background of this paper?
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.
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 (Bahdanau et al., 2014; Vaswani et al., 2017) and Chinese-English (Hassan et al., 2018).
Answer: 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  and Chinese-English.
Question: Give me five books that I should read today.
Answer: There are many books that you could read today, however, if I could recommend a few, I would recommend the following: The Great Gatsby, by F. Scott Fitzgerald, Let The Great World Spin, by Colum McCann, 1984 by George Orwell, Pride and Prejudice by Jane Austen, and The Diary of Anne Frank, by Anne Frank.
Question: Is Castle Island really an Island?
Evidence: Castle Island is a peninsula in South Boston on the shore of Boston Harbor. In 1928, Castle Island was connected to the mainland by a narrow strip of land and is thus no longer an island. It has been the site of a fortification since 1634, and is currently a 22-acre (8.9 ha) recreation site and the location of Fort Independence.
Answer: No, Castle Island is a peninsula off Boston Harbor as of 1928, when it was connected to the Massachusetts mainland.  It is a recreation area that is the site of Fort Independence.
Question: Is it possible to prevent Type 2 Diabetes?
Answer: Type 2 diabetes is largely preventable by staying a normal weight, exercising regularly, and eating a healthy diet (high in fruits and vegetables and low in sugar and saturated  fats).Treatment involves exercise and dietary changes.  If blood sugar levels are not adequately lowered, the medication metformin is typically recommended. Many people may eventually also require insulin injections. In those on insulin, routinely checking blood sugar levels is advised; however, this may not be needed in those who are not on insulin therapy. Bariatric surgery often improves diabetes in those who are obese.
Question: How much do clouds weigh?
Answer: The weight of a cloud depends on how you define it. It will include the weight of water droplets in the cloud itself. You may also define weight to include the weight of the air, mostly pressing down on the cloud. If only defined by water droplets, clouds have a density of about 0.5 grams of water per cubic meter. A 1 cubic kilometer cloud contains 1 billion cubic meters. That would be 500 million grams or 500 thousand kilograms. That is equivalent to 1.1 million pounds or 551 tons of water droplets.
Question: What is parkrun
Evidence: Parkrun (stylised as parkrun) is a collection of 5-kilometre (3.1 mi) events for walkers, runners and volunteers that take place every Saturday morning at more than 2,000 locations in 22 countries across six continents. Junior Parkrun (stylised as junior parkrun) is a spin-off event that provides a 2 kilometres (1+1⁄4 mi) event for children aged 4–14 on a Sunday morning. Parkrun events are free to enter and are delivered by volunteers, supported by a small group of staff at its headquarters.
Answer: Parkrun is a community event that takes place each Saturday morning where entrants walk, jog, or run a 5-kilometre course. Events take place at a range of general locations including parks, forests, rivers, lakes, beaches, and nature reserves. The first ever parkrun event took place on 2 October 2004 in Bushy Park, London involving 13 runners. Today more than 3 million people across more than 2,000 locations in 22 countries participate in parkrun each week. Parkrun events are free to enter and are delivered by volunteers.