DataHammer/scimrc huggingface dataset:
Question: What is the baseline?
Evidence: Evaluation metric. Since our problem is imbalanced, we use the F1 score as our evaluation metric. For the tagging approach, we average the labels of words with the same stemmed version to obtain a single prediction for the stemmed word. To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances).
To examine the utility of our features in a neural framework, we further adapt our word-level task as a tagging task, and use LSTM as a baseline. Specifically, we concatenate an OP and PC with a special token as the separator so that an LSTM model can potentially distinguish the OP from PC, and then tag each word based on the label of its stemmed version. We use GloVe embeddings to initialize the word embeddings BIBREF40. We concatenate our proposed features of the corresponding stemmed word to the word embedding; the resulting difference in performance between a vanilla LSTM demonstrates the utility of our proposed features. We scale all features to $[0, 1]$ before fitting the models. As introduced in Section SECREF3, we split our tuples of (OP, PC, explanation) into training, validation, and test sets, and use the validation set for hyperparameter tuning. Refer to the supplementary material for additional details in the experiment.
 To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances).
To examine the utility of our features in a neural framework, we further adapt our word-level task as a tagging task, and use LSTM as a baseline. Specifically, we concatenate an OP and PC with a special token as the separator so that an LSTM model can potentially distinguish the OP from PC, and then tag each word based on the label of its stemmed version. We use GloVe embeddings to initialize the word embeddings BIBREF40. We concatenate our proposed features of the corresponding stemmed word to the word embedding; the resulting difference in performance between a vanilla LSTM demonstrates the utility of our proposed features.
Answer: The answers are shown as follows:
* random method 
* LSTM
Question: Who has won the most grand slams in men's singles tennis in open era?
Evidence: The Open Era is the current era of professional tennis. It began in 1968 when the Grand Slam tournaments allowed professional players to compete with amateurs, ending the division that had persisted since the dawn of the sport in the 19th century. The first open tournament was the 1968 British Hard Court Championships held in April, followed by the inaugural open Grand Slam tournament, the 1968 French Open, a month later. Unless otherwise sourced, all records are based on data from the Association of Tennis Professionals (ATP), the International Tennis Federation (ITF), and the official websites of the four Grand Slam tournaments. All rankings-related records are based on ATP rankings, which began in 1973.
Answer: Rafael Nadal and Novak Djokovic won 22 times.
Question: How much F1 was improved after adding skip connections?
Evidence: Table TABREF20 reports the F1 and EM scores obtained for the experiments on the base model. The first column reports the base BERT baseline scores, while the second reports the results for the C2Q/Q2C attention addition. The two skip columns report scores for the skip connection connecting the BERT embedding layer to the coattention output (Simple Skip) and the scores for the same skip connection containing a Transformer block (Transformer Skip). The final column presents the result of the localized feature extraction added inside the C2Q/Q2C architecture (Inside Conv - Figure FIGREF8).
FLOAT SELECTED: Table 2: Performance results for experiments relative to BERT base
Table TABREF20 reports the F1 and EM scores obtained for the experiments on the base model. The first column reports the base BERT baseline scores, while the second reports the results for the C2Q/Q2C attention addition. The two skip columns report scores for the skip connection connecting the BERT embedding layer to the coattention output (Simple Skip) and the scores for the same skip connection containing a Transformer block (Transformer Skip).
FLOAT SELECTED: Table 2: Performance results for experiments relative to BERT base
Answer: Simple Skip improves F1 from 74.34 to 74.81
Transformer Skip improes F1 from 74.34 to 74.95
Question: What story does your family always tell about you?
Answer: My family loves to tell the story from when I was little. I used to tell everyone that I thought I would own a large white airplane with pink seats. My parents have a video recording of me saying that I love to fly, and that when I am older, I would have a beautiful white airplane. I do not have an airplane, nor is it white. Nor does it have pink seats! I'm not sure why I thought this was important or possible. I believe my family tells this story because a toddler believing that they can have an airplane is surprising.
Question: Could you write a poem about a standing desk?
Answer: The Standing Desk


Two doors down
Standing in the corner
Looking out the window 
Is your desk
Back turned
Wailing at all of us
Wondering why we can’t hear it
Whispering sometimes with secrets for the room

When the door closes, it is particularly loud 
Screaming, screaming mad
Angry and lonely 
Moaning now for us to find its partner 
Groaning now for why, why, why
And only when it hears the occupants in the room questioning and turning to stare is it mollified 

We unplugged it to keep it quiet
Left it tall and high at the position you liked best
Chair tucked neatly under
Gleaming clean and bear, beckoning for the next master 
And when everyone is busy 
Meeting our many meetings
It hums softly (usually that same song you taught it), seemingly content and happy 

People enter, come and go
And the Observer watches and waits for a pause 
Wanting to insert something weird and funny
I glare at it when I come in
It knows, I know.
I know, I know! Shhhhhh! 
“We’re working on it,” I say
It raises a brow at me 
I roll my eyes at it and shake my head
“Not enough!”, it throws at me as I slink out walking backwards 

Searching, I stay out of sight
Looking for its new companion
Specs on hand, omissions at the back of my throat
Someone smart, yet dumb enough
Someone commanding, yet submissive
Someone current, yet mature
Someone good but not too much 
“Someone who will stay,” the desk whispers hopefully 

I grimace and say: 
“I’ll take someone tall enough for the desk!”

It taunts and laughs at me
While the memories flood in 
And I start to laugh too
And the desk and I share a grin 

“Not funny,” I say
The desk shrugs and finally stays quiet 

One day, when everyone was gone, I sneak in and stand before it 
Power it up and press the down button so I can rest my elbows on its smooth surface 
And I look out the dark window
In unison, we share a heavy sigh 
Choked up, I whisper: “A new person will be here soon and I’ll be gone.
Watch over everyone, will you?”
The desk nods quietly, lump in throat 

I take an orange sticker and press it gently underneath as a reminder of its promise 
And then walk two doors down to add one to my own desk before heading home for the night
As the elevator takes me down, the desk scans the empty office, takes a deep breath, and stands at the ready, a white knight brave but trembling, and starts humming your song.
Question: Why does the model yield poor performance on the test and validation sets?
Evidence: During training, we find that the model can easily overfit the training data, which yields poor performance on the test and validation sets. To make the model more stable on the unseen data, we apply dropout to the word embeddings, POS embeddings, vectors after the convolutional layers and the stacked recurrent layers. In Figure FIGREF24 , we examine the accuracies dropout rates in INLINEFORM0 . We find that adding dropout alleviates overfitting issues on the training set. If we reduce the dropout rate to INLINEFORM1 , which means randomly setting some values to zero with probability INLINEFORM2 , the training F1-Multi increases rapidly and the validation F1-multi score is the lowest among all the settings. Preliminary results proved best for a dropout rate of INLINEFORM3 , so we use this in all the experiments.
During training, we find that the model can easily overfit the training data, which yields poor performance on the test and validation sets.
Answer: Because the model can easily overfit the training data.
Question: Apart from sarcasm in text, what other reasons may cause longer fixation duration?
Evidence: Since sarcasm in text can be expected to induce cognitive load, it is reasonable to believe that it would require more processing time BIBREF14 . Hence, fixation duration normalized over total word count should usually be higher for a sarcastic text than for a non-sarcastic one. We observe this for all participants in our dataset, with the average fixation duration per word for sarcastic texts being at least 1.5 times more than that of non-sarcastic texts. To test the statistical significance, we conduct a two-tailed t-test (assuming unequal variance) to compare the average fixation duration per word for sarcastic and non-sarcastic texts. The hypothesized mean difference is set to 0 and the error tolerance limit ( INLINEFORM0 ) is set to 0.05. The t-test analysis, presented in Table TABREF11 , shows that for all participants, a statistically significant difference exists between the average fixation duration per word for sarcasm (higher average fixation duration) and non-sarcasm (lower average fixation duration). This affirms that the presence of sarcasm affects the duration of fixation on words.
It is important to note that longer fixations may also be caused by other linguistic subtleties (such as difficult words, ambiguity and syntactically complex structures) causing delay in comprehension, or occulomotor control problems forcing readers to spend time adjusting eye-muscles. So, an elevated average fixation duration per word may not sufficiently indicate the presence of sarcasm. But we would also like to share that, for our dataset, when we considered readability (Flesch readability ease-score BIBREF15 ), number of words in a sentence and average character per word along with the sarcasm label as the predictors of average fixation duration following a linear mixed effect model BIBREF16 , sarcasm label turned out to be the most significant predictor with a maximum slope. This indicates that average fixation duration per word has a strong connection with the text being sarcastic, at least in our dataset.
We now analyze scanpaths to gain more insights into the sarcasm comprehension process.
It is important to note that longer fixations may also be caused by other linguistic subtleties (such as difficult words, ambiguity and syntactically complex structures) causing delay in comprehension, or occulomotor control problems forcing readers to spend time adjusting eye-muscles.
Answer: Longer fixations may also be caused by other linguistic subtleties (such as difficult words, ambiguity and syntactically complex structures) causing delay in comprehension, or occulomotor control problems forcing readers to spend time adjusting eye-muscles.
Question: What is the main difference among RAML, MaliGAN and the framework in this paper?
Evidence: The most similar works to our framework are RAML BIBREF17 and MaliGAN BIBREF8 . The main difference among them is the training objective of their generators. We have shown different objective functions in Table TABREF26 . For comparison, we use the form with no input for all the three models.
Our model is greatly inspired by RAML, which gets samples from a non-parametric distribution INLINEFORM0 constructed based on a specific reward. Compared to RAML, our reward comes from a learnable discriminator which varies as the adversarial training proceeds rather than a specific reward function. This difference equips our framework with the ability to adapt to the text generation tasks with no explicit evaluation metrics as rewards.
Our model is also similar to MaliGAN, which gets samples from the generator's distribution. In MaliGAN's training objective, INLINEFORM0 also indicates the generator's distribution but it's used in the sampling phase and fixed at each optimization step. The weight of samples INLINEFORM1 . Different from our model, MaliGAN acquires samples from the generator's distribution INLINEFORM2 , which usually brings samples with low rewards even with careful pre-training for the generator, leading to training instability. Instead, our framework gets samples from a stationary distribution INLINEFORM3 around real data, thus our training process is more stable.
The most similar works to our framework are RAML (Norouzi et al., 2016) and MaliGAN (Che et al., 2017). The main difference among them is the training objective of their generators.
Answer: The main difference among them is the training objective of their generators.
Question: Please share some important points about actress Zendaya using given passage as a reference
Evidence: Born and raised in Oakland, California, Zendaya began her career as a child model and backup dancer. She made her television debut as Rocky Blue on the Disney Channel sitcom Shake It Up (2010–2013) and starred as the titular character in the sitcom K.C. Undercover (2015–2018), for the channel. Her feature film debut came in 2017 with the superhero film Spider-Man: Homecoming, and she later starred in its sequels. Zendaya's role as Rue Bennett, a struggling drug addicted teenager, in the HBO teen drama series Euphoria (2019–present) made her the youngest recipient of the Primetime Emmy Awards for Outstanding Lead Actress in a Drama Series, which she won twice. Her film roles include the musical The Greatest Showman (2017), the romantic drama Malcolm & Marie (2021), and the science fiction epic Dune (2021).
Answer: 1.	Zendaya is born and grown up in Oakland, California.
2.	She started her career as a child model and made her television debut as Rocky Blue in  Shake It Up (2010–2013) and starred as the titular character in K.C. Undercover (2015–2018), on Disney Channel.
3.	Zendaya made her film debut in 2017 with Spider-Man: Homecoming, and she later starred in its sequels.
4.	Zendaya is the youngest recipient of the Primetime Emmy Awards for Outstanding Lead Actress in a Drama Series, which she won twice for Zendaya's role as Rue Bennett, a struggling drug addicted teenager, in the HBO teen drama series Euphoria (2019–present).
5.	Few of her film roles include the musical The Greatest Showman (2017), the romantic drama Malcolm & Marie (2021), and the science fiction epic Dune (2021).
Question: Classify these movies based on their release year
Kedarnath, Bala, Raazi, Kaashi, Article 15
Answer: 2018 - Kedarnath, Raazi, Kaashi
2019 - Bala, Article 15
Question: Does Amazon use doors for desks?
Answer: Jeff Bezos initially used doors purchased from the local hardware store and added legs, and door desk was born.  He did that because funds were limited, and this was the least expensive way for him to purchase a desk.  Today, Amazon still uses wooden doors with four pieces of wood attached for legs.  New employees receive directions to calculate the optimum desk height, then fill out a form, and maintenance cuts the wooden legs to achieve the correct size.  The company does provide adjustable desks by request only.
Question: Is their data from Twitter or Facebook?
Evidence: Change is a universal property of language. For example, English has changed so much that Renaissance-era texts like The Canterbury Tales must now be read in translation. Even contemporary American English continues to change and diversify at a rapid pace—to such an extent that some geographical dialect differences pose serious challenges for comprehensibility BIBREF0 . Understanding language change is therefore crucial to understanding language itself, and has implications for the design of more robust natural language processing systems BIBREF1 .
Language change is a fundamentally social phenomenon BIBREF2 . For a new linguistic form to succeed, at least two things must happen: first, speakers (and writers) must come into contact with the new form; second, they must decide to use it. The first condition implies that language change is related to the structure of social networks. If a significant number of speakers are isolated from a potential change, then they are unlikely to adopt it BIBREF3 . But mere exposure is not sufficient—we are all exposed to language varieties that are different from our own, yet we nonetheless do not adopt them in our own speech and writing. For example, in the United States, many African American speakers maintain a distinct dialect, despite being immersed in a linguistic environment that differs in many important respects BIBREF4 , BIBREF5 . Researchers have made a similar argument for socioeconomic language differences in Britain BIBREF6 . In at least some cases, these differences reflect questions of identity: because language is a key constituent in the social construction of group identity, individuals must make strategic choices when deciding whether to adopt new linguistic forms BIBREF7 , BIBREF8 , BIBREF9 . By analyzing patterns of language change, we can learn more about the latent structure of social organization: to whom people talk, and how they see themselves.
But, while the basic outline of the interaction between language change and social structure is understood, the fine details are still missing: What types of social network connections are most important for language change? To what extent do considerations of identity affect linguistic differences, particularly in an online context? Traditional sociolinguistic approaches lack the data and the methods for asking such detailed questions about language variation and change.
In this paper, we show that large-scale social media data can shed new light on how language changes propagate through social networks. We use a data set of Twitter users that contains all public messages for several million accounts, augmented with social network and geolocation metadata. This data set makes it possible to track, and potentially explain, every usage of a linguistic variable as it spreads through social media. Overall, we make the following contributions:
We use a data set of Twitter users that contains all public messages for several million accounts, augmented with social network and geolocation metadata.
Answer: Twitter.
Question: Tell me whether these are movies or books: Jurassic, Harry Potter and the Philosopher's Stone, Angels and Demons
Answer: That is a tricky question to answer because these are both books and movies! I will say that the books mentioned in your question were written before the movies were made.
Question: How do cryptic crosswords work?
Answer: Normal crosswords simply give a clue that defines their answer. For example, "Feline - 3 letters" would be answered by "cat". Cryptic crosswords clues have various forms but generally they have part of the clue that is a definition and the rest of the clue that hints at the same definition through coded methods like wordplay or anagrams. For example "Smallest half of the alphabet - 4 letters" is solvable as "atom" where "atom" is theoretically the smallest possible particle of an element, and "A to M" is half of the alphabet.
Question: What are the continents from largest to smallest?
Answer: The continents by size, from largest to smallest are: Asia, Africa, North America, South America, Antarctica, Europe, and Australia.
Question: Did the Croatian group have a concrete idea for tackling alignment?
Evidence: At this point, we are leaving the historical analysis behind to speculate on what the group might have discovered if they had had access to a computer. First of all, did the Croatian group have a concrete idea for tackling alignment? Not really. However, an approach can be read between the lines of primarily BIBREF16 and BIBREF17. In BIBREF17, Pranić addresses the Soviet model by Andreev, looking at it as if it was composed of two modules – an understanding module and a generation module. Following the footsteps of Andreev, their interaction should be over an idealized language. Laszlo BIBREF16 notes that such an idealized language should be encoded by keeping the entropy in mind. He literally calls for using entropy to eliminate redundancy while translating to an artificial language, and as Mulić notes BIBREF7, Andreev's idea (which should be followed) was to use an artificial language as an intermediary language, which has all the essential structures of all the languages one wishes to translate.
The step which was needed here was to eliminate the notion of structure alignment and just seek sentential alignment. This, in theory, can be done by using only entropy. A simple alignment could be made by using word entropies in both languages and aligning the words by decreasing entropy. This would work better for translating into a language with no articles. A better approach, which was not beyond the thinking of the group since it was already proposed by Matković in his dissertation from 1957 BIBREF20, would be to use word bigrams and align them. It is worth mentioning that, although the idea of machine translation in the 1950s in Croatia did not have a significant influence on development of the field, it shows that Croatian linguists had contemporary views and necessary competencies for its development. But, unfortunately, the development of machine translation in Croatia had been stopped because of the previously discussed circumstances. In 1964, Laszlo went to the USA, where he spent the next seven years, and after returning to Croatia, he was active as a university professor, but because of disagreement with the ruling political option regarding Croatian language issues, he published very rarely and was mainly focused on other linguistic issues in that period, but his work was a major influence on the later development of computational linguistics in Croatia.
First of all, did the Croatian group have a concrete idea for tackling alignment? Not really.
Answer: Not really.
Question: What are the primary causes of climate change?
Evidence: In common usage, climate change describes global warming—the ongoing increase in global average temperature—and its effects on Earth's climate system. Climate change in a broader sense also includes previous long-term changes to Earth's climate. The current rise in global average temperature is more rapid than previous changes, and is primarily caused by humans burning fossil fuels. Fossil fuel use, deforestation, and some agricultural and industrial practices increase greenhouse gases, notably carbon dioxide and methane. Greenhouse gases absorb some of the heat that the Earth radiates after it warms from sunlight. Larger amounts of these gases trap more heat in Earth's lower atmosphere, causing global warming.
Answer: The primary physical causes or drivers of climate change are:
- Humans burning fossil fuels
- Greenhouse gases such as carbon dioxide, methane and nitrous oxide
- Aerosols such as sulfur dioxide, organic carbon, black carbon and ammonia
Question: Name some characters from the TV show "Friends"?
Answer: The main characters on the TV show "Friends" are Rachel Green, Ross Geller, Monica Geller, Chandler Bing, Joey Tribiani, and Phoebe Buffay. Secondary characters include Gunther,  manager of Central Perk, Richard, Monica's ex-boyfriend, and Janice, Chandler's ex-girlfriend. There are two main children on the show, Emma and Ben. Ross is the father of both children. Rachel is Emma's mother. Carol is Ben's mother. Carol was in a relationship with Ross but later married Susan. Additionally, Phoebe Buffay was a surrogate for her brother who has triplets.