DataHammer/scimrc huggingface dataset:
Question: What does the author aim to do in future work?
Evidence: In this paper, we presented LD2NL, a framework for verbalizing SW languages, especially on RDF and OWL while including the SPARQL verbalization provided by SPARQL2NL. Our evaluation with 86 persons revealed that our framework generates NL that can be understood by lay users. While the OWL verbalization was close to NL, the RDF was less natural but still sufficient to convey the meaning expressed by the corresponding set of triples. In future work, we aim to extend LD2NL to verbalize the languages SWRL BIBREF23 and SHACL BIBREF24.
In future work, we aim to extend LD2NL to verbalize the languages SWRL (Horrocks et al., 2004) and SHACL (Knublauch and Kontokostas, 2017).
Answer: In future work, the authors aim to extend LD2NL to verbalize the languages SWRL and SHACL.
Question: Which of the following are perfect square roots? Classify them as 'perfect' and 'not perfect' - 1, 23, 4, 6, 9, 10, 42, 112, 81, 100, 55, 16, 32, 25.
Answer: 'perfect', 'not perfect', 'perfect', 'not perfect', 'perfect', 'not perfect', 'not perfect', 'not perfect', 'perfect', 'perfect', 'not perfect', 'perfect', 'not perfect', 'perfect'.
Question: What is the biggest advantage of their SERA compared to ROUGE?
Evidence: Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
Answer: SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
Question: What is Fortran?
Answer: Fortran is a general-purpose, compiled imperative programming language that is especially suited to numeric computation and scientific computing.
Question: What performance does the Entity-GCN get on WIKIHOP?
Evidence: FLOAT SELECTED: Table 2: Accuracy of different models on WIKIHOP closed test set and public validation set. Our Entity-GCN outperforms recent prior work without learning any language model to process the input but relying on a pretrained one (ELMo – without fine-tunning it) and applying R-GCN to reason among entities in the text. * with coreference for unmasked dataset and without coreference for the masked one.
FLOAT SELECTED: Table 2: Accuracy of different models on WIKIHOP closed test set and public validation set. Our Entity-GCN outperforms recent prior work without learning any language model to process the input but relying on a pretrained one (ELMo – without fine-tunning it) and applying R-GCN to reason among entities in the text. * with coreference for unmasked dataset and without coreference for the masked one.
Answer: During testing: 67.6 for single model without coreference, 66.4 for single model with coreference, 71.2 for ensemble of 5 models
Question: How many tweats does MMHS150k contains, 150000?
Evidence: Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
Answer: The answers are shown as follows:
* $150,000$ tweets
Question: What criticism do people make about the death growl vocal technique?
Evidence: A death growl, or simply growl, is an extended vocal technique usually employed in extreme styles of music, particularly in death metal and other extreme subgenres of heavy metal music. Death growl vocals are sometimes criticized for their "ugliness", but their unintelligibility contributes to death metal's abrasive style and often dark and obscene subject matter.
Answer: The death growl vocal technique is described as having a harsh tone which is called ugly.
Question: Which is the best meal delivery service?
Answer: It depends on what is important to you. Some people like Blue Apron because the ingredients are from sustainable sources and the recipe cards have pictures and are easy to follow. But some people don't like Blue Apron and other meal delivery services like Hello Fresh, Factor, and Dinnerly, because you only get the ingredients, not complete meals. So you have to spend time on meal prep. For completely assembled ready-to-eat meals, people like Freshly, Unity Kitchen, or Splendid Spoon. If you have young children, it's probably a good idea to order the ready-to-eat meals so you can save time. The pre-assembled meals may be more expensive, but it could really be worth it for a young family.
Question: What year was Genghis Khan born?
Answer: Genghis Khan was born in the year 1162.
Question: What was most important invention by Humans?
Answer: Humans have invented many things and it really depends on each generation what was the most important invention. There are few that come to mind below

1) Fire: Ability to create fire led to significant increase in Humans ability to survive and thrive.

2) Wheel: Ability to create and control wheel led to Humans being able to cover more distances than they previously could do physically. This led to increased ability to hunt and also run away from predators

3) Farming: Ability to Farm food led to Humans not needing to hunt and led to creation of societies that led to more cooperation & more inventions

There are many others like Gun, Engine, Drugs, Electricity, Airplanes, Nuclear energy, Computer, iphone.
Question: What is a self-compiled corpus?
Evidence: As a first corpus, we compiled INLINEFORM0 that represents a collection of 80 excerpts from scientific works including papers, dissertations, book chapters and technical reports, which we have chosen from the well-known Digital Bibliography & Library Project (DBLP) platform. Overall, the documents were written by 40 researchers, where for each author INLINEFORM1 , there are exactly two documents. Given the 80 documents, we constructed for each author INLINEFORM2 two verification problems INLINEFORM3 (a Y-case) and INLINEFORM4 (an N-case). For INLINEFORM5 we set INLINEFORM6 's first document as INLINEFORM7 and the second document as INLINEFORM8 . For INLINEFORM9 we reuse INLINEFORM10 from INLINEFORM11 as the known document and selected a text from another (random) author as the unknown document. The result of this procedure is a set of 80 verification problems, which we split into a training and test set based on a 40/60% ratio. Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms. The average time span between both documents of an author is 15.6 years. The minimum and maximum time span are 6 and 40 years, respectively. Besides the temporal aspect of INLINEFORM12 , another challenge of this corpus is the formal (scientific) language, where the usage of stylistic devices is more restricted, in contrast to other genres such as novels or poems.
Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms.
Answer: The answers are shown as follows:
*  restrict the content of each text to the abstract and conclusion of the original work
* considered other parts of the original works such as introduction or discussion sections
* extracted text portions are appropriate for the AV task, each original work was preprocessed manually
* removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms
Question: Tell me which of the following are body parts: head, ankle, banana, finger, earlobe, kiwi, toe, car, shoulder, bed
Answer: head, ankle, finger, earlobe, toe, shoulder
Question: Given this reference text about the career of Charles Henry Bromedge Caldwell, name the ships he commanded after his promotion to the rank of Commander.
Evidence: Charles Henry Bromedge Caldwell was born in Hingham, Massachusetts on February 24, 1823. He entered the navy as midshipman February 27, 1838, and became lieutenant September 4, 1852. He commanded the USS Vandalia in the US Navy's second Fiji expedition, an 1859 punitive expedition against Waya Island warriors who had cannibalized two American traders. In 1862, he commanded the gun-boat Itasca, of the western gulf blockading squadron, and took part in the bombardment of Forts Jackson and St. Philip. On the night of April 20, his gun-boat, with the Pinola, was sent on an expedition under the command of Fleet-Capt. Bell, to make a passage for the fleet through the chain obstructions near the forts. Lieut. Caldwell and his party boarded one of the hulks that held the chains, and succeeded in detaching the latter, in spite of the heavy fire to which they were subjected. The Itasca was then swept on shore by the current, in full sight of the forts, and it was half an hour before she was afloat again. She was unable to pass the forts with the rest of the fleet, owing to a shot that penetrated her boiler. Lieut. Caldwell was in the action at Grand Gulf, June 9, 1862, and was promoted to commander on July 16 1862.

He commanded the iron-clad Essex, of the Mississippi squadron in 1862–63, and took part in the operations at Port Hudson, from March to July 1863, in command of the Essex and the mortar flotilla.

He commanded the Glaucus of the North Atlantic blockading squadron from 1863 till 1864, and the R. R. Cuyler, of the same squadron, from 1864 till 1865. He became captain, December 12, 1867, chief of staff of the North Atlantic fleet in 1870, and commodore on June 14, 1874.

He died on November 30, 1877.
Answer: Essex
Glaucus
R. R. Cuyler
Question: What are the existing methods mentioned in the paper?
Evidence: In DDI extraction task, NLP methods or machine learning approaches are proposed by most of the work. Chowdhury BIBREF14 and Thomas et al. BIBREF11 proposed methods that use linguistic phenomenons and two-stage SVM to classify DDIs. FBK-irst BIBREF10 is a follow-on work which applies kernel method to the existing model and outperforms it.
Neural network based approaches have been proposed by several works. Liu et al. BIBREF9 employ CNN for DDI extraction for the first time which outperforms the traditional machine learning based methods. Limited by the convolutional kernel size, the CNN can only extracted features of continuous 3 to 5 words rather than distant words. Liu et al. BIBREF8 proposed dependency-based CNN to handle distant but relevant words. Sahu et al. BIBREF12 proposed LSTM based DDI extraction approach and outperforms CNN based approach, since LSTM handles sentence as a sequence instead of slide windows. To conclude, Neural network based approaches have advantages of 1) less reliance on extra NLP toolkits, 2) simpler preprocessing procedure, 3) better performance than text analysis and machine learning methods.
Chowdhury BIBREF14 and Thomas et al. BIBREF11 proposed methods that use linguistic phenomenons and two-stage SVM to classify DDIs. FBK-irst BIBREF10 is a follow-on work which applies kernel method to the existing model and outperforms it.
Neural network based approaches have been proposed by several works. Liu et al. BIBREF9 employ CNN for DDI extraction for the first time which outperforms the traditional machine learning based methods.
 Sahu et al. BIBREF12 proposed LSTM based DDI extraction approach and outperforms CNN based approach, since LSTM handles sentence as a sequence instead of slide windows.
Answer: The answers are shown as follows:
* Chowdhury BIBREF14 and Thomas et al. BIBREF11
* FBK-irst BIBREF10
* Liu et al. BIBREF9
* Sahu et al. BIBREF12
Question: How many feature maps are generated for a given triple?
Evidence: FLOAT SELECTED: Figure 1: Process involved in ConvKB (with the embedding size k = 4, the number of filters τ = 3 and the activation function g = ReLU for illustration purpose).
Our ConvKB uses different filters $\in \mathbb {R}^{1\times 3}$ to generate different feature maps. Let ${\Omega }$ and $\tau $ denote the set of filters and the number of filters, respectively, i.e. $\tau = |{\Omega }|$ , resulting in $\tau $ feature maps. These $\tau $ feature maps are concatenated into a single vector $\in \mathbb {R}^{\tau k\times 1}$ which is then computed with a weight vector ${w} \in \mathbb {R}^{\tau k\times 1}$ via a dot product to give a score for the triple $(h, r, t)$ . Figure 1 illustrates the computation process in ConvKB.
FLOAT SELECTED: Figure 1: Process involved in ConvKB (with the embedding size k = 4, the number of filters τ = 3 and the activation function g = ReLU for illustration purpose).
Our ConvKB uses different filters $\in \mathbb {R}^{1\times 3}$ to generate different feature maps. Let ${\Omega }$ and $\tau $ denote the set of filters and the number of filters, respectively, i.e. $\tau = |{\Omega }|$ , resulting in $\tau $ feature maps. 
Answer: 3 feature maps for a given tuple
Question: What is an Italian Greyhound?
Answer: Italian greyhounds are small dogs resembling racing greyhounds but are much smaller at around 15 pounds. Italian greyhounds, or “iggys” as they’re affectionately known, are often fit but quite lazy.
Question: Do the tweets come from any individual?
Evidence: We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like “fire”, “earthquake”, “theft”, “robbery”, “drunk driving”, “drunk driving accident” etc. Later, we manually label tweets with <emergency>and <non-emergency>labels for classification as stage one. Our dataset contains 1313 tweet with positive label <emergency>and 1887 tweets with a negative label <non-emergency>. We create another dataset with the positively labeled tweets and provide them with category labels like “fire”, “accident”, “earthquake” etc.
We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like “fire”, “earthquake”, “theft”, “robbery”, “drunk driving”, “drunk driving accident” etc. 
Answer: Yes.
Question: Think up a comma separated list of tropical fruits
Answer: Mangos, Pineapples, Coconuts, Papayas, Guavas, Pitayas, Kiwis, Passionfruits, Bananas
Question: Based on this paragraph, what was Joe Biden's mother's maiden's name, what city was he born in, how many siblings did he have growing up, and what religion was he as a child?
Evidence: Joseph Robinette Biden Jr. was born on November 20, 1942, at St. Mary's Hospital in Scranton, Pennsylvania, to Catherine Eugenia "Jean" Biden (née Finnegan) and Joseph Robinette Biden Sr. The oldest child in a Catholic family, he has a sister, Valerie, and two brothers, Francis and James. Jean was of Irish descent, while Joseph Sr. had English, Irish, and French Huguenot ancestry. Biden's paternal line has been traced to stonemason William Biden, who was born in 1789 in Westbourne, England, and emigrated to Maryland in the United States by 1820.
https://en.wikipedia.org/wiki/Joe_Biden
Answer: Based on this paragraph, Joe Biden's mother's maiden name was Finnegan, he was born in the city of Stranton, he had three siblings (a younger sister and two younger brothers), and he was raised as a member of the Catholic church.
Question: What about the setting of preprocessing materials?
Evidence:  This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified.
While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:
In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text.
Answer: They prepared three incremental levels of document preprocessing for the experimented: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document.
Question: What is a mirepoix?
Answer: A mirepoix is rooted in French cooking but used widely across many cuisines.  It consists of equal amounts of small, diced carrots, onions, and celery.  A mirepoix if often used in soup making as a fundamental component that adds flavor while enhances other flavors.
Question: How do they study the model sensitivity for each sentence?
Evidence: To study model sensitivity, for each sentence, we perturb one randomly-chosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 12 , we count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.
The neutral backoff variant has the lowest sensitivity (Table 5 ). This is expected, as it returns a fixed neutral word whenever the ScRNN predicts an UNK, therefore reducing the number of unique outputs it predicts. Open vocabulary (i.e. char-only, word+char, word-piece) downstream classifiers consider every unique combination of characters differently, whereas word-only classifiers internally treat all out of vocabulary (OOV) words alike. Hence, for char-only, word+char, and word-piece models, the pass-through version is more sensitive than the background variant, as it passes words as is (and each combination is considered uniquely). However, for word-only models, pass-through is less sensitive as all the OOV character combinations are rendered identical.
Ideally, a preferred defense is one with low sensitivity and word error rate. In practice, however, we see that a low error rate often comes at the cost of sensitivity. We see this trade-off in Figure 2 , where we plot WER and sensitivity on the two axes, and depict the robustness when using different backoff variants. Generally, sensitivity is the more dominant factor out of the two, as the error rates of the considered variants are reasonably low.
We verify if the sentiment (of the reviews) is preserved with char-level attacks. In a human study with 50 attacked (and subsequently misclassified), and 50 unchanged reviews, it was noted that 48 and 49, respectively, preserved the sentiment.
To study model sensitivity, for each sentence, we perturb one randomlychosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 1, we count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.
Answer: They  perturb one randomlychosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 1, they count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.
Question: Give me a list of some of the famous stock market indices.
Answer: 1. MSCI World: It comprises enterprises in developed countries based on market cap.
2. FTSE All World: It comprise of enterprises in both developed and developing economies with larger focus over large cap companies.
3. S&P 500: It is one of the most famous index comprise 500 big enterprises from United States
4. EURO STOXX 50: Top 50 enterprises present in Euro Zone.