DataHammer/scimrc huggingface dataset:
Question: Tell me about the life and career of Alison Calder
Evidence: Alison Calder (born 21 December 1969) is a Canadian poet, literary critic and educator.

Life and career
Calder was born in London, England on 21 December 1969 and grew up in Saskatoon, Saskatchewan, Canada. She studied at the University of Saskatchewan, where she earned a BA, and at the University of Western Ontario where she earned an MA and a PhD in English Literature. She was also a Distinguished Junior Scholar in Residence at the Peter Wall Institute for Advanced Studies, University of British Columbia.

In 2004, she won the RBC Bronwen Wallace Award for Emerging Writers.

She wrote a collection of essays in 2005 called History, Literature, and the Writing of the Canadian Prairies which examines literary critism.

Her debut collection of poetry was called Wolf Tree and was published in 2007. It won the 2008 Aqua Books Lansdowne Prize for Poetry and the Eileen McTavish Sykes Award for Best First Book by a Manitoba Author at the 2008 Manitoba Book Awards. It was a finalist for the Pat Lowther Memorial Award and the Gerald Lampert Memorial Award. Her second collection, In the Tiger Park, was published in 2014 and was a finalist for the Lansdowne Prize for Poetry.

She also co-wrote the chapbook Ghost Works: Improvisations in Letters and Poems, with Jeanette Lynes.

She lives in Winnipeg, Manitoba and works at the University of Manitoba where she teaches literature and creative writing. She is married to writer Warren Cariou.
Answer: Alison Calder (born 21 December 1969) is a Canadian poet, literary critic and educator.

Calder was born in London, England on 21 December 1969 and grew up in Saskatoon, Saskatchewan, Canada. She studied at the University of Saskatchewan, where she earned a BA, and at the University of Western Ontario where she earned an MA and a PhD in English Literature. She was also a Distinguished Junior Scholar in Residence at the Peter Wall Institute for Advanced Studies, University of British Columbia.

In 2004, she won the RBC Bronwen Wallace Award for Emerging Writers.

She wrote a collection of essays in 2005 called History, Literature, and the Writing of the Canadian Prairies which examines literary critism.

Her debut collection of poetry was called Wolf Tree and was published in 2007. It won the 2008 Aqua Books Lansdowne Prize for Poetry and the Eileen McTavish Sykes Award for Best First Book by a Manitoba Author at the 2008 Manitoba Book Awards. It was a finalist for the Pat Lowther Memorial Award and the Gerald Lampert Memorial Award. Her second collection, In the Tiger Park, was published in 2014 and was a finalist for the Lansdowne Prize for Poetry.

She also co-wrote the chapbook Ghost Works: Improvisations in Letters and Poems, with Jeanette Lynes.

She lives in Winnipeg, Manitoba and works at the University of Manitoba where she teaches literature and creative writing. She is married to writer Warren Cariou.
Question: How long does it take our sun to orbit the center of our galaxy
Answer: 230 million years!
Question: Where is the data collected from？
Evidence: Presenting pronunciation data in several languages to the network might create problems because different languages have different pronunciation patterns. For example, the string `real' is pronounced differently in English, German, Spanish, and Portuguese. We solve this problem by prepending each grapheme sequence with an artificial token consisting of the language's ISO 639-3 code enclosed in angle brackets. The English word `real', for example, would be presented to the system as
 INLINEFORM0 eng INLINEFORM1 r e a l
The artificial token is treated simply as an element of the grapheme sequence. This is similar to the approach taken by johnson2016google in their zero-shot NMT system. However, their source-side artificial tokens identify the target language, whereas ours identify the source language. An alternative approach, used by ostling2017continuous, would be to concatenate a language embedding to the input at each time step. They do not evaluate their approach on grapheme-to-phoneme conversion.
This corpus consists of spelling-pronunciation pairs extracted from Wiktionary.
Answer: All are extracted from Wiktionary.
Question: Why do some people like horror films?
Answer: Horror films can provide a safe way for us to mentally rehearse how we'd handle real danger.  Some people actually enjoy the scary emotions that horror films elicit.   It can provide a unique adrenaline rush, somewhat similar to other fearful activities, like white water rafting, jumping out of airplanes, amusement park rides and flying in a small aircraft.  For many, horror films are their least liked movie genre for those same reasons.  They might prefer a romantic comedy or action triller, without all the blood and diabolical antagonists.
Question: Are there any hyperparameter setting methods?
Evidence: We used the same hyperparameters as the ones used in MADDPG, except with a reduced learning rate and tau (lr = 0.001 and τ = 0.005). We used the same parameters for all our experiments
Answer: We based our hyperparameters on the ones used in MADDPG, but reduced the learning rate and tau to be able to reproduce their results (lr=0.001;  τ=0.005). We used the same parameters for all our experiments. We would like to emphasize that the point we are trying to convey is that all the learned models are significantly below the heuristic&oracle (for as much as an order of magnitude).
Question: Why and how are people choosing to electrify their homes?
Answer: An increasingly popular trend is to replace dirty appliances (in other words, those that consume gas or other carbon-based fuel sources) with clean energy appliances (typically those powered by electricity that is in turn generated by solar or wind power). The main motivation for doing so is to reduce a household's carbon footprint and help address global warming. Government incentives, economies of scale and technological innovation have also made it possible for consumers to save money by changing to electric appliances. The most common household appliances that have traditionally been powered by gas are the furnace, water heater, stove, air conditioning unit, and dryer. Replacements for these appliances include heat pumps that can replace both the furnace and air conditioner, electric water heaters and dryers, and induction stoves. To power these electric appliances in environmentally friendly ways, consumers are either generating the electricity on their property by installing rooftop solar panels or they are selecting green energy sources when they pick their plan from their local utility provider.
Question: What datasets used for evaluation?
Evidence: We conduct experiments on several different types of public datasets including news and encyclopedia corpus. The training set is AIDA-Train and Wikipedia datasets, where AIDA-Train contains 18448 mentions and Wikipedia contains 25995 mentions. In order to compare with the previous methods, we evaluate our model on AIDA-B and other datasets. These datasets are well-known and have been used for the evaluation of most entity linking systems. The statistics of the datasets are shown in Table 1.
AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets.
ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents.
MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.)
AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press.
WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset.
WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation.
In order to compare with the previous methods, we evaluate our model on AIDA-B and other datasets. 
AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets.

ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents.

MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.)

AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press.

WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset.

WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation.
Answer: The answers are shown as follows:
* AIDA-B
* ACE2004
* MSNBC
* AQUAINT
* WNED-CWEB
* WNED-WIKI
Question: How does one state something as an opinion, belief, or preference rather than as if something is a fact?
Answer: Begin the statement with "I think" or "I believe" or "In my opinion." This will make it clear that you are expressing personal thoughts about something, rather than stating it as if it were a fact.
Question: Is it advisable to dress in costume for a musical theater audition?
Answer: No, this is usually not advisable. It is often considered "too much" or the sign of an inexperienced actor to come to the call in full costume. It is recommended that an auditioner dress for confidence, and for comfort and movement (especially if there will be a dance call), However, it is always good advice to keep the appearance, style and personality of the character you are most interested in top of mind when choosing how to present yourself at an audition.
Question: Cuzco, Machu Pichu is a famous place located in which country?
Answer: Peru
Question: Who won the San Diego Film Critics Society Award for Best Cinematography in 2012?
Evidence: The San Diego Film Critics Society Award for Best Cinematography is an award given by the San Diego Film Critics Society to honor the finest directing achievements in filmmaking.

Winners
2000s
Year	Winner	Cinematographer(s)
2000	Gladiator	John Mathieson
2001	The Man Who Wasn't There	Roger Deakins
2002	Road to Perdition	Conrad L. Hall
2003	Girl with a Pearl Earring	Eduardo Serra
2004	Hero (Ying xiong)	Christopher Doyle
The Phantom of the Opera	John Mathieson
2005	The New World	Emmanuel Lubezki
2006	The Illusionist	Dick Pope
2007	No Country for Old Men	Roger Deakins
2008	Slumdog Millionaire	Anthony Dod Mantle
2009	The Road	Javier Aguirresarobe
2010s
Year	Winner	Cinematographer(s)
2010	Inception	Wally Pfister
2011	The Tree of Life	Emmanuel Lubezki
2012	Life of Pi	Claudio Miranda
2013	To the Wonder	Emmanuel Lubezki
2014	Nightcrawler	Robert Elswit
2015	Sicario	Roger Deakins
2016	Hell or High Water	Giles Nuttgens
2017	Dunkirk	Hoyte van Hoytema
2018	The Ballad of Buster Scruggs	Bruno Delbonnel
The Rider	Joshua James Richards
2019	The Lighthouse	Jarin Blaschke
2020s
Year	Winner	Cinematographer(s)
2020	Nomadland	Joshua James Richards
Answer: Claudio Miranda won the San Diego Film Critics Society Award for Best Cinematography in 2012 as cinematographer for Life of Pi
Question: What does the submodel consist of?
Evidence: Adversarial learning has been an effective mechanism to resolve the problem of the input features between the training and test examples having large divergences BIBREF27 , BIBREF13 . It has been successfully applied on domain adaption BIBREF7 , cross-lingual learning BIBREF15 and multi-task learning BIBREF17 . All settings involve feature shifting between the training and testing.
In this paper, our setting is different. We are using the annotations from non-experts, which are noise and can influence the final performances if they are not properly processed. Directly learning based on the resulting corpus may adapt the neural feature extraction into the biased annotations. In this work, we assume that individual workers have their own guidelines in mind after short training. For example, a perfect worker can annotate highly consistently with an expert, while common crowdsourcing workers may be confused and have different understandings on certain contexts. Based on the assumption, we make an adaption for the original adversarial neural network to our setting.
Our adaption is very simple. Briefly speaking, the original adversarial learning adds an additional discriminator to classify the type of source inputs, for example, the domain category in the domain adaption setting, while we add a discriminator to classify the annotation workers. Solely the features from the input sentence is not enough for worker classification. The annotation result of the worker is also required. Thus the inputs of our discriminator are different. Here we exploit both the source sentences and the crowd-annotated NE labels as basic inputs for the worker discrimination.
In the following, we describe the proposed adversarial learning module, including both the submodels and the training method. As shown by the left part of Figure 1 , the submodel consists of four parts: (1) a common Bi-LSTM over input characters; (2) an additional Bi-LSTM to encode crowd-annotated NE label sequence; (3) a convolutional neural network (CNN) to extract features for worker discriminator; (4) output and prediction.
As shown by the left part of Figure 1, the submodel consists of four parts: (1) a common Bi-LSTM over input characters; (2) an additional Bi-LSTM to encode crowd-annotated NE label sequence; (3) a convolutional neural network (CNN) to extract features for worker discriminator; (4) output and prediction.
Answer: The submodel consists of four parts: (1) a common Bi-LSTM over input characters; (2) an additional Bi-LSTM to encode crowd-annotated NE label sequence; (3) a convolutional neural network (CNN) to extract features for worker discriminator; (4) output and prediction.
Question: What is the probability of dropout in their input layer?
Evidence:  Training Data. We choose WIT3's TED corpus BIBREF12 as the basis of our experiments since it might be the only high-quality parallel data of many low-resourced language pairs. TED is also multilingual in a sense that it includes numbers of talks which are commonly translated into many languages. In addition, we use a much larger corpus provided freely by WMT organizers when we evaluate the impact of our approach in a real machine translation campaign. It includes the paralled corpus extracted from the digital corpus of European Parliament (EPPS), the News Commentary (NC) and the web-crawled parallel data (CommonCrawl). While the number of sentences in popular TED corpora varies from 13 thousands to 17 thousands, the total number of sentences in those larger corpus is approximately 3 million sentences.
Neural Machine Translation Setup. All experiments have been conducted using NMT framework Nematus, Following the work of Sennrich2016a, subword segmentation is handled in the prepocessing phase using Byte-Pair Encoding (BPE). Excepts stated clearly in some experiments, we set the number of BPE merging operations at 39500 on the joint of source and target data. When training all NMT systems, we take out the sentence pairs exceeding 50-word length and shuffle them inside every minibatch. Our short-list vocabularies contain 40,000 most frequent words while the others are considered as rare words and applied the subword translation. We use an 1024-cell GRU layer and 1000-dimensional embeddings with dropout at every layer with the probability of 0.2 in the embedding and hidden layers and 0.1 in the input and ourput layers. We trained our systems using gradient descent optimization with Adadelta BIBREF13 on minibatches of size 80 and the gradient is rescaled whenever its norm exceed 1.0. All the trainings last approximately seven days if the early-stopping condition could not be reached. At a certain time, an external evaluation script on BLEU BIBREF14 is conducted on a development set to decide the early-stopping condition. This evaluation script has also being used to choose the model archiving the best BLEU on the development set instead of the maximal loglikelihood between the translations and target sentences while training. In translation, the framework produces INLINEFORM0 -best candidates and we then use a beam search with the beam size of 12 to get the best translation.
Our short-list vocabularies contain 40,000 most frequent words while the others are considered as rare words and applied the subword translation. We use an 1024-cell GRU layer and 1000-dimensional embeddings with dropout at every layer with the probability of 0.2 in the embedding and hidden layers and 0.1 in the input and ourput layers.
Answer: 0.1.
Question: Why Phuket is so famous
Answer: Because of island like phi phi, Krabi. Its just stand still mountains inside sea
Question: Please provide me with ideas for dinner tonight.
Answer: Sure! Here are 2 ideas. 

1) Ordering in:

You can see what restaurants in your area offer home delivery, and select something that works well for you! Some common things to order are Pizza, Sushi, Tacos, Fried Rice, and more! 

2) Check in your fridge and pantry, and cook at home! Here are a few easy meal ideas: 

- Pasta
- Stir Fry
- Flat bread 
- Chicken, rice, and vegetables
- Wraps or sandwiches
Question: How did the authors demonstrate that showing a hate speech definition caused annotators to partially align their own opinion with the definition?
Evidence: Participants who were shown the definition were more likely to suggest to ban the tweet. In fact, participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%). This suggests that participants in that group aligned their own opinion with the definition.
Participants who were shown the definition were more likely to suggest to ban the tweet. In fact, participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%). This suggests that participants in that group aligned their own opinion with the definition.
Answer: The answers are shown as follows:
* participants in group one very rarely gave different answers to questions one and two (18 of 500 instances or 3.6%)
Question: What is the purpose of a shot clock in basketball?
Answer: The shot clock is intended to increase the pace of play. By having a limited amount of time to shoot, teams are encouraged to shoot more frequently, leading to a faster moving game.
Question: What is a tri-state area?
Answer: This term is used in multiple parts of the United States to refer to regions where three states come together.
Question: Give me some tips to impress my aspiring Father-in-law.
Answer: 1. Try to be authentic self. It will cause you less stress in long run and also provide a chance to the other person to get to know you better.
2. Be respectful. It is always good to understand other person's perspective specially when they are coming from a different generation.
3. Light the conversation over the lighter topics or if required keep yourself up to date with the recent events happening in the world. Bonus points if you also know about the topics of interest for the other party.
4. Relax and try to enjoy the time together. At the end of the day, both of you would like to know each other better. There can always be some judgement in the equation but with openness and mindful transparency, things should evolve constructively.
Question: What does Korla stand for?
Evidence: Korla, also known as Kurla or from Mandarin Chinese as Ku'erle or Kuerle, is the second largest city in Xinjiang. It is a county-level city and the seat of the Bayingolin Mongol Autonomous Prefecture, the largest prefecture of China.

Korla has existed since at least the Han Dynasty. Korla is known for its production of fragrant pears and is a production center for the Tarim oil fields.
Answer: Korla is the second largest city in Xinjiang, China. It is also known as Kurla or Kuerle. It is located in the Bayingolin Mongol Autonomous Prefecture. It is known for fragrant pears and oil fields.
Question: What are some useful ways to repurpose a cardboard box?
Answer: That depends on the person and what they find useful. I might find a box useful for clearing my desk of all the mess I accumulate throughout the week. Meanwhile, my kids would much prefer to turn the box into a play house, a spaceship, or maybe a jail to imprison each other in.  A cat might repurpose it as a hiding place or as a new scratching toy.
Question: What are fun things to do in New York City
Answer: 1. Plan a trip to Central Park 
2. Go shopping on Fifth Avenue 
3. Get a New York City Bagel 
4. Take the Subway to visit the Statue of Liberty
Question: What kind of activity do good sales people do?
Answer: Good sales people build good relationships first by questioning and finding their interest. Then, they stat discovery questions. They don’t start product presentation until they finish discovery and find a space to listen in their customers. When they give some discovery questions they often use SPIN. S stands for situational questions, P stands for problem questions, I stands for Implication questions, N stands for need questions. 
When they talk about their products they always how this product solves customers problems. They mention “You” or customer name frequently so that the customer imagine how they can change by that product.
Question: Is voluntary childlessness rising? If yes, then what are the major drivers? What could be its impact?
Answer: The fast paced life and easy availability of effective contraception have increased the number of couples opting for childfree life. There are numerous reasons why being childfree is trending. The decision to have a child comes with responsibilities and many couples do not want to embrace it. Some couples avoid the idea of having children based on past experience in the family or simply because not having a child opens new avenues to explore and spend time doing other meaningful things in life. Unwillingness to bear the financial burden of raising a child is another reason why some couples avoid the idea.

Nevertheless, the trend represents the rise in deterioration of interpersonal relationships. It prevents overpopulation and indirectly impacts events like recession, inflation, global warming etc. and in a consumer centric world the resonance of this trend cannot be ignored in the global economy.
Question: Which is a bird or fish: Redhorse sucker, Red-necked grebe
Answer: Red-necked grebe is a bird, Redhorse sucker is a fish
Question: What are the words of House Wendwater?
Answer: "For All Seasons"
Question: What in-domain data is used to continue pre-training?
Evidence: We view different varieties of Arabic as different domains, and hence introduce a simple, yet effective, `in-domain' training measure where we further pre-train BERT on a dataset closer to task domain (in that it involves dialectal tweet data).
We view different varieties of Arabic as different domains, and hence introduce a simple, yet effective, `in-domain' training measure where we further pre-train BERT on a dataset closer to task domain (in that it involves dialectal tweet data).
Answer: The answers are shown as follows:
* dialectal tweet data
Question: Classify the following animals as mammal or not: whale, kangaroo, mouse, snake, fish, human, koala
Answer: whale - mammal
kangaroo - mammal
mouse - mammal
snake - not mammal
fish - not mammal
human - mammal
koala - mammal
Question: How much performance gap between their approach and the strong handcrafted method?
Evidence: FLOAT SELECTED: Table 4: Compared with other systems (bold is best).
FLOAT SELECTED: Table 4: Compared with other systems (bold is best).
Answer: 0.007 MAP on Task A, 0.032 MAP on Task B, 0.055 MAP on Task C
Question: Identify which instrument is string or woodwind: Asor, Aulochrome
Answer: Aulochrome is woodwind, Asor is string.
Question: What are the words of House Bolton?
Answer: "Our Blades are Sharp"