DataHammer/scimrc huggingface dataset:
Question: What makes it possible to delve deep into the cognitive underpinnings of sarcasm understanding?
Evidence: Sarcasm, in general, has been the focus of research for quite some time. In one of the pioneering works jorgensen1984test explained how sarcasm arises when a figurative meaning is used opposite to the literal meaning of the utterance. In the word of clark1984pretense, sarcasm processing involves canceling the indirectly negated message and replacing it with the implicated one. giora1995irony, on the other hand, define sarcasm as a mode of indirect negation that requires processing of both negated and implicated messages. ivanko2003context define sarcasm as a six tuple entity consisting of a speaker, a listener, Context, Utterance, Literal Proposition and Intended Proposition and study the cognitive aspects of sarcasm processing.
Computational linguists have previously addressed this problem using rule based and statistical techniques, that make use of : (a) Unigrams and Pragmatic features BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 (b) Stylistic patterns BIBREF4 and patterns related to situational disparity BIBREF5 and (c) Hastag interpretations BIBREF6 , BIBREF7 .
Most of the previously done work on sarcasm detection uses distant supervision based techniques (ex: leveraging hashtags) and stylistic/pragmatic features (emoticons, laughter expressions such as “lol” etc). But, detecting sarcasm in linguistically well-formed structures, in absence of explicit cues or information (like emoticons), proves to be hard using such linguistic/stylistic features alone.
With the advent of sophisticated eye-trackers and electro/magneto-encephalographic (EEG/MEG) devices, it has been possible to delve deep into the cognitive underpinnings of sarcasm understanding. Filik2014, using a series of eye-tracking and EEG experiments try to show that for unfamiliar ironies, the literal interpretation would be computed first. They also show that a mismatch with context would lead to a re-interpretation of the statement, as being ironic. Camblin2007103 show that in multi-sentence passages, discourse congruence has robust effects on eye movements. This also implies that disrupted processing occurs for discourse incongruent words, even though they are perfectly congruous at the sentence level. In our previous work BIBREF8 , we augment cognitive features, derived from eye-movement patterns of readers, with textual features to detect whether a human reader has realized the presence of sarcasm in text or not.
The recent advancements in the literature discussed above, motivate us to explore gaze-based cognition for sarcasm detection. As far as we know, our work is the first of its kind.
With the advent of sophisticated eye-trackers and electro/magneto-encephalographic (EEG/MEG) devices, it has been possible to delve deep into the cognitive underpinnings of sarcasm understanding.
Answer: The advent of sophisticated eye-trackers and electro/magneto-encephalographic (EEG/MEG) devices.
Question: What do they plan to do in the future?
Evidence: In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework. By treating words in different languages as different words and force the attention and translation to the direction of desired target language, we are able to employ attention-enable NMT toward a multilingual translation system. Our proposed approach alleviates the need of complicated architecture re-designing when accommodating attention mechanism. In addition, the number of free parameters to learn in our network does not go beyond that magnitute of a single NMT system. With its universality, our approach has shown its effectiveness in an under-resourced translation task with considerable improvements. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.
Nevertheless, there are issues that we can continue working on to address in future work. A more balancing data would be helpful for this framework. The mechanism of forcing the NMT system to the right target language could be improved. We could conduct more detailed analyses of the various strategies under the framework to show its universarity.
Nevertheless, there are issues that we can continue working on to address in future work. A more balancing data would be helpful for this framework. The mechanism of forcing the NMT system to the right target language could be improved. We could conduct more detailed analyses of the various strategies under the framework to show its universarity.
Answer: They may create more balancing data, improve the mechanism of forcing the NMT system to the right target language and analyze the various strategies in more detail under the framework to show its universality.
Question: How is the The U.S. Naturalization Interview and test done?
Answer: During your naturalization interview, a USCIS officer will ask you questions about your application and background. Unless you qualify for an exemption, you will also take a naturalization test which is made up of two components, an English and civics test.

During the English test, you must demonstrate an understanding of the English language including the ability to read, write, and speak basic English. 
During the civics test, you will answer important questions about American government and history.
Question: How did the Getty Museum in Los Angeles respond to the global pandemic?
Evidence: Many museums turned to their existing social media presences to engage their audience online during the COVID-19 pandemic. Inspired by the Rijksmuseum in Amsterdam and Instagram accounts such as the Dutch Tussen Kunst & Quarantaine (“between art and quarantine”) and Covid Classics, the Getty sponsored the Getty Museum Challenge, inviting people to use everyday objects to recreate works of art and share their creations on social media, prompting thousands of submissions. The museum was among those singled out for particular praise by industry analysts for their successful social media content strategy during the shutdown, both for the challenge and for incorporating its works into the popular video game Animal Crossing.
Answer: Like other art institutions, the Getty Museum leveraged its social media presence in a creative way to keep engagement levels high during the pandemic. The Getty invited people to reimagine classic works of art using every day objects, the results of which were posted on social media for others' enjoyment and commentary.  Thousands of people were inspired to post submissions, and industry analysts praised the museum for this successful social media strategy.
Question: How many versions of One Night game available?
Evidence: The One Night Trilogy, comprising three games, One Night, One Night 2: The Beyond and One Night: Full Circle, is a series of 2D tile-based overhead psychological horror games. The three games tell the story of an attempt to invade Earth by a race of supernatural shadow people and a collection of protagonists who must survive the attacks and fight against them. The origins of the creatures and their motives are detailed in the prequel, One Night 2: The Beyond, while the first and third games deal with subsequent invasion attempts and the conclusion to the conflict.
Answer: The One Night Trilogy, comprising three games, One Night, One Night 2: The Beyond and One Night
Question: What is a difference between alligators and crocodiles?
Answer: A difference between alligators and crocodiles is the shape of their heads. Crocodiles have more narrow and longer heads than alligators.
Question: Extract the locations where Nachum lived from the text below and list them in alphabetical order and separated by a semicolon.
Evidence: Nachum Gutman was born in Teleneşti, Bessarabia Governorate, then a part of the Russian Empire (now in the Republic of Moldova). He was the fourth child of Simha Alter and Rivka Gutman. His father was a Hebrew writer and educator who wrote under the pen name S. Ben Zion. In 1903, the family moved to Odessa, and two years later, to Ottoman Palestine. In 1908, Gutman attended the Herzliya Gymnasium in what would later become Tel Aviv. In 1912, he studied at the Bezalel School in Jerusalem. In 1920–26, he studied art in Vienna, Berlin and Paris.

Gutman was married to Dora, with whom he had a son. After Gutman's death in 1980, Dora asked two Tel Aviv gallery owners, Meir Stern of Stern Gallery and Miriam Tawin of Shulamit Gallery, to appraise the value all of the works left in his estate.
Answer: Berlin;Jerusalem;Odessa;Palestine;Paris;Tel Aviv;Vienna
Question: Best Actress Award in Drama series in 27th Screen Actors Guild(SAG) Awards was won by  whom?
Answer: Gillian Anderson for her performance as Margaret Thatcher
Question: What's the Platinum Jubilee?
Evidence: The Platinum Jubilee of Elizabeth II was the international celebration in 2022 marking the 70th anniversary of the accession of Queen Elizabeth II on 6 February 1952. It was the first time that any monarch in British history celebrated a platinum jubilee, as is the case in the histories of the other Commonwealth realms.[n 1]

Initiatives to commemorate the jubilee were announced by the governments of many realms—including Australia, Canada, New Zealand, Papua New Guinea, and the United Kingdom—of territories, such as the Cayman Islands and Gibraltar, and celebrations were also held in other Commonwealth member states, like the Gambia, Malaysia, Malta, Pakistan, and Samoa. Leaders from across the world, including from China, France, Germany, Israel, and the United States, sent messages of congratulations to the Queen on reaching the milestone. In the United Kingdom, there was an extra bank holiday on 3 June and the usual spring bank holiday was moved from the end of May to 2 June to create the four-day Platinum Jubilee Central Weekend from Thursday, 2 June, to Sunday, 5 June. Commemorative stamps and coins were issued by several Commonwealth nations and beacons were lit in every Commonwealth capital for the first time. In many places, trees were planted in the Queen's honour.

The Queen died in her Platinum Jubilee year, on 8 September, at the age of 96. Her funeral was held on 19 September 2022 and she was buried at the King George VI Memorial Chapel later that day.
Answer: The Platinum Jubilee was a 2022 celebration commemorating British Monarch Queen Elizabeth II's 70th anniversary on the throne. No other monarch in British history has lived long enough to celebrate this milestone, and celebrations were held in her honor all over the world, and several Commonwealth nations commemorated the occasion by issuing special stamps and coins.
Question: Why are baselines such as FedMoCoV1 and FedMoCoV2 missing from Table 7?
Evidence: Table 7 supplements the semi-supervised learning results on Table 4, providing additional results using ResNet-50 as the network architecture for the encoder. FedEMA consistently outperforms all the other methods.****Table 7: Top-1 accuracy comparison on using 1% and 10% of labeled data for semi-supervised
learning on the non-IID settings of CIFAR datasets. FedEMA outperforms all other methods.
Answer: We have run these experiments (FedSimCLR, FedSimSiam, FedMoCoV1, and FedMoCoV2) and added them in Table 7 in Appendix. C.
Question: What are the previously published methods?
Evidence: Subword segmentation has become a standard preprocessing step in many neural approaches to natural language processing (NLP) tasks, e.g Neural Machine Translation (NMT) BIBREF0 and Automatic Speech Recognition (ASR) BIBREF1. Word level modeling suffers from sparse statistics, issues with Out-of-Vocabulary (OOV) words, and heavy computational cost due to a large vocabulary. Word level modeling is particularly unsuitable for morphologically rich languages, but subwords are commonly used for other languages as well. Subword segmentation is best suited for languages with agglutinative morphology.
While rule-based morphological segmentation systems can achieve high quality, the large amount of human effort needed makes the approach problematic, particularly for low-resource languages. The systems are language dependent, necessitating use of multiple tools in multilingual setups. As a fast, cheap and effective alternative, data-driven segmentation can be learned in a completely unsupervised manner from raw corpora. Unsupervised morphological segmentation saw much research interest until the early 2010's; for a survey on the methods, see hammarstrom2011unsupervised. Semi-supervised segmentation with already small amounts of annotated training data was found to improve the accuracy significantly when compared to a linguistic segmentation; see ruokolainen2016comparative for a survey. While this line of research has been continued in supervised and more grammatically oriented tasks BIBREF2, the more recent work on unsupervised segmentation is less focused on approximating a linguistically motivated segmentation. Instead, the aim has been to tune subword segmentations for particular applications. For example, the simple substitution dictionary based Byte Pair Encoding segmentation algorithm BIBREF3, first proposed for NMT by sennrich2015neural, has become a standard in the field. Especially in the case of multilingual models, training a single language-independent subword segmentation method is preferable to linguistic segmentation BIBREF4.
In this study, we compare three existing and one novel subword segmentation method, all sharing the use of a unigram language model in a generative modeling framework. The previously published methods are Morfessor Baseline BIBREF5, Greedy Unigram Likelihood BIBREF6, and SentencePiece BIBREF7. The new Morfessor variant proposed in this work is called Morfessor EM+Prune.
The contributions of this article are
a better training algorithm for Morfessor Baseline, with reduction of search error during training, and improved segmentation quality for English, Finnish and Turkish;
comparing four similar segmentation methods, including a close look at the SentencePiece reference implementation, highlighting details omitted from the original article BIBREF7;
and showing that the proposed Morfessor EM+Prune with particular hyper-parameters yields SentencePiece.
The previously published methods are Morfessor Baseline, Greedy Unigram Likelihood, and SentencePiece.
Answer: Morfessor Baseline, Greedy Unigram Likelihood, and SentencePiece.
Question: Classify these cities into the countries that they are in.
Paris, Munich, Frankfurt, Nice, Lyon, Dresden
Answer: France - Paris, Nice, Lyon
Germany - Munich, Frankfurt, Dresden
Question: What is the process by which utilities rates are set in the US
Evidence: Utility ratemaking is the formal regulatory process in the United States by which public utilities set the prices (more commonly known as "rates") they will charge consumers. Ratemaking, typically carried out through "rate cases" before a public utilities commission, serves as one of the primary instruments of government regulation of public utilities.
Answer: The price of a utility's products and services will affect its consumption. As with most demand curves, a price increase decreases demand. Through a concept known as rate design or rate structure, regulators set the prices (known as "rates" in the case of utilities) and thereby affect the consumption. With declining block rates, the per-unit price of utility consumption decreases as the energy consumption increases. Typically a declining block rate is offered only to very large consumers. If conservation is the goal, regulators can promote conservation by letting prices rise. A third possible rate design is a flat rate which charges the same price for all consumption.
Question: What is cloud computing in simple terms?
Answer: In simple terms cloud computing allows you to use preconfigured hardware devices and software services managed by somebody else on internet. Most cases you pay only for the resources you use and it is very easy to procure more as you need more services. 
Cloud computing makes it easy to setup the technology infrastructure at a low cost and scale as you grow.
Question: Name a Christopher Nolan movie that was remade into an Indian movie.
Answer: Ghajini movie in Tamil language was the first Indian unofficial remake of Christopher Nolan's Memento.
Question: Do you know any other languages besides english?can you name colors in ARABIC?
Answer: yes , i could speak a little bit of ARABIC and a few other languages aswell here are the colors in ARABIC along with its english spelling aswell أبيض abyad.
أسود aswad.
أحمر ahmar.
أصفر asfar.
أخضر akhdar.
أزرق azraq.
بنى bunni.
برتقالى burtuqali. orange
Question: Has the capability of adversarial domain adaptation for SQA been explored before?
Evidence: Question answering (QA) has drawn a lot of attention in the past few years. QA tasks on images BIBREF0 have been widely studied, but most focused on understanding text documents BIBREF1 . A representative dataset in text QA is SQuAD BIBREF1 , in which several end-to-end neural models have accomplished promising performance BIBREF2 . Although there is a significant progress in machine comprehension (MC) on text documents, MC on spoken content is a much less investigated field. In spoken question answering (SQA), after transcribing spoken content into text by automatic speech recognition (ASR), typical approaches use information retrieval (IR) techniques BIBREF3 to find the proper answer from the ASR hypotheses. One attempt towards QA of spoken content is TOEFL listening comprehension by machine BIBREF4 . TOEFL is an English examination that tests the knowledge and skills of academic English for English learners whose native languages are not English. Another SQA corpus is Spoken-SQuAD BIBREF5 , which is automatically generated from SQuAD dataset through Google Text-to-Speech (TTS) system. Recently ODSQA, a SQA corpus recorded by real speakers, is released BIBREF6 .
To mitigate the impact of speech recognition errors, using sub-word units is a popular approach for speech-related downstream tasks. It has been applied to spoken document retrieval BIBREF7 and spoken term detection BIBREF8 The prior work showed that, using phonectic sub-word units brought improvements for both Spoken-SQuAD and ODSQA BIBREF5 .
Instead of considering sub-word features, this paper proposes a novel approach to mitigate the impact of ASR errors. We consider reference transcriptions and ASR hypotheses as two domains, and adapt the source domain data (reference transcriptions) to the target domain data (ASR hypotheses) by projecting these two domains in the shared common space. Therefore, it can effectively benefit the SQA model by improving the robustness to ASR errors in the SQA model.
Domain adaptation has been successfully applied on computer vision BIBREF9 and speech recognition BIBREF10 . It is also widely studied on NLP tasks such as sequence tagging and parsing BIBREF11 , BIBREF12 , BIBREF13 . Recently, adversarial domain adaptation has already been explored on spoken language understanding (SLU). Liu and Lane learned domain-general features to benefit from multiple dialogue datasets BIBREF14 ; Zhu et al. learned to transfer the model from the transcripts side to the ASR hypotheses side BIBREF15 ; Lan et al. constructed a shared space for slot tagging and language model BIBREF16 . This paper extends the capability of adversarial domain adaptation for SQA, which has not been explored yet.
This paper extends the capability of adversarial domain adaptation for SQA, which has not been explored yet.
Answer: No.
Question: From this paragraph please summarize the notable California regions where pinot noir is produced.
Evidence: Pinot noir's home is France's Burgundy region, particularly Côte-d'Or. It is also planted in Argentina, Australia, Austria, Bulgaria, Canada, Chile, northern parts of Croatia, Czech Republic, England, the Republic of Georgia, Germany, Greece, Israel, Italy, Hungary, Kosovo, the Republic of Macedonia, Moldova, New Zealand, Romania, Serbia, Slovakia, Slovenia, South Africa, Switzerland, Ukraine, United States, and Uruguay. The United States has increasingly become a major pinot noir producer, with some of the best regarded coming from Oregon's Willamette Valley, and California's Sonoma County with its Russian River Valley and Sonoma Coast appellations. Lesser-known appellations are found in Mendocino County's Anderson Valley, the Central Coast's Santa Lucia Highlands appellation, the Santa Maria Valley, and Sta. Rita Hills American Viticulture Area in Santa Barbara County. In New Zealand, it is principally grown in Martinborough, Marlborough, Waipara, and Central Otago.
Answer: Notable California regions that produce pinot noir include Sonoma County (Russian River Valley and Sonoma Coast), Mendocino County (Anderson Valley), Central Coast (Santa Lucia Highlands), and Santa Barbara County (Santa Maria Valley, Sta. Rita Hills American Viticulture Area).
Question: Which similarity measure do they use in their n-gram retrieval approach?
Evidence: Our baseline approach relies on a simple inverse document frequency (IDF) based similarity score. We define the IDF score of any token, INLINEFORM0 , as INLINEFORM1 , where INLINEFORM2 is the number of sentence pairs in training corpus and INLINEFORM3 is the number of sentences INLINEFORM4 occurs in. Let any two sentence pairs in the corpus be INLINEFORM5 and INLINEFORM6 . Then we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0
Motivated by phrase based SMT, we retrieve neighbors which have high local, sub-sentence level overlap with the source sentence. We adapt our approach to retrieve n-grams instead of sentences. We note that the similarity metric defined above for sentences is equally applicable for n-gram retrieval.
Then we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0
We note that the similarity metric defined above for sentences is equally applicable for n-gram retrieval.
Answer: The answers are shown as follows:
* we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0
Question: How does the multi-turn dialog system learns?
Evidence: Usually the probability distribution $p(\mathbf {y}\,|\,\mathbf {X})$ can be modeled by an RNN language model conditioned on $\mathbf {X}$. When generating the word $y_t$ at time step $t$, the context $\mathbf {X}$ is encoded into a fixed-sized dialog context vector $\mathbf {c}_t$ by following the hierarchical attention structure in HRAN BIBREF13. Additionally, we extract the emotion information from the utterances in $\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\mathbf {e}$, which is combined with $\mathbf {c}_t$ to produce the distribution. The overall architecture of the model is depicted in Figure FIGREF4. We are going to elaborate on how to obtain $\mathbf {c}_t$ and $\mathbf {e}$, and how they are combined in the decoding part.
When generating the word $y_t$ at time step $t$, the context $\mathbf {X}$ is encoded into a fixed-sized dialog context vector $\mathbf {c}_t$ by following the hierarchical attention structure in HRAN BIBREF13. Additionally, we extract the emotion information from the utterances in $\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\mathbf {e}$, which is combined with $\mathbf {c}_t$ to produce the distribution.
Answer: The answers are shown as follows:
* we extract the emotion information from the utterances in $\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\mathbf {e}$, which is combined with $\mathbf {c}_t$ to produce the distribution