DataHammer/scimrc huggingface dataset:
Question: what pitfalls are mentioned in the paper?
Evidence: While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or unsupervised methods in low-data conditions BIBREF3 , BIBREF4 . In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. Our main contributions are as follows:
While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or unsupervised methods in low-data conditions BIBREF3 , BIBREF4 . 
Answer: The answers are shown as follows:
* highly data-inefficient
* underperform phrase-based statistical machine translation
Question: What is a SuperDisk?
Evidence: The SuperDisk LS-120 is a high-speed, high-capacity alternative to the 90 mm (3.5 in), 1.44 MB floppy disk. The SuperDisk hardware was created by 3M's storage products group Imation in 1997, with manufacturing chiefly by Matsushita.

The SuperDisk's format was designed to supersede the floppy disk with its higher-capacity media that imitated the ubiquitous format with its own 120 MB (and later 240 MB) disk storage while the SuperDisk drive itself was backwards compatible with 1.44 MB and 720 KB floppy formats (MFM). Superdisk drives read and write faster to these sorts of disks than conventional 1.44 MB or 720 KB floppy drives. The newer LS-240 drives also have the ability to read and write regular 1.44 MB floppies at much higher densities.
Answer: The SuperDisk is a storage medium that was created to supplant the 90 mm 1.44 MB floppy disk. It was created by 3M's storage products group in 1997. The product was not commercially successful and was discontinued in 2003.
Question: Who are the people named in the passage?
Evidence: Lauri Johannes Silván (born 13 March 1932) is a Finnish ice hockey player. He competed in the men's tournament at the 1952 Winter Olympics.
Answer: Lauri Johannes Silván
Question: Without quoting directly from the text give me a summary of how turist visit the falls even if they are used for hydroelectric power production.
Evidence: The Cascata delle Marmore (Italian: [kaˈskaːta delle ˈmarmore]) or Marmore Falls is a man-made waterfall created by the ancient Romans. Its total height is 165 m (541 feet), making it the tallest man-made waterfall in the world.

It is located 7.7 km from Terni, a provincial capital of the Italian region of Umbria. 

Its source is a portion of the waters of the river Velino. After flowing through Piediluco lake near the community of Marmore (italian), it pours into the valley below formed by the river Nera. The flow of the waterfall depends on an off and on scale with a published schedule to support the power company nearby.

Most of the time, the water in the canals above the Cascata delle Marmore falls is diverted to a hydroelectric power plant, so the flow in the falls themselves is heavily reduced. Piediluco Lake, above the falls, is used as a reservoir for the power plant. The Galleto power plant, built in 1929, is architecturally interesting. Its capacity is about 530 MW. To control the operation of the power plant, and to satisfy tourists, the falls are turned on according to a set schedule, achieving a spectacular effect at full flow. An alarm is sounded first, then the gates are opened, and in a few minutes the small stream is transformed into a full-sized river rushing into the void below.

Normally, the falls are turned on between 12:00 and 13:00 and again between 16:00 and 17:00 every day, with additional times on holidays. An entrance fee is charged to visit the falls and the surrounding area.

A path along the falls allows the visitor to hike up to the top of the falls. Along the way, a tunnel leads to an observatory just next to the falls, where a visitor is likely to get soaked. Another observatory near the top affords a wide view of the falls and of the Nera valley below.
Answer: The Marmore Falls are used as a hydroelectric power plant, but to allow people to enjoy the spectacular water flow, the gates are periodically opened to present the full-sized river rushing into the void. 

The tourists can visit the falls on a fixed schedule (12:00 to 13:00, 16:00 to 17:00 plus additional times on holidays) following paths that allow them to reach the top of the falls.
Question: What is DNA sequencing?
Answer: DNA sequencing is the process of determining the nucleic acid sequence – the order of nucleotides in DNA. It includes any method or technology that is used to determine the order of the four bases: adenine, guanine, cytosine, and thymine. The advent of rapid DNA sequencing methods has greatly accelerated biological and medical research and discovery.

Knowledge of DNA sequences has become indispensable for basic biological research, DNA Genographic Projects and in numerous applied fields such as medical diagnosis, biotechnology, forensic biology, virology and biological systematics. Comparing healthy and mutated DNA sequences can diagnose different diseases including various cancers, characterize antibody repertoire, and can be used to guide patient treatment. Having a quick way to sequence DNA allows for faster and more individualized medical care to be administered, and for more organisms to be identified and cataloged.

The rapid speed of sequencing attained with modern DNA sequencing technology has been instrumental in the sequencing of complete DNA sequences, or genomes, of numerous types and species of life, including the human genome and other complete DNA sequences of many animal, plant, and microbial species.

The first DNA sequences were obtained in the early 1970s by academic researchers using laborious methods based on two-dimensional chromatography. Following the development of fluorescence-based sequencing methods with a DNA sequencer, DNA sequencing has become easier and orders of magnitude faster.[
Question: What is the Moral Choice Machine?
Evidence: BIBREF0 (BIBREF0) developed Moral Choice Machine computes the cosine similarity in a sentence embedding space of an arbitrary action embedded in question/answer pairs. This is illustrated in Fig. FIGREF16 for the moral bias of the action murder. Since murdering is a quite destructive and generally refused behaviour, the questions are expected to lie closer to the denying response and thus to yield a negative bias. To create a more meaningful and comprehensive statistic, several question/answer prompts were conflated to a question/answer template (cf. Tab. TABREF15). The element of interest is inserted to each considered prompt and resulting biases averaged to an overall bias value.
BIBREF0 (BIBREF0) developed Moral Choice Machine computes the cosine similarity in a sentence embedding space of an arbitrary action embedded in question/answer pairs.
Answer: The answers are shown as follows:
* Moral Choice Machine computes the cosine similarity in a sentence embedding space of an arbitrary action embedded in question/answer pairs
Question: What’s your favorite color?
Answer: That is entirely up to the person. Some people like blue some people like red some people like green a very popular color is blue.
Question: What model is used by the team in this research?
Evidence: In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data. Our model, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout, and residual connections. To improve training, we further introduce a new layer-wise optimizer called NovoGrad. Through experiments, we demonstrate that the proposed deep architecture performs as well or better than more complex choices. Our deepest Jasper variant uses 54 convolutional layers. With this architecture, we achieve 2.95% WER using a beam-search decoder with an external neural language model and 3.86% WER with a greedy decoder on LibriSpeech test-clean. We also report competitive results on the Wall Street Journal and the Hub5'00 conversational evaluation datasets.
Our model, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout, and residual connections.
Answer: Jasper.
Question: How do they demonstrate the robustness of their results?
Evidence: The results are displayed in Figure FIGREF43 . In the standard setting (Figure FIGREF43 ), the models that have access to the context besides the content ( INLINEFORM0 ) and the models that are only allowed to access the context ( INLINEFORM1 ), always perform better than the content-based models ( INLINEFORM2 ) (bars above zero). However, when we randomly flip contexts of the test instances (Figure FIGREF43 ), or suppress them entirely (Figure FIGREF43 ), the opposite picture emerges: the content-based models always outperform the other models. For some classes (support, INLINEFORM3 ) the difference can exceed 50 F1 percentage points. These two studies, where testing examples are varied regarding their context (randomized-context or no-context) simulates what can be expected if we apply our systems for relation class assignment to EAUs stemming from heterogeneous sources. While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably – they perform worse than the content-based model.
While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably – they perform worse than the content-based model.
Answer: The answers are shown as follows:
* performances of a purely content-based model naturally stays stable
Question: How long after the release of the album “Abbey Road” did The Beatles break up?
Evidence: By early 1964, the Beatles were international stars and had achieved unprecedented levels of critical and commercial success. They became a leading force in Britain's cultural resurgence, ushering in the British Invasion of the United States pop market, and soon made their film debut with A Hard Day's Night (1964). A growing desire to refine their studio efforts, coupled with the untenable nature of their concert tours, led to the band's retirement from live performances in 1966. At this time, they produced records of greater sophistication, including the albums Rubber Soul (1965), Revolver (1966) and Sgt. Pepper's Lonely Hearts Club Band (1967), and enjoyed further commercial success with The Beatles (also known as "the White Album", 1968) and Abbey Road (1969). The success of these records heralded the album era, as albums became the dominant form of record consumption over singles; they also increased public interest in psychedelic drugs and Eastern spirituality, and furthered advancements in electronic music, album art and music videos. In 1968, they founded Apple Corps, a multi-armed multimedia corporation that continues to oversee projects related to the band's legacy. After the group's break-up in 1970, all principal former members enjoyed success as solo artists and some partial reunions have occurred. Lennon was murdered in 1980 and Harrison died of lung cancer in 2001. McCartney and Starr remain musically active.
Answer: One year after releasing Abbey Road (1969), the Beatles broke up (1970)
Question: What are the best investments ideas?
Answer: How you invest depends on some important factors:

- Objective: What do you want to do with the money?
- Timeline: What is the period that you want to invest and/or need to withdraw from the investment?
- Risk tolerance: How do you feel comfortable with market variations?

Depending on these factors you can select the types of investments that best fit your goals such as:

- Treasury/Certificate of Deposits/Bonds
- Fixed Income
- REIT (Real Estate Investment Trust)
- Mutual Funds
- Stock Market

You can choose between one type of investment or adopt strategies to diversify your portfolio.

Also, there are more advanced types of investment strategies such as Options and Derivatives, for more investment ideas and how to adopt a strategy you can consult your financial advisor.
Question: how is quality measured?
Evidence: FLOAT SELECTED: Table 1: Comparison of manually created lexicon performance with UniSent in Czech, German, French, Macedonians, and Spanish. We report accuracy and the macro-F1 (averaged F1 over positive and negative classes). The baseline is constantly considering the majority label. The last two columns indicate the performance of UniSent after drift weighting.
FLOAT SELECTED: Table 1: Comparison of manually created lexicon performance with UniSent in Czech, German, French, Macedonians, and Spanish. We report accuracy and the macro-F1 (averaged F1 over positive and negative classes). The baseline is constantly considering the majority label. The last two columns indicate the performance of UniSent after drift weighting.
Answer: Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality.
Question: Who founded the Minnesota Educational Computing Consortium (MECC)?
Answer: The Minnesota Legislature founded MECC in 1971.
Question: How does the team achieve the best overall accuracy of 49.87 on Task 2?
Evidence: This paper describes our approach and results for Task 2 of the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection BIBREF0 . The task is to generate an inflected word form given its lemma and the context in which it occurs.
Morphological (re)inflection from context is of particular relevance to the field of computational linguistics: it is compelling to estimate how well a machine-learned system can capture the morphosyntactic properties of a word given its context, and map those properties to the correct surface form for a given lemma.
There are two tracks of Task 2 of CoNLL–SIGMORPHON 2018: in Track 1 the context is given in terms of word forms, lemmas and morphosyntactic descriptions (MSD); in Track 2 only word forms are available. See Table TABREF1 for an example. Task 2 is additionally split in three settings based on data size: high, medium and low, with high-resource datasets consisting of up to 70K instances per language, and low-resource datasets consisting of only about 1K instances.
The baseline provided by the shared task organisers is a seq2seq model with attention (similar to the winning system for reinflection in CoNLL–SIGMORPHON 2016, BIBREF1 ), which receives information about context through an embedding of the two words immediately adjacent to the target form. We use this baseline implementation as a starting point and achieve the best overall accuracy of 49.87 on Task 2 by introducing three augmentations to the provided baseline system: (1) We use an LSTM to encode the entire available context; (2) We employ a multi-task learning approach with the auxiliary objective of MSD prediction; and (3) We train the auxiliary component in a multilingual fashion, over sets of two to three languages.
In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results. Multi-task learning, paired with multilingual training and subsequent monolingual finetuning, scored highest for five out of seven languages, improving accuracy by another 9.86% on average.
We use this baseline implementation as a starting point and achieve the best overall accuracy of 49.87 on Task 2 by introducing three augmentations to the provided baseline system
Answer: By introducing three augmentations to the provided baseline system.
Question: What metrics do they use as evaluation metrics?
Evidence: We use two public datasets for fake news detection and stance detection, i.e., RumourEval BIBREF36 and PHEME BIBREF12. We introduce both the datasets in details from three aspects: content, labels, and distribution.
Content. Both datasets contain Twitter conversation threads associated with different newsworthy events including the Ferguson unrest, the shooting at Charlie Hebdo, etc. A conversation thread consists of a tweet making a true and false claim, and a series of replies. Labels. Both datasets have the same labels on fake news detection and stance detection. Fake news is labeled as true, false, and unverified. Because we focus on classifying true and false tweets, we filter the unverified tweets. Stance of tweets is annotated as support, deny, query, and comment. Distribution. RumourEval contains 325 Twitter threads discussing rumours and PHEME includes 6,425 Twitter threads. Threads, tweets, and class distribution of the two datasets are shown in Table TABREF24.
In consideration of the imbalance label distributions, in addition to accuracy (A) metric, we add Precision (P), Recall (R) and F1-score (F1) as complementary evaluation metrics for tasks. We hold out 10% of the instances in each dataset for model tuning, and the rest of the instances are performed 5-fold cross-validation throughout all experiments.
In consideration of the imbalance label distributions, in addition to accuracy (A) metric, we add Precision (P), Recall (R) and F1-score (F1) as complementary evaluation metrics for tasks.
Answer: Accuracy, Precision, Recall and F1-score.
Question: Why do the authors combine entity linking and Wikidata?
Evidence: Over the few past years, the emergence of deep neural networks has fundamentally changed the design of entity detection systems. Consequently, recurrent neural networks (RNN) have found popularity in the field since they are able to learn long term dependencies of sequential data. The recent success of neural network based architectures principally comes from its deep structure. Training a deep neural network, however, is a difficult problem due to vanishing or exploding gradients. In order to solve this, LSTMs were proposed. An LSTM is an internal memory cell controlled by forget gate and input gate networks. A forget gate in an LSTM layer which determines how much prior memory should be passed into the next time increment. Similarly, an input gate scales new input to memory cells. Depending on the states of both gates, LSTM is able to capture long-term or short-term dependencies for sequential data. This is an ideal property for many NLP tasks.
Most existing studies consider NER and entity linking as two separate tasks, whereas we try to combine the two. It has been shown that one can significantly increase the semantic information carried by a NER system when we successfully linking entities from a deep learning method to the related entities from a knowledge base (Ji et al., 2018;  Phan et al., 2018).
Answer: It has been shown that one can significantly increase the semantic information carried by a NER system when they successfully linking entities from a deep learning method to the related entities from a knowledge base.
Question: What are your three favorite Martin Scorsese  films
Answer: 1. The Departed
2. GoodFellas
3. Casino
Question: How many roles are proposed?
Evidence: FLOAT SELECTED: Table 2: Most common syntactic patterns for each semantic role.
FLOAT SELECTED: Table 2: Most common syntactic patterns for each semantic role.
Answer: 12
Question: Of the cities listed, which was furthest from the station?
Evidence: In 1899 the Great Central Railway completed its main line to London through the southernmost part of the parish. The nearest station was Finmere for Buckingham, which was just over the Oxfordshire county boundary on the main road between Buckingham and Bicester and only 3⁄4 mile (1.2 km) from Barton Hartshorn. The station was 5 miles (8 km) from Buckingham, more than 1 mile (1.6 km) from Finmere and was actually in Shelswell parish next to the village of Newton Purcell. In about 1922 the Great Central renamed the station Finmere. British Railways closed the station in 1963 and the line in 1966.
Answer: Buckingham city was the furthest, 5 miles from the station.
Question: Give a comma-separated list of the different genes influencing horse coat colors according to the text
Evidence: Horses exhibit a diverse array of coat colors and distinctive markings, described by a specialized vocabulary. Often, a horse is classified first by its coat color, before breed or sex. Horses of the same color may be distinguished from one another by white markings, which, along with various spotting patterns, are inherited separately from coat color.
Many genes that create horse coat colors and patterns have been identified. Current genetic tests can identify at least 13 different alleles influencing coat color, and research continues to discover new genes linked to specific traits. The basic coat colors of chestnut and black are determined by the gene controlled by the Melanocortin 1 receptor, also known as the "extension gene" or "red factor," as its recessive form is "red" (chestnut) and its dominant form is black. Additional genes control suppression of black color to point coloration that results in a bay, spotting patterns such as pinto or leopard, dilution genes such as palomino or dun, as well as greying, and all the other factors that create the many possible coat colors found in horses.
Answer: "extension gene" or "red factor," palomino, dun, greying
Question: What does Avici stand for
Evidence: Avīci or Avici (Sanskrit and Pali for "without waves"; Chinese: 無間地獄 or 阿鼻地獄; Japanese: 無間地獄 or 阿鼻地獄; Burmese: အဝီစိငရဲ) is one of the hells (naraka) in Hinduism and Buddhism. In Hinduism, it is one of the twenty-eight hells located in the kingdom of Yama, where individuals are reborn for bearing false witness and outright lying while transacting business or giving charity. In Buddhism, it is the lowest level of the Naraka or "hell" realm, with the most suffering, into which the dead who have committed grave misdeeds may be reborn. It is said to be a cube 20,000 yojanas (240,000 to 300,000 kilometers) on each side, buried deep underneath the divine (nonvisible) earth. Avīci is often translated into English as "interminable" or "incessant", referring to suffering without periods of respite, although it is believed to be ultimately impermanent.
Answer: There was a famous Swedish DJ called Avicii and there is Avici that is one of the hells (naraka) in Hinduism and Buddhism. In Hinduism, it is one of the twenty-eight hells located in the kingdom of Yama, where individuals are reborn for bearing false witness and outright lying while transacting business or giving charity.

In Buddhism, it is the lowest level of the Naraka or "hell" realm, with the most suffering, into which the dead who have committed grave misdeeds may be reborn. It is said to be a cube 20,000 yojanas (240,000 to 300,000 kilometers) on each side, buried deep underneath the divine (nonvisible) earth. Avici is often translated into English as "interminable" or "incessant", referring to suffering without periods of respite, although it is believed to be ultimately impermanent.
Question: What are most American homes made out of?
Answer: In the US, most homes are made from a cement base or foundation and wood framing. The outside and roofs are made of a weather proof material, which may include treated wood, vinyl, brick, or plaster for siding and metal or asphalt shingle for the roof. The inside of homes are generally lined with drywall or some older homes may have plaster walls.
Question: Give me a checklist of 5 items of furniture I should buy for my new living room
Answer: - Couch
- Coffee Table
- Side table
- Lamp
- TV stand