DataHammer/scimrc huggingface dataset:
Question: Does the model proposed by the team include unstructured sentences?
Evidence: Table TABREF30 shows automatic evaluation results for our model and baselines (copied from their papers). Our proposed model which combines structured answer-relevant relations and unstructured sentences achieves significant improvements over proximity-based answer-aware models BIBREF9, BIBREF15 on both dataset splits. Presumably, our structured answer-relevant relation is a generalization of the context explored by the proximity-based methods because they can only capture short dependencies around answer fragments while our extractions can capture both short and long dependencies given the answer fragments. Moreover, our proposed framework is a general one to jointly leverage structured relations and unstructured sentences. All compared baseline models which only consider unstructured sentences can be further enhanced under our framework.
Recall that existing proximity-based answer-aware models perform poorly when the distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question is large (Table TABREF2). Here we investigate whether our proposed model using the structured answer-relevant relations can alleviate this issue or not, by conducting experiments for our model under the same setting as in Table TABREF2. The broken-down performances by different relative distances are shown in Table TABREF40. We find that our proposed model outperforms Hybrid (our re-implemented version for this experiment) on all ranges of relative distances, which shows that the structured answer-relevant relations can capture both short and long term answer-relevant dependencies of the answer in sentences. Furthermore, comparing the performance difference between Hybrid and our model, we find the improvements become more significant when the distance increases from “$0\sim 10$” to “$>10$”. One reason is that our model can extract relations with distant dependencies to the answer, which greatly helps our model ignore the extraneous information. Proximity-based answer-aware models may overly emphasize the neighboring words of answers and become less effective as the useful context becomes further away from the answer in the complex sentences. In fact, the breakdown intervals in Table TABREF40 naturally bound its sentence length, say for “$>10$”, the sentences in this group must be longer than 10. Thus, the length variances in these two intervals could be significant. To further validate whether our model can extract long term dependency words. We rerun the analysis of Table TABREF40 only for long sentences (length $>$ 20) of each interval. The improvement percentages over Hybrid are shown in Table TABREF40, which become more significant when the distance increases from “$0\sim 10$” to “$>10$”.
Our proposed model which combines structured answer-relevant relations and unstructured sentences achieves significant improvements over proximity-based answer-aware models (Zhou et al., 2017; Sun et al., 2018) on both dataset splits.
Answer: Yes.
Question: How would you describe Dance?
Evidence: Dance is an art form consisting of sequences of body movements with aesthetic and often symbolic value, either improvised or purposefully selected.[nb 1] Dance can be categorized and described by its choreography, by its repertoire of movements, done simultaneously with music or with instruments; or by its historical period or place of origin.
Answer: Dance is an art consisting of precision sequence of body movements either improvised or intentionally selected.   Dance can be described by the category of choreography and repertoire of movements done simultaneously with music or with instruments.   It can also be defined by its historical period or place of origin.   Students studying the performing art of dance often attend a Dance conservatory or university.   An example of a highly regarded dance conservatory is Point Park University in Pittsburgh, Pa.
Question: How are entities mapped onto polar coordinate system?
Evidence: Combining the modulus part and the phase part, HAKE maps entities into the polar coordinate system, where the radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively. That is, HAKE maps an entity $h$ to $[\textbf {h}_m;\textbf {h}_p]$, where $\textbf {h}_m$ and $\textbf {h}_p$ are generated by the modulus part and the phase part, respectively, and $[\,\cdot \,; \,\cdot \,]$ denotes the concatenation of two vectors. Obviously, $([\textbf {h}_m]_i,[\textbf {h}_p]_i)$ is a 2D point in the polar coordinate system. Specifically, we formulate HAKE as follows:
Combining the modulus part and the phase part, HAKE maps entities into the polar coordinate system, where the radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively.
Answer: The answers are shown as follows:
* radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively
Question: What task is their method especially useful for?
Evidence: In recent years, the micro-blogging site Twitter has become a major social media platform with hundreds of millions of users. The short (140 character limit), noisy and idiosyncratic nature of tweets make standard information retrieval and data mining methods ill-suited to Twitter. Consequently, there has been an ever growing body of IR and data mining literature focusing on Twitter. However, most of these works employ extensive feature engineering to create task-specific, hand-crafted features. This is time consuming and inefficient as new features need to be engineered for every task.
In this paper, we present Tweet2Vec, a method for generating general-purpose vector representation of tweets that can be used for any classification task. Tweet2Vec removes the need for expansive feature engineering and can be used to train any standard off-the-shelf classifier (e.g., logistic regression, svm, etc). Tweet2Vec uses a CNN-LSTM encoder-decoder model that operates at the character level to learn and generate vector representation of tweets. Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics BIBREF0 , BIBREF1 ).
There has been several works on generating embeddings for words, most famously Word2Vec by Mikolov et al. BIBREF2 ). There has also been a number of different works that use encoder-decoder models based on long short-term memory (LSTM) BIBREF3 , and gated recurrent neural networks (GRU) BIBREF4 . These methods have been used mostly in the context of machine translation. The encoder maps the sentence from the source language to a vector representation, while the decoder conditions on this encoded vector for translating it to the target language. Perhaps the work most related to ours is the work of Le and Mikolov le2014distributed, where they extended the Word2Vec model to generate representations for sentences (called ParagraphVec). However, these models all function at the word level, making them ill-suited to the extremely noisy and idiosyncratic nature of tweets. Our character-level model, on the other hand, can better deal with the noise and idiosyncrasies in tweets. We plan to make our model and the data used to train it publicly available to be used by other researchers that work with tweets.
Our method is especially useful for natural language processing tasks on Twitter where it is particularly difficult to engineer features, such as speech-act classification and stance detection (as shown in our previous works on these topics [13, 12]).
Answer: Natural language processing tasks on Twitter.
Question: Classify the following as either dark-colored beers or light colored beers: porter, pilsner, stout, amber, lager
Answer: Dark-colored beers: porter, stout, amber
Light-colored beers: pilsner, lager
Question: What University has most Heisman Trophy winners?
Answer: USC, The University of Southern California, has the most Heisman Trophy Winners: 1965 Mike Garrett, 1968 O. J. Simpson, 1979 Charles White, 1981 Marcus Allen, 2002 Carson Palmer, 2004 Matt Leinart, 2005, Reggie Bush, 2022 Caleb Williams. The Heisman Trophy is considered the most prestigious collegiate individual athletic award.
Question: What's a romatic poem about the universe and time?
Answer: In the universe I know

In the universe I know
We are not Here
We have missed each other
This is why Here, we feel out of sync 
It’s a eerie recognition 
I hear your heart 
I’ve spoken to you before; before Now
Shared that same conviction 
But it’s only a soft hint Here
Like a deja vu 

Is it time or space? 
It would never be the past 
The past is old and it’s the has been, done that 
The past is when we acknowledge Now; the moment we reflect 

The moment we realize that again time has failed us 
With its seeds of doubt
And badge of wisdom...
that necessary caution built brick by brick

In the universe I know, we have made it Here
To our moment
To the moment I’ve been waiting for

And then I realize abruptly I’m here alone
My thoughts are mine
Like an empty stare  
or worse yet
Like a joking friend 

There are no questions
Because questions are for the curious 
And yours are for Science, not love 
And when I open my map I see you blazing another path, sirens blaring 
Coast to coast, I know you are made to conquer 
But I already made my way down that road 
And I am not there 

In the universe I know, we are not Here
Instead, we are for another Future
And I hope you’ll know me then
Question: What was the profession of Araber Rahaman?
Evidence: Araber Rahaman (died 28 April 2013) was an Indian politician. He was the Tripura MLA for Boxanagar from 1978–1988.
Answer: He was a politician.
Question: From the given paragraph extract all the cities that got the rail along with the year in the format {City} - {Year} and show as a bullet list
Evidence: India's first passenger train, operated by the Great Indian Peninsula Railway and hauled by three steam locomotives (Sahib, Sindh and Sultan), ran for 34 kilometres (21 mi) with 400 people in 14 carriages on 1,676 mm (5 ft 6 in) broad gauge track between Bori Bunder (Mumbai) and Thane on 16 April 1853. The Thane viaducts, India's first railway bridges, were built over the Thane creek when the Mumbai-Thane line was extended to Kalyan in May 1854. Eastern India's first passenger train ran 39 km (24 mi) from Howrah, near Kolkata, to Hoogly on 15 August 1854. The first passenger train in South India ran 97 km (60 mi) from Royapuram-Veyasarapady (Madras) to Wallajaroad (Arcot) on 1 July 1856.

On 24 February 1873, a horse-drawn 3.8 km (2.4 mi) tram opened in Calcutta between Sealdah and Armenian Ghat Street. On 9 May 1874, a horse-drawn tramway began operation in Bombay between Colaba and Parel. In 1879, the Nizam's Guaranteed State Railway was established which built several railway lines across the then Hyderabad State with Kachiguda Railway Station serving as its headquarters. In 1897, lighting in passenger coaches was introduced by many railway companies. On 3 February 1925, the first electric passenger train in India ran between Victoria Terminus and Kurla.
Answer: Bori Bunder (Mumbai) - 1853
Thane - 1853
Howrah - 1854
Hoogly - 1854
Royapuram-Veyasarapady (Madras) - 1856
Wallajaroad (Arcot) - 1856
Sealdah - 1873
Armenian Ghat Street - 1873
Colaba - 1874
Parel - 1874
Kachiguda - 1879
Victoria Terminus - 1925
Kurla - 1925
Question: Extract the reasons why inverted yield curve could happen, and place them in a bullet list.
Evidence: Under unusual circumstances, investors will settle for lower yields associated with low-risk long-term debt if they think the economy will enter a recession in the near future. For example, the S&P 500 experienced a dramatic fall in mid 2007, from which it recovered completely by early 2013. Investors who had purchased 10-year Treasuries in 2006 would have received a safe and steady yield until 2015, possibly achieving better returns than those investing in equities during that volatile period.

Economist Campbell Harvey's 1986 dissertation showed that an inverted yield curve accurately forecasts U.S. recessions. An inverted curve has indicated a worsening economic situation in the future eight times since 1970.

In addition to potentially signaling an economic decline, inverted yield curves also imply that the market believes inflation will remain low. This is because, even if there is a recession, a low bond yield will still be offset by low inflation. However, technical factors, such as a flight to quality or global economic or currency situations, may cause an increase in demand for bonds on the long end of the yield curve, causing long-term rates to fall. Falling long-term rates in the presence of rising short-term rates is known as "Greenspan's Conundrum".
Answer: There are two reasons why an inverted yield curve could happen
1. When investors think the economy will enter a recession in the near future, they will settle for lower yields associated with low-risk long-term debt, which would likely result in higher returns than investments in equities during the same time.
2. When the market believes inflation will remain low, there will be more demand for bonds on the long end of the yield curve, causing long-term rates to fall.
Question: What is Michael Pollan's top bestselling book?
Answer: The Omnivores Dilemma is Michael Pollan's highest bestseller, published in 2006.
Question: What do you think: can we release this without a UI and add that later? Can we release it as GA without a UI?
Answer: I think we're better off releasing it as an update to. the public preview if we aren't going to have a UI. And the API needs to be very well documented. If we do release it as GA without a UI, we should definitely message to customers that a UI is coming (although not promise a time frame or ETA).
Question: What was used for the extraction of morphological rules and the matching with the tags?
Evidence: Different labels were found at the dataset and were matched to a label map, where for each label the part of the speech and their morphology are analyzed. In more detail, the first two characters refer to the part of speech and accordingly extend to more information about it. The label map supports 16 standard part of speech tags: Adjective, Adposition, Adverb, Coordinating Conjuction, Determiner, Interjection, Noun, Numeral, Particle, Pronoun, Proper Noun, Punctuation, Subordinating Conjuction, Symbol, Verb and Other. Each tag describes morphological features of the word, depending on the part of the speech to which it refers like the gender, the number, and the case BIBREF6. It must be mentioned that the extraction of morphological rules and the matching with the tags was done using the Greek version of the Universal Dependencies BIBREF7.
It must be mentioned that the extraction of morphological rules and the matching with the tags was done using the Greek version of the Universal Dependencies [8].
Answer: The Greek version of the Universal Dependencies.
Question: what would I need of the following for a hiking trip?
headphones
internet
trek shoes
winter gear
camp
water
food
car
towels
jeans
guitar
Answer: Need: trek shoes, winter gear, camp, water, food, towels
Dont need: headphones, internet, car, jeans, guitar
Question: Why do people think that cricket is a boring sport?
Answer: People think that cricket is a boring sport because they fundamentally do not understand how the various forms of the game work and what their respective laws are.  They are blind to its beauty. 

There are three main forms of cricket, T20 (lasting approximately three to four hrs), One Day (lasting a playing day) and First Class/Test Matches (lasting four to five playing days). 

Each form of the game includes specific laws but fundamentally the objective is to score more runs than your opponents whilst giving away fewer wickets. It is a game of real skill and strategy, requiring physical fitness, agility, strength and stamina as well as mental rigour, concentration, resilience and also stamina. 

In the longest form of the game, it is indeed a possible outcome that two teams can play each other for five days and a DRAW is still possible. It is the hardest sport in the world to win if you can play for five days and still not win! 

How can anyone say that's boring...?
Question: What happened at the 1936 United States presidential election
Answer: The 1936 United States presidential election in Nevada took place on November 3, 1936, as part of the 1936 United States presidential election. State voters chose three representatives, or electors, to the Electoral College, who voted for president and vice president.

Nevada was won by incumbent President Franklin D. Roosevelt (D–New York), running with Vice President John Nance Garner, with 72.81% of the popular vote, against Governor Alf Landon (R–Kansas), running with Frank Knox, with 27.19% of the popular vote.

As of the 2020 presidential election, this is the last occasion Douglas County voted for a Democratic presidential candidate, and the last time a Democratic nominee won every single county in the state.
Question: Mahatma Gandhi gave a slogan on Aug 8, 1942 .What was the slogan?
Answer: Do or Die for Independence
Question: What are some common types of winter squash for cooking?
Answer: Winter squash is defined as the fruit of a squash plant that grows with a hard outer shell. Common types of winter squash used in cooking are pumpkin, butternut squash, acorn squash, kabocha squash, spaghetti squash, and delicata squash.
Question: Which of these are capitals of states in USA: Richmond, San Diego, San Francisco, Sacramento, New orleans, New York, Seattle, Carson City, Portland, Jamaica City, Salem, San louis obispo, Trenton, Bellevue, Redmond, Renton, Kirkland
Answer: Richmond, Sacramento, Carson city, Salem and Trenton are all state capitals.
Question: What are five popular Taylor Swift songs?
Answer: Five popular Taylor Swift songs are Shake it Off, Wildest Dreams, Bad Blood, Look What You Made Me Do, and Love Story
Question: Whether the author's sentiment analyzer works better than the one in Brian's work?
Evidence: The most well-known publication in this area is by Bollen BIBREF10 . They investigated whether the collective mood states of public (Happy, calm, Anxiety) derived from twitter feeds are correlated to the value of the Dow Jones Industrial Index. They used a Fuzzy neural network for their prediction. Their results show that public mood states in twitter are strongly correlated with Dow Jones Industrial Index. Chen and Lazer BIBREF11 derived investment strategies by observing and classifying the twitter feeds. Bing et al. BIBREF12 studied the tweets and concluded the predictability of stock prices based on the type of industry like Finance, IT etc. Zhang BIBREF13 found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Recently, Brian et al. BIBREF14 investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stocks. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation. Sentiment analyzer is used to classify the sentiments in tweets extracted.The human annotated dataset in our work is also exhaustive. We have shown that a strong correlation exists between twitter sentiments and the next day stock prices in the results section. We did so by considering the tweets and stock opening and closing prices of Microsoft over a year.
The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation.
Answer: Yes.
Question: Tell me if the following activities are healthy or harmful: sleeping, running, eating fruit, smoking, listening to music, drinking alcohol, occasional cocaine usage, hiking by yourself in the desert with no water, eating McDonald’s
Answer: There are a variety of activities here, some are healthy while others will result in doing harm to your body.  The clear healthy activities include: sleeping, running, eating fruit and listening to music.  The activities that are clearly harmful include: smoking, occasional cocaine usage, hiking by yourself in the desert with no water and eating McDonald’s.  There is one activity that is widely thought to be healthy when done in moderation: drinking alcohol.
Question: From the passage provided, extract the state Lincoln was born in
Evidence: Abraham Lincoln (/ˈlɪŋkən/ LINK-ən; February 12, 1809 – April 15, 1865) was an American lawyer, politician and statesman who served as the 16th president of the United States from 1861 until his assassination in 1865. Lincoln led the Union through the American Civil War to defend the nation as a constitutional union and succeeded in abolishing slavery, bolstering the federal government, and modernizing the U.S. economy.

Lincoln was born into poverty in a log cabin in Kentucky and was raised on the frontier, primarily in Indiana. He was self-educated and became a lawyer, Whig Party leader, Illinois state legislator, and U.S. Congressman from Illinois. In 1849, he returned to his successful law practice in central Illinois. In 1854, he was angered by the Kansas–Nebraska Act, which opened the territories to slavery, and he re-entered politics. He soon became a leader of the new Republican Party. He reached a national audience in the 1858 Senate campaign debates against Stephen A. Douglas. Lincoln ran for president in 1860, sweeping the North to gain victory. Pro-slavery elements in the South viewed his election as a threat to slavery, and Southern states began seceding from the nation. During this time, the newly formed Confederate States of America began seizing federal military bases in the south. Just over one month after Lincoln assumed the presidency, the Confederate States attacked Fort Sumter, a U.S. fort in South Carolina. Following the bombardment, Lincoln mobilized forces to suppress the rebellion and restore the union.
Answer: Lincoln was born in the state of Kentucky
Question: What is your worst habit?
Answer: My worst habit is going to sleep too late at night as it reduces my overall time for sleeping leading to sleep deprivation. There are some benefits to staying up late, including additional time to scroll the internet aimlessly and endeavor in creative pursuits. Ultimately, I believe going to sleep late is my worst habit as it detrimentally impacts several facets of my personal overall health and well-being.
Question: I want you to break these emotions down into happy and sad groups: Upset, Joyful, Elated, Grief, Amused, Upbeat and Melancholy.
Answer: Elated, Joyful Amused and Upbeat are happy emotions. Upset, Grief, and Melancholy are sad emotions.
Question: Find the bird in this list: Aquatic warbler, Tiger, Shark
Answer: Aquatic warbler
Question: What was the first Harry Bosch book called?
Evidence: Detective Hieronymus "Harry" Bosch is a fictional character created by American author Michael Connelly. Bosch debuted as the lead character in the 1992 novel The Black Echo, the first in a best-selling police procedural series now numbering 24 novels.

The novels are more or less coincident in timeframe with the year in which they were published. Harry, as he is commonly known by his associates, is a veteran police homicide detective with the Los Angeles Police Department. He was named after the 15th-century Dutch artist Hieronymus Bosch.

Titus Welliver portrayed the title character from 2015 to 2021 in Bosch, a television series adapted from the novels, and from 2022 in its spin-off series Bosch: Legacy.
Answer: The first novel in the Harry Bosch series  by Michael Connelly was The Black Echo, published in 1992.