DataHammer/scimrc huggingface dataset:
Question: What are the three core innovations of the encoder-decoder architecture used in the task?
Evidence: This paper documents the Team Copenhagen system which placed first in the CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection, Task 2 with an overall accuracy of 49.87. Task 2 focuses on morphological inflection in context: generating an inflected word form, given the lemma of the word and the context it occurs in. Previous SIGMORPHON shared tasks have focused on context-agnostic inflection---the"inflection in context"task was introduced this year. We approach this with an encoder-decoder architecture over character sequences with three core innovations, all contributing to an improvement in performance: (1) a wide context window; (2) a multi-task learning approach with the auxiliary task of MSD prediction; (3) training models in a multilingual fashion.
We approach this with an encoder-decoder architecture over character sequences with three core innovations, all contributing to an improvement in performance: (1) a wide context window; (2) a multi-task learning approach with the auxiliary task of MSD prediction; (3) training models in a multilingual fashion.
Answer: (1) a wide context window; (2) a multi-task learning approach with the auxiliary task of MSD prediction; (3) training models in a multilingual fashion.
Question: What is data science?
Answer: Data science is a field of study that deals with using mathematics like statistics and large scale computing to build algorithms and models that explain information or predict outcomes and insights from structured, unstructured, and semi-structured.  Data scientists spend 80% of their time doing exploratory data analysis and about 20% of their building the model and hyper parameter tuning.
Question: What is an office space?
Answer: An office space is a room or group of rooms in a building that a company or entity can own or rent for activities such as business activities.  Each office space can be a single person work area with a phone, computer, headset, keyboard and mouse.  These office space items are use for working and collaborated with other co-workers in other office spaces.  Office spaces may also be converted into storage space or other group collaboration spaces too.
Question: How many canals are in Amsterdam?
Answer: There are a total of 14 canals in Amsterdam, including the Singel, Herengracht, Keizersgracht, and Prinsengracht canals.
Question: How many annotators participated?
Evidence: Two thousand narration files were created. We used Audacity to record the narrations. We used a normal laptop in a quiet room and minimized the background noise. However, we could not manage to avoid the noise of the fan of the laptop. A single speaker narrated the 2000 sentences, which took several days. We set the Audacity software to have a sampling rate of 16, 16-bit bit rate, and a mono (single) channel. The noise reduction db was set to 6, the sensitivity to 4.00, and the frequency smoothing to 0.
A single speaker narrated the 2000 sentences, which took several days. 
Answer: 1
Question: What is Caracas?
Evidence: Caracas (/kəˈrækəs, -ˈrɑːk-/, Spanish: [kaˈɾakas]), officially Santiago de León de Caracas, abbreviated as CCS, is the capital and largest city of Venezuela, and the center of the Metropolitan Region of Caracas (or Greater Caracas). Caracas is located along the Guaire River in the northern part of the country, within the Caracas Valley of the Venezuelan coastal mountain range (Cordillera de la Costa). The valley is close to the Caribbean Sea, separated from the coast by a steep 2,200-meter-high (7,200 ft) mountain range, Cerro El Ávila; to the south there are more hills and mountains. The Metropolitan Region of Caracas has an estimated population of almost 5 million inhabitants.
Answer: Caracas (/kəˈrækəs, -ˈrɑːk-/, Spanish: [kaˈɾakas]), officially Santiago de León de Caracas, abbreviated as CCS, is the capital and largest city of Venezuela, and the center of the Metropolitan Region of Caracas (or Greater Caracas). Caracas is located along the Guaire River in the northern part of the country, within the Caracas Valley of the Venezuelan coastal mountain range (Cordillera de la Costa). The valley is close to the Caribbean Sea, separated from the coast by a steep 2,200-meter-high (7,200 ft) mountain range, Cerro El Ávila; to the south there are more hills and mountains. The Metropolitan Region of Caracas has an estimated population of almost 5 million inhabitants.

The center of the city is Catedral, located near Bolívar Square, though some consider the center to be Plaza Venezuela, located in the Los Caobos area. Businesses in the city include service companies, banks, and malls. Caracas has a largely service-based economy, apart from some industrial activity in its metropolitan area. The Caracas Stock Exchange and Petróleos de Venezuela (PDVSA) are headquartered in Caracas. Empresas Polar is the largest private company in Venezuela. Caracas is also Venezuela's cultural capital, with many restaurants, theaters, museums, and shopping centers. Caracas has some of the tallest skyscrapers in Latin America, such as the Parque Central Towers. The Museum of Contemporary Art of Caracas is one of the most important in South America.
Question: What caused Africa's integration into the Roman System?
Evidence: Following the conquest of North Africa's Mediterranean coastline by the Roman Empire, the area was integrated economically and culturally into the Roman system. Roman settlement occurred in modern Tunisia and elsewhere along the coast. The first Roman emperor native to North Africa was Septimius Severus, born in Leptis Magna in present-day Libya—his mother was Italian Roman and his father was Punic.
Answer: Rome's conquest of North Africa's Mediterranean coastline lead to Africa's integration into the Roman System.
Question: Based on this paragraph, what might be some appealing things about Bloomington?
Evidence: Bloomington is a city in and the county seat of Monroe County, Indiana, United States. It is the seventh-largest city in Indiana and the fourth-largest outside the Indianapolis metropolitan area. According to the Monroe County History Center, Bloomington is known as the "Gateway to Scenic Southern Indiana". The city was established in 1818 by a group of settlers from Kentucky, Tennessee, the Carolinas, and Virginia who were so impressed with "a haven of blooms" that they called it Bloomington.
Answer: Bloomington appears to be a scenic place with natural beauty such as fields of flowers. As the fourth largest city in Indiana outside of Indianapolis, it may be a small and quiet town which may be appealing to those looking for a laid back place to spend time.
Question: Was this benchmark automatically created from an existing dataset?
Evidence: As mentioned above, step 1, 2 and 4 in the QA4IE framework can be solved by existing work. Therefore, in this paper, we mainly focus on step 3. According to the recent progress in QA and MRC, deep neural networks are very good at solving this kind of problem with a large-scale dataset to train the network. However, all previous IE benchmarks BIBREF18 are too small to train neural network models typically used in QA, and thus we need to build a large benchmark. Inspired by WikiReading BIBREF33 , a recent large-scale QA benchmark over Wikipedia, we find that the articles in Wikipedia together with the high quality triples in knowledge bases such as Wikidata BIBREF34 and DBpedia can form the supervision we need. Therefore, we build a large scale benchmark named QA4IE benchmark which consists of 293K Wikipedia articles and 2M golden relation triples with 636 different relation types.
Incorporating DBpedia. Unlike WikiData, DBpedia is constructed automatically without human verification. Relations and properties in DBpedia are coarse and noisy. Thus we fix the existing 636 relation types in WikiData and build a projection from DBpedia relations to these 636 relation types. We manually find 148 relations which can be projected to a WikiData relation out of 2064 DBpedia relations. Then we gather all the DBpedia triples with the first entity is corresponding to one of the above 3.5M articles and the relation is one of the projected 148 relations. After the same clipping process as above and removing the repetitive triples, we obtain 394K additional triples in 302K existing Wikipedia articles.
However, all previous IE benchmarks BIBREF18 are too small to train neural network models typically used in QA, and thus we need to build a large benchmark.
Therefore, we build a large scale benchmark named QA4IE benchmark which consists of 293K Wikipedia articles and 2M golden relation triples with 636 different relation types.
We manually find 148 relations which can be projected to a WikiData relation out of 2064 DBpedia relations.
Answer: No.
Question: What drawback do they solve in their work?
Evidence: Fake News Detection Exist studies for fake news detection can be roughly summarized into two categories. The first category is to extract or construct comprehensive and complex features with manual ways BIBREF5, BIBREF8, BIBREF17. The second category is to automatically capture deep features based on neural networks. There are two ways in this category. One is to capture linguistic features from text content, such as semantic BIBREF7, BIBREF18, writing styles BIBREF4, and textual entailments BIBREF19. The other is to focus on gaining effective features from the organic integration of text and user interactions BIBREF20, BIBREF21. User interactions include users' behaviours, profiles, and networks between users. In this work, following the second way, we automatically learn representations of text and stance information from response and forwarding (users' behaviour) based on multi-task learning for fake news detection.
Stance Detection The researches BIBREF22, BIBREF23 demonstrate that the stance detected from fake news can serve as an effective credibility indicator to improve the performance of fake news detection. The common way of stance detection in rumors is to catch deep semantics from text content based on neural networksBIBREF24. For instance, Kochkina et al.BIBREF25 project branch-nested LSTM model to encode text of each tweet considering the features and labels of the predicted tweets for stance detection, which reflects the best performance in RumourEval dataset. In this work, we utilize transformer encoder to acquire semantics from responses and forwarding of fake news for stance detection.
Multi-task Learning A collection of improved models BIBREF26, BIBREF27, BIBREF28 are developed based on multi-task learning. Especially, shared-private model, as a popular multi-task learning model, divides the features of different tasks into private and shared spaces, where shared features, i.e., task-irrelevant features in shared space, as supplementary features are used for different tasks. Nevertheless, the shared space usually mixes some task-relevant features, which makes the learning of different tasks introduce noise. To address this issue, Liu et al. BIBREF29 explore an adversarial shared-private model to alleviate the shared and private latent feature spaces from interfering with each other. However, these models transmit all shared features in the shared layer to related tasks without distillation, which disturb specific tasks due to some useless and even harmful shared features. How to solve this drawback is the main challenge of this work.
However, these models transmit all shared features in the shared layer to related tasks without distillation, which disturb specific tasks due to some useless and even harmful shared features. How to solve this drawback is the main challenge of this work.
Answer: Previous models transmit all shared features in the shared layer to related tasks without distillation, which disturbs specific tasks due to some useless and even harmful shared features.
Question: What is the best time of the year to visit Paris?
Answer: Paris is well known as a global centre of art, culture, fashion, and gastronomy. But what it really stands out for are the amazing landmarks such as Louvre Museum, Eiffel Tower, the Gothic Notre-Dame cathedral, the Versailles palace, or Basilica of Sacre-Coeur. While you will enjoy visiting these any time of the year, walking down the Avenue des Champs-Elysees is certainly most enjoyable during spring and summer.
Question: Which two kinds of submodules does the module contain?
Evidence: To solve the issues described above, we present Entity2Topic (E2T), a module that can be easily attached to any sequence-to-sequence based abstractive summarization model. E2T encodes the linked entities extracted from the text and transforms them into a single topic vector. This vector is ultimately concatenated to the decoder hidden state vectors. The module contains two submodules specifically for the issues presented by the entity linking systems: the entity encoding submodule with selective disambiguation and the pooling submodule with firm attention.
Overall, our full architecture can be illustrated as in Figure 2 , which consists of an entity linking system (ELS), a sequence-to-sequence with attention mechanism model, and the E2T module. We note that our proposed module can be easily attached to more sophisticated abstractive summarization models BIBREF13 , BIBREF14 that are based on the traditional encoder-decoder framework and consequently can produce better results. The code of the base model and the E2T are available online.
The module contains two submodules specifically for the issues presented by the entity linking systems: the entity encoding submodule with selective disambiguation and the pooling submodule with firm attention.
Answer: The entity encoding submodule with selective disambiguation and the pooling submodule with firm attention.
Question: What dataset was used in this work?
Evidence: The recently introduced How2 dataset BIBREF2 has stimulated research around multimodal language understanding through the availability of 300h instructional videos, English subtitles and their Portuguese translations. For example, BIBREF3 successfully demonstrates that semantically rich action-based visual features are helpful in the context of machine translation (MT), especially in the presence of input noise that manifests itself as missing source words. Therefore, we hypothesize that a speech-to-text translation (STT) system may also benefit from the visual context, especially in the traditional cascaded framework BIBREF4, BIBREF5 where noisy automatic transcripts are obtained from an automatic speech recognition system (ASR) and further translated into the target language using a machine translation (MT) component. The dataset enables the design of such multimodal STT systems, since we have access to a bilingual corpora as well as the corresponding audio-visual stream. Hence, in this paper, we propose a cascaded multimodal STT with two components: (i) an English ASR system trained on the How2 dataset and (ii) a transformer-based BIBREF0 visually grounded MMT system.
Hence, in this paper, we propose a cascaded multimodal STT with two components: (i) an English ASR system trained on the How2 dataset and (ii) a transformer-based BIBREF0 visually grounded MMT system.
Answer: The answers are shown as follows:
* How2
Question: Who won the Men's Madison at the 2018 UCI Track Cycling World Championships?
Evidence: The Men's madison competition at the 2018 UCI Track Cycling World Championships was held on 4 March 2018.

Results
200 laps (50km) with 20 sprints were raced.

Rank	Name	Nation	Laps points	Sprint points	Total points
1st place, gold medalist(s)	Roger Kluge
Theo Reinhardt	 Germany	20	33	53
2nd place, silver medalist(s)	Albert Torres
Sebastián Mora	 Spain	20	25	45
3rd place, bronze medalist(s)	Cameron Meyer
Callum Scotson	 Australia	20	17	37
4	Oliver Wood
Mark Stewart	 Great Britain	0	36	36
5	Andreas Graf
Andreas Müller	 Austria	20	12	32
6	Niklas Larsen
Casper von Folsach	 Denmark	0	29	29
7	Benjamin Thomas
Morgan Kneisky	 France	0	24	24
8	Kenny De Ketele
Moreno De Pauw	 Belgium	0	23	23
9	Felix English
Mark Downey	 Ireland	0	11	11
10	Simone Consonni
Liam Bertazzo	 Italy	0	10	10
11	Roy Pieters
Wim Stroetinga	 Netherlands	0	1	1
12	Tristan Marguet
Gaël Suter	 Switzerland	–20	2	–18
13	Wojciech Pszczolarski
Daniel Staniszewski	 Poland	–40	3	–37
Regan Gough
Thomas Sexton	 New Zealand	0	0	DNF
Daniel Holloway
Adrian Hegyvary	 United States	–60	5
Leung Chun Wing
Cheung King Lok	 Hong Kong	–60	0
Answer: The Men's Madison at the 2018 UCI Track Cycling World Championships was won by Roger Kluge and Theo Reinhardt of Germany, with a total of 53 points. This was 8 points over their nearest competitors, Albert Torres and Sebastián Mora of Spain, who had 45 points.
Question: Help me pick a Lego set for my friends.
Answer: Lego has different sets for people in different ages. Each set usually has age guidance on the box. For small kids below 3 years old, I would recommend Duplo, because Duplo bricks have bigger size, and won't be swallowed by accident. For kids above 3 years old, but less than 10 years old, I would recommend Lego Classic. For those sets, people can use their imagination to create whatever things they like. For kids above 10 and adults, Lego Technic and Icons are super cool and good for display. 

For people interested in specific themes, like Star War, Harry Potter, etc, lego also has sets in those themes for you to choose from.
Question: Identify which car manufacturer is Chinese or American: Ford, Higer
Answer: Ford is American, Higer is Chinese
Question: What are some good sci-fi books I can read?
Answer: Dune, Neuromancer, Snow Crash, Foundation, 1984 and Brave New World are all great Science Fiction books.
Question: Based on the paragraph below, what bone was used to estimate the height of William the Conquerer?
Evidence: No authentic portrait of William has been found; the contemporary depictions of him on the Bayeux Tapestry and on his seals and coins are conventional representations designed to assert his authority. There are some written descriptions of a burly and robust appearance, with a guttural voice. He enjoyed excellent health until old age, although he became quite fat in later life. He was strong enough to draw bows that others were unable to pull and had great stamina. Geoffrey Martel described him as without equal as a fighter and as a horseman. Examination of William's femur, the only bone to survive when the rest of his remains were destroyed, showed he was approximately 5 feet 10 inches (1.78 m) in height.
Answer: His femur.
Question: Are the authors the first to use a LM for generating adversarial examples?
Evidence: Recent studies have shown the vulnerability of ML models to adversarial attacks, small perturbations which lead to misclassification of inputs. Adversarial example generation in NLP BIBREF0 is more challenging than in common computer vision tasks BIBREF1, BIBREF2, BIBREF3 due to two main reasons: the discrete nature of input space and ensuring semantic coherence with the original sentence. A major bottleneck in applying gradient based BIBREF4 or generator model BIBREF5 based approaches to generate adversarial examples in NLP is the backward propagation of the perturbations from the continuous embedding space to the discrete token space.
Recent works for attacking text models rely on introducing errors at the character level in words BIBREF6, BIBREF7 or adding and deleting words BIBREF8, BIBREF9, BIBREF10, etc. for creating adversarial examples. These techniques often result in adversarial examples which are unnatural looking and lack grammatical correctness, and thus can be easily identified by humans.
TextFooler BIBREF11 is a black-box attack, that uses rule based synonym replacement from a fixed word embedding space to generate adversarial examples. These adversarial examples do not account for the overall semantics of the sentence, and consider only the token level similarity using word embeddings. This can lead to out-of-context and unnaturally complex replacements (see Table ), which can be easily identifiable by humans.
The recent advent of powerful language models BIBREF12, BIBREF13 in NLP has paved the way for using them in various downstream applications. In this paper, we present a simple yet novel technique: BAE (BERT-based Adversarial Examples), which uses a language model (LM) for token replacement to best fit the overall context. We perturb an input sentence by either replacing a token or inserting a new token in the sentence, by means of masking a part of the input and using a LM to fill in the mask (See Figure FIGREF1). BAE relies on the powerful BERT masked LM for ensuring grammatical correctness of the adversarial examples. Our attack beats the previous baselines by a large margin and confirms the inherent vulnerabilities of modern text classification models to adversarial attacks. Moreover, BAE produces more richer and natural looking adversarial examples as it uses the semantics learned by a LM.
To the best of our knowledge, we are the first to use a LM for adversarial example generation. We summarize our major contributions as follows:

We propose BAE, a novel strategy for generating natural looking adversarial examples using a masked language model.
We introduce 4 BAE attack modes, all of which are almost always stronger than previous baselines on 7 text classification datasets.
We show that, surprisingly, just a few replace/insert operations can reduce the accuracy of even a powerful BERT-based classifier by over $80\%$ on some datasets.

To the best of our knowledge, we are the first to use a LM for generating adversarial examples.
Answer: Yes, they are.
Question: Why are cats better than dogs?
Answer: This is a subjective question whose answer depends on individual preferences. There are many reasons that some people prefer cars over dogs. For example, some cats are very affectionate and become very attached to their owners. They have personalities that appeal to some people more than dogs. They also may be easier to care for and cheaper to own. Because they are typically smaller house pets, they can be easier to keep clean. These are just a few examples of how cats may be preferred to dogs for some people.
Question: Classify the below numbers into odd or even.
1, 4, 5, 333, 67, 88, 100
Answer: Odd - 1, 5, 333, 67
Even - 4, 88, 100
Question: List the five most important quick keys on a PC
Answer: 1. Control C
2. Control V
3. Control Alt Delete
4. Control Print Screen
5. Control z
Question: What is the difference between a detail and an Inference
Answer: A detail is the words you use to describe something, but an inference is an informational guess. Occasionally you can use details to make an inference
Question: Why are there so many different types of cheeses in France?
Answer: Cheese production has a long history in France. It is believed that cheese was first introduced into France during Roman times. After the fall of the Roman empire, monasteries continued the production of cheese. Experimentation within the monasteries, regional isolation and differences in climate and vegetation across France lead to the creation of regional varieties of cheese (some of which still exist today). From about the 13th century onwards, cheese production started to develop on French farms. Over time, the recipes for making cheese were handed down from generation to generation (and tweaked and improved by each generation), further increasing the number of varieties. 

As a result, today it is commonly considered that there are between 350 and 400 different types of French cheese – although there are actually almost 1,000 varieties! These cheeses are made from cows, sheep, goats or buffalo milk.

Charles de Gaulle, a famous French president, once asked - "How can you govern a country which has 246 varieties of cheese?" It would be interesting to hear what his thoughts would be today now that there are almost 1000 different varieties.
Question: Classify the below places based on whether they are mountains or river.
Everest, Niles, Hudson, Nanga Parbat
Answer: Everest, Nanga Parbat - Mountains
Niles, Hudson- River