DataHammer/scimrc huggingface dataset:
Question: Who is Jeffrey Archer?
Evidence: Jeffrey Howard Archer, Baron Archer of Weston-super-Mare (born 15 April 1940) is an English novelist, life peer and former politician. Before becoming an author, Archer was a Member of Parliament (1969–1974), but did not seek re-election after a financial scandal that left him almost bankrupt.

Archer revived his fortunes as a novelist. His 1979 novel Kane and Abel remains one of the best-selling books in the world, with an estimated 34 million copies sold worldwide. Overall his books have sold more than 320 million copies worldwide.

Archer became deputy chairman of the Conservative Party (1985–86), before resigning after a newspaper accused him of paying money to a prostitute. In 1987, he won a court case and was awarded large damages because of this claim. He was made a life peer in 1992 and subsequently became Conservative candidate to be the first elected Mayor of London. He resigned his candidacy in 1999 after it emerged that he had lied in his 1987 libel case. He was imprisoned (2001–2003) for perjury and perverting the course of justice, ending his active political career.
Answer: Jeffrey Archer is British Author and was also British Parliament member.One of his most famous books include Kane & Abel.
Question: Categorize these modes of transportation into "would use to go across continents" "would use for daily commuting" "would use for fun": car, bicycle, boat, airplane, dune buggy, skateboard, rv, truck, sled, train
Answer: Would use to go across continents: airplane, car, truck, train, boat
Would use for daily commuting:car, truck, train, bicycle, skateboard, boat
Would use for fun: sled, rv, dune buggy, sled, skateboard, bicycle, car, truck, boat
Question: Why is the monolingual corpus better than bilingual corpus?
Evidence: The word embeddings of the source and target language sentences are used as initial vectors of the model to improve contextualization. The skip gram model of the word2vec algorithm optimizes the vectors by accounting for the average log probability of context words given a source word. DISPLAYFORM0 
where k is the context window taken for the vectorization, INLINEFORM0 refers to the INLINEFORM1 word of the corpus and INLINEFORM2 is the size of the training corpus in terms of the number of words. Here, the probabily INLINEFORM3 is computed as a hierarchical softmax of the product of the transpose of the output vector of INLINEFORM4 and the input vector of INLINEFORM5 for each and every pair over the entire vocabulary. The processes of negative sampling and subsampling of frequent words that were used in the original model aren't used in this experiment BIBREF3 .
For the process of creating semantically meaningful word embeddings, a monolingual corpus of 569,772 Tamil sentences was used. This gave the vectors more contextual richness due to the increased size of the corpus as opposed to using just the bilingual corpus' target side sentences BIBREF3 .
In the experiment, the word2vec model was trained using a vector size of 100 to ensure that the bulk of the limited memory of the GPU will be used for the neural attention translation model. It has been shown that any size over that of 150 used for word vectorization gives similar results and that a size of 100 performs close to the model with 150-sized word vectors BIBREF7 . A standard size of 5 was used as window size and the model was trained over 7 worker threads simultaneously. A batch size of 50 words was used for training. The negative sampling was set at 1 as it is the nature of morphologically rich languages to have a lot of important words that don't occur more than once in the corpus. The gensim word2vec toolkit was used to implement this word embedding process BIBREF8 .
For the process of creating semantically meaningful word embeddings, a monolingual corpus of 569,772 Tamil sentences was used. This gave the vectors more contextual richness due to the increased size of the corpus as opposed to using just the bilingual corpus' target side sentences (Mikolov and Dean 2013).
Answer: A monolingual corpus that contains 569,772 Tamil sentences gave the vectors more contextual richness due to the increased size.
Question: What are some common onion varieties used in cooking?
Answer: Common onions used in cooking are yellow onion, white onion, red onion, sweet onion, green onion, spring onion, leeks, and shallots.
Question: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?
Evidence: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. Notice that our best Closed model can almost perform as well as the state-of-the-art model while the latter utilizes pre-trained word embeddings. Besides, performance gap between three models under Open setting is very small. It indicates that the representation ability of BERT is so powerful and may contains rich syntactic information. At last, the Gold result is much higher than the other models, indicating that there is still large space for improvement for this task.
Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.
Answer: The answers are shown as follows:
* our Open model achieves more than 3 points of f1-score than the state-of-the-art result
Question: Identify which instrument is string or percussion: Zill, Epinette des Vosges
Answer: Epinette des Vosges is string, Zill is percussion.
Question: Which is a species of fish? Barracuda or Barracks
Answer: Barracuda
Question: What do they use to optimize the model?
Evidence: In the preprocessing step, we used pre-trained GloVe vectors for word embeddings, and they would not be updated during training; We padded sentence number in each passage to 101, all word number in each sentence to 100. Word number of queries and choices were padded to 50. For all kernels of CNN, INLINEFORM0 , each of which has three different kernel width INLINEFORM1 ; each of them has same kernel number INLINEFORM2 . We utilized dropout in each CNN layer with dropout rate 0.8. We used Adam BIBREF8 optimizer to optimize our model with initial learning rate 0.001.
We used Adam [9] optimizer to optimize our model with initial learning rate 0.001.
Answer: Adam optimizer.
Question: Who is the most influential president in history?
Answer: Abraham Lincoln is mostly regarded as the greatest president for his leadership during the Civil War and the abolition of slavery. His main contender is Franklin D. Roosevelt, for leading the country out of the Great Depression and during World War II.
Question: What is the average number of turns per dialog?
Evidence: Multiple turns: The average number of utterances per dialog is about 23 which ensures context-rich language behaviors.
Multiple turns: The average number of utterances per dialog is about 23 which ensures context-rich language behaviors.
Answer: The answers are shown as follows:
* The average number of utterances per dialog is about 23
Question: Extract the reasons the Atcama Desert is one of the driest places on earth and list them in bullet format.
Evidence: One of the driest places on Earth is the Atacama Desert. It is virtually devoid of life because it is blocked from receiving precipitation by the Andes mountains to the east and the Chilean Coast Range to the west. The cold Humboldt Current and the anticyclone of the Pacific are essential to keep the dry climate of the Atacama. The average precipitation in the Chilean region of Antofagasta is just 1 mm (0.039 in) per year. Some weather stations in the Atacama have never received rain. Evidence suggests that the Atacama may not have had any significant rainfall from 1570 to 1971. It is so arid that mountains that reach as high as 6,885 m (22,589 ft) are completely free of glaciers and, in the southern part from 25°S to 27°S, may have been glacier-free throughout the Quaternary, though permafrost extends down to an altitude of 4,400 m (14,400 ft) and is continuous above 5,600 m (18,400 ft). Nevertheless, there is some plant life in the Atacama, in the form of specialist plants that obtain moisture from dew and the fogs that blow in from the Pacific.
Answer: -blocked from receiving precipitation by the Andes mountains to the east and the Chilean Coast Range to the west
-cold Humboldt Current and the anticyclone of the Pacific
Question: What is Grimsthorpe?
Evidence: Grimsthorpe is a hamlet in the South Kesteven district of Lincolnshire, England. It is situated on the A151 road, and 3 miles (5 km) north-west from Bourne. Grimsthorpe falls within the civil parish of Edenham, which is governed by Edenham Grimsthorpe Elsthorpe & Scottlethorpe Parish Council.

Grimsthorpe Castle is 500 yards (460 m) to the west.

John Marius Wilson's 1870 Imperial Gazetteer of England and Wales described Grimsthorpe as: a hamlet in Edenham parish, Lincoln; on the river Glen, 1½ mile W of Edenham village. Pop., 135. Grimsthorpe Park was the seat once of the Duke of Ancaster, afterwards of Lord Gwyder; is now the seat of Lord Willoughby d'Eresby; was built partly in the time of Henry III., but principally by the Duke of Suffolk, to entertain Henry VIII.; is a large, irregular, but magnificent structure; and stands in an ornate park, about 16 miles in circuit. A Cistertian abbey, founded about 1451, by the Earl of Albemarle, and called Vallis Dei, or, vulgarly, Vaudy, formerly stood in the park, about a mile from the castle.
Answer: Grimsthorpe is a hamlet in the South Kesteven district of Lincolnshire, England. It is situated on the A151 road, and 3 miles (5 km) north-west from Bourne. Grimsthorpe falls within the civil parish of Edenham, which is governed by Edenham Grimsthorpe Elsthorpe & Scottlethorpe Parish Council.

Grimsthorpe Castle is 500 yards (460 m) to the west.

John Marius Wilson's 1870 Imperial Gazetteer of England and Wales described Grimsthorpe as:

a hamlet in Edenham parish, Lincoln; on the river Glen, 1½ mile W of Edenham village. Pop., 135. Grimsthorpe Park was the seat once of the Duke of Ancaster, afterwards of Lord Gwyder; is now the seat of Lord Willoughby d'Eresby; was built partly in the time of Henry III., but principally by the Duke of Suffolk, to entertain Henry VIII.; is a large, irregular, but magnificent structure; and stands in an ornate park, about 16 miles in circuit. A Cistertian abbey, founded about 1451, by the Earl of Albemarle, and called Vallis Dei, or, vulgarly, Vaudy, formerly stood in the park, about a mile from the castle.
Question: How many letters there are in the Japanese language?
Answer: here are 46 basic characters in the Japanese alphabet and additional characters/sounds that can be made from the basic 46. Each of the alphabet symbols forms one syllable made of the 21 romaji, which is phonetic and always read the same way.
Question: How do I optimize my Kafka consumption in Spark for low latency?
Answer: You can tune the batch size and processing time to ensure that your Spark job can keep up with the data volume and maintain low latency.
Question: Which of the following surnames have appeared on the Wimbledon Men's Championship trophy in the last sixty years? Federer, Djokovic, Navratilova, Connors, Borg, McEnroe, Becker, Laver, Evert, Court, Nastase, Sullivan, Leconte, Bahrami.
Answer: Wimbledon is a famous tennis tournament that is played, every two weeks of the summer, in a suburb of London in the United Kingdom. The correct answer is: Federer, Djokovic, Connors, Borg, McEnroe, Becker, Laver. The tennis players called Nastase, Sullivan, Leconte, and Bahrami have never won the Wimbledon Championships. The names 'Evert', 'Navratilova' and 'Court' belong to female champions (who have won the women's event in the last sixty years).
Question: Why are semantic HTML elements important to use?
Answer: The main benefit of using semantic HTML elements is that it improves the accessibility of a website when used correctly. For example, text that is styled to look like a heading for a section of content, may not be exposed as a heading to a screen reader or search engine unless that text is wrapped in a heading element — h1, h2, h3, h4, h5, or h6.
Question: What is the LD2NL presented in this paper?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL.
Answer: It is an open-source holistic NLG framework for the SW.
Question: What four representative datasets are used for bechmark?
Evidence: Recently large and powerful pre-trained language models such as BERT BIBREF8 have been achieving the state-of-the-art (SOTA) results on various tasks, however, its potency on MCQA datasets has been severely limited by the data insufficiency. For example, the MCTest dataset has two variants: MC160 and MC500, which are curated in a similar way, and MC160 is considered easier than MC500 BIBREF9. However, BERT-based models perform much worse on MC160 compared with MC500 (8–10% gap) since the data size of the former is about three times smaller. To tackle this issue, we investigate how to improve the generalization of BERT-based MCQA models with the constraint of limited training data using four representative MCQA datasets: DREAM, MCTest, TOEFL, and SemEval-2018 Task 11.
To tackle this issue, we investigate how to improve the generalization of BERT-based MCQA models with the constraint of limited training data using four representative MCQA datasets: DREAM, MCTest, TOEFL, and SemEval-2018 Task 11.
Answer: The answers are shown as follows:
* DREAM, MCTest, TOEFL, and SemEval-2018 Task 11
Question: How to interact between every two tasks in the model?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer.
Answer: In the model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer.
Question: Tell me if the following tourist attractions in Barcelona are free or paid: Park Güell, Parc de la Ciutadella, La Boqueria, Tibidabo, Casa Battló, La Rambla, Born Centre Cultural, La Sagrada Familia, La Pedrera, La Barceloneta, Palau de Música Catalana, Camp Nou
Answer: Park Güell - paid
Parc de la Ciutadella - free
La Boqueria - free
Tibidabo - free
Casa Battló - paid
La Rambla - free
Born Centre Cultural - free
La Sagrada Familia - paid
La Pedrera - paid
La Barceloneta - free
Palau de Música Catalana - paid
Camp Nou - paid
Question: Without directly quoting from the text, give me a summary of the second home controversy in Gwynedd, Wales.
Evidence: Controversy erupted in mid-winter 2001 when Seimon Glyn, Gwynedd Council's housing committee chairman and Plaid Cymru member, voiced frustration over "English immigrants" moving into traditionally Welsh speaking communities. Glyn was commenting on a report underscoring the dilemma of rocketing house prices outstripping what locals could pay, with the report warning that "...traditional Welsh communities could die out..." as a consequence.

In 2001 nearly a third of all purchases of properties in Gwynedd were by buyers from out of the county, with some communities reporting as many as a third of local homes used as holiday homes. Holiday home owners typically spend less than six months of the year in the local community.

The issue of locals being priced out of the local housing market is common to many rural communities throughout Britain, but in Wales the added dimension of language further complicates the issue, as many new residents do not learn the Welsh language.

Concerned for the Welsh language under these pressures, Glyn said "Once you have more than 50% of anybody living in a community that speaks a foreign language, then you lose your indigenous tongue almost immediately". His comments attracted strong criticism of Plaid Cymru from other national parties.

By spring 2002 both the Snowdonia National Park (Welsh: Parc Cenedlaethol Eryri) and Pembrokeshire Coast National Park (Welsh: Parc Cenedlaethol Arfordir Penfro) authorities began limiting second home ownership within the parks, following the example set by Exmoor. According to planners in Snowdonia and Pembroke applicants for new homes must demonstrate a proven local need or the applicant must have strong links with the area.
Answer: By the end of 2001, as much as a third of the homes in the Gwynedd county was owned by owners not using these houses as a primary home. The local community was concerned that the use of the Welsh language was at risk because a growing amount of owners did only speak English, not Welsh. To combat this phenomenon, two national parks in the county started limiting second home ownership in the area, requiring proof of local need or strong ties in the area.
Question: Do Texans have a Southern accent?
Answer: It depends on who you ask.  Most Texans would answer the question with no.  Texans would describe their accent as a Texas drawl.  The sound of a Texas drawl differs from what is commonly considered a southern accent.  A southern accent is common in states such as Alabama, Georgia, the Carolinas, Mississippi, and others.
Question: Categorize each of the following classes as either 'hard', 'medium', 'easy': english writing, math, history, chemistry, physics.
Answer: English writing in general is considered as easy, however to master the skills of writing is actually pretty hard. 

Math is in general considered as hard in school, since it's less intuitive and requires multiple step thinking process to solve problems.

History is considered easy at school while chemistry is considered as medium.

Physics is considered as hard. It combines math and science. It has lots of concepts to learn, and it's cumulative: one new concept is built on top of many previous concepts, one physics concept may depends on concepts from math, science, etc.