DataHammer/scimrc huggingface dataset:
Question: What type of simulations of real-time data feeds are used for validaton?
Evidence: To support future large-scale operations, a multi-protocol message passing system was used for inter-module communication. This modular design also allows different components to be swapped out seamlessly, provided they continue to communicate via the expected interface. Routines were developed to simulate input data based on the authors experience with real healthcare data. The reasons for this choice were twofold: One, healthcare data can be high in incidental complexity, requiring one-off code to handle unusual inputs, but not necessarily in such a way as to significantly alter the fundamental engineering choices in a semantic enrichment engine such as this one. Two, healthcare data is strictly regulated, and the process for obtaining access to healthcare data for research can be cumbersome and time-consuming.
A simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting, was used for simulation. In a production setting, the Java module that generates simulation data would be replaced by either a data source that directly writes to the input message queue or a Java module that intercepts or extracts production data, transforms it as needed, and writes it to the input message queue. A component-level view of the systems architecture is shown in Figure FIGREF7
Routines were developed to simulate input data based on the authors experience with real healthcare data. The reasons for this choice were twofold: One, healthcare data can be high in incidental complexity, requiring one-off code to handle unusual inputs, but not necessarily in such a way as to significantly alter the fundamental engineering choices in a semantic enrichment engine such as this one. Two, healthcare data is strictly regulated, and the process for obtaining access to healthcare data for research can be cumbersome and time-consuming.

A simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting, was used for simulation.
Answer: The answers are shown as follows:
* simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting
Question: What online text resources are used to test binomial lists?
Evidence: The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names — a key setting for word ordering which has been difficult to study by other means.
 We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. 
Answer: The answers are shown as follows:
* news publications, wine reviews, and Reddit
Question: What is a CPT as it relates to Healthcare?
Answer: A CPT is an acronym for Common Procedural Terminology and it is a standard for billing in the medical industry. For example, CPT code 77427 is for radiation treatment management and is billed once per five treatment sessions regardless of the actual time period in which the services are furnished. The services need not be furnished on consecutive days.
Question: What are the baselines?
Evidence: The Title-to-Story system is a baseline, which generates directly from topic.
The Title-to-Story system is a baseline, which generates directly from topic.
Answer: The answers are shown as follows:
* Title-to-Story system
Question: Should I go to the beach or mountains for my next vacation?
Answer: We have vacation homes in many locations to fit your needs! You can enjoy warmer temperatures at the beach with beautiful sunsets across the water. Go for a run on the sand and stop by the many local shops and boutiques as you mingle through the day. Are you an adventure seeker? If so, check out our mountain rental homes. You can go mountain biking, hiking, or even skiing, depending on the time of year. A mountain vacation is a bit more of a thrill seeker dream but can be relaxing as well as spending time in nature.
Question: Does the base model for each round perform well on that round's test set?
Evidence: Table TABREF13 reports the main results. In addition to BERT BIBREF10 and RoBERTa BIBREF25, we also include XLNet BIBREF30 as an example of a strong, but different, model architecture. We show test set performance on the ANLI test sets per round, the total ANLI test set, and the exclusive test subset (examples from test-set-exclusive workers). We also show accuracy on the SNLI test set and the MNLI development (for the purpose of comparing between different model configurations across table rows) set. In what follows, we briefly discuss our observations.
Notice that the base model for each round performs very poorly on that round’s test set.
Answer: No, it doesn't.
Question: What is the task-specific composition function used to do?
Evidence: Deep learning models have been widely used in many natural language processing (NLP) tasks. A major challenge is how to design and learn the semantic composition function while modeling a text sequence. The typical composition models involve sequential BIBREF0 , BIBREF1 , convolutional BIBREF2 , BIBREF3 , BIBREF4 and syntactic BIBREF5 , BIBREF6 , BIBREF7 compositional models.
In spite of their success, these models have two major limitations. First, they usually use a shared composition function for all kinds of semantic compositions, even though the compositions have different characteristics in nature. For example, the composition of the adjective and the noun differs significantly from the composition of the verb and the noun. Second, different composition functions are learned from scratch in different tasks. However, given a certain natural language, its composition functions should be the same (on meta-knowledge level at least), even if the tasks are different.
To address these problems, we need to design a dynamic composition function which can vary with different positions and contexts in a sequence, and share it across the different tasks. To share some meta-knowledge of composition function, we can adopt the multi-task learning BIBREF8 . However, the sharing scheme of most neural multi-task learning methods is feature-level sharing, where a subspace of the feature space is shared across all the tasks. Although these sharing schemes are successfully used in various NLP tasks BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , they are not suitable to share the composition function.
In this paper, inspired by recent work on dynamic parameter generation BIBREF15 , BIBREF16 , BIBREF17 , we propose a function-level sharing scheme for multi-task learning, in which a shared meta-network is used to learn the meta-knowledge of semantic composition among the different tasks. The task-specific semantic composition function is generated by the meta-network. Then the task-specific composition function is used to obtain the task-specific representation of a text sequence. The difference between two sharing schemes is shown in Figure 1 . Specifically, we use two LSTMs as meta and basic (task-specific) network respectively. The meta LSTM is shared for all the tasks. The parameters of the basic LSTM are generated based on the current context by the meta LSTM, therefore the composition function is not only task-specific but also position-specific. The whole network is differentiable with respect to the model parameters and can be trained end-to-end.
We demonstrate the effectiveness of our architectures on two kinds of NLP tasks: text classification and sequence tagging. Experimental results show that jointly learning of multiple related tasks can improve the performance of each task relative to learning them independently.
Our contributions are of three-folds:
Then the task-specific composition function is used to obtain the task-specific representation of a text sequence.
Answer: To obtain the task-specific representation of a text sequence.
Question: What kind of method it is?
Evidence: Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3 . Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4 . Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios.
However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child). On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs. However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En). Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9 .
In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. Furthermore, the amount of indirect in-domain parallel corpora, i.e., Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small. As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9 .
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows:
To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling and domain adaptation.
Answer: A method that combines multilingual modeling and domain adaptation.
Question: What kind of dog should I get?
Answer: There are many dog breeds to choose from. Choosing a dog breed is a personal choice. Consider what kind of lifestyle you live and pick a dog that fits your lifestyle. For example, if you are allergic to dogs you may consider a poodle, or poodle mix as they tend to be hypoallergenic.
Question: Why are people afraid of sharks?
Answer: Many people are afraid of sharks from irrational fear from media, and from losing control. Only about 80 humans are attacked by sharks each year, with a very low death rate. Galeophobia is the term used for being afraid of sharks.
Question: What non autoregressive NMT models are used for comparison?
Evidence: We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8.
We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8.
Answer: The answers are shown as follows:
* NAT w/ Fertility
* NAT-IR
* NAT-REG
* LV NAR
* CTC Loss
* CMLM
Question: give me a list of ways to cool down
Answer: - drink a slushy 
- take a cold shower
- dip in glacier water
- take your clothes off
- participate in a polar bear plunge
- stand very still
- take shelter under shade
- sit in front of a fan
- roll down the windows in a moving car
Question: Which characters belong to DC or Marvel Universe? Nova Corps or Green Lantern Corps
Answer: Nova Corps is Marvel, Green Lantern Corps is DC
Question: How many angels can dance on the head of a pin?
Answer: This exact question puzzled theologians for many hundreds of years, and led to many differing points of view. The issue with the question is that there is no correct answer- firstly, are angels real? If they are, how big are they? are they even able to dance? if they could dance, how much space would they need? Secondly, how big is the pin? is the pin a normal sized Earthly pin? or something much larger, on a celestial scale?

The answer relates to faith- if you believe in angels, you believe in their ability to dance, and believe in their specific ability to dance upon a pin, and can have an argument in any direction based on your faith that they underlying premise of the question could be a likelihood. If you do not have that faith, then your outlook would likely view this question with a great degree of cynicism, and you would probably view it as a pointless question.

But with a degree of humour, and a tip of the hat to Douglas Adams, I'm happy to conclude that the answer is 42.
Question: What are the baselines?
Evidence: We compare our model with two sets of baselines:
MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . It encodes question and evidence with a bag-of-word method and stores the representations of evidences in an external memory. A recurrent attention model is used to retrieve relevant information from the memory to answer the question.
Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. The simpler Attentive Reader uses a similar way as our work to compute attention for the evidence. And the more complex Impatient Reader computes attention after processing each question word.
We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 .
Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings.
Answer: The answers are shown as follows:
* MemN2N BIBREF12
* Attentive and Impatient Readers BIBREF6
Question: Based on this paragraph, what was the main focus of Mohammad Shah I's reign over his sultanate?
Evidence: When Mohammad inherited the newly born sultanate from his father Alauddin, the land was still infested with thieves and robbers. As such he spent the entire part of his reign in establishing law in his land. He ordered his governors to never give refuge to anyone who defied his authority. This campaign was successful and at the end of his reign,the land became peaceful and law abiding. He also sat on the takht-ē-firoza or The Turquoise throne which was gifted to him by Kapaya Nayaka.
Answer: Mohammad was mainly focused on establishing law and order within his territory during his reign in order to remove thieves and robbers.
Question: Why is the Chrysler Hemi V8 so legendary?
Answer: The Chrysler "Hemi" V8 are a series of American V8 internal combustion engines known for their simplicity, reliability, low cost, high power and torque output. Originally the Hemi was named for the Hemispherical shaped combustion chamber that increased power and efficiency.  The very first Hemi engines were originally developed during World War II for the Republic P-47 and the M47 Patton Tank and they were first introduced into passenger cars in 1951 as the Chrysler FirePower. 

The Hemi is now in its third generation (3G) being built from 2003 to the present at Chrysler's Saltillo Engine plant in Ramos Arizpe Mexico.  The 3G Hemi's have had power outputs from 345 horsepower in the first 3G 5.7 Liter to 1025 horsepower in the 6.2 High Output Demon 170 engine.   The latest 6.2 Hemi in the Demon 170 is now one of the most powerful engines installed in a passenger car in the world at a fraction of the cost of other 1000 horsepower vehicles.

All of these factors have added up to make a tough, powerful and extremely desirable engine for automotive enthusiasts around the world cementing the Hemi Legend.
Question: Classify these vehicles by which go in the water or on land: dune buggy, cruise ship, pirate ship, bulldozer, submarine, sailboat, truck, car, ATV
Answer: Water: cruise ship, pirate ship, submarine, sailboat
Land: dune buggy, bulldozer, truck, car, ATV
Question: Name five states in the USA that don't touch the ocean.
Answer: Five states in the USA that don't touch the ocean are Iowa, Illinois, Indiana, Ohio, and Utah.
Question: What is Purchasing power parity (PPP)?
Answer: Purchasing power parity (PPP) is a measurement of the price of specific goods in different countries and is used to compare the absolute purchasing power of the countries' currencies. PPP is effectively the ratio of the price of a basket of goods at one location divided by the price of the basket of goods at a different location. The PPP inflation and exchange rate may differ from the market exchange rate because of tariffs, and other transaction costs.
Question: Where is Hoober Stand located?
Evidence: Hoober Stand is a 30-metre-high (98 ft) tower and Grade II* listed building on a ridge in Wentworth, South Yorkshire in northern England. It was designed by Henry Flitcroft for the Whig aristocrat Thomas Watson-Wentworth, Earl of Malton (later the 1st Marquess of Rockingham) to commemorate the quashing of the 1745 Jacobite rebellion. It lies close to his country seat Wentworth Woodhouse. Its site is approximately 157 metres (515 ft) above sea level and from the top there are long-distance views on a clear day. Hoober Stand is one of several follies in and around Wentworth Woodhouse park; the others include Needle's Eye and Keppel's Column. Sidney Oldall Addy, the Sheffield author calls the structure Woburn Stand in his 1888 book, A glossary of words used in the neighbourhood of Sheffield.
Answer: Wentworth, South Yorkshire in northern England
Question: Are you going for a walk today?
Answer: After these last rainy days, I'm definitely going for a walk today! With this sunny weather, staying at home would be a shame!
Question: What is a cat?
Answer: A cat is a cute, cuddly animal with whiskers, pointed ears, four legs and paws, and a tail. Cats are companion animals to humans. Cats can come in many different varieties, such as hairless, short-haired, medium-haired, and very fluffy; they also come in many colors, such as black, white, grey, brown, orange, and a combination of multiple colors.
Question: Which Gene in the body is responsible for a persons height
Answer: A variant called HMGA2 is responsible for persons height
Question: How do they extract causality from text?
Evidence: Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. The word `cause' was not included due to its use as a popular contraction for `because'. One `cause-word' per document restricted the analysis to single relationships between two relata. Documents that contain bidirectional words (`associate', `relate', `connect', `correlate', and any of their stems) were also not selected for analysis. This is because our focus is on causality, an inherently one-sided relationship between two objects. We also did not consider additional synonyms of these cause words, although that could be pursued for future work. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. The end result of this procedure identified 965,560 causal and 965,560 control documents. Each of the three “cause-words”, `caused', `causes', and `causing' appeared in 38.2%, 35.0%, and 26.8% of causal documents, respectively.
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'.
Answer: They identify documents that contain the unigrams 'caused', 'causing', or 'causes'
Question: What is Big Data?
Answer: Big data is a term used to describe sets of data that are too large and complex to be analyzed and understood using traditional methods and tools.
Question: Tell me whether these are literary genres or titles of books: Non-fiction, 1984, Mystery, The lost girls of paris, Fiction, Narrative, The one and only Ruby, Mary Poppins, Science fiction.
Answer: Sure, literary genres include: Non-fiction, Mystery, Fiction, Narrative, and Science fiction. Titles of books from this list are: 1984, The Lost girls of Paris, The One and Only Ruby, Marry Poppins.
Question: What issues do the linked entities extracted from ELS's have?
Evidence: Despite its usefulness, linked entities extracted from ELS's have issues because of low precision rates BIBREF11 and design challenges in training datasets BIBREF12 . These issues can be summarized into two parts: ambiguity and coarseness.
First, the extracted entities may be ambiguous. In the example, the entity “South Korean” is ambiguous because it can refer to both the South Korean person and the South Korean language, among others. In our experimental data, we extract (1) the top 100 entities based on frequency, and (2) the entities extracted from 100 randomly selected texts, and check whether they have disambiguation pages in Wikipedia or not. We discover that $71.0\%$ of the top 100 entities and $53.6\%$ of the entities picked at random have disambiguation pages, which shows that most entities are prone to ambiguity problems.
Second, the linked entities may also be too common to be considered an entity. This may introduce errors and irrelevance to the summary. In the example, “Wednesday” is erroneous because it is wrongly linked to the entity “Wednesday Night Baseball”. Also, “swap” is irrelevant because although it is linked correctly to the entity “Trade (Sports)”, it is too common and irrelevant when generating the summaries. In our experimental data, we randomly select 100 data instances and tag the correctness and relevance of extracted entities into one of four labels: A: correct and relevant, B: correct and somewhat relevant, C: correct but irrelevant, and D: incorrect. Results show that $29.4\%$ , $13.7\%$ , $30.0\%$ , and $26.9\%$ are tagged with A, B, C, and D, respectively, which shows that there is a large amount of incorrect and irrelevant entities.
Despite its usefulness, linked entities extracted from ELS’s have issues because of low precision rates (Hasibi et al., 2016) and design challenges in training datasets (Ling et al., 2015).  These issues can be summarized into two parts: ambiguity and coarseness.
Answer: These issues can be summarized into two parts: ambiguity and coarseness.
Question: Identify which animal species is alive or extinct: Carolina Parakeet, Orangutan
Answer: Orangutan is alive, Carolina Parakeet is extinct.