DataHammer/scimrc huggingface dataset:
Question: Extract the names of the climbers who pioneered The Nose route on El Captain.  List them in order by date first and then alphabetically by last name.  Format them {Year} - {Name}.  Separate the pairs with a semicolon.
Evidence: The Nose was climbed in 1958 by Warren Harding, Wayne Merry and George Whitmore in 47 days using "siege" tactics: climbing in an expedition style using fixed ropes along the length of the route, linking established camps along the way. The fixed manila ropes allowed the climbers to ascend and descend from the ground throughout the 18-month project, although they presented unique levels of danger as well, sometimes breaking due to the long exposure to cold temperatures. The climbing team relied heavily on aid climbing, using rope, pitons and expansion bolts to make it to the summit. The second ascent of The Nose was in 1960 by Royal Robbins, Joe Fitschen, Chuck Pratt and Tom Frost, who took seven days in the first continuous climb of the route without siege tactics. The first solo climb of The Nose was done by Tom Bauman in 1969. The first ascent of The Nose in one day was accomplished in 1975 by John Long, Jim Bridwell and Billy Westbay.
Answer: 1958 - Warren Harding; 1958 - Wayne Merry; 1958 - George Whitmore; 1960 - Joe Fitschen; 1960 - Tom Frost; 1960 - Chuck Pratt; 1960 - Royal Robbins; 1969 - Tom Bauman; 1975 - Jim Bridwell; 1975 - John Long; 1975 - Billy Westbay
Question: What is the difference between table salt and kosher salt?
Answer: Most salt shakers contain table salt, a fine-grained salt often including iodine - called iodized salt.  Table salt is most commonly applied “at the table” when eating, however many home cooks don’t tend to keep multiple types of salt on hand so they might season their food with table salt during the cooking process.  
Kosher salt is a more flaky salt and typically used either in larger quantities for brining or creating a salt crust on proteins prior to cooking.  Most professional chefs will choose Kosher salt over table salt as they prefer the texture and usability.  Table salt, being very fine-grained, is more dense than kosher salt and as such, recipes using salt should be weighed rather than using a volume measurement to ensure that a different type of salt doesn’t ruin the recipe.
Question: What are the three evaluation methods used to verify the performance of the model?
Evidence: Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena, and human evaluation.
Answer: The BLEU score, contrastive evaluation of translation of several discourse phenomena, and human evaluation.
Question: What method do the authors base for the development of a news generation system?
Evidence: Automated, or robotic, journalism aims at news generation from structured data sources, either as the final product or as a draft for subsequent post-editing. At present, automated journalism typically focuses on domains such as sports, finance and similar statistics-based reporting, where there is a commercial product potential due to the high volume of news, combined with the expectation of a relatively straightforward task.
News generation systems—especially those deployed in practice—tend to be based on intricate template filling, aiming to give the users the full control of the generated facts, while maintaining a reasonable variability of the resulting text. This comes at the price of having to develop the templates and specify their control logic, neither of which are tasks naturally fitting journalists' work. Further, this development needs to be repeated for every domain, as the templates are not easily transferred across domains. Examples of the template-based news generation systems for Finnish are Voitto by the Finnish Public Service Broadcasting Company (YLE) used for sports news generation, as well as Vaalibotti BIBREF0, a hybrid machine learning and template-based system used for election news.
BIBREF1 suggested a neural template generation, which jointly models latent templates and text generation. Such a system increases interpretability and controllability of the generation, however, recent sequence-to-sequence systems represent the state-of-the-art in data-to-text generation. BIBREF2
In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods. In order to train such a system, we compile a corpus of news based on over 2000 game reports from the Finnish News Agency STT. While developing this corpus into a form suitable for training of end-to-end systems naturally requires manual effort, we argue that compiling and refining a set of text examples is a more natural way for journalists to interact with the system, in order for them to codify their knowledge and to adapt it for new domains.
Our aim is to generate reports that give an overview of a game based on information inferrable from the statistics. Such reports can be used either as a basis for further post-editing by a journalist imprinting own insights and background information, or even used directly as a news stream labelled as machine-generated.
In the following, we will introduce the news dataset and the process of its creation, introduce an end-to-end model for news generation, and evaluate its output respective to the abovementioned objectives.
In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods.
Answer: Based on sequence-to-sequence methods.
Question: The Award Palme d’or was given to whom?
Answer: Triangle of Sadness(directed by Ruben Ostlund)
Question: What is molecular biology?
Answer: Molecular biology is the branch of biology that seeks to understand the molecular basis of biological activity in and between cells, including biomolecular synthesis, modification, mechanisms, and interactions.[ The study of chemical and physical structure of biological macromolecules is known as molecular biology.

Molecular biology was first described as an approach focused on the underpinnings of biological phenomena - uncovering the structures of biological molecules as well as their interactions, and how these interactions explain observations of classical biology.

In 1945 the term molecular biology was used by physicist William Astbury. In 1953 Francis Crick, James Watson, Rosalind Franklin, and colleagues, working at Medical Research Council unit, Cavendish laboratory, Cambridge (now the MRC Laboratory of Molecular Biology), made a double helix model of DNA which changed the entire research scenario. They proposed the DNA structure based on previous research done by Rosalind Franklin and Maurice Wilkins. This research then lead to finding DNA material in other microorganisms, plants and animals.

Molecular biology is not simply the study of biological molecules and their interactions; rather, it is also a collection of techniques developed since the field's genesis which have enabled scientists to learn about molecular processes. In this way it has both complemented and improved biochemistry and genetics as methods (of understanding nature) that began before its advent. One notable technique which has revolutionized the field is the polymerase chain reaction (PCR), which was developed in 1983. PCR is a reaction which amplifies small quantities of DNA, and it is used in many applications across scientific disciplines.

The central dogma of molecular biology describes the process in which DNA is transcribed into RNA, which is then translated into protein.

Molecular biology also plays a critical role in the understanding of structures, functions, and internal controls within individual cells, all of which can be used to efficiently target new drugs, diagnose disease, and better understand cell physiology. Some clinical research and medical therapies arising from molecular biology are covered under gene therapy whereas the use of molecular biology or molecular cell biology in medicine is now referred to as molecular medicine.
Question: What do the authors want to do in the future?
Evidence: In this paper, we proposed a new character-level, attention-based encoder-decoder model for question answering. In our approach, embeddings of questions, entities, and predicates are all jointly learned to directly optimize the likelihood of generating the correct KB query. Our approach improved the state-of-the-art accuracy on the SimpleQuestions benchmark significantly, using much less data than previous work. Furthermore, thanks to character-level modeling, we have a compact model that is robust to unseen entities. Visualizations of the attention distribution reveal that our model, although built on character-level inputs, can learn higher-level semantic concepts required to answer a natural language question with a structured KB. In the future we would like to extend our system to handle multi-relation questions.
In the future we would like to extend our system to handle multirelation questions.
Answer: They would like to extend their system to handle multirelation questions.
Question: Are these holidays Christian or Jewish? Passover, Hanukkah, Christmas, Easter, Valentines Day, Yom Kippur, Sukkot, Rosh Hashanah
Answer: Passover, Hanukkah, Yom Kippur, Sukkot and Rosh Hashanah are all Jewish holidays. Christmas, Easter and Valentines Day are all Christian holidays.
Question: In what year did George Washington die and how old was he?
Evidence: Washington's death came more swiftly than expected. On his deathbed, out of fear of being entombed alive, he instructed his private secretary Tobias Lear to wait three days before his burial. According to Lear, Washington died between 10 p.m. and 11 p.m. on December 14, 1799, with Martha seated at the foot of his bed. His last words were "'Tis well", from his conversation with Lear about his burial. He was 67.
Answer: George Washington died in the year 1799 at the age of 67.
Question: How do I keep great oral health?
Answer: You can have and maintain good oral health by minimizing the proliferation of bacteria in your mouth and tongue surface, flossing your teeth and brushing your teeth at least twice a day in the morning after waking up and in the evening before going to bed.
Question: How should I deal with my kid's allergy?
Answer: Find out what your kid is allergic to by conducting a series of tests. Also make sure the kid changes cloth after coming back from school to avoid spreading allergy agents, e.g. dust or pollens, into the bedroom and taking shower right after school is a good idea too. In some severe cases doctors may prescribe allergy shots, so do ask about that option if necessary.
Question: Are the document vectors that the authors introduce evaluated in any way other than the new way the authors propose?
Evidence: In this section we describe how to evaluate and compare the outcomes of algorithms which assign relevance scores to words (such as LRP or SA) through intrinsic validation. Furthermore, we propose a measure of model explanatory power based on an extrinsic validation procedure. The latter will be used to analyze and compare the relevance decompositions or explanations obtained with the neural network and the BoW/SVM classifier. Both types of evaluations will be carried out in section "Results" .
In this section we describe how to evaluate and compare the outcomes of algorithms which assign relevance scores to words (such as LRP or SA) through intrinsic validation. Furthermore, we propose a measure of model explanatory power based on an extrinsic validation procedure.
Answer: Yes.
Question: Identify which instrument is string or percussion: Handchime, Nyatiti
Answer: Nyatiti is string, Handchime is percussion
Question: What are the uncanny semantic structures of the embedding space?
Evidence: We mainly concern with the two following structures of the embedding space.
Semantic similarity structure: Semantically similar entities are close to each other in the embedding space, and vice versa. This structure can be identified by a vector similarity measure, such as the dot product between two embedding vectors. The similarity between two embedding vectors is computed as:
Semantic direction structure: There exist semantic directions in the embedding space, by which only one semantic aspect changes while all other aspects stay the same. It can be identified by a vector difference, such as the subtraction between two embedding vectors. The semantic direction between two embedding vectors is computed as:
We mainly concern with the two following structures of the embedding space.

Semantic similarity structure: Semantically similar entities are close to each other in the embedding space, and vice versa.
Semantic direction structure: There exist semantic directions in the embedding space, by which only one semantic aspect changes while all other aspects stay the same. It can be identified by a vector difference, such as the subtraction between two embedding vectors.
Answer: The answers are shown as follows:
* Semantic similarity structure
* Semantic direction structure
Question: How many people who contract COVID-19 end up experiencing symptoms and what symptoms might they experience?
Evidence: Coronavirus disease 2019 (COVID-19) is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic.

The symptoms of COVID‑19 are variable but often include fever, cough, headache, fatigue, breathing difficulties, loss of smell, and loss of taste. Symptoms may begin one to fourteen days after exposure to the virus. At least a third of people who are infected do not develop noticeable symptoms. Of those who develop symptoms noticeable enough to be classified as patients, most (81%) develop mild to moderate symptoms (up to mild pneumonia), while 14% develop severe symptoms (dyspnea, hypoxia, or more than 50% lung involvement on imaging), and 5% develop critical symptoms (respiratory failure, shock, or multiorgan dysfunction). Older people are at a higher risk of developing severe symptoms. Some people continue to experience a range of effects (long COVID) for months after recovery, and damage to organs has been observed. Multi-year studies are underway to further investigate the long-term effects of the disease.
Answer: Approximately two-thirds of people who are infected with COVID-19 develop noticeable symptoms. Of those who develop symptoms noticeable enough to be classified as patients, most (81%) develop mild to moderate symptoms (up to mild pneumonia), while 14% develop severe symptoms (dyspnea, hypoxia, or more than 50% lung involvement on imaging), and 5% develop critical symptoms (respiratory failure, shock, or multiorgan dysfunction).
Question: How is chocolate made?
Answer: Chocolate comes from cacao beans. These beans need to be cleaned and roasted at low temperatures to develop flavor. The shells are seperated from the nibs through winnowing. Nibs are finely ground into cocoa mass which becomes solid at room temperature. Other ingredients then get added to the solid and then machines knead the chocolate paste making sweet chocolate.
Question: Can you name a few of Leonardo's famous works
Evidence: Leonardo is identified as one of the greatest painters in the history of art and is often credited as the founder of the High Renaissance. Despite having many lost works and fewer than 25 attributed major works—including numerous unfinished works—he created some of the most influential paintings in Western art. His magnum opus, the Mona Lisa, is his best known work and often regarded as the world's most famous painting. The Last Supper is the most reproduced religious painting of all time and his Vitruvian Man drawing is also regarded as a cultural icon. In 2017, Salvator Mundi, attributed in whole or part to Leonardo, was sold at auction for US$450.3 million, setting a new record for the most expensive painting ever sold at public auction.
Answer: Mona Lisa, The Last Supper, Vitruvian Man, Salvator Mundi
Question: If we intend to claim a " core" set of documents has some distinctive property, what do we need to do?
Evidence: After identifying the data source(s), the next step is compiling the data. This step is fundamental: if the sources cannot support a convincing result, no result will be convincing. In many cases, this involves defining a “core" set of documents and a “comparison" set. We often have a specific set of documents in mind: an author's work, a particular journal, a time period. But if we want to say that this “core" set has some distinctive property, we need a “comparison" set. Expanding the collection beyond the documents that we would immediately think of has the beneficial effect of increasing our sample size. Having more sources increases the chance that we will notice something consistent across many individually varying contexts.
Comparing sets of documents can sometimes support causal inference, presented as a contrast between a treatment group and a control. In BIBREF0 , the treatment consisted of the text written in the two forums that were eventually closed by Reddit. However, identifying a control group required a considerable amount of time and effort. Reddit is a diverse platform, with a wide variety of interactional and linguistic styles; it would be pointless to compare hate speech forums against forums dedicated to, say, pictures of wrecked bicycles. Chandrasekharan et al. used a matching design, populating the control group with forums that were as similar as possible to the treatment group, but were not banned from Reddit. The goal is to estimate the counterfactual scenario: in this case, what would have happened had the site not taken action against these specific forums? An ideal control would make it possible to distinguish the effect of the treatment — closing the forums — from other idiosyncratic properties of texts that were treated.
We also look for categories of documents that might not be useful. We might remove documents that are meta-discourse, like introductions and notes, or documents that are in a language that is not the primary language of the collection, or duplicates when we are working with archived web pages. However, we need to carefully consider the potential consequences of information we remove. Does its removal alter the data, or the interpretation of the data, we are analyzing? Are we losing anything that might be valuable at a later stage?
In many cases, this involves defining a “core” set of documents and a “comparison” set. We often have a specific set of documents in mind: an author’s work, a particular journal, a time period. But if we want to say that this “core” set has some distinctive property, we need a“comparison” set.
Answer: You need a“comparison” set of documents.
Question: What is the baseline model used?
Evidence: DrQA is a CRC baseline coming with the CoQA dataset. Note that this implementation of DrQA is different from DrQA for SQuAD BIBREF8 in that it is modified to support answering no answer questions by having a special token unknown at the end of the document. So having a span with unknown indicates NO ANSWER. This baseline answers the research question RQ1.
DrQA+CoQA is the above baseline pre-tuned on CoQA dataset and then fine-tuned on $(\text{RC})_2$ . We use this baseline to show that even DrQA pre-trained on CoQA is sub-optimal for RCRC. This baseline is used to answer RQ1 and RQ3.
BERT is the vanilla BERT model directly fine-tuned on $(\text{RC})_2$ . We use this baseline for ablation study on the effectiveness of pre-tuning. All these BERT's variants are used to answer RQ2.
BERT+review first tunes BERT on domain reviews using the same objectives as BERT pre-training and then fine-tunes on $(\text{RC})_2$ . We use this baseline to show that a simple domain-adaptation of BERT is not good.
BERT+CoQA first fine-tunes BERT on the supervised CoQA data and then fine-tunes on $(\text{RC})_2$ . We use this baseline to show that pre-tuning is very competitive even compared with models trained from large-scale supervised data. This also answers RQ3.
DrQA is a CRC baseline coming with the CoQA dataset. Note that this implementation of DrQA is different from DrQA for SQuAD BIBREF8 in that it is modified to support answering no answer questions by having a special token unknown at the end of the document. 
DrQA+CoQA is the above baseline pre-tuned on CoQA dataset and then fine-tuned on $(\text{RC})_2$ . 
BERT is the vanilla BERT model directly fine-tuned on $(\text{RC})_2$ . We use this baseline for ablation study on the effectiveness of pre-tuning.
BERT+review first tunes BERT on domain reviews using the same objectives as BERT pre-training and then fine-tunes on $(\text{RC})_2$ . We use this baseline to show that a simple domain-adaptation of BERT is not good.
BERT+CoQA first fine-tunes BERT on the supervised CoQA data and then fine-tunes on $(\text{RC})_2$ . We use this baseline to show that pre-tuning is very competitive even compared with models trained from large-scale supervised data.
Answer: The baseline models used are DrQA modified to support answering no answer questions, DrQA+CoQA which is pre-tuned on CoQA dataset, vanilla BERT, BERT+review tuned on domain reviews, BERT+CoQA tuned on the supervised CoQA data