DataHammer/scimrc huggingface dataset:
Question: What are the two subsets of the emotionline?
Evidence: EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. The former comes from the scripts of the Friends TV sitcom. The other is made up of Facebook messenger chats. Each subset includes $1,000$ English dialogues, and each dialogue can be further divided into a few consecutive utterances. All the utterances are annotated by five annotators on a crowd-sourcing platform (Amazon Mechanical Turk), and the labeling work is only based on the textual content. Annotator votes for one of the seven emotions, namely Ekman’s six basic emotions BIBREF1, plus the neutral. If none of the emotion gets more than three votes, the utterance will be marked as “non-neutral”.
For the datasets, there are properties worth additional mentioning. Although Friends and EmotionPush share the same data format, they are quite different in nature. Friends is a speech-based dataset which is annotated dialogues from the TV sitcom. It means most of the utterances are generated by the a few main characters. The personality of a character often affects the way of speaking, and therefore “who is the speaker" might provide extra clues for emotion prediction. In contrast, EmotionPush does not have this trait due to the anonymous mechanism. In addition, features such as typo, hyperlink, and emoji that only appear in chat-based data will need some domain-specific techniques to process.
Incidentally, the objective of the challenge is to predict the emotion for each utterance. Just, according to EmotionX 2019 specification, there are only four emotions be selected as our label candidates, which are Joy, Sadness, Anger, and Neutral. These emotions will be considered during performance evaluation. The technical detail will also be introduced and discussed in following Section SECREF13 and Section SECREF26.
EmotionLines is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues.
Answer: Friends and EmotionPush.
Question: What is the author's plan for his future work?
Evidence: Our future work will be mainly focused on providing a baseline system for interactive learning which will be evaluated on the dataset. We are also planning improvements for dialog management that is used to gain explanations during the data collection. We believe that with conversation about specific aspects of the discussed question it will be possible to gain even more interesting information from users. The other area of our interest is in possibilities to improve question answering accuracy on test questions of Simple question dataset with the extra information contained in the collected dialogs.
Our future work will be mainly focused on providing a baseline system for interactive learning which will be evaluated on the dataset. We are also planning improvements for dialog management that is used to gain explanations during the data collection.
Answer: Their future work will be mainly focused on providing a baseline system for interactive learning which will be evaluated on the dataset. They are also planning improvements for dialog management that is used to gain explanations during the data collection.
Question: What is contrast therapy used for?
Evidence: Contrast bathing can be used to reduce swelling around injuries or to aid recovery from exercise. It can also significantly improve muscle recovery following exercise by reducing the levels of blood lactate concentration. For any injury presenting with palpable swelling and heat, and visible redness - such as a strain/sprain - contrast baths are contraindicated during the acute inflammation stage. Acute inflammation begins at the time of injury and lasts for approximately 72 hours.
Answer: Contrast bathing can be used to reduce swelling around injuries or to aid recovery from exercise.
Question: What is a snook?
Answer: A snook is an inshore fish species.  They cannot be commercially fished.  If you want to eat a snook, you will need to catch it yourself.  Snook catch limits are highly regulated by Florida fish and wildlife.
Question: What are the authors’ major contributions in this project?
Evidence: Recent migration to transformer based architectures in language modeling from LSTM models is justified as Transformer-XL obtains strong perplexity results. BERT model also obtains very low pseudo-perplexity scores but it is inequitable to the unidirectional models. Our major contributions in this project, is the use of Transformer-XL architectures for the Finnish language in a sub-word setting, and the formulation of pseudo perplexity for the BERT model. Further comparisons between the transformer architectures can be made by downstreaming it to an ASR task, which will be explored in the future.
Our major contributions in this project, is the use of Transformer-XL architectures for the Finnish language in a sub-word setting, and the formulation of pseudo perplexity for the BERT model.
Answer: The use of Transformer-XL architectures for the Finnish language in a sub-word setting, and the formulation of pseudo perplexity for the BERT model.
Question: By how much does SPNet outperforms state-of-the-art abstractive summarization methods on evaluation metrics?
Evidence: We show all the models' results in Table TABREF24. We observe that SPNet reaches the highest score in both ROUGE and CIC. Both Pointer-Generator and Transformer achieve high ROUGE scores, but a relative low CIC scores. It suggests that the baselines have more room for improvement on preserving critical slot information. All the scaffolds we propose can be applied to different neural network models. In this work we select Pointer-Generator as our base model in SPNet because we observe that Transformer only has a small improvement over Pointer-Generator but is having a higher cost on training time and computing resources. We observe that SPNet outperforms other methods in all the automatic evaluation metrics with a big margin, as it incorporates all the three semantic scaffolds. Semantic slot contributes the most to SPNet's increased performance, bringing the largest increase on all automatic evaluation metrics.
FLOAT SELECTED: Table 1: Automatic evaluation results on MultiWOZ. We use Pointer-Generator as the base model and gradually add different semantic scaffolds.
We show all the models' results in Table TABREF24
FLOAT SELECTED: Table 1: Automatic evaluation results on MultiWOZ. We use Pointer-Generator as the base model and gradually add different semantic scaffolds.
Answer: SPNet vs best baseline:
ROUGE-1: 90.97 vs 90.68
CIC: 70.45 vs 70.25
Question: Give me a few recipes you could make with bread, butter, eggs, and cheese where I have a stovetop, toaster, and a frying pan.
Answer: Here are a few recipes you could make with the ingredients and tools you mentioned.
1. Grilled cheese and sunny-side up eggs: 
Warm up your frying pan on the stove top. Heat a small amount of butter on the frying pan until melted and then place two slices of bread side-by-side into the pan. Place cheese on one slice of bread and heat until bread is crispy and cheese has started to melt. Then cover the slice of bread with melted cheese with the other slice of bread and remove from the frying pan.

Gently crack an egg and place it into the frying pan. Cook until the egg whites are hardened. You may then cook until the yolks are of your desired consistency.
2. Toast and cheese omelette: 
Place bread into toaster and cook to desired level. Spread desired amount of butter on bread.

Warm up your frying pan on the stove top. Heat a small amount of butter on the frying pan until melted. In the meantime, gently crack eggs into a bowl and whisk eggs. Place whisked eggs into heated frying pan until eggs have started to harden. Place cheese onto eggs and fold.
3. Scrambled eggs with toast:
Place bread into toaster and cook to desired level. Spread desired amount of butter on bread.

Warm up your frying pan on the stove top. Heat a small amount of butter on the frying pan until melted. In the meantime, gently crack eggs into a bowl and whisk eggs. Place whisked eggs into heated frying pan and consistently mix. Place cheese into pan and continue mixing until eggs set.
Question: Give me a bulleted list of the top five most followed people on instagram.
Answer: 1. @cristiano
2. @leomessi
3. @selenagomez
4. @kyliejenner
5. @therock
Question: What is an experiment?
Answer: An experiment is a scientific process that uses the element of discovery and hypothesis to test out a known fact.  A series of actions are executed and then a person observes the outcomes of those actions.  Experiments also give us insight into cause and effect.  The best way to develop an experiment is to come up with a set of questions to test a hypothesis about something.
Question: Which is stronger - a lion or a tiger?
Answer: It really depends on what attributes are being judged.  If we look at one of the most telling traits between the two - the family - the lion would be the clear winner.  They live in prides and hunt as a family unit.  They also rely on the pride to protect their young and their territory from invaders.  A tiger is a solitary animal, so it is independent and does not have the social constructs as that of a lion.
Question: Do they explore how much traning data is needed for which magnitude of improvement for WER? 
Evidence: What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance.
What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance.
Answer: Yes.
Question: Given this paragraph about Gheorge Hagi, tell me which were his top achievements in the Romanian football team.
Evidence: At international level, Hagi played for the Romania national team in three FIFA World Cups, in 1990, 1994 (where he was named in the World Cup All-Star Team after helping his nation to the quarter-finals of the tournament) and 1998; as well as in three UEFA European Championships, in 1984, 1996 and 2000. He won a total of 124 caps for Romania between 1983 and 2000, making him the second-most capped Romanian player of all time, behind only Dorinel Munteanu; he is also the joint all-time leading goalscorer of the Romania national side (alongside Adrian Mutu) with 35 goals.
Answer: Gheorghe Hagi is arguably the best Romanian football player holding the record for most goals scored with the national team. He participated in 3 World Cups and 3 European Championships and he was awarded a spot in the World Cup All-Start Team in 1994.
Question: What are 5 different edible uses for Tomatoes?
Answer: - Marinara Sauce
- Salsa
- Hamburger Topping
- Bruschetta 
- BLT
Question: How do I do a headstand?
Answer: Start in a tabletop position with your knees on the ground and your hands placed underneath your shoulders.  Draw your chin into your chest and lower the crown of your head onto the ground.  Curl your toes under and lift your knees up off the ground.  Place each knee on your tricep.  Push into the heal of your palm to lift your knees off your tricep.  With your low core deeply engaged, continue to lift your knees higher until your legs are fully extended.  Make sure you keep your breath slow while inverted, and then slowly reverse the steps.  You have now done your first head stand!
Question: Which leaders claimed the English throne in 1066?
Evidence: In 1066, several rival claimants to the English throne emerged. Among them were Harold Godwinson (recognised as king by the Witenagemot after the death of Edward the Confessor), Harald Hardrada (King of Norway who claimed to be the rightful heir of Harthacnut) and Duke William II of Normandy (vassal to the King of France, and first cousin once-removed of Edward the Confessor). Harald and William both invaded separately in 1066. Godwinson successfully repelled the invasion by Hardrada, but ultimately lost the throne of England in the Norman conquest of England.
Answer: Harold Godwinson, Harald Hardrada, and Duke William II of Normandy all claimed the English throne in 1066.
Question: What are the characteristics of chihuahuas?
Answer: Chihuahuas are among the smallest of the dog breeds. Small in size but confident and big-dog natured. They originated from Mexico with lineage going back to ancient kingdoms of pre-Columbian times. Chihuahuas are extremely loyal, known for having close attachments with their owners. Their distinct features include oversized, pointy ears and expressive eyes. Coats come in many colors ranging from white to brown to black with many different pattern variations, and will be either short or long. Short hair chihuahuas are known for shivering and needing sweaters in cold weather. They are ideal city dogs given their small size.
Question: How large is the runway of the Richton-Perry County Airport?
Evidence: Richton-Perry County Airport covers an area of 55 acres (22 ha) at an elevation of 167 feet (51 m) above mean sea level. It has one runway designated 18/36 with an asphalt surface measuring 3,000 by 60 feet (914 x 18 m).

For the 12-month period ending November 17, 2011, the airport had 1,400 general aviation aircraft operations, an average of 116 per month. At that time there were two single-engine aircraft based at this airport.
Answer: The single runway at the Richton-Perry County Airport is 18000 square feet in area (it's dimensions are 3,000 by 60 feet)
Question: How do you make an Old Fashioned Cocktail?
Answer: Making an Old Fashioned consists of five steps. The ingredients for an Old Fashioned cocktail include your choice of bourbon or rye, an orange, aromatic bitters, a sugar cube, a dash of soda water, Luxardo cherry and ice.

First, grab your cocktail glass and add two dashes of aromatic bitters along with a spritz of soda water and the sugar cube.
Second, add an ounce and a half of your choice of bourbon or rye.
Third, add ice and stir for around 30 seconds.
Fourth, peel an orange, express the oils over the glass and drop it in.
Fifth, add the Luxardo cherry and enjoy.
Question: When did Lebron James become the NBA's all time leading scorer?
Answer: Lebron James passed Kareem Abdul-Jabbar as the NBA's all time leading scorer on February 7, 2023.
Question: What automated metrics authors investigate?
Evidence: One of the most popular metrics for QG, BLEU BIBREF21 provides a set of measures to compare automatically generated texts against one or more references. In particular, BLEU-N is based on the count of overlapping n-grams between the candidate and its corresponding reference(s).
Within the field of Computational Creativity, Diversity is considered a desirable property BIBREF31. Indeed, generating always the same question such as “What is the meaning of the universe?" would be an undesirable behavior, reminiscent of the “collapse mode" observed in Generative Adversarial Networks (GAN) BIBREF32. Therefore, we adopt Self-BLEU, originally proposed by BIBREF33, as a measure of diversity for the generated text sequences. Self-BLEU is computed as follows: for each generated sentence $s_i$, a BLEU score is computed using $s_i$ as hypothesis while the other generated sentences are used as reference. When averaged over all the references, it thus provides a measure of how diverse the sentences are. Lower Self-BLEU scores indicate more diversity. We refer to these metrics as Self-B* throughout this paper.
Therefore, given a question-context pair as input to a QA model, two type of metrics can be computed:
n-gram based score: measuring the average overlap between the retrieved answer and the ground truth.
probability score: the confidence of the QA model for its retrieved answer; this corresponds to the probability of being the correct answer assigned by the QA model to the retrieved answer.
One of the most popular metrics for QG, BLEU BIBREF21 provides a set of measures to compare automatically generated texts against one or more references. In particular, BLEU-N is based on the count of overlapping n-grams between the candidate and its corresponding reference(s).
Therefore, we adopt Self-BLEU, originally proposed by BIBREF33, as a measure of diversity for the generated text sequences. 
Therefore, given a question-context pair as input to a QA model, two type of metrics can be computed:

n-gram based score: measuring the average overlap between the retrieved answer and the ground truth.

probability score: the confidence of the QA model for its retrieved answer; this corresponds to the probability of being the correct answer assigned by the QA model to the retrieved answer.
Answer: The answers are shown as follows:
* BLEU
* Self-BLEU
* n-gram based score
* probability score
Question: In which years did the Women's New Zealand cricket team win the world cup
Answer: 2000
Question: Who is the top baseball player based on offensive categories that are not in the major league baseball hall of fame with no chance of getting in while alive.
Answer: While there is an exhaustive amount of ways one could approach this answer, there is arguable only one player in all of the major offensive categories that is not in the hall of fame.  That player is Pete Rose.   Players like Barry Bonds and Albert Pujols may have better overall statistics in the power categories of baseball, but they still have an outside chance to get into the hall of fame, whereas Pete Rose has been banned for life by major league baseball and does not have any current pathway into the hall of fame.  Speculation is that this will change posthumously, but the executives of major league baseball will not consider any change to this status while he is alive.
Question: How did Jim Henson die?
Answer: The famous puppeteer succumbed to toxic shock syndrome, resulting from abscesses in his lungs.
Question: What should I think about when driving a car in a city?
Answer: Cities can be crowded and there are many things to consider when driving in a city. In cities, the driving pace can vary between slow and fast speeds, and one needs to be extra attentive. Other vehicles may switch lanes hastily without indicating, or break with no warning. In some cities, one also has trams to be aware of. Pedestrians, bicyclists, and other electric mobility vehicles might appear unexpectedly in traffic. Another thing to take note of is the time of day, and day of the week, as traffic jams can occur when too many people are in traffic at the same time. In some situations, it might be faster to walk, cycle, or take public transportation than drive in cities. Finally, parking and tolls might be expensive when there is a lot of traffic, yet free when cities tend to be empty.
Question: Tell me about Crater Lake National Park
Answer: Crater Lake National Park, located in Oregon is an astonishing national park full of beautiful views and pristine natural surroundings.  The Park itself was officially founded in 1902 but prior to that it was considered a holy place by Native American Indians for hundreds of years.  The water in Crater Lake is a deep, dark blue and remains relatively cold year round.  Crater Lake is actually the remnant of an imploded volcano that filled with water of the centuries and is in fact, the deepest lake in the United States.   The caldera itself is 6 miles by 5 miles and more than a half mile deep.  The rim offers some spectacular views of the lake itself and surrounding caldera formation.
Question: Give me a list of the stations on the Island Line of Hong Kong's MTR network
Answer: - Kennedy Town
- HKU
- Sai Ying Pun
- Sheung Wan
- Central
- Admiralty
- Wan Chai
- Causeway Bay
- Tin Hau
- Fortress Hill
- North Point
- Quarry Bay
- Tai Koo
- Sai Wan Ho
- Shau Kei Wan
- Heng Fa Chuen
- Chai Wan
Question: How old were the indigenous civilizations in Mexico prior to European contact?
Evidence: Human presence in Pre-Columbian Mexico goes back to 8,000 BCE and it went to become one of the world's six cradles of civilization. In particular, the Mesoamerican region was home to many intertwined civilizations, including the Olmec, Maya, Zapotec, Teotihuacan, and Purepecha. Last were the Aztecs, who dominated the region in the century before European contact. In 1521, the Spanish Empire and its indigenous allies conquered the Aztec Empire from its capital Tenochtitlan (now Mexico City), establishing the colony of New Spain.
Answer: There were many civilizations that came prior to European contact, some that date back as early as 8,000 BCE. The last civilization to dominate Mexico a century prior to European contact were the Aztecs, whose capital, Tenochtitlan, became what is now Mexico City.
Question: Although many people have studied eye-tracking and EEG recordings of natural reading, what shortcoming still existed?
Evidence: Some eye-tracking corpora of natural reading (e.g. the Dundee BIBREF2, Provo BIBREF3 and GECO corpus BIBREF4), and a few EEG corpora (for example, the UCL corpus BIBREF5) are available. It has been shown that this type of cognitive processing data is useful for improving and evaluating NLP methods (e.g. barrett2018sequence,hollenstein2019cognival, hale2018finding). However, before the Zurich Cognitive Language Processing Corpus (ZuCo 1.0), there was no available data for simultaneous eye-tracking and EEG recordings of natural reading. dimigen2011coregistration studied the linguistic effects of eye movements and EEG co-registration in natural reading and showed that they accurately represent lexical processing. Moreover, the simultaneous recordings are crucial to extract word-level brain activity signals.
While the above mentioned studies analyze and leverage natural reading, some NLP work has used eye-tracking during annotation (but, as of yet, not EEG data). mishra2016predicting and joshi2014measuring recorded eye-tracking during binary sentiment annotation (positive/negative). This data was used to determine the annotation complexity of the text passages based on eye movement metrics and for sarcasm detection BIBREF6. Moreover, eye-tracking has been used to analyze the word sense annotation process in Hindi BIBREF7, named entity annotation in Japanese BIBREF8, and to leverage annotator gaze behaviour for coreference resolution BIBREF9. Finally, tomanek2010cognitive used eye-tracking data during entity annotation to build a cost model for active learning. However, until now there is no available data or research that analyzes the differences in the human processing of normal reading versus annotation.
However, until now there was no available data or research that analyzes the differences in the human processing of normal reading versus annotation.
Answer: There was no available data or research that analyzes the differences in the human processing of normal reading versus annotation.
Question: In the series A Song of Ice and Fire, who is the founder of House Banefort?
Answer: The name nor identity of the Banefort house's founder is unknown. They are only referred to as The Hooded Man.