DataHammer/scimrc huggingface dataset:
Question: What can an organization do to increase employee satisfaction apart from providing monetary benefits? What can be the focus areas? Are there any daily lists of activities which can be beneficial in this aspect?
Answer: In modern times people are more concerned about physical well being. An organization can increase employee satisfaction and their productivity simultaneously by focussing on certain aspects which would help in improving their physical health. They should indulge in group activities which will engage the employees to follow healthier habits and lifestyle changes.

To improve physical health, organizations can take help from any collaborative app which tracks physical exercise and integrate it with internal social networks. Weekly/Monthly activity goals and new fitness challenges can be set and leaderboards can be published to encourage participation. Apart from that, the importance of a healthy diet and rest needs to be stressed as well. Strategic partnerships with healthy food manufacturers, organic grocery retailers and giving employee discounts will incentivize the process.

One other aspect which is vastly ignored is mental health. Encouraging employees to perform yoga or meditation are some of the key focus areas which an organization can explore.

Are there any daily lists of activities which can be beneficial in this aspect?

Here are few activities which can be performed as a step towards healthier lifestyle:
1. Complete at least 5k steps everyday
2. Meditate for 15 mins thrice a week
3. Perform high-intensity interval training twice a week
4. Switch to organic foods
5. Get at least 7 hours of sleep
Question: Given the reference text below the eruption of Mount Vesuvius, how high was the mountain after the disaster?
Evidence: In December 1631, Mount Vesuvius in Italy erupted. The eruption began on 16 December 1631 and culminated the day after. The Volcanic Explosivity Index was VEI-5, and it was a Plinian eruption that buried many villages under the resulting lava flows. It is estimated that between 4,000 people were killed by the eruption, making it the highest death toll for a volcanic disaster in the Mediterranean in the last 1800 years.[citation needed] The 1631 eruption was considered to be of minor proportions regarding its eruptive magnitude and erupted volumes compared to the AD 79 eruption, but the damage was not.[citation needed] By the 1631 eruption, the summit of Mount Vesuvius had been reduced by 450m, making its total height lower than that of Mount Somma.
Answer: Mount Vesuvius had a reduced summit by 450 meters.
Question: How do they obtain parsed source sentences?
Evidence: We use an off-the-shelf parser, in this case Stanford CoreNLP BIBREF11 , to create binary constituency parses. These parses are linearized as shown in Table TABREF6 . We tokenize the opening parentheses with the node label (so each node label begins with a parenthesis) but keep the closing parentheses separate from the words they follow. For our task, the parser failed on one training sentence of 5.9 million, which we discarded, and succeeded on all test sentences. It took roughly 16 hours to parse the 5.9 million training sentences.
We use an off-the-shelf parser, in this case Stanford CoreNLP BIBREF11 , to create binary constituency parses.
Answer: The answers are shown as follows:
* Stanford CoreNLP BIBREF11
Question: What is the capital city of the United States?
Answer: Washington DC is the capital city of the United States
Question: Do the authors equate drunk tweeting with drunk texting? 
Evidence: The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. We refer to tweets written under the influence of alcohol as `drunk tweets', and the opposite as `sober tweets'.
In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. 
Answer: Yes.
Question: What are the teams in the NL Central Division in Major League Baseball?
Answer: The teams that are included in the National League Central Division for Major League Baseball are the St Louis Cardinals, Chicago Cubs, Pittsburg Pirates, Milwaukee Brewers, and the Cincinnati Reds.
Question: From the passage provided, extract the number of episodes that made up the seventh series of Waterloo Road.
Evidence: The seventh series of the British television drama series Waterloo Road began broadcasting on 4 May 2011, and ended on 25 April 2012 on BBC One. The series follows the lives of the faculty and pupils of the eponymous school, a failing inner-city comprehensive school. It consists of thirty episodes, shown in three blocks of ten episodes each. The seventh series achieved an average of 5.30 million viewers in the ratings. It is the last series to be set in Rochdale, England.
Answer: The seventh series of Waterloo Road had thirty episodes.
Question: Given a reference text about Valery Moiseevich Leibin, tell me where he is a member of an editorial board and when he died.
Evidence: Valery Moiseevich Leibin (born 8 March 1942 in the village of Khmelyovka in Zuevskiy district of Kirov region in Russia) is a Russian psychoanalyst, Ph.D., head of the department of history and theory of psychoanalysis in Institute of Psychoanalysis, Professor of Moscow State Medical Stomatological University, an honorary Doctor of East European Institute of Psychoanalysis, an honorary member of the interregional public organization Russian Psychoanalytical Society, a member of the Academy of Pedagogical and Social Sciences, a chief scientist of Institute for Systems Analysis of Russian Academy of Sciences, a member of the editorial boards of the "Russian Psychoanalytic Bulletin" (since 1991), the philosophic psychoanalytic journal "Archetype" (since 1996), "Psychoanalytic Review" (since 1997) and "Review of Psychoanalysis" (since 2005). According to the rating given by "Psychological newspaper" Valery Leibin is in the top ten most prominent psychoanalysts in Russia.
Answer: Valery Moiseevich Leibin is still alive. He is a member of the following editorial boards:
- "Russian Psychoanalytic Bulletin"
- "Archetype"
- "Psychoanalytic Review"
- "Review of Psychoanalysis"
Question: What approach does this paper introduce?
Evidence: Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
We introduce an approach to context-aware machine translation using only monolingual document-level data.
Answer: An approach to context-aware machine translation using only monolingual document-level data.
Question: Name the Khals known to currently be alive in A Song of Ice and Fire.
Answer: Jhaqo, Pono, Moro, Jommo, Zekko, and Motho.
Question: What do they measure a concept’s controversiality from?
Evidence: Analysis of controversy in Wikipedia, online news and social media has attracted considerable attention in recent years. Exploiting the collaborative structure of Wikipedia, estimators of the level of controversy in a Wikipedia article were developed based on the edit-history of the article BIBREF0, BIBREF3. Along these lines, BIBREF4 detect controversy based on mutual reverts, bi-polarity in the collaboration network, and mutually-reinforced scores for editors and articles. Similarly, BIBREF1 classify whether a Wikipedia page is controversial through the combined evaluation of the topically neighboring set of pages.
Content analysis of controversial Wikipedia articles has been used to evaluate the level of controversy of other documents (e.g., web pages) by mapping them to related Wikipedia articles BIBREF5. BIBREF6 further build a language model, which enhances predictions made by existing classifiers, by inferring word probabilities from Wikipedia articles prominent in Wikipedia controversy features (mainly signals in edit history as discussed above) and from articles retrieved by manually selected query terms, believed to indicate controversy.
BIBREF7 detect controversy in news items by inspecting terms with excessive frequency in contexts containing sentiment words, and BIBREF8 study controversy in user comments of news articles using lexicons. Finally, BIBREF9 suggest that controversy is not a universal but rather a community-related concept, and, therefore, should be studied in context.
Here we measure a concept's controversiality from the explicit sentence-level context in which it is mentioned. In this, our approach is reminiscent of BIBREF10, who suggest a similar approach to detect abstract concepts.
Here we measure a concept’s controversiality from the explicit sentence-level context in which it is mentioned.
Answer: From the explicit sentence-level context in which it is mentioned.
Question: How many samples did they generate for the artificial language?
Evidence: In a new experiment, we train the models on pairs of sentences with length 5, 7 or 8, and test on pairs of sentences with lengths 6 or 9. As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively. Results are shown in Table UID19 .
As before, the training and test sets contain some 30,000 and 5,000 sentence pairs, respectively
Answer: 70,000
Question: When was rolling luggage invented?
Answer: Rolling luggage was invented by Bernard D. Sadow in 1970. His invention was described in a patent, numbered 3,653,474, which emphasized the growth of air travel as a motivating use-case. The purported inspiration for attaching wheels directly to the luggage was an observation by Sadow of a porter stacking luggage on a rolling cart.
Question: What type of kitchen knife should I buy?
Answer: Every household should have at a minimum: a serrated knife, a paring knife, and a chef's knife. The serrated knife is good for cutting breads. A paring knife is handy for peeling and slicing smaller objects such as fruits. A chef's knife is a multi-purpose workhorse that can be used to accomplish most tasks such as slicing and dicing meats, vegetables, and fruits.
Question: At what temperature does water boil?
Answer: While some say a "watched pot never boils," water reaches its boiling point at 100 degrees celsius or 212 degrees fahrenheit.
Question: From the passage provided, extract the  population of Seattle metropolitan area
Evidence: Seattle (/siˈætəl/ (listen) see-AT-əl) is a seaport city on the West Coast of the United States. It is the seat of King County, Washington. With a 2020 population of 737,015, it is the largest city in both the state of Washington and the Pacific Northwest region of North America. The Seattle metropolitan area's population is 4.02 million, making it the 15th-largest in the United States. Its growth rate of 21.1% between 2010 and 2020 made it one of country's fastest-growing large cities.
Answer: The Seattle metropolitan area's population is 4.02 million, making it the 15th-largest in the United States
Question: How many moons does Mars have?
Answer: Mars has two moons, they were named Phobos and Deimos by the American astronomer Asaph Hall, who discovered them in August 1877.
Question: Given a reference text about Nicholas Theodore, tell me where he went to medical school and  what he is known for.
Evidence: Nicholas Theodore is an American neurosurgeon and researcher at Johns Hopkins University School of Medicine. He is known for his work in spinal trauma, minimally invasive surgery, robotics, and personalized medicine. He is Director of the Neurosurgical Spine Program at Johns Hopkins and Co-Director of the Carnegie Center for Surgical Innovation at Johns Hopkins.

Dr. Theodore graduated from Cornell University, where he was the recipient of a Cornell Tradition Academic Fellowship. He attended medical school at Georgetown University, where he graduated with honors. After completing his internship at Bethesda Naval Hospital, Dr. Theodore served as a Senior General Medical Officer with the United States Marine Corps in Okinawa, Japan.

Dr. Theodore completed his neurosurgical residency and a fellowship in spinal surgery at the Barrow Neurological Institute. After completing his residency in 2001, he served as Chief of the Division of Neurosurgery at Naval Medical Center San Diego, overseeing the largest neurosurgery complement in the Navy.

In 2003, Dr. Theodore joined the faculty at the Barrow Neurological Institute, and assumed the position of Director of Neurotrauma. In 2004 he was appointed Associate Director of the Neurosurgery Residency Program at Barrow. The Neurosurgery Residency Program at Barrow is the largest in the United States, training four residents per academic year, for a total of 28 residents. In 2009 he became the Chief of the Spine Section at the Barrow Neurological Institute and was appointed the Volker K.H. Sonntag Chair in 2015. In 2016 he became the second Donlin M. Long Professor of Neurosurgery at Johns Hopkins Hospital. Dr. Theodore also holds professorships in Orthopedics and Biomedical Engineering at Johns Hopkins. He is also actively involved in the area of preventative medicine within neurosurgery. He has been associated with the ThinkFirst Foundation for several years, having served as the foundation’s Medical Director and President. In 2017, Dr. Theodore was appointed to the National Football League’s Head, Neck and Spine Committee, of which he became Chairman in 2018. In 2020, Michael J. Fox revealed in his memoir that Dr. Theodore performed a risky but successful surgery on him to remove an ependymoma in Fox’s spinal cord.
Answer: Nicholas Theodore went to medical school at Georgetown University and is known for his work in spinal trauma, robotics, minimally invasive surgery, and personalized medicine.
Question: What's fun about disc golf?
Answer: Disc golf (also known as frisbee golf) is a sport with a similar set of rules to golf but instead played with specially designed discs (aka frisbees) that are thrown. Similar to golf, the discs are designed with different qualities to produce different outcomes. For example, there is a driver for long straight throws and a putter for shorter, direct throws. There is strategy and skill involved in choosing the correct disc for the situation. Players must also take into account terrain, vegetation (e.g., trees), and climate (e.g., wind). The combination of challenges makes disc golf a game that is accessible to new players while also being challenging to experienced players.
Question: Has anyone extends the capability of adversarial domain adaptation for SQA before this paper?
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: Identify which instrument is string or percussion: Cimboa, Bamboo clapper
Answer: Bamboo clapper is percussion, Cimboa is string.
Question: From the passage provided, extract the year which Presto Software Foundation was announced
Evidence: Presto was originally designed and developed at Facebook, Inc. (later renamed Meta) for their data analysts to run interactive queries on its large data warehouse in Apache Hadoop. The first four developers were Martin Traverso, Dain Sundstrom, David Phillips, and Eric Hwang. Before Presto, the data analysts at Facebook relied on Apache Hive for running SQL analytics on their multi-petabyte data warehouse. Hive was deemed too slow for Facebook's scale and Presto was invented to fill the gap to run fast queries. Original development started in 2012 and deployed at Facebook later that year. In November 2013, Facebook announced its open source release.

In 2014, Netflix disclosed they used Presto on 10 petabytes of data stored in the Amazon Simple Storage Service (S3). In November, 2016, Amazon announced a service called Athena that was based on Presto. In 2017, Teradata spun out a company called Starburst Data to commercially support Presto, which included staff acquired from Hadapt in 2014. Teradata's QueryGrid software allowed Presto to access a Teradata relational database.

In January 2019, the Presto Software Foundation was announced. The foundation is a not-for-profit organization for the advancement of the Presto open source distributed SQL query engine. At the same time, Presto development forked: PrestoDB maintained by Facebook, and PrestoSQL maintained by the Presto Software Foundation, with some cross pollination of code.

In September 2019, Facebook donated PrestoDB to the Linux Foundation, establishing the Presto Foundation. Neither the creators of Presto, nor the top contributors and committers, were invited to join this foundation.

By 2020, all four of the original Presto developers had joined Starburst. In December 2020, PrestoSQL was rebranded as Trino, since Facebook had obtained a trademark on the name "Presto" (also donated to the Linux Foundation).

Another company called Ahana was announced in 2020, with seed funding from GV (formerly Google Ventures, an arm of Alphabet, Inc.), to commercialize the PrestoDB fork as a cloud service, while also offering an open-source version. A $20 million round of funding for Ahana was announced in August 202
Answer: 2019
Question: Which algorithm is used in the UDS-DFKI system?
Evidence: Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. We use multi-head attention to jointly attend to information at different positions from different representation subspaces.
Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions. The transformer uses positional encoding to encode the input and output sequences, and computes both self- and cross-attention through so-called multi-head attentions, which are facilitated by parallelization. We use multi-head attention to jointly attend to information at different positions from different representation subspaces.
Answer: The answers are shown as follows:
* Our transference model extends the original transformer model to multi-encoder based transformer architecture. The transformer architecture BIBREF12 is built solely upon such attention mechanisms completely replacing recurrence and convolutions.