DataHammer/scimrc huggingface dataset:
Question: Is it possible to build a society that runs completely on socialism without any capitalism elements?
Answer: It is theoretically possible to build a society that runs completely on socialism without any capitalism elements, but based on the human history so far, there are challenges that are difficult to overcome. As a socialist society relies on a centralized organization for distribution of resources rather than relying on free market, the challenges include inefficient decision making, corruption of power, etc.
Question: What is a benefit of salsa dancing?
Answer: Salsa dancing is a great form of exercise. It provides a great cardio workout and helps you work on balance and coordination. In addition, it keeps you engaged mentally, making it a workout of the body and the mind.
Question: What's the capital of Brazil?
Answer: The capital of Brazil is Rio de Janeiro.
Question: Creative ways to call in sick at work
Answer: Here are a few ways to call in sick at work: 

- "Unfortunately, the salmon from last night did not sit well with me and I've been having severe stomach issues" 

- "I've been experiencing flu like symptoms and would need to take the day off to check for any Covid related infections" 

- "I need to visit the doctor urgently for a medical issue" 

- "I have a bad migraine and will not be able to join in any meetings today, I will still be available on email and chat, if needed"
Question: Is there any Bengali standard corpus of questions?
Evidence: Though Bengali is the seventh most spoken language in terms of number of native speakers BIBREF23, there is no standard corpus of questions available BIBREF0. We have collected total 3500 questions from the Internet and other sources such as books of general knowledge questions, history etc. The corpus contains the questions and the classes each question belongs to.
The set of question categories is known as question taxonomy BIBREF0. We have used two layer taxonomy which was proposed by Xin Li, Dan Roth BIBREF24. This two layer taxonomy is made up of two classes which are Coarse Class and Finer Class. There are six coarse classes such as Numeric, Location, Entity, Description, Human and Abbreviation and fifty finer classes such as city, state, mountain, distance, count, definition, group, expression, substance, creative, vehicle etc as shown in the Table I BIBREF0. A coarse-grained description of a system denotes large components while a fine-grained description denotes smaller sub-components of which the larger ones are composed of.
Though Bengali is the seventh most spoken language in terms of number of native speakers [6], there is no standard corpus of questions available [14].
Answer: No, there isn't.
Question: What is constructed to select the core entity of the question?
Evidence: We introduce an open domain question answering system based on Chinese knowledge graph in this paper. We analyze the questions and find that most of the answers to the questions are within two hops. Therefore, we only solve the problem within two hops to reduce the complexity of the system. The system consists of a topic entity selection module, a relationship recognition module and an answer selection module. Firstly, we construct a scoring mechanism to select the core entity of the question; Then we score the relationship in the two-hop subgraph of the topic entity; Finally, we build a classier to judge whether a question is simple or complicated, so that we can choose the final relationship and generate sparql query.
Firstly, we construct a scoring mechanism to select the core entity of the question; Then we score the relationship in the two-hop subgraph of the topic entity; Finally, we build a classier to judge whether a question is simple or complicated, so that we can choose the final relationship and generate sparql query.
Answer: A scoring mechanism.
Question: What KB is used in this work?
Evidence: In this study, we investigate KB literal canonicalization using a combination of RNN-based learning and semantic technologies. We first predict the semantic types of a literal by: (i) identifying candidate classes via lexical entity matching and KB queries; (ii) automatically generating positive and negative examples via KB sampling, with external semantics (e.g., from other KBs) injected for improved quality; (iii) training classifiers using relevant subject-predicate-literal triples embedded in an attentive bidirectional RNN (AttBiRNN); and (iv) using the trained classifiers and KB class hierarchy to predict candidate types. The novelty of our framework lies in its knowledge-based learning; this includes automatic candidate class extraction and sampling from the KB, triple embedding with different importance degrees suggesting different semantics, and using the predicted types to identify a potential canonical entity from the KB. We have evaluated our framework using a synthetic literal set (S-Lite) and a real literal set (R-Lite) from DBpedia BIBREF0 . The results are very promising, with significant improvements over several baselines, including the existing state-of-the-art.
We have evaluated our framework using a synthetic literal set (S-Lite) and a real literal set (R-Lite) from DBpedia BIBREF0 . 
Answer: The answers are shown as follows:
* DBpedia
Question: What should I wear in the summer?
Answer: In the summer people usually wear shorts, tank tops or tee shirts. Women may choose to wear skirts, skorts, or sun dresses.  Flip flops and sandals are the most common footwear choice but sneakers are also a good choice. To protect against sun damage, most people wear a hat, visor, or light jackets to protect skin from sun exposure.
Question: Who is Sean Connery?
Evidence: Sir Sean Connery (born Thomas Connery; 25 August 1930 – 31 October 2020) was a Scottish actor. He was the first actor to portray fictional British secret agent James Bond on film, starring in seven Bond films between 1962 and 1983. Originating the role in Dr. No, Connery played Bond in six of Eon Productions' entries and made his final Bond appearance in the non-Eon-produced Never Say Never Again; if non-Eon-produced Bond movies are included, Connery shares the record for the most portrayals as James Bond with Roger Moore (with 7 apiece). Following his third appearance as Bond in Goldfinger (1964), in June 1965 Time magazine observed "James Bond has developed into the biggest mass-cult hero of the decade"
Answer: Scottish Actor
Question: Which countries became members of NATO between the years of 1999 and 2009?
Evidence: Politically, the organization sought better relations with the newly autonomous Central and Eastern European states, and diplomatic forums for regional cooperation between NATO and its neighbours were set up during this post-Cold War period, including the Partnership for Peace and the Mediterranean Dialogue initiative in 1994, the Euro-Atlantic Partnership Council in 1997, and the NATO–Russia Permanent Joint Council in 1998. At the 1999 Washington summit, Hungary, Poland, and the Czech Republic officially joined NATO, and the organization also issued new guidelines for membership with individualized "Membership Action Plans". These plans governed the addition of new alliance members: Bulgaria, Estonia, Latvia, Lithuania, Romania, Slovakia, and Slovenia in 2004, Albania and Croatia in 2009, Montenegro in 2017, and North Macedonia in 2020. The election of French President Nicolas Sarkozy in 2007 led to a major reform of France's military position, culminating with the return to full membership on 4 April 2009, which also included France rejoining the NATO Military Command Structure, while maintaining an independent nuclear deterrent.
Answer: Hungary, Poland, Czech Republic, Bulgaria, Estonia, Latvia, Lithuania, Romania, Slovakia, Slovenia, Albania, France (rejoined)
Question: What turn out to be more important high volume or high quality data?
Evidence: The Spearman $\rho $ correlation for fastText models on the curated small dataset (clean), C1, improves the baselines by a large margin ($\rho =0.354$ for Twi and 0.322 for Yorùbá) even with a small dataset. The improvement could be justified just by the larger vocabulary in Twi, but in the case of Yorùbá the enhancement is there with almost half of the vocabulary size. We found out that adding some noisy texts (C2 dataset) slightly improves the correlation for Twi language but not for the Yorùbá language. The Twi language benefits from Wikipedia articles because its inclusion doubles the vocabulary and reduces the bias of the model towards religious texts. However, for Yorùbá, noisy texts often ignore diacritics or tonal marks which increases the vocabulary size at the cost of an increment in the ambiguity too. As a result, the correlation is slightly hurt. One would expect that training with more data would improve the quality of the embeddings, but we found out with the results obtained with the C3 dataset, that only high-quality data helps. The addition of JW300 boosts the vocabulary in both cases, but whereas for Twi the corpus mixes dialects and is noisy, for Yorùbá it is very clean and with full diacritics. Consequently, the best embeddings for Yorùbá are obtained when training with the C3 dataset, whereas for Twi, C2 is the best option. In both cases, the curated embeddings improve the correlation with human judgements on the similarity task a $\Delta \rho =+0.25$ or, equivalently, by an increment on $\rho $ of 170% (Twi) and 180% (Yorùbá).
One would expect that training with more data would improve the quality of the embeddings, but we found out with the results obtained with the C3 dataset, that only high-quality data helps.
Answer: The answers are shown as follows:
* only high-quality data helps
Question: What is the currently available word segmentation corpus in Vietnamese？
Evidence: In general, building corpus is carried out through four stages: (1) choose target of corpus and source of raw data; (2) building a guideline based on linguistics knowledge for annotation; (3) annotating or tagging corpus based on rule set in the guideline; and (4) reviewing corpus to check the consistency issue.
Encoding word segmentation corpus using B-I-O tagset can be applied, where B, I, and O denoted begin of word, inside of word, and others, respectively. For example, the sentence “Megabit trên giây là đơn vị đo tốc đọ truyền dẫn dữ liệu ." (”Megabit per second is a unit to measure the network traffic.” in English) with the word boundary result “Megabit trên giây là đơn_vị đo tốc_độ truyền_dẫn dữ_liệu ." is encoded as “Megabit/B trên/B giây/B là/B đơn/B vị/I đo/B tốc/B độ/I truyền/B dẫn/I dữ/B liệu/I ./O" .
Annotation guidelines can be applied to ensure that annotated corpus has less errors because the manual annotation is applied. Even though there are guidelines for annotating, the available output corpora are still inconsistent. For example, for the Vietnamese Treebank corpus of the VLSP project, Nguyen et al. listed out several Vietnamese word segmentation inconsistencies in the corpus based on POS information and n-gram sequences BIBREF14 .
Currently, there are at least three available word segmentation corpus used in Vietnamese word segmentation studies and systems. Firstly, Dinh et al. built the CADASA corpus from CADASA’s books BIBREF15 . Secondly, Nguyen et al. built vnQTAG corpus from general news articles BIBREF7 . More recently, Ngo et al. introduced the EVBCorpus corpus, which is collected from four sources, news articles, books, law documents, and novels. As a part of EVBCorpus, EVBNews, was annotated common tags in NLP, such as word segmentation, chunker, and named entity BIBREF16 . All of these corpora are collected from news articles or book stories, and they are manually annotated the word boundary tags (as shown in Table I).
Currently, there are at least three available word segmentation corpus used in Vietnamese word segmentation studies and systems. Firstly, Dinh et al. built the CADASA corpus from CADASA's books [16]. Secondly, Nguyen et al. built vnQTAG corpus from general news articles [8]. More recently, Ngo et al. introduced the EVBCorpus corpus, which is collected from four sources, news articles, books, law documents, and novels. As a part of EVBCorpus, EVBNews, was annotated common tags in NLP, such as word segmentation, chunker, and named entity [17]. All of these corpora are collected from news articles or book stories, and they are manually annotated the word boundary tags (as shown in Table I).
Answer: CADASA corpus from CADASA's books，vnQTAG corpus from general news articles and EVBCorpus from news articles, books, law documents, and novels.
Question: Who is Kamala Harris?
Evidence: Kamala Devi Harris (/ˈkɑːmələ ˈdeɪvi/ (listen) KAH-mə-lə DAY-vee; born October 20, 1964) is an American politician and attorney who is the 49th and current vice president of the United States. She is the first female vice president and the highest-ranking female official in U.S. history, as well as the first African-American and first Asian-American vice president. A member of the Democratic Party, she previously served as the attorney general of California from 2011 to 2017 and as a United States senator representing California from 2017 to 2021.
Answer: Kamala Devi Harris (/ˈkɑːmələ ˈdeɪvi/ (listen) KAH-mə-lə DAY-vee; born October 20, 1964) is an American politician and attorney who is the 49th and current vice president of the United States. She is the first female vice president and the highest-ranking female official in U.S. history, as well as the first African-American and first Asian-American vice president. A member of the Democratic Party, she previously served as the attorney general of California from 2011 to 2017 and as a United States senator representing California from 2017 to 2021.

Born in Oakland, California, Harris graduated from Howard University and the University of California, Hastings College of the Law. She began her career in the Alameda County District Attorney's Office, before being recruited to the San Francisco District Attorney's Office and later the City Attorney of San Francisco's office. In 2003, she was elected district attorney of San Francisco. She was elected Attorney General of California in 2010 and re-elected in 2014. Harris served as the junior United States senator from California from 2017 to 2021; she defeated Loretta Sanchez in the 2016 Senate election to become the second African-American woman and the first South Asian American to serve in the United States Senate. As a senator, she advocated for healthcare reform, federal de-scheduling of cannabis, a path to citizenship for undocumented immigrants, the DREAM Act, a ban on assault weapons, and progressive tax reform. She gained a national profile for her pointed questioning of Trump administration officials during Senate hearings, including Trump's second Supreme Court nominee Brett Kavanaugh, who was accused of sexual assault.

Harris sought the 2020 Democratic presidential nomination, but withdrew from the race prior to the primaries. Joe Biden selected her as his running mate, and their ticket went on to defeat the incumbent president, Donald Trump, and vice president, Mike Pence, in the 2020 election. Harris became vice president upon being inaugurated on January 20, 2021, alongside President Joe Biden.
Question: Tell me whether these athletes play or have played in the National Football League (NFL) or the National Basketball Association (NBA): Kobe Bryant, Kawhi Leonard, LeBron James, Larry Bird, Michael Jordan
Answer: National Basketball Association (NBA)
Question: Why did Captain Bligh sail to Tahiti?
Answer: The British Navy assigned then-Lieutenant William Bligh the task of sailing to Tahiti to collect seedlings of the breadfruit tree and transport them to British colonies in the Caribbean.  The goal was to establish the breadfruit tree as a food source for the growing population in those Caribbean colonies.
Question: Can you please share some context on Figure skating using the passage given as a reference
Evidence: Figure skating is a sport in which individuals, pairs, or groups perform on figure skates on ice. It was the first winter sport to be included in the Olympic Games, when contested at the 1908 Olympics in London. The Olympic disciplines are men's singles, women's singles, pair skating, and ice dance; the four individual disciplines are also combined into a team event, first included in the Winter Olympics in 2014. The non-Olympic disciplines include synchronized skating, Theater on Ice, and four skating. From intermediate through senior-level competition, skaters generally perform two programs (the short program and the free skate), which, depending on the discipline, may include spins, jumps, moves in the field, lifts, throw jumps, death spirals, and other elements or moves.
Answer: 1.	Individuals, couples, or groups compete in the sport of figure skating on ice. When it was played at the 1908 Olympics in London, it became the first winter sport to be featured in the Olympic Games.
2.	Men's singles, women's singles, pair skating, and ice dancing are the Olympic disciplines. In addition, the four individual disciplines are merged into a team event, which debuted in the Winter Olympics in 2014.
3.	Skaters compete in two programmes—the short programme and the free skate—from intermediate to senior levels. Depending on the discipline, these programmes may contain spins, leaps, field moves, lifts, throw jumps, death spirals, and other features or moves.
4.	The non-Olympic disciplines include synchronized skating, Theater on Ice, and four skating.
Question: How did they measure effectiveness?
Evidence: We trained the DQN agent using an $\epsilon $-greedy policy with $\epsilon $ decreasing linearly from 1 to $0.1$ over $100,000$ steps. Additionally, we tuned a window size to include previous dialog turns as input and set $\gamma $ to $0.9$. We ran the method 30 times for $150,000$ steps, and in each run, after every 10,000 steps, we sampled $3,000$ dialog episodes with no exploration to evaluate the performance. The optimal parameters were found using Hyperopt BIBREF23 (see Appendix B). Figure FIGREF9 shows the simulation results during training. The Y-axis in the figure is the success rate of the agent (measured in terms of number of dialogs that resulted in launching a skill divided by total number of dialogs), and the X-axis is the number of learning steps. Given our choice of reward function, the increase in success rate is indicative of the agent learning to improve its policy over time. Furthermore, the RL agent outperformed the rule-based agent with average success rate of $68.00\% (\pm 2\%$) in simulation.
The Y-axis in the figure is the success rate of the agent (measured in terms of number of dialogs that resulted in launching a skill divided by total number of dialogs), and the X-axis is the number of learning steps. 
Answer: The answers are shown as follows:
* number of dialogs that resulted in launching a skill divided by total number of dialogs
Question: Write a short story about a young aboriginal man seeking guidance on his place in the world.  Have him consult a wise elder, who will share wisdom and perspective.
Answer: The young man stood apart from the other young men and watched them tease the girls and play around in general.
He sighed deeply. "Why am I so shy?", he asked himself: "Why can't I join in the games like the others?" The young man had asked himself these questions many times, and each time the answer was the same. He just didn't know. Was it that he felt that he was not worthy? Hardly. He was a kind and respectful person, and, he had to admit, a fine hunter. So what was wrong with him? He decided to speak to the Koradji, the wise man. Surely he would know. The Koradji was peering into his fire, deep in thought, when the young man approached, but somehow knew of his presence. "You are troubled," he asked and continued to gaze into the fire. "I seek vour wisdom, Uncle," he replied, his voice hardly more than a whisper. "Wisdom comes at a price" said the Koradji, without raising his head. "But I have nothing to offer," the young man told him. "I think you have," said the Koradji." then added: "But that can wait. Now sit and tell me what is troubling vou." The young man sat opposite the old man and told him everything. When he had finished, the Koradji nodded his head thoughtfully, then gazed into the young man's eyes.
"Now listen carefully and heed my words," he said. Then he continued. "This land is to you as it is to all living things.
Therefore, we have the right to breathe its air, drink its water and nourish our bodies with its bounty. We have no need to stand apart from others because we have much to contribute, and how much we contribute is in accordance with our own measure." There was a pause while he added wood to the fire. Then he continued. "Now hear these words and keep them in your mind. No one has the right to tell another that they may not do these things, because each of us are children of Mother Earth. Of course," he added quickly, "there will be those who stand above others, and those who are stronger, or of a different tribe, but it matters not, because we each have an equal right to our place in the sun, to live in accordance with our conscience." He waved a mosquito from his arm and smiled. "And, yes, even a mosquito". There was a long silence after the Koradji had finished speaking, then the young man spoke finally. "I have listened to your words Uncle, and they are very wise," he said, "but how can I pay you?"
Question: Why is California the best state?
Answer: California has the best landscape in the country. We have some of the best mountains to ski on, most iconic beach spots, and richest soil for agriculture. What else could you ask for?
Question: What is the difference between racquetball and tennis?
Answer: While racquetball and tennis are similar in that they both involve using a racquet to hit a ball back and forth, there are many differences between racquetball and tennis.

Racquetball is played in an enclosed room where players hit the ball back and forth against the same wall. In tennis, players also hit the ball back and forth but they hit it to each other over a net. In tennis, the ball has a fuzzy outer layer which causes the ball's trajectory to be more influenced by the spin on the ball. The rackets in tennis are larger and have higher tension.

A tennis court is much larger than a racquetball court measuring 78 ft long and 36 ft wide. This width is reduced in tennis to 27 ft for a singles match.

These are just a few of the differences but there are many more: scoring, attire, court markings, etc.
Question: Who created Sesame Street?
Answer: Sesame Street was created by Jim Henson, Lloyd Morrisett, and Joan Ganz Cooney