DataHammer/scimrc huggingface dataset:
Question: How many attention layers are there in their model?
Evidence: The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.
The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. 
Answer: one
Question: What do the authors find by analyzing the linguistic roles of identified important words?
Evidence: Neural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, such as English-French BIBREF0, BIBREF1 and Chinese-English BIBREF2. However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
The understanding of NMT models has been approached primarily from two complementary perspectives. The first thread of work aims to understand the importance of representations by analyzing the linguistic information embedded in representation vectors BIBREF3, BIBREF4 or hidden units BIBREF5, BIBREF6. Another direction focuses on understanding the importance of input words by interpreting the input-output behavior of NMT models. Previous work BIBREF7 treats NMT models as black-boxes and provides explanations that closely resemble the attention scores in NMT models. However, recent studies reveal that attention does not provide meaningful explanations since the relationship between attention scores and model output is unclear BIBREF8.
In this paper, we focus on the second thread and try to open the black-box by exploiting the gradients in NMT generation, which aims to estimate the word importance better. Specifically, we employ the integrated gradients method BIBREF9 to attribute the output to the input words with the integration of first-order derivatives. We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
We analyze the linguistic behaviors of words with the importance and show its potential to improve NMT models. First, we leverage the word importance to identify input words that are under-translated by NMT models. Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods. Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language. For example, nouns are more important for Chinese$\Rightarrow $English translation, while prepositions are more important for English-French and -Japanese translation. This finding can inspire better design principles of NMT architectures for different language pairs. For instance, a better architecture for a given language pair should consider its own language characteristics.
Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language.
Answer: Find that words of certain syntactic categories have higher importance while the categories vary across language.
Question: How is the price of an Uber ride calculated?
Answer: The price of an Uber ride is calculated based on supply and demand while considering the time, distance, and destination of the trip.
Question: What are steps to become a US citizen?
Answer: Step 1. Determine if you are already a U.S. citizen
Step 2. Determine if you are eligible to become a U.S. citizen
Step 3. Prepare your Form N-400, Application for Naturalization
Step 4. Submit your Form N-400 and pay your fees
Step 5. Go to your biometrics appointment, if applicable
Step 6. Complete the interview
Step 7. Receive a decision from USCIS on your Form N-400
Step 8. Receive a notice to take the Oath of Allegiance
Step 9. Take the Oath of Allegiance to the United States
Step 10. Understanding U.S. citizenship
Question: How many annotators are used to write natural language explanations to SNLI-VE-2.0?
Evidence: We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. We required the workers to have a previous approval rate above 90%. No restriction was put on the workers' location.
We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54.
Answer: The answers are shown as follows:
* 2,060 workers
Question: What happened to Captain Bligh after the Mutiny on the Bounty?
Answer: Captain Bligh and eighteen other men were set adrift in mid-ocean by the mutineers.  Navigating by memory and astronomical observation alone, Bligh led the tiny boat all the way to the Dutch colony of Timor.  The men suffered greatly from hunger, thirst, and dangerous weather.  After reaching Timor, they sailed a proper ship to Batavia and, from there, returned to England.
Question: Which characters belong to DC or Marvel Universe? Ultron, Brainiac
Answer: Brainiac is DC, Ultron is Marvel
Question: What are the different types of shots in ice hockey
Answer: -Slapshot
-Wrist shot
-Backhand
-Snapshot
Question: Why should I move to New York City?
Answer: New York, New York, also known as New York City or NYC, is the largest city in the United States. Comprised of 5 boroughs (Manhattan, Staten Island, Brooklyn, Bronx, Queens), each neighborhood has its own distinct culture and purpose that contributes to the energy of the city. Being that it is such a large city, there is something for everyone. It has an incredibly diverse population, a culture of historical culinary tradition with new cuisines arriving every day, a strongly developed art scene for film-lovers, musicians, visual artists, and so much more. There are so many reasons why it is considered a global city. It is incredibly walkable and public transit-friendly, and the green spaces alleviate the concrete jungle that New York is so well-known for. You will meet all types of people here. While the people may be controversially and arguably known for being unfriendly, they are surely kind. It is truly an unmatched experience to live, know, and love the city of New York.
Question: How better is accuracy of new model compared to previously reported models?
Evidence: Table TABREF29 presents the quantitative results for the visual reasoning tasks in RecipeQA. In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF. Note that the model performances in the multi-task training setting are worse than single-task performances. We believe that this is due to the nature of the tasks that some are more difficult than the others. We think that the performance could be improved by employing a carefully selected curriculum strategy BIBREF20.
FLOAT SELECTED: Table 1: Quantitative comparison of the proposed PRN model against the baselines.
Table TABREF29 presents the quantitative results for the visual reasoning tasks in RecipeQA. In single-task training setting, PRN gives state-of-the-art results compared to other neural models.
In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF.
FLOAT SELECTED: Table 1: Quantitative comparison of the proposed PRN model against the baselines.
Answer: Average accuracy of proposed model vs best prevous result:
Single-task Training: 57.57 vs 55.06
Multi-task Training: 50.17 vs 50.59
Question: What are transistors made from?
Evidence: Most transistors are made from very pure silicon, and some from germanium, but certain other semiconductor materials are sometimes used. A transistor may have only one kind of charge carrier, in a field-effect transistor, or may have two kinds of charge carriers in bipolar junction transistor devices. Compared with the vacuum tube, transistors are generally smaller and require less power to operate. Certain vacuum tubes have advantages over transistors at very high operating frequencies or high operating voltages. Many types of transistors are made to standardized specifications by multiple manufacturers.
Answer: silicon, germanium
Question: What is Sensory processing disorder?
Evidence: Sensory processing disorder (SPD, formerly known as sensory integration dysfunction) is a condition in which multisensory input is not adequately processed in order to provide appropriate responses to the demands of the environment. Sensory processing disorder is present in many people with autism spectrum disorder and attention deficit hyperactivity disorder. Individuals with SPD may inadequately process visual, auditory, olfactory (smell), gustatory (taste), tactile (touch), vestibular (balance), proprioception (body awareness), and interoception (internal body senses) sensory stimuli.

Sensory integration was defined by occupational therapist Anna Jean Ayres in 1972 as "the neurological process that organizes sensation from one's own body and from the environment and makes it possible to use the body effectively within the environment".Sensory processing disorder has been characterized as the source of significant problems in organizing sensation coming from the body and the environment and is manifested by difficulties in the performance in one or more of the main areas of life: productivity, leisure and play or activities of daily living.
Sources debate whether SPD is an independent disorder or represents the observed symptoms of various other, more well-established, disorders.SPD is not included in the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association, and the American Academy of Pediatrics has recommended in 2012 that pediatricians not use SPD as a stand-alone diagnosis.
Answer: Sensory processing disorder (SPD, formerly known as sensory integration dysfunction) is a condition in which multisensory input is not adequately processed to provide appropriate responses to the demands of the environment. Sensory processing disorder is present in many people with autism spectrum disorder and attention deficit hyperactivity disorder. Individuals with SPD may inadequately process visual, auditory, olfactory (smell), gustatory (taste), tactile (touch), vestibular (balance), proprioception (body awareness), and interoception (internal body senses) sensory stimuli.
Question: What is the meaning of life and how do I have a fulfilling one?
Answer: The meaning of life is to live a long and fulfilling one.  A good life may comprise of having many friends, meaningful relationships, a family, kids, and being a constructive member of society through hard work and positive interactions.  It is always best to leave a positive impression on those you meet or who you come into contact with during your daily routine.  You will want to focus and put positive intention in all of your actions so that you leave a good impression on all those who come into contact with you.  Don't forget that part of life is enjoying experiences and having fun too.  Find hobbies you enjoy, and do them often.  We say this is important because it will lead to never ending happiness.  As you age you will get wiser, and it will also be important to impart wisdom on those individuals who are younger than you who still need to experience life in order to gain the same wisdom.
Question: Classify each of the following as a conductor or insulator: steel, copper, rubber, wood, cardboard, aluminum
Answer: Steel: Conductor
Copper: Conductor
Rubber: Insulator
Wood: Insulator
Cardboard: Insulator
Aluminum: Conductor
Question: How did Chiune Sugihara save lives?
Evidence: Chiune Sugihara (杉原 千畝, Sugihara Chiune, 1 January 1900 – 31 July 1986) was a Japanese diplomat who served as vice-consul for the Japanese Empire in Kaunas, Lithuania. During the Second World War, Sugihara helped thousands of Jews flee Europe by issuing transit visas to them so that they could travel through Japanese territory, risking his job and the lives of his family. The fleeing Jews were refugees from German-occupied Western Poland and Soviet-occupied Eastern Poland, as well as residents of Lithuania. In 1985, the State of Israel honored Sugihara as one of the Righteous Among the Nations for his actions. He is the only Japanese national to have been so honored. The year 2020 was "The Year Chiune Sugihara" in Lithuania. It has been estimated as many as 100,000 people alive today are the descendants of the recipients of Sugihara visas.
Answer: He issued transit visas to Jews fleeing Europe during World War II, so they could travel through Japanese territory.
Question: Who wrote the Great Gatsby?
Answer: The Great Gatsby was written by F. Scott Fitzgerald
Question: What is the origin of Hello ?
Answer: According to the Oxford English Dictionary, hello is an alteration of hallo, hollo, which came from Old High German "halâ, holâ, emphatic imperative of halôn, holôn to fetch, used especially in hailing a ferryman". It also connects the development of hello to the influence of an earlier form, holla, whose origin is in the French holà (roughly, 'whoa there!', from French là 'there')
Question: What is the role of crowd-sourcing?
Evidence: The sentences and their corresponding triples were then divided. Each task contained 10 sentences and all of their unique corresponding triples from a particular OIE systems. Half of the ten sentences were randomly selected from SCI and the other half were randomly selected from WIKI. Crowd workers were asked to mark whether a triple was correct, namely, did the triple reflect the consequence of the sentence. Examples of correct and incorrect triples were provided. Complete labelling instructions and the presentation of the HITS can be found with the dataset. All triples were labelled by at least 5 workers.
Crowd workers were asked to mark whether a triple was correct, namely, did the triple reflect the consequence of the sentence.
Answer: The answers are shown as follows:
* Crowd workers were asked to mark whether a triple was correct, namely, did the triple reflect the consequence of the sentence.
Question: What is Acyl-CoA?
Answer: Acyl-CoA is a group of coenzymes that metabolize fatty acids. Acyl-CoA's are susceptible to beta oxidation, forming, ultimately, acetyl-CoA. The acetyl-CoA enters the citric acid cycle, eventually forming several equivalents of ATP. In this way, fats are converted to ATP, the universal biochemical energy carrier.
Question: How to compile a corpus by the authors?
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 order to train such a system, we compile a corpus of news based on over 2000 game reports from the Finnish News Agency STT.
Answer: The authors compile a corpus of news based on over 2000 game reports from the Finnish News Agency STT.
Question: Given the reference text below, what is the official name of the church San Vincenzo?
Evidence: The Basilica of Santa Maria della Sanità is a basilica church located over the Catacombs of San Gaudioso, on a Piazza near where Via Sanità meets Via Teresa degli Scalzi, in the Rione of the Sanità, in Naples, Italy. The church is also called San Vincenzo or San Vincenzo della Sanità, due to the cult of an icon of San Vincenzo Ferrer, also called locally O' Monacone (the big monk).
Answer: The church called San Vincenzo is officially known as the Basilica of Santa Maria della Sanità, located over the Catacombs of San Gaudioso.
Question: What conclusions are drawn from the syntactic analysis?
Evidence: We highlight the problem of translating between languages with different morphological systems, in which the target translation must contain gender and number information that is not available in the source. We propose a method for injecting such information into a pre-trained NMT model in a black-box setting. We demonstrate the effectiveness of this method by showing an improvement of 2.3 BLEU in an English-to-Hebrew translation setting where the speaker and audience gender can be inferred. We also perform a fine-grained syntactic analysis that shows how our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them. In future work we would like to explore automatic generation of the injected context, or the use of cross-sentence context to infer the injected information.
We also perform a fine-grained syntactic analysis that shows how our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them.
Answer: The answers are shown as follows:
*  our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them
Question: What are close cities to Legonice?
Evidence: Łęgonice [wɛnɡɔˈnit͡sɛ] is a village in the administrative district of Gmina Nowe Miasto nad Pilicą, within Grójec County, Masovian Voivodeship, in east-central Poland. It lies approximately 3 kilometres (2 mi) west of Nowe Miasto nad Pilicą, 36 km (22 mi) south-west of Grójec, and 74 km (46 mi) south-west of Warsaw.

The village has a population of 440.
Answer: Close cities to Legonice are Nowe Miasto nad Pilica, Grojec, and Warsaw.
Question: Who were the annotators?
Evidence: We base our annotation procedure on the guidelines and schemas presented in BIBREF0 , discussed in detail in section "Classification Structure" . As a warm-up procedure, the first 100 posts were annotated by two annotators (the author and the supervisor) and the results compared. This was used as an opportunity to refine the mutual understanding of the task at hand and to discuss the mismatches in these annotations for each sub-task.
In light of these findings our internal guidelines were refined so that no post should be labeled as offensive by interpreting any context that is not directly visible in the post itself and that any post containing any form of profanity should automatically be labeled as offensive. These stricter guidelines made the annotation procedure considerably easier while ensuring consistency. The remainder of the annotation task was performed by the author, resulting in 3600 annotated samples.
As a warm-up procedure, the first 100 posts were annotated by two annotators (the author and the supervisor) and the results compared.
The remainder of the annotation task was performed by the author, resulting in 3600 annotated samples.
Answer: The answers are shown as follows:
* the author and the supervisor
Question: What are fast recipes to make for dinner?
Answer: Fast recipes for dinner include:
1. Tacos - corn tortillas, ground beef, diced tomatoes, onions and shredded cheese
2. Stir fry - cook rice or noodle with vegetables in a wok and use some teriyaki sauce
3. Breakfast for dinner - eggs are quick to cook and make nutritious  dinner meals as well
Question: On which task does do model do best?
Evidence: N-GrAM ranked first in all cases except for the language variety task. In this case, the baseline was the top-ranked system, and ours was second by a small margin. Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task.
Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task.
Answer: Variety prediction task