DataHammer/scimrc huggingface dataset:
Question: Why were the participants not instructed to annotate whether a sentence is sarcastic or not?
Evidence: The task assigned to annotators was to read sentences one at a time and label them with with binary labels indicating the polarity (i.e., positive/negative). Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not., to rule out the Priming Effect (i.e., if sarcasm is expected beforehand, processing incongruity becomes relatively easier BIBREF12 ). The setup ensures its “ecological validity” in two ways: (1) Readers are not given any clue that they have to treat sarcasm with special attention. This is done by setting the task to polarity annotation (instead of sarcasm detection). (2) Sarcastic sentences are mixed with non sarcastic text, which does not give prior knowledge about whether the forthcoming text will be sarcastic or not.
The eye-tracking experiment is conducted by following the standard norms in eye-movement research BIBREF13 . At a time, one sentence is displayed to the reader along with the “aspect” with respect to which the annotation has to be provided. While reading, an SR-Research Eyelink-1000 eye-tracker (monocular remote mode, sampling rate 500Hz) records several eye-movement parameters like fixations (a long stay of gaze) and saccade (quick jumping of gaze between two positions of rest) and pupil size.
The accuracy of polarity annotation varies between 72%-91% for sarcastic texts and 75%-91% for non-sarcastic text, showing the inherent difficulty of sentiment annotation, when sarcasm is present in the text under consideration. Annotation errors may be attributed to: (a) lack of patience/attention while reading, (b) issues related to text comprehension, and (c) confusion/indecisiveness caused due to lack of context.
For our analysis, we do not discard the incorrect annotations present in the database. Since our system eventually aims to involve online readers for sarcasm detection, it will be hard to segregate readers who misinterpret the text. We make a rational assumption that, for a particular text, most of the readers, from a fairly large population, will be able to identify sarcasm. Under this assumption, the eye-movement parameters, averaged across all readers in our setting, may not be significantly distorted by a few readers who would have failed to identify sarcasm. This assumption is applicable for both regular and multi-instance based classifiers explained in section SECREF6 .
Note that, the participants were not instructed to annotate whether a sentence is sarcastic or not., to rule out the Priming Effect (i.e., if sarcasm is expected beforehand, processing incongruity becomes relatively easier (Gibbs, 1986)).
Answer: To rule out the Priming Effect.
Question: How many topics does the corpus contain?
Evidence: The corpus consists of document clusters for 30 different topics. Each of them contains around 40 documents with on average 2413 tokens, which leads to an average cluster size of 97,880 token. With these characteristics, the document clusters are 15 times larger than typical DUC clusters of ten documents and five times larger than the 25-document-clusters (Table TABREF26 ). In addition, the documents are also more variable in terms of length, as the (length-adjusted) standard deviation is twice as high as in the other corpora. With these properties, the corpus represents an interesting challenge towards real-world application scenarios, in which users typically have to deal with much more than ten documents.
Because we used a large web crawl as the source for our corpus, it contains documents from a variety of genres. To further analyze this property, we categorized a sample of 50 documents from the corpus. Among them, we found professionally written articles and blog posts (28%), educational material for parents and kids (26%), personal blog posts (16%), forum discussions and comments (12%), commented link collections (12%) and scientific articles (6%).
In addition to the variety of genres, the documents also differ in terms of language use. To capture this property, we follow Zopf.2016 and compute, for every topic, the average Jensen-Shannon divergence between the word distribution of one document and the word distribution in the remaining documents. The higher this value is, the more the language differs between documents. We found the average divergence over all topics to be 0.3490, whereas it is 0.3019 in DUC 2004 and 0.3188 in TAC 2008A.
The corpus consists of document clusters for 30 different topics. Each of them contains around 40 documents with on average 2413 tokens, which leads to an average cluster size of 97,880 token.
Answer: 30 different topics.
Question: Provide a few different ideas for games for 4 adults to play.
Answer: There are a number of popular board games that would be suitable for four adult players. Catan is a popular strategy and resource board game for two to four players, Scrabble is a good option and poker is always a popular game for adults and only requires a pack of playing cards and either poker chips or objects that can be used in place of betting chips, such as buttons.
Question: Is a hotdog a sandwich?
Answer: That’s a tricky question because most people would consider a hotdog to not be a sandwich; however, if a sandwich is considered to be meat between two pieces of bread, then it is a sandwich.  There are several counter points to this argument.  A burrito is usually not considered a sandwich but is very similar to a hotdog.  An ice cream sandwich is called a sandwich but has no meat.
Question: Has the TREC considered the idea of out-of-scope data?
Evidence: In most other analyses and datasets, the idea of out-of-scope data is not considered, and instead the output classes are intended to cover all possible queries (e.g., TREC BIBREF7). Recent work by BIBREF8 considers a similar problem they call out-of-distribution detection. They use other datasets or classes excluded during training to form the out-of-distribution samples. This means that the out-of-scope samples are from a small set of coherent classes that differ substantially from the in-distribution samples. Similar experiments were conducted for evaluating unknown intent discovery models in BIBREF9. In contrast, our out-of-scope queries cover a broad range of phenomena and are similar in style and often similar in topic to in-scope queries, representing things a user might say given partial knowledge of the capabilities of a system.
Table TABREF20 compares our dataset with other short-query intent classification datasets. The Snips BIBREF0 dataset and the dataset presented in BIBREF10 are the most similar to the in-scope part of our work, with the same type of conversational agent requests. Like our work, both of these datasets were bootstrapped using crowdsourcing. However, the Snips dataset has only a small number of intents and an enormous number of examples of each. Snips does present a low-data variation, with 70 training queries per intent, in which performance drops slightly. The dataset presented in BIBREF10 has a large number of intent classes, yet also contains a wide range of samples per intent class (ranging from 24 to 5,981 queries per intent, and so is not constrained in all cases).
BIBREF11 created datasets with constrained training data, but with very few intents, presenting a very different type of challenge. We also include the TREC query classification datasets BIBREF7, which have a large set of labels, but they describe the desired response type (e.g., distance, city, abbreviation) rather than the action intents we consider. Moreover, TREC contains only questions and no commands. Crucially, none of the other datasets summarized in Table TABREF20 offer a feasible way to evaluate out-of-scope performance.
The Dialog State Tracking Challenge (DSTC) datasets are another related resource. Specifically, DSTC 1 BIBREF12, DSTC 2 BIBREF13, and DSTC 3 BIBREF14 contain “chatbot style" queries, but the datasets are focused on state tracking. Moreover, most if not all queries in these datasets are in-scope. In contrast, the focus of our analysis is on both in- and out-of-scope queries that challenge a virtual assistant to determine whether it can provide an acceptable response.
In most other analyses and datasets, the idea of out-of-scope data is not considered, and instead the output classes are intended to cover all possible queries (e.g., TREC (Li and Roth, 2002)).
Answer: No, it hasn't.
Question: From the passage provided, extract Tagore's nickname
Evidence: The name Tagore is the anglicised transliteration of Thakur. The original surname of the Tagores was Kushari. They were Pirali Brahmin ('Pirali’ historically carried a stigmatized and pejorative connotation) originally belonged to a village named Kush in the district named Burdwan in West Bengal. The biographer of Rabindranath Tagore, Prabhat Kumar Mukhopadhyaya wrote in the first volume of his book Rabindrajibani O Rabindra Sahitya Prabeshak that

The Kusharis were the descendants of Deen Kushari, the son of Bhatta Narayana; Deen was granted a village named Kush (in Burdwan zilla) by Maharaja Kshitisura, he became its chief and came to be known as Kushari.

Life and events
Early life: 1861–1878
Main article: Early life of Rabindranath Tagore

Young Tagore in London, 1879
The last two days a storm has been raging, similar to the description in my song—Jhauro jhauro borishe baridhara  [... amidst it] a hapless, homeless man drenched from top to toe standing on the roof of his steamer [...] the last two days I have been singing this song over and over [...] as a result the pelting sound of the intense rain, the wail of the wind, the sound of the heaving Gorai River, [...] have assumed a fresh life and found a new language and I have felt like a major actor in this new musical drama unfolding before me.

— Letter to Indira Devi.
The youngest of 13 surviving children, Tagore (nicknamed "Rabi") was born on 7 May 1861 in the Jorasanko mansion in Calcutta, the son of Debendranath Tagore (1817–1905) and Sarada Devi (1830–1875).

Black-and-white photograph of a finely dressed man and woman: the man, smiling, stands with the hand on the hip and elbow turned outward with a shawl draped over his shoulders and in Bengali formal wear. In front of him, the woman, seated, is in elaborate dress and shawl; she leans against a carved table supporting a vase and flowing leaves.
Tagore and his wife Mrinalini Devi, 1883
Tagore was raised mostly by servants; his mother had died in his early childhood and his father travelled widely. The Tagore family was at the forefront of the Bengal renaissance. They hosted the publication of literary magazines; theatre and recitals of Bengali and Western classical music featured there regularly. Tagore's father invited several professional Dhrupad musicians to stay in the house and teach Indian classical music to the children. Tagore's oldest brother Dwijendranath was a philosopher and poet. Another brother, Satyendranath, was the first Indian appointed to the elite and formerly all-European Indian Civil Service. Yet another brother, Jyotirindranath, was a musician, composer, and playwright. His sister Swarnakumari became a novelist. Jyotirindranath's wife Kadambari Devi, slightly older than Tagore, was a dear friend and powerful influence. Her abrupt suicide in 1884, soon after he married, left him profoundly distraught for years.
Answer: Rabi
Question: How many people live in the city of Oakland in 2022?
Evidence: Oakland population in 2022 is estimated to be 439,483
Answer: About 450,000 people live in Oakland, Ca during the last census
Question: What is the most famous sport in the world
Answer: Soccer is one of the most famous sports . With Fan base of ~4 Billion soccer is one support which can be considered to be most viewed sport in the the world.
Question: Retrieve the teams Doug Pederson was affiliated with as a coach
Evidence: Douglas Irvin Pederson (born January 31, 1968) is an American football coach and former quarterback who is the head coach for the Jacksonville Jaguars of the National Football League (NFL). Pederson spent most of his 13-season playing career as a backup to Brett Favre on the Green Bay Packers, where he was part of the team that won a Super Bowl title in Super Bowl XXXI. He was also a backup to Dan Marino on the Miami Dolphins and a starter for the Philadelphia Eagles and Cleveland Browns until retiring in 2004.

Pederson began his coaching career under Andy Reid, serving as an assistant for the Eagles from 2009 to 2012. After Reid became the head coach of the Kansas City Chiefs in 2013, Pederson followed him to serve as the Chiefs' offensive coordinator. He returned to the Eagles as their head coach in 2016, a position he held for five seasons. His greatest success was when he led the franchise to its first Super Bowl title in 2017's Super Bowl LII, making him one of four individuals to win a Super Bowl as a player and head coach.
Answer: Jacksonville Jaguars, Philadelphia Eagles, Kansas City Chiefs
Question: How can UV light be used?
Answer: UV light is electromagnetic radiation.  It has a wavelength that is shorter than regular visible light.  UV light can be used in water purification to kill viruses and bacteria in city water.  Continuous flow UV lights can kill virus and bacteria up to 99.999%.  This is very helpful because it keeps people from ingesting virus and bacteria in their water source.  UV light has other applications such as tanning beds.  UV light can also be used in disinfecting services too.
Question: What does W stand for in the name W. Rex Black?
Evidence: Wilford Rex Black (January 31, 1920 – December 12, 2012) was an American politician.

Wilford Rex Black represented the 2nd Utah Senate District from 1973 to 1997. W. Rex Black, as he was often referred to, was a member of the Democratic party and represented the party as the Minority leader in the Utah Senate for many years.
Answer: Wilford
Question: Identify which instrument is string or percussion: Boomwhacker, Ukelin
Answer: Boomwhacker is percussion, Ukelin is string.
Question: How was art used for manipulative purpose in racial discuss in the 20th century?
Evidence: Racial categories have historically been used as a way to enable an oppressive figure or group to discriminate against other groups or individuals which were seen as different from that of the oppressor. In nineteenth and early twentieth century Europe, artwork was a common form of racialization which targeted countries in the Middle East and Asia. The artwork, predominantly paintings, were portrayed in order to instill prejudice in the Western populations through sexualizing and manipulating images. One of the most prominent examples of Orientalist work in art is a piece by Eugène Delacroix titled Women of Algiers in their Apartment. Dating back to 1834, it portrays three women resting in a harem in exotic clothing while an African woman is dressed in plain clothing, depicting her role as a servant. Fine textiles, hookahs, and other paraphernalia adorn the room, which represents a European fantasy of an exotic scene. Attempts to portray these cultures as strange, foreign and exotic through Orientalism led to intolerance towards the Arab and Asian communities in Europe and the United States. Others argue that Delacroix, who travelled in North Africa sketching extensively, was depicting a realistic scene of the era based on his first-hand knowledge and experience. In such an interpretation the clothing, for example, is consistent with the times, as Arab North Africans dressed differently from Europeans, and kept black slaves who would not have been treated as equals.
Answer: In the 19th to 20th century, Europeans commonly use artworks to portray prejudice towards Middle Eastern and Asian people.
Question: Why do humans play sports?
Answer: Humans play sports for a variety of reasons but one reason is to have fun.   Playing sports is also a good form of exercise which helps humans stay in shape.  Not everyone enjoys playing sports but for those that do they often enjoy competing against one another.
Question: How long is a chukker in Polo?
Answer: A chukker is 7.5 minutes
Question: What are all the input modalities considered in prior work in question generation?
Evidence: Question generation is an NLG task for which the input has a wealth of possibilities depending on applications. While a host of input modalities have been considered in other NLG tasks, such as text summarization BIBREF24 , image captioning BIBREF25 and table-to-text generation BIBREF26 , traditional QG mainly focused on textual inputs, especially declarative sentences, explained by the original application domains of question answering and education, which also typically featured textual inputs.
Recently, with the growth of various QA applications such as Knowledge Base Question Answering (KBQA) BIBREF27 and Visual Question Answering (VQA) BIBREF28 , NQG research has also widened the spectrum of sources to include knowledge bases BIBREF29 and images BIBREF10 . This trend is also spurred by the remarkable success of neural models in feature representation, especially on image features BIBREF30 and knowledge representations BIBREF31 . We discuss adapting NQG models to other input modalities in Section "Wider Input Modalities" .
While a host of input modalities have been considered in other NLG tasks, such as text summarization BIBREF24 , image captioning BIBREF25 and table-to-text generation BIBREF26 , traditional QG mainly focused on textual inputs, especially declarative sentences, explained by the original application domains of question answering and education, which also typically featured textual inputs.

Recently, with the growth of various QA applications such as Knowledge Base Question Answering (KBQA) BIBREF27 and Visual Question Answering (VQA) BIBREF28 , NQG research has also widened the spectrum of sources to include knowledge bases BIBREF29 and images BIBREF10 .
Answer: Textual inputs, knowledge bases, and images.
Question: Classify the following as TV Shows or movies: Ted Lasso, Schitt's Creek, The Office, Seinfeld, The Elephant Whisperers, Harry Potter and the Sorcerer's Stone, Delhi Crime, The Social Network.
Answer: The classification in order is: 'show', 'show', 'show', 'show', 'movie', 'movie', 'show', 'movie'.
Question: What is the performance of the system balikasg?
Evidence: Automatically predicting the level of English of non-native speakers from their written text is an interesting text mining task. Systems that perform well in the task can be useful components for online, second-language learning platforms as well as for organisations that tutor students for this purpose. In this paper I present the system balikasg that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems. In order to achieve the best performance in the challenge, I decided to use a variety of features that describe an essay's readability and syntactic complexity as well as its content. For the prediction step, I found Gradient Boosted Trees, whose efficiency is proven in several data science challenges, to be the most efficient across a variety of classifiers.
The rest of the paper is organized as follows: in Section 2 I frame the problem of language level as an ordinal classification problem and describe the available data. Section 3 presents the feature extaction and engineering techniques used. Section 4 describes the machine learning algorithms for prediction as well as the achieved results. Finally, Section 5 concludes with discussion and avenues for future research.
In this paper I present the system balikasg that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems.
Answer: It achieved the state-of-the-art performance in the language-level prediction task of CAp 2018 data science challenge among 14 systems.
Question: A lead or A manager?
Answer: Both are importent,

1. A manager is important to keep your work organized and get the timeline done
2. A leader is needed to guide you technically
Question: Why the translation of pronouns is hard?
Evidence: Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
Even though machine translation has improved considerably with the advent of neural machine translation (NMT) (Sutskever et al., 2014; Bahdanau et al., 2015), the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
Answer: Becasue they often require context outside the current sentence.