DataHammer/scimrc huggingface dataset:
Question: Extract the names of the winners of the Berlin Marathon from the reference text and provide them as a bulleted list.
Evidence: The Berlin Marathon was established in 1974 by Horst Milde, a baker and running enthusiast in the city. The race had 244 finishers; 234 men and 10 women, and was won by Günter Hallas and Jutta von Haase in times of 2:44:53 and 3:22:01 respectively. In 1977, Christa Vahlensieck established a new women's world record at the Berlin Marathon, running 2:34:47. Four years later, the race started outside the Reichstag for the first time, by which time it had grown to 2,583 finishers, making it the largest city road race in Germany. The following year, before German unification, some East Germans secretly ran the race, registering under false names to avoid recriminations from the East German secret service. In 1989, a children's race was added to the event, and eight years later an inline skating marathon was contested.

In 1998, Ronaldo da Costa set a new men's world record, the only South American to hold a marathon world record. Two years later, one of the race's pacemakers, Simon Biwott won the race, after one of the favourites had pulled out in the second half of the race. In 2001, Naoko Takahashi became the first woman to run a sub-2:20 marathon, completing the Berlin Marathon in 2:19:46. The race had continued to be the venue of world records; between 2003 and 2018, seven men's records were set at the race; the most recent remains as the current world record time; Eliud Kipchoge's 2:01:39.

The event was held in mid-September in 2000 and 2018, because of a conflict with the Women's Olympic Marathon, and due to German Unity Day preparations, respectively.

The 2020 edition of the event was prohibited from being held on its originally scheduled dates due to the coronavirus pandemic, and was eventually cancelled once it was clear that it would not be possible to hold it at a later date in 2020. All registrants were given the option of either transferring their entry to 2021 or obtaining a refund.
Answer: * Günter Hallas
* Jutta van Hasse
* Christa Vahlensieck
* Ronaldo da Costa
* Simon Biwott
* Naoko Takahashi
* Eliud Kipchoge
Question: What WER did their system achieve on the VLSP 2019 data sets?
Evidence: Automatic Speech Recognition (ASR) systems have been evolving quickly and reaching human parity in certain cases. The systems usually perform pretty well on reading style and clean speech, however, most of the available systems suffer from situation where the speaking style is conversation and in noisy environments. It is not straight-forward to tackle such problems due to difficulties in data collection for both speech and text. In this paper, we attempt to mitigate the problems using language models combination techniques that allows us to utilize both large amount of writing style text and small number of conversation text data. Evaluation on the VLSP 2019 ASR challenges showed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER on the VLSP 2019 data sets.
Evaluation on the VLSP 2019 ASR challenges showed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER on the VLSP 2019 data sets.
Answer: 15.09% WER.
Question: How many teams are in the MLB American League
Evidence: The American League of Professional Baseball Clubs, known simply as the American League (AL), is one of two leagues that make up Major League Baseball (MLB) in the United States and Canada.
Answer: The American League consists of 15 teams (including one Canadian team) aligned in three divisions: the AL East, comprising the Baltimore Orioles, Boston Red Sox, New York Yankees, Tampa Bay Rays, and Toronto Blue Jays; the AL Central, comprising the Chicago White Sox, Cleveland Indians, Detroit Tigers, Kansas City Royals, and Minnesota Twins; and the AL West, comprising the Houston Astros, Los Angeles Angels of Anaheim, Oakland Athletics, Seattle Mariners, and Texas Rangers.
Question: Based on this paragraph about the potential Kontinental Hockey League expansion, how many divisions and how many conferences would the proposed league have?
Evidence: The proposed league would have consisted of two conferences split into four divisions. The current KHL would have made up one of the conferences. The other conference would have been composed of a Scandinavian division and a Central European division. The winners of the two conferences would have played each other for the overall championship.
Answer: The league would have two conferences and four divisions.
Question: Give me a title of one of the "Harry Potter" novels.
Answer: Harry Potter and the Goblet of Fire
Question: How is education in Singapore
Evidence: Education in Singapore is managed by the Ministry of Education (MOE). It controls the development and administration of state schools receiving taxpayers' funding, but also has an advisory and supervisory role in respect of private schools. For both private and state schools, there are variations in the extent of autonomy in their curriculum, scope of taxpayers' aid and funding, tuition burden on the students, and admission policy.
Answer: Education spending usually makes up about 20 per cent of the annual national budget, which subsidises state education and government-assisted private education for Singaporean citizens and funds the Edusave programme. Non-citizens bear significantly higher costs of educating their children in Singapore government and government-aided schools. In 2000, the Compulsory Education Act codified compulsory education for children of primary school age (excepting those with disabilities), and made it a criminal offence for parents to fail to enroll their children in school and ensure their regular attendance. Exemptions are allowed for homeschooling or full-time religious institutions, but parents must apply for exemption from the Ministry of Education and meet a minimum benchmark.

The main language of instruction in Singapore is English, which was officially designated the first language within the local education system in 1987. English is the first language learned by half the children by the time they reach preschool age and becomes the primary medium of instruction by the time they reach primary school. Although Malay, Mandarin and Tamil are also official languages, English is the language of instruction for nearly all subjects except the official Mother Tongue languages and the literatures of those languages; these are generally not taught in English, although there is provision for the use of English at the initial stages. Certain schools, such as secondary schools under the Special Assistance Plan (SAP), encourage a richer use of the mother tongue and may occasionally teach subjects in Mandarin Chinese.

Singapore's education system has been consistently ranked as one of the highest in the world by the OECD. It is believed that this comes from the style of teaching that is implemented in Singapore. Teachers focus on making sure that each of their students thoroughly move through the syllabus before moving on. By doing this teachers in Singapore teach a much more narrow but deeper type of instruction. Furthermore, it has been described as "world-leading" and in 2010 was among those picked out for commendation by the Conservative former UK Education Secretary Michael Gove. According to PISA, an influential worldwide study on educational systems, Singapore has the highest performance in international education and tops in global rankings. In 2020, Singaporean students made up half of the perfect scorers in the International Baccalaureate (IB) examinations worldwide.
Question: How many Indian princely states were united by Sardar Patel during 1947-1949
Answer: 565 princely states
Question: Who is the Denver Broncos best player in franchise history?
Answer: John Elway is widely considered the best player for the Denver Broncos.  In addition to leading the team to five Super Bowls, and winning three, he is also a holder of numerous franchise and NFL records.  He became a fan favorite for his toughness.  His most memorable moment was the "helicopter play" in Super Bowl XXXII, a key moment in the game. Other notable players include Steve Atwater, Shannon Sharpe, Randy Gradishar, Terrell Davis, Rod Smith, Peyton Manning, Jake Plummer, and Tom Jackson.
Question: What is the former prince of Wales is now referred as?
Answer: King Charles III of United Kingdom
Question: Identify which instrument is string or woodwind: Chrotta, Frula
Answer: Chrotta is string, Frula is woodwind.
Question: What are four well known movies with Samuel L Jackson?
Answer: Four well known movies with Samuel L Jackson are Pulp Fiction, Goodfellas, The Incredibles, and Marvel's the Avengers
Question: Why is Pickleball a popular sport?
Answer: Pickleball's popularity has been on the rise, thanks in large part to its low entry barrier. With elements of tennis, ping-pong, and badminton, players with experience in racket sports can easily pick up the game. The court is smaller than a tennis court, and the slower-moving ball allows for players of varying athletic abilities to participate with ease
Question: Who is Kenny Ortega?
Evidence: Kenneth John Ortega (born April 18, 1950) is an American filmmaker, touring manager, and choreographer.
Answer: Kenneth John Ortega (born April 18, 1950) is an American filmmaker, touring manager, and choreographer. He is known for directing the films Newsies, Hocus Pocus, The Cheetah Girls 2, High School Musical, Michael Jackson's This Is It, Descendants, and Julie and the Phantoms. He also choreographed the films St. Elmo's Fire, Pretty in Pink, Ferris Bueller's Day Off, and Dirty Dancing; and directed multiple concert tours for Cher, Gloria Estefan, Miley Cyrus, and Michael Jackson. For his work, Ortega was honored with both a star on the Hollywood Walk of Fame and a Disney Legend Award in 2019.
Question: What are some good body weight exercises for shoulders?
Answer: Good body weight exercises for shoulders include plank front arm reaches, pike pushups, plank shoulder taps, arm circles, and dolphin presses.
Question: How do you make a good cappuccino?
Answer: There are multiple steps involved in making a good cappuccino:

Step 1: Find fresh coffee beans and grind them finely

For optimal taste, the coffee beans should be fresh and only slightly oxidized. It is important to use a high quality grinder that grinds the beans uniformly and finely. You should fill a portafilter with 18-21g of grounds and tamp the grounds.

Step 2: Pull the shot

Turn on the espresso machine, wait for it to heat up, and press the button to start the shot. The goal is to make ~40g of espresso in 25-30 seconds starting from when you initiated the pull.

Step 3: Steam the milk and pour

Pour milk into a carafe and use the steam wand to steam the milk. You want to aerate the milk, but not too much or too little. You want to avoid making macrobubbles and also heat up the milk so that the carafe is hot to the touch. Now pour the milk into the espresso, and you're done!
Question: Which dataset do they use?
Evidence: The data used for this task is drawn from the Switchboard conversational English corpus BIBREF31 . The word segments range from 50 to 200 frames in length. The acoustic features in each frame (the input to the word embedding models INLINEFORM0 ) are 39-dimensional MFCCs+ INLINEFORM1 + INLINEFORM2 . We use the same train, development, and test partitions as in prior work BIBREF13 , BIBREF11 , and the same acoustic features as in BIBREF13 , for as direct a comparison as possible. The train set contains approximately 10k example segments, while dev and test each contain approximately 11k segments (corresponding to about 60M pairs for computing the dev/test AP). As in BIBREF13 , when training the classification-based embeddings, we use a subset of the training set containing all word types with a minimum of 3 occurrences, reducing the training set size to approximately 9k segments.
The data used for this task is drawn from the Switchboard conversational English corpus BIBREF31 .
Answer: The answers are shown as follows:
* Switchboard conversational English corpus
Question: Why does adding water to a hot pan of oil cause fire?
Answer: Oil and water do not mix. Water is denser than oil and will settle in the bottom. When you have hot oil in a pan and add a droplet of water the water will sink to the bottom and rapidly heat up to boiling point and vaporize into steam. The steam escapes through the oil leading to a steam explosion. The explosion takes the oil with it and the hot oil becomes fuel which ignites on the nearby stove flame causing a fire.
Question: What models are their methods based on?
Evidence: Automatic Text Summarization deals with the task of condensing documents into a summary, whose level is similar to a human-generated summary. It is mostly distributed into two distinct domains, i.e., Abstractive Summarization and Extractive Summarization. Abstractive summarization( Dejong et al. ,1978) involves models to deduce the crux of the document. It then presents a summary consisting of words and phrases that were not there in the actual document, sometimes even paraphrasing BIBREF1 . A state of art method proposed by Wenyuan Zeng BIBREF2 produces such summaries with length restricted to 75. There have been many recent developments that produce optimal results, but it is still in a developing phase. It highly relies on natural language processing techniques, which is still evolving to match human standards. These shortcomings make abstractive summarization highly domain selective. As a result, their application is skewed to the areas where NLP techniques have been superlative. Extractive Summarization, on the other hand, uses different methods to identify the most informative/dominant sentences through the text, and then present the results, ranking them accordingly. In this paper, we have proposed two novel stand-alone summarization methods.The first method is based on Glove Model BIBREF3 ,and other is based on Facebook's InferSent BIBREF4 . We have also discussed how we can effectively subdue shortcomings of one model by using it in coalition with models which capture the view that other faintly held.
In this paper, we have proposed two novel stand-alone summarization methods.The first method is based on Glove Model,and other is based on Facebook's InferSent.
Answer: Glove Model and InferSent model.
Question: How to collect the data?
Evidence: Keyphrases are single or multi-word lexical units that best summarise a document BIBREF0. As such, they are of great importance for indexing, categorising and browsing digital libraries BIBREF1. Yet, very few documents have keyphrases assigned, thus raising the need for automatic keyphrase generation systems. This task falls under the task of automatic keyphrase extraction which can also be the subtask of finding keyphrases that only appear in the input document. Generating keyphrases can be seen as a particular instantiation of text summarization, where the goal is not to produce a well-formed piece of text, but a coherent set of phrases that convey the most salient information. Those phrases may or may not appear in the document, the latter requiring some form of abstraction to be generated. State-of-the-art systems for this task rely on recurrent neural networks BIBREF2, BIBREF3, BIBREF4, and hence require large amounts of annotated training data to achieve good performance. As gold annotated data is expensive and difficult to obtain BIBREF5, previous works focused on readily available scientific abstracts and used author-assigned keyphrases as a proxy for expert annotations. However, this poses two major issues: 1) neural models for keyphrase generation do not generalize well across domains, thus limiting their use in practice; 2) author-assigned keyphrases exhibit strong consistency issues that negatively impacts the model's performance. There is therefore a great need for annotated data from different sources, that is both sufficiently large to support the training of neural-based models and that comprises gold-standard labels provided by experts. In this study, we address this need by providing KPTimes, a dataset made of 279 923 news articles that comes with editor-assigned keyphrases.
Online news are particularly relevant to keyphrase generation since they are a natural fit for faceted navigation BIBREF6 or topic detection and tracking BIBREF7. Also, and not less importantly, they are available in large quantities and are sometimes accompanied by metadata containing human-assigned keyphrases initially intended for search engines. Here, we divert these annotations from their primary purpose, and use them as gold-standard labels to automatically build our dataset. More precisely, we collect data by crawling selected news websites and use heuristics to draw texts paired with gold keyphrases. We then explore the resulting dataset to better understand how editors tag documents, and how these expert annotations differ from author-assigned keyphrases found in scholarly documents. Finally, we analyse the performance of state-of-the-art keyphrase generation models and investigate their transferability to the news domain and the impact of domain shift.
More precisely, we collect data by crawling selected news websites and use heuristics to draw texts paired with gold keyphrases.
Answer: By crawling selected news websites.
Question: Classify each of the following as either a stringed instrument or a percussion instrument: violin, guitar, drums, congas, bongos, bass guitar.
Answer: The violin, guitar and bass guitar are stringed instruments. Drums, congas, and bongos are percussion instruments.
Question: Which evaluation metric was chosen?
Evidence: Following lebret2016neural, we used BLEU-4, NIST-4 and ROUGE-4 as the evaluation metrics. We first make a few observations based on the results on the English dataset (Table TABREF15 ). The basic seq2seq model, as well as the model proposed by weather16, perform better than the model proposed by lebret2016neural. Our final model with bifocal attention and gated orthogonalization gives the best performance and does 10% (relative) better than the closest baseline (basic seq2seq) and 21% (relative) better than the current state of the art method BIBREF0 . In Table TABREF16 , we show some qualitative examples of the output generated by different models.
Following Lebret et al. (2016), we used BLEU- 4, NIST-4 and ROUGE-4 as the evaluation metrics.
Answer: They used BLEU- 4, NIST-4 and ROUGE-4 as the evaluation metrics.
Question: What external sources are used?
Evidence: FLOAT SELECTED: Table 3: Statistics of external data.
Neural network models for NLP benefit from pretraining of word/character embeddings, learning distributed sementic information from large raw texts for reducing sparsity. The three basic elements in our neural segmentor, namely characters, character bigrams and words, can all be pretrained over large unsegmented data. We pretrain the five-character window network in Figure FIGREF13 as an unit, learning the MLP parameter together with character and bigram embeddings. We consider four types of commonly explored external data to this end, all of which have been studied for statistical word segmentation, but not for neural network segmentors.
Raw Text. Although raw texts do not contain explicit word boundary information, statistics such as mutual information between consecutive characters can be useful features for guiding segmentation BIBREF11 . For neural segmentation, these distributional statistics can be implicitly learned by pretraining character embeddings. We therefore consider a more explicit clue for pretraining our character window network, namely punctuations BIBREF10 .
Automatically Segmented Text. Large texts automatically segmented by a baseline segmentor can be used for self-training BIBREF13 or deriving statistical features BIBREF12 . We adopt a simple strategy, taking automatically segmented text as silver data to pretrain the five-character window network. Given INLINEFORM0 , INLINEFORM1 is derived using the MLP in Figure FIGREF13 , and then used to classify the segmentation of INLINEFORM2 into B(begining)/M(middle)/E(end)/S(single character word) labels. DISPLAYFORM0
Heterogenous Training Data. Multiple segmentation corpora exist for Chinese, with different segmentation granularities. There has been investigation on leveraging two corpora under different annotation standards to improve statistical segmentation BIBREF16 . We try to utilize heterogenous treebanks by taking an external treebank as labeled data, training a B/M/E/S classifier for the character windows network. DISPLAYFORM0
POS Data. Previous research has shown that POS information is closely related to segmentation BIBREF14 , BIBREF15 . We verify the utility of POS information for our segmentor by pretraining a classifier that predicts the POS on each character, according to the character window representation INLINEFORM0 . In particular, given INLINEFORM1 , the POS of the word that INLINEFORM2 belongs to is used as the output. DISPLAYFORM0
FLOAT SELECTED: Table 3: Statistics of external data.
We consider four types of commonly explored external data to this end, all of which have been studied for statistical word segmentation, but not for neural network segmentors.
Raw Text.
Automatically Segmented Text. 
Heterogenous Training Data.
POS Data.
Answer: Raw data from Gigaword, Automatically segmented text from Gigaword, Heterogenous training data from People's Daily, POS data from People's Daily
Question: Where do Macarons originate from?
Evidence: According to popular belief, the macaron has been introduced in France by the Italian chef of queen Catherine de Medici during the Renaissance but this appears to be a legend as the macaron is already mentioned in France in the 8th century in the Cormery Abbey.
Answer: The Macaron originates from France and is generally thought to have been created by an Italian chef of Catherine de Medici when she was the Queen of France during the 16th century. However the time period and original creator maybe a legend as there are documents referring to the Macaron in the 8th century from Cormery Abbey in France.
Question: By how much do they outperform standard BERT?
Evidence: In this paper we presented a way of enriching BERT with knowledge graph embeddings and additional metadata. Exploiting the linked knowledge that underlies Wikidata improves performance for our task of document classification. With this approach we improve the standard BERT models by up to four percentage points in accuracy. Furthermore, our results reveal that with task-specific information such as author names and publication metadata improves the classification task essentially compared a text-only approach. Especially, when metadata feature engineering is less trivial, adding additional task-specific information from an external knowledge source such as Wikidata can help significantly. The source code of our experiments and the trained models are publicly available.
With this approach we improve the standard BERT models by up to four percentage points in accuracy.
Answer: The answers are shown as follows:
* up to four percentage points in accuracy