DataHammer/scimrc huggingface dataset:
Question: How does the QuaSP+Zero model work?
Evidence: We present and evaluate a model that we have developed for this, called QuaSP+Zero, that modifies the QuaSP+ parser as follows: During decoding, at points where the parser is selecting which property to include in the LF (e.g., Figure FIGREF31 ), it does not just consider the question tokens, but also the relationship between those tokens and the properties INLINEFORM0 used in the qualitative model. For example, a question token such as “longer” can act as a cue for (the property) length, even if unseen in the training data, because “longer” and a lexical form of length (e.g.,“length”) are similar. This approach follows the entity-linking approach used by BIBREF11 Krishnamurthy2017NeuralSP, where the similarity between question tokens and (words associated with) entities - called the entity linking score - help decide which entities to include in the LF during parsing. Here, we modify their entity linking score INLINEFORM1 , linking question tokens INLINEFORM2 and property “entities” INLINEFORM3 , to be: INLINEFORM4
We present and evaluate a model that we have developed for this, called QuaSP+Zero, that modifies the QuaSP+ parser as follows: During decoding, at points where the parser is selecting which property to include in the LF (e.g., Figure FIGREF31 ), it does not just consider the question tokens, but also the relationship between those tokens and the properties INLINEFORM0 used in the qualitative model.
This approach follows the entity-linking approach used by BIBREF11 Krishnamurthy2017NeuralSP, where the similarity between question tokens and (words associated with) entities - called the entity linking score - help decide which entities to include in the LF during parsing.
Answer: The answers are shown as follows:
* does not just consider the question tokens, but also the relationship between those tokens and the properties
Question: Classify each of the following as a liquid or solid: water, mailbox, soda, coffee, bush, grass, milk
Answer: Water: Liquid
Mailbox: Solid
Soda: Liquid
Coffee: Liquid
Bush: Solid
Grass: Solid
Milk: Liquid
Question: What are the functions of the crosslingual latenfut variables in the model proposed?
Evidence: We propose a Bayesian model of semantic role induction (SRI) that uses crosslingual latent variables to capture role alignments in parallel corpora. The crosslingual latent variables capture correlations between roles in different languages, and regularize the parameter estimates of the monolingual models. Because this is a joint Bayesian model of multilingual SRI, we can apply the same model to a variety of training scenarios just by changing the inference procedure appropriately. We evaluate monolingual SRI with a large unlabeled dataset, bilingual SRI with a parallel corpus, bilingual SRI with annotations available for the source language, and monolingual SRI with a small labeled dataset. Increasing the amount of monolingual unlabeled data significantly improves SRI in German but not in English. Adding word alignments in parallel sentences results in small, non significant improvements, even if there is some labeled data available in the source language. This difficulty in showing the usefulness of parallel corpora for SRI may be due to the current assumptions about role alignments, which mean that only a small percentage of roles are aligned. Further analyses reveals that annotating small amounts of data can easily outperform the performance gains obtained by adding large unlabeled dataset as well as adding parallel corpora.
Future work includes training on different language pairs, on more than two languages, and with more inclusive models of role alignment.
The crosslingual latent variables capture correlations between roles in different languages, and regularize the parameter estimates of the monolingual models.
Answer: The crosslingual latent variables capture correlations between roles in different languages, and regularize the parameter estimates of the monolingual models.
Question: Do the SVM and the FNN versions of the system consistently outperform all three versions of ClaimBuster on all measures?
Evidence: Table TABREF21 shows the performance of our models when using all features described in Section SECREF4: see the SVM$_{All}$ and the FNN$_{All}$ rows. In order to put the numbers in perspective, we also show the results for five increasingly competitive baselines.
First, there is a random baseline, followed by an SVM classifier based on a bag-of-words representation with TF.IDF weights learned on the training data. Then come three versions of the ClaimBuster system: CB-Platform uses scores from the online demo, which we accessed on December 20, 2016, and SVM$_{CBfeat}$ and FNN$_{CBfeat}$ are our re-implementations, trained on our dataset.
We can see that all systems perform well above the random baseline. The three versions of ClaimBuster also outperform the TF.IDF baseline on most measures. Moreover, our reimplementation of ClaimBuster performs better than the online platform in terms of MAP. This is expected as their system is trained on a different dataset and it may suffer from testing on slightly out-of-domain data. At the same time, this is reassuring for our implementation of the features, and allows for a more realistic comparison to the ClaimBuster system.
More importantly, both the SVM and the FNN versions of our system consistently outperform all three versions of ClaimBuster on all measures. This means that the extra information coded in our model, mainly more linguistic, structural, and contextual features, has an important contribution to the overall performance.
We can further see that the neural network model, FNN$_{All}$, clearly outperforms the SVM model: consistently on all measures. As an example, with the precision values achieved by FNN$_{All}$, the system would rank on average 4 positive examples in the list of its top-5 choices, and also 14-15 in the top-20 list. Considering the recall at the first $R$ sentences, we will be able to encounter 43% of the total number of check-worthy sentences. This is quite remarkable given the difficulty of the task.
More importantly, both the SVM and the FNN versions of our system consistently outperform all three versions of ClaimBuster on all measures.
Answer: Yes, they do.
Question: What is the current recommendation for a healthy diet?
Answer: The current guidance on eating a healthy diet consists of eating a balance of healthy foods. Most of your meal should consist of a mix of fruits and vegetables, taking into account that potatoes are not considered to count as a vegetable as they have a negative impact on blood sugar. A quarter of your meal should consist of whole grains, things like whole wheat, quinoa, oats, brown rice and things made from them such as whole wheat bread and pasta. The other quarter of your plate should consist of proteins such as fish, poultry, beans and nuts. You should limit red meat and avoid processed meat such as bacon and sausages. You should choose to cook or dress your food with healthy plant oils, such as olive oil or sunflower oil and avoid partially hydrogenated oils which contain trans fats. You should drink water, coffee or tea, avoid sugary drinks and limit fruit juice to only one small glass a day.
Question: Classify each as either a vodka drink or a gin drink: Screwdriver, Bloody Mary, French 75, White Russian, Gimlet, Cosmopolitan, Tom Collins, Negroni
Answer: Screwdrivers, Bloody Mary's, White Russians, and Cosmopolitans are traditionally vodka cocktails, whereas French 75's, Gimlets, Tom Collins', and Negronis are traditionally gin cocktails.
Question: What streaming shows are worth binging right now?
Answer: First series that comes to mind is the HBO show Succession. This series dives into the dynamic of a family business on a big scale and the power shifts and crazy situations the children of the CEO get themselves into. On a total different viewing experience is Love is Blind on Netflix. This reality show involves an experiment that is trying to determine if Love is Blind. Contestants date in pods, with a with in between them and only can talk to one another and not physically see the other person. Love does come about and so does heart break.
Question: Who is the company Geberit
Evidence: Geberit (Alemannic German pronunciation: [ˈɡeberɪ:t] is a Swiss multinational group specialized in manufacturing and supplying sanitary parts and related systems. It is a leader in its field in Europe with a global presence through its subsidiaries.
Answer: Geberit is a Swiss multinational group specialized in manufacturing and supplying sanitary parts and related systems. It is a leader in its field in Europe with a global presence through its subsidiaries.
Question: Who is the Aston Martin formula 1 team principal?
Answer: The Aston Martin Aramco Team Principal is Mike Krack
Question: Did they compare to Transformer based large language models?
Evidence: We compared our models with the following state-of-the-art baselines:
Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 .
Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively.
HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation.
HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention.
HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge.
We compared our models with the following state-of-the-art baselines:

Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 .

Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively.

HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation.

HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention.

HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge.
Answer: No.
Question: What can I do with a broken stapler?
Answer: - Use the broken stapler as a paper weight
- Use the broken stapler to hold staples for a new stapler
- Use the broken stapler to hold a door ajar
- Use the broken stapler to hold paper that needs to be filed
- Use the broken stapler as a nutcracker
Question: Do they measure computation time of their factorizations compared to other word embeddings?
Evidence: When considering going from two dimensions to three, it is perhaps necessary to discuss the computational issues in such a problem size increase. However, it should be noted that the creation of pre-trained embeddings can be seen as a pre-processing step for many future NLP tasks, so if the training can be completed once, it can be used forever thereafter without having to take training time into account. Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively.
Despite this, we found that the training of our embeddings was not considerably slower than the training of order-2 equivalents such as SGNS. Explicitly, our GPU trained CBOW vectors (using the experimental settings found below) in 3568 seconds, whereas training CP-S and JCP-S took 6786 and 8686 seconds respectively.
Answer: Yes.
Question: What is the art of command
Answer: It is the art of influencing human behavior in order to achieve the objective assigned by the superior.
Question: What is The Dark Side of the Moon?
Evidence: The Dark Side of the Moon is the eighth studio album by the English rock band Pink Floyd, released on 1 March 1973 by Harvest Records. Developed during live performances before recording began, it was conceived as a concept album that would focus on the pressures faced by the band during their arduous lifestyle, and also deal with the mental health problems of former band member Syd Barrett, who departed the group in 1968. New material was recorded in two sessions in 1972 and 1973 at EMI Studios (now Abbey Road Studios) in London.
Answer: The Dark Side of the Moon is Pink Floyd's 8th studio album. It is about the band's difficult lifestyle and touches on the mental health problems of former band member Syd Barrett.
Question: Extract all unique names from the provided paragraphs separated by comma.
Evidence: Neville shows to Det. Delroy a recently shot love scene between Keefe and Elaine, willingly pointing out a spousicide-related detail which compromises the already delicate position of the young widower. In light of this new evidence, Delroy tells the director that he will arrest the young man very soon. Neville telephones Keefe to warn him, telling him to come to his house at a certain time. The director also calls Elaine to his home, where he attempts to recreate the night of Andrea's murder, this time using Elaine. Det. Vickers watches Elaine enter the director's house from the street, but he misses Keefe, who breaks in through a window. He disrupts the liaison by pulling plug fuses out of the breaker box, shutting off the lights. Detective Vickers notices the lights shut off from the street and unsuccessfully tries to break in. In the house, Neville attacks Keefe with a pair of scissors, and the two struggle. Keefe throws Neville over a balcony, and he lands in an indoor fountain below together with a light. Simultaneously, Elaine restores electricity to the house, unknowingly electrocuting Neville to death.
Answer: Neville, Delroy, Keefe, Elaine, Andrea, Vickers
Question: What is the difference between a coastal cruising and blue water sailboat?
Answer: The designs, accommodations and compromises for these boats can be very different and reflect the likely sea conditions these boats would encounter, expected time at sea and different experiences of passenger comfort.  The primary difference between these boats begins with the shape of the hull since many other design choices tend to be dictated by hull shape.

Coastal cruisers, which are generally sailed near shore and in safer weather conditions, 
tend to have a flatter hull shapes since these boats, which means they tend to "slap" the water when coming off a wave.  With a flatter hull shape, boat architects can make greater use of the cabin space and so coastal cruisers will generally have a more spacious interior.  The helm (or the outdoor area from which the boat is navigated) is generally less protected and generally speaking, offers greater comfort and ease of moving around.  Safety, protection and expected time away from marinas mean these boats can refill fresh water, recharge batteries, empty waste tanks more frequently and therefore, tend to have lower capacities.

Blue water boats, because they are intended to sail in open waters (hence the moniker "blue water") in more unpredictable weather, generally have tapered (or tear drop shaped) hull shapes with heavier keels (generally, lead) and deeper drafts.  With these designs, blue water boats can withstand heavier seas and will "cut through" waves rather than "slap."  Generally speaking, blue water boats offer a much more comfortable experience in rougher seas and will have bigger fresh water and fuel tanks and will likely feature generators and other equipment that allows these boats to away for longer periods.

Blue water boats feature tapered hulls so the beam or width of the boat narrows the closer it gets to the water line (and below), leaving less room in the interior cabin than an equally sized coastal cruiser.  Foot for foot, blue water boats will feature fewer berths and will have more cramped quarters because of the narrowing beam.

Because blue water boats are intended to sail longer distances and will carry more fuel and fresh water, these boats interior space is further restricted to accomadate these practical requirements.

The differences in comfort, stability and likelihood of capsizing are all captured in mathematical calculations that can provide an abstracted comparison between these boat types so prospective buyers can anticipate the the experiences and trade offs each hull type would provide.
Question: Given this text about James Pressdee, tell me what sports they played, which awards they won, when they were born, and if they are still alive.
Evidence: James Stuart Pressdee (19 June 1933 – 20 July 2016) was a Welsh first class cricketer. He was a left-arm spinner and aggressive right-handed batsman. He also played association football in the Football League and Welsh Football League where he played as a left-back.

Football Career
He won a Welsh youth cap at football and was on the books at Swansea Town where provided reliable defensive cover. He turned professional at the Vetch Field in August 1951. He was given his League debut in Swansea's 2–0 defeat at home to Everton in March 1954, making eight Second Division appearances before concentrating on his cricket career. He also had spells with Brecon Corinthians and Llanelly in the Welsh Football League.

Cricket Career
Pressdee made his debut for Glamorgan against Nottinghamshire at Cardiff Arms Park when just 16 years of age, making him the county's youngest player in the post-war period.

A regular in the Glamorgan team from 1955 onwards, Pressdee scored a thousand runs in a season on six occasions, with 1911 in 1962 being his best. He scored 13 centuries including one for North-Eastern Transvaal. Playing 322 games for Glamorgan CCC between 1949 and 1965.

Pressdee was an inconsistent bowler. Having taken 71 wickets in 1955, he captured only 54 during the next seven seasons, but then in 1963 and 1964 he came to prominence as an all-rounder, completing the double (1000 runs and 100 wickets) in each season. In 1965, he took 9 for 43 against Yorkshire at Swansea. Pressdee emigrated to South Africa in 1965 where he played for North-Eastern Transvaal until 1969–70. He won the South African Cricket Annual Cricketer of the Year award in 1966 after taking 33 wickets.

Pressdee returned to Wales in the 1980s and captained the Glamorgan Colts side in the South Wales Cricket Association.
Answer: James Stuart Pressdee played both football and cricket. Though he was an inconsistent bowler, he won the South African Cricket Annual Cricketer of the Year award in 1966. Prior to his professional sports career, James won a Welsh youth cap in football in Swansea Town. Jame was born on June 19th, 1933, and he died on July 20th, 2016 at the age of 83.
Question: What is chosen as the target domain data for training and testing?
Evidence: We utilize fasttext BIBREF18 to generate the embeddings of all words from both Text-SQuAD and Spoken-SQuAD. We adopt the phoneme sequence embeddings to replace the original character sequence embeddings using the method proposed by Li et al. BIBREF5 . The source domain model and the target domain model share the same set of word embedding matrix to improve the alignment between these two domains.
W-GAN is adopted for our domain discriminator BIBREF19 , which stacks 5 residual blocks of 1D convolutional layers with 96 filters and filter size 5 followed by one linear layer to convert each input vector sequence into one scalar value.
All models used in the experiments are trained with batch size 20, using adam with learning rate INLINEFORM0 and the early stop strategy. The dimension of the hidden state is set to 96 for all layers, and the number of self-attention heads is set to 2. The setup is slightly different but better than the setting suggested by the original QAnet.
Spoken-SQuAD is chosen as the target domain data for train ing and testing.
Answer: Spoken-SQuAD
Question: What were the word embeddings trained on?
Evidence: In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. The latter was chosen for application in the present proposal.
Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 . The work is focused on the analysis of gender bias associated with professions in word embeddings. So therefore into the evaluation of the accuracy of the associations generated, aiming at achieving results as good as possible without prejudicing the evaluation metrics.
In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. 
Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 . 
Answer: The answers are shown as follows:
* large Portuguese corpus
Question: How many prototypes were made?
Evidence: The first prototype was entirely mechanical. Three prototypes were made, and they were improved over one year. In the meantime, sure of having found a solution to the problem of taking 360° photos, Leme applied for a patent registration, a process that went on for some years.
Answer: Three prototypes were made and improved over one year.
Question: Which is a species of fish? Black Mamba or Black Sea Bass
Answer: Black Sea Bass
Question: In Digital Marketing, what is the difference between SEO and SEM?
Answer: SEO stands for Search Engine Optimization and it focuses on the creation and enhancement of webpages to improve their rankings in organic search results. SEM stands for Search Engine Marketing and it focuses on the use of paid advertisements to rank at the top of Search Engine Results Page.
Question: Given a reference text about José Francisco Miguel António de Mendonça, tell me when he was appointed patriarch and when he died.
Evidence: José Francisco Miguel António de Mendonça, or rather, Mendoça (Lisbon, October 2, 1725 - Lisbon, February 11, 1808), was the fifth Patriarch of Lisbon under the name of D. José II.

Biography
He was the son of Nuno Manuel de Mendoça, 4th Count of Vale de Reis, and his wife, D. Leonor Maria Antónia de Noronha. He was the younger brother of the 5th Count and 1st Count of Azambuja, and of D. João Rafael de Mendonça, Bishop of Porto.

He graduated in canon law, having been canon of the Patriarchal See of Lisbon, Monsignor and still principal of the same church.

José Mendonca succeeded D. Francisco de Lemos as rector of the University of Coimbra from 1780 to 1785. During his tenure he attempted introduce a number of reforms.

In 1786, he was appointed Patriarch of Lisbon. By 1792 he was still trying to get permission from the Real Mesa Censória (Royal Censorial Court) to publish two pastoral letters. A law passed on April 5, 1768 reaffirmed the right of "temporal sovereignty" over the prohibition of "pernicious books and papers" in the interest of political defense. As the Mesa had jurisdiction over all printed materials, this included pastoral letters.

Pope Pius VI made him a cardinal in 1788. He died in 1808 and his body is buried in the Convent of Grace.
Answer: José Francisco Miguel António de Mendonça was appointed Patriarch of Lisbon in 1786 and died in 1808.