DataHammer/scimrc huggingface dataset:
Question: What learning paradigms do they cover in this survey?
Evidence: Past research took a reductionist approach, separately considering these two problems of “what” and “how” via content selection and question construction. Given a sentence or a paragraph as input, content selection selects a particular salient topic worthwhile to ask about and determines the question type (What, When, Who, etc.). Approaches either take a syntactic BIBREF11 , BIBREF12 , BIBREF13 or semantic BIBREF14 , BIBREF3 , BIBREF15 , BIBREF16 tack, both starting by applying syntactic or semantic parsing, respectively, to obtain intermediate symbolic representations. Question construction then converts intermediate representations to a natural language question, taking either a tranformation- or template-based approach. The former BIBREF17 , BIBREF18 , BIBREF13 rearranges the surface form of the input sentence to produce the question; the latter BIBREF19 , BIBREF20 , BIBREF21 generates questions from pre-defined question templates. Unfortunately, such QG architectures are limiting, as their representation is confined to the variety of intermediate representations, transformation rules or templates.
In contrast, neural models motivate an end-to-end architectures. Deep learned frameworks contrast with the reductionist approach, admitting approaches that jointly optimize for both the “what” and “how” in an unified framework. The majority of current NQG models follow the sequence-to-sequence (Seq2Seq) framework that use a unified representation and joint learning of content selection (via the encoder) and question construction (via the decoder). In this framework, traditional parsing-based content selection has been replaced by more flexible approaches such as attention BIBREF22 and copying mechanism BIBREF23 . Question construction has become completely data-driven, requiring far less labor compared to transformation rules, enabling better language flexibility compared to question templates.
Past research took a reductionist approach, separately considering these two problems of “what” and “how” via content selection and question construction. 
In contrast, neural models motivate an end-to-end architectures. Deep learned frameworks contrast with the reductionist approach, admitting approaches that jointly optimize for both the “what” and “how” in an unified framework. 
Answer: Considering "What" and "How" separately versus jointly optimizing for both.
Question: If I have a dollar, two nickels and a quarter, how much money in total do I have. Please respond with words only, no symbols or numbers.
Answer: A dollar, two nickels and a quarter is equal to one dollar and thirty five cents.
Question: Is PRE-DICTED strategy performing better on BLEU?
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
We see that the PRE- DICTED strategy outperforms the others both in terms of BLEU and METEOR, albeit slightly.
Answer: Yes.
Question: Was Dracula a real person?
Answer: Dracula is a fictitious character created by the British novelist Bram Stoker. Stoker drew inspiration from a Romanian prince called Vlad Dracul. Vlad led a Romanian province situated in the south of Transylvania for parts of the 15th century. Vlad was renown at the time for his cruel measures to punish his enemies through impaling them. For this reason, he was also called Vlad the Impaler or, in Romanian, Vlad Tepeș
Question: Do they explore similarity of texts across different doctors?
Evidence: Clustering was performed separately for each specialty of doctors. Figure FIGREF11 illustrates two-dimensional projections of visit embeddings coloured by clusters. The projections were created by t-SNE algorithm BIBREF25 . For some domains clusters are very clear and separated (Figure FIGREF11 ). This corresponds with the high stability of clustering measured by Rand index.
We also examined the distribution of doctors' IDs in the obtained clusters. It turned out that some clusters covered almost exactly descriptions written by one doctor. This situation took place in the specialties where clusters are separated with large margins (e.g. psychiatry, pediatrics, cardiology). Figure FIGREF13 (a) shows correspondence analysis between doctors' IDs and clusters for psychiatry clustering.
Clustering was performed separately for each specialty of doctors.
We also examined the distribution of doctors' IDs in the obtained clusters. It turned out that some clusters covered almost exactly descriptions written by one doctor. This situation took place in the specialties where clusters are separated with large margins (e.g. psychiatry, pediatrics, cardiology).
Answer: Yes.
Question: What three machine architectures are analyzed?
Evidence: We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22.
We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22.
Answer: The answers are shown as follows:
* LSTM+attention
* Transformer 
* Universal Transformer
Question: Identify which car manufacturer is German or American: Dodge, Volkswagen
Answer: Dodge is American, Volkswagen is German
Question: what are the different types of music genres
Answer: The music genre is the categorisation of music. Some examples of music genres are - rock, pop, hip-hop, jazz etc.
Question: What are the key elements to a companies income statement?
Answer: Gross Revenue, Net Revenue, Cost of Goods Sold, Gross Profit, Sales of Goods & Administration, Marketing, Operational Expenses, Operating Income
Question: What is the .NET Framework?
Answer: .NET Framework is a managed execution environment for Windows that allows software developers to create a software application in one programming language and be assured that the app will be able to work with code written in other languages.
Question: What weaponry was the U-3030 submarine equipped with?
Evidence: The submarine had a maximum surface speed of 15.6 knots (28.9 km/h; 18.0 mph) and a submerged speed of 17.2 knots (31.9 km/h; 19.8 mph). When running on silent motors the boat could operate at a speed of 6.1 knots (11.3 km/h; 7.0 mph). When submerged, the boat could operate at 5 knots (9.3 km/h; 5.8 mph) for 340 nautical miles (630 km; 390 mi); when surfaced, she could travel 15,500 nautical miles (28,700 km; 17,800 mi) at 10 knots (19 km/h; 12 mph). U-3030 was fitted with six 53.3 cm (21.0 in) torpedo tubes in the bow and four 2 cm (0.79 in) C/30 anti-aircraft guns. She could carry twenty-three torpedoes or seventeen torpedoes and twelve mines. The complement was five officers and fifty-two men.
Answer: The U-3030 submarine could carry twenty-three torpedoes or seventeen torpedoes and twelve mines.
Question: What is a DAW?
Answer: DAW is an acronym for Digital Audio Workstation.  A DAW can either be a specialized digital hardware device (or multiple devices), or more commonly a software application, a computer, an audio interface and/or midi (musical instrument digital interface), all combined to enable digital recording, sampling, editing, mixing, and mastering of audio content. Software based DAWs come in many forms.  Most are created to emulate the workflow of a traditional analog multi-track recording environment.  However some DAWs are created to cater to the needs of electronic music creators, video content producers, and live performers.  Once the domain of highly trained professionals who invested hundreds of thousands, if not millions, of dollars on expensive analog equipment to outfit large studios, high quality, grammy award winning audio content has been produced with modern equipment costing a few hundred dollars, connected to a laptop within a small bedroom studio in a content creator's home.
Question: What is the name of the flag carrier airline of the Netherlands?
Answer: The flag carrier airline of the Netherlands is KLM.
Question: Identify which instrument is string or percussion: Fontomfrom, Ruan
Answer: Ruan is string, Fontomfrom is percussion.
Question: How do I fix the engine of my car?
Answer: This would depend on how complex the repair job is. But essentially you will need 
1. A basic set of tools including spanners, screwdrivers and pliers.
2. An automotive manual for the model of your car. For example a Haynes manual.
3. Once you have diagnosed the problem, watch some videos on YouTube on how to fix the problem.
4. Based on the knowledge that you have gathered, attempt to fix your car

If you are enable to fix it, go to an experienced mechanic.
Question: What trait do you think everyone should have?
Answer: There are many important traits and virtues that can lead to a life of meaning. Perhaps the most important of all is resilience. Resilience allows one to live with the ability to adapt, operate from a mindset of hopefulness and willingness to learn new things. Undoubtedly life will present many trials and obstacles. Resilience will allow you to move forward from difficult situations with optimism for the future and the ability to cultivate learning from mistakes.
Question: What are the benefits of using positional encoding and self-attention mechanism?
Evidence: We train NMT with RAT to achieve better query translations. We improve a recently proposed NMT baseline, Transformer, that achieves state-of-the-art results for sentence pairs in some languages BIBREF8 . We discuss Transformer, RAT, and our multi-task learning architecture that achieves balanced translation.
For this study, we use a state-of-the-art NMT model, Transformer (Vaswani et al., 2017), that uses positional encoding and self attention mechanism to achieve three benefits over the existing convolutional or recurrent neural network based models: (a) reduced computational complexity of each layer, (b) parallel computation, and (c) path length between long-range dependencies.
Answer: (a) reduced computational complexity of each layer, (b) parallel computation, and (c) path length between long-range dependencies.
Question: Based on what model the dataset used by them was constructed?
Evidence: Specifically, we conducted a feature ablation study to assess the informativeness of each feature group and a feature elimination study to determine the optimal feature sets for classifying Twitter tweets. We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms BIBREF12 , BIBREF13 . The dataset contains 9,473 annotations for 9,300 tweets. Each tweet is annotated as no evidence of depression (e.g., “Citizens fear an economic depression") or evidence of depression (e.g., “depressed over disappointment"). If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e.g., “feeling down in the dumps"), disturbed sleep (e.g., “another restless night"), or fatigue or loss of energy (e.g., “the fatigue is unbearable") BIBREF10 . For each class, every annotation (9,473 tweets) is binarized as the positive class e.g., depressed mood=1 or negative class e.g., not depressed mood=0.
We leveraged an existing, annotated Twitter dataset that was constructed based on a hierarchical model of depression-related symptoms.
Answer: A hierarchical model of depression-related symptoms.
Question: How many unique intervention responses are in the datasets?
Evidence: Removing duplicates, there are 21,747 unique intervention responses in the aggregated Gab dataset and 7,641 in the aggregated Reddit dataset. Despite the large diversity of the collected responses for intervention, we find workers tend to have certain strategies for intervention.

Identify Hate Keywords: One of the most common strategies is to identify the inappropriate terms in the post and then urge the user to stop using that work. For example, “The C word and language attacking gender is unacceptable. Please refrain from future use.” This strategy is often used when the hatred in the post is mainly conveyed by specific hate keywords.

Categorize Hate Speech: This is another common strategy used by the workers. The workers classify hate speech into different categories, such as racist, sexist, homophobic, etc. This strategy is often combined with identifying hate keywords or targets of hatred. For example, “The term ""fa**ot"" comprises homophobic hate, and as such is not permitted here.”

Positive Tone Followed by Transitions: This is a strategy where the response consists of two parts combined with a transitional word, such as “but” and “even though”. The first part starts with affirmative terms, such as “I understand”, “You have the right to”, and “You are free to express”, showing kindness and understanding, while the second part is to alert the users that their post is inappropriate. For example, “I understand your frustration, but the term you have used is offensive towards the disabled community. Please be more aware of your words.”. Intuitively, compared with the response that directly warns, this strategy is likely more acceptable for the users and be more likely to clam down a quarrel full of hate speech.

Suggest Proper Actions: Besides warning and discouraging the users from continuing hate speech, workers also suggest the actions that the user should take. This strategy can either be combined with other strategies mentioned above or be used alone. In the latter case, a negative tone can be greatly alleviated. For example, “I think that you should do more research on how resources are allocated in this country.”
Removing duplicates, there are 21,747 unique intervention responses in the aggregated Gab dataset and 7,641 in the aggregated Reddit dataset. Despite the large diversity of the collected responses for intervention, we find workers tend to have certain strategies for intervention.
Answer: Removing duplicates, there are 21,747 unique intervention responses in the aggregated Gab dataset and 7,641 in the aggregated Reddit dataset.
Question: Which is an imperial or a metric measurement: gallon, litre
Answer: gallon is imperial, litre is metric
Question: What is Sea Hear Now
Answer: Sea Hear Now is a music festival that's held annually in Asbury Park, New Jersey. It's held on the beach at the end of the boardwalk next to the convention center. There are three stages, two of which are in the sand with varying acts spanning multiple genres. Sea Hear know is known for its incorporation of local artists and surfers along with great music. Sea Hear now is also family friendly which is an added bonus
Question: For each of these countries, tell whether it lies in the Western Hemisphere, Eastern Hemisphere, or both:  Thailand, United Kingdom, Belize, Japan, Algeria
Answer: Thailand - Eastern
United Kingdom - Both
Belize - Western
Japan - Eastern
Algeria - Both
Question: ways to reduce solid waste
Answer: Use biodegradable plates
Use reusable personal bottles
Use less plastic products
Use less detergent
Use more biodegradable FMCG products
Question: How does the author's "domino" approach work?
Evidence: Each document INLINEFORM0 is represented by two vectors in INLINEFORM1 and INLINEFORM2 . The first vector exists in a “monolingual space” (of dimensionality INLINEFORM3 ) and is based on a bag-of-words representation of the document. The second vector exists in a “crosslingual space” (of dimensionality INLINEFORM4 ) which is common to all languages. More details about these representations are discussed in § SECREF4 .
Our method of “domino-toppling” works by making (potentially sequences) of changes to previous clustering decisions for the crosslingual clusters, at each step placing a residual monolingual cluster in a crosslingual cluster that is most similar to it.
Answer: Their method of “domino-toppling” works by making (potentially sequences) of changes to previous clustering decisions for the crosslingual clusters, at each step placing a residual monolingual cluster in a crosslingual cluster that is most similar to it.
Question: What is Lean manufacturing
Evidence: Lean manufacturing is a production method aimed primarily at reducing times within the production system as well as response times from suppliers and to customers. It is closely related to another concept called just-in-time manufacturing (JIT manufacturing in short). Just-in-time manufacturing tries to match production to demand by only supplying goods which have been ordered and focuses on efficiency, productivity (with a commitment to continuous improvement) and reduction of "wastes" for the producer and supplier of goods. Lean manufacturing adopts the just-in-time approach and additionally focuses on reducing cycle, flow and throughput times by further eliminating activities which do not add any value for the customer. Lean manufacturing also involves people who work outside of the manufacturing process, such as in marketing and customer service.
Answer: Lean is a production method.
Question: what are the methods they compare with in the korean-english dataset?
Evidence: Table TABREF21 shows results for Korean INLINEFORM0 English, using the same configurations (1, 2 and 8) as for German–English. Our results confirm that the techniques we apply are successful across datasets, and result in stronger systems than previously reported on this dataset, achieving 10.37 BLEU as compared to 5.97 BLEU reported by gu-EtAl:2018:EMNLP1.
Table TABREF21 shows results for Korean INLINEFORM0 English, using the same configurations (1, 2 and 8) as for German–English. Our results confirm that the techniques we apply are successful across datasets, and result in stronger systems than previously reported on this dataset, achieving 10.37 BLEU as compared to 5.97 BLEU reported by gu-EtAl:2018:EMNLP1.
Answer: The answers are shown as follows:
* gu-EtAl:2018:EMNLP1
Question: There is a strong assumption that 'we know the displacement changes of the agent after each action'. Several previous works have made similar assumptions. However, those approaches are suitable only for simulation. In reality, how robust the proposed approach is against noise in the agent pose?
Evidence: While AI2THOR adopts a discrete action/state space for the agent, we believe the idea behind our multimodal exploration design and waypoint-based semantic representation is applicable to generic embodied tasks. In the presence of motion noise and pose sensor noise, Active Neural SLAM shows that such inaccuracy can be solved to an acceptable level by simply learning a pose estimator.
Answer: In the presence of motion noise and pose sensor noise, Active Neural SLAM shows that these can be solved to an acceptable level by simply adding a pose estimator.
Question: What text-based features are used?
Evidence: Feature-based models. We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words). Several classical machine learning classifiers were tested with several feature combinations, among them Random Forest (RF) achieved the best result with all features. Neural model with monolingual embeddings. We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29. For the embeddings, we relied on $AraVec$ BIBREF30 for Arabic, FastText BIBREF31 for French, and Word2vec Google News BIBREF32 for English . For the three languages, the size of the embeddings is 300 and the embeddings were fine-tuned during the training process. The CNN network was tuned with 20% of the training corpus using the $Hyperopt$ library.
 We used state-of-the-art features that have shown to be useful in ID: some of them are language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities) while others are language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words).
Answer: The answers are shown as follows:
* language-independent (e.g., punctuation marks, positive and negative emoticons, quotations, personal pronouns, tweet's length, named entities)
*  language-dependent relying on dedicated lexicons (e.g., negation, opinion lexicons, opposition words)
Question: How many people of Egypt do not live in urban areas
Evidence: Islam is the official religion of Egypt and Arabic is its official language. With over 100 million inhabitants, Egypt is the most populous country in North Africa, the Middle East, and the Arab world, the third-most populous in Africa (after Nigeria and Ethiopia), and the fourteenth-most populous in the world. The great majority of its people live near the banks of the Nile River, an area of about 40,000 square kilometres (15,000 sq mi), where the only arable land is found. The large regions of the Sahara desert, which constitute most of Egypt's territory, are sparsely inhabited. About 43% of Egypt's residents live across the country's urban areas, with most spread across the densely populated centres of greater Cairo, Alexandria and other major cities in the Nile Delta.
Answer: About 43% of Egypt's residents live across the country's urban areas, that means 57% of Egypt population not liveing in urban areas.
Question: Give me a list of some preparations in which stracchino cheese is used/
Evidence: Stracchino is usually eaten on its own but also as a filling for some kinds of bread: in Recco, on the Ligurian riviera east of Genoa, focaccia col formaggio ("cheese focaccia") or focaccia di Recco is typically filled with crescenza, while in Romagna and in parts of some nearby regions (e.g. northern Marche, Umbria and eastern Tuscany) it is a common filling for the cascione which is made out of piadina, a thin flat bread.
Answer: - eaten on its own
- focaccia col formaggio
- cascione, which is made out of a thin flat bread called piadina filled with stracchino cheese
Question: per this reference of Itsunen Shoyu, who did he convince to bring Zen Buddhism to Japan?
Evidence: In 1642 he travelled to Nagasaki as a trader in Chinese medicine, and in 1644 entered Kofukuji Temple, becoming its 3rd abbot in 1645. Established by monks immigrated from China, the temple was then a base of the Ōbaku school of Zen Buddhism in Japan. In 1654 after multiple requests he succeeded in persuading Yinyuan Longqi (Ingen Ryuki), the 33rd abbot of Wanfu Temple (Mount Huangbo, Fujian) to emigrate to Japan, where he founded Ōbaku, the third and final major Japanese Zen sect.
Answer: Yinyuan Longqi (Ingen Ryuki) the 33rd abbot of Wanfu Temple