DataHammer/scimrc huggingface dataset:
Question: I am on a week long vacation to India and specifically want to explore Himalayan state of Himachal Pradesh. How should I plan my trip ?
Answer: Firstly , fly to Delhi or Chandigarh. These cities are well connected via flights from across the world.  Next , take an overnight bus to Manali.  There are ample buses available connecting Manali from any cities.  Spend 4 days of your time  enjoying local cuisine and sight seeing.   Do carry wollens  as temperature drops sub zero during the night. Next , head towards Shimla and spend 2 days here for local sightseeing enjoying marvels of British architectures and old mall road.  Wrap up your trip travelling back to Delhi or Chandigarh. Spend time doing some local shopping before you fly back home.
Question: What languages are involved in the experiment？
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for DE- EN, the KFTT corpus for JA-EN (Neubig, 2011), and the WMT 2016 dataset for RO-EN.
Answer: English, Germany, Romanian, and Japanese.
Question: What models do previous work use?
Evidence: Related Work ::: Transfer learning based approaches
Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.
The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work.
Related Work ::: Hybrid models
In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture.
 Transfer learning based approaches
Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.

The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work.

Related Work ::: Hybrid models
In another localized setting of Vietnamese language, Nguyen et al. in 2017 proposed a Hybrid multi-channel CNN and LSTM model where they build feature maps for Vietnamese language using CNN to capture shorterm dependencies and LSTM to capture long term dependencies and concatenate both these feature sets to learn a unified set of features on the messages. These concatenated feature vectors are then sent to a few fully connected layers. They achieved an accuracy rate of 87.3% with this architecture.
Answer: The answers are shown as follows:
* Ternary Trans-CNN 
* Hybrid multi-channel CNN and LSTM
Question: Make me a list of the oldest board games that I might not know about, and where they were invented.
Answer: Here is a list of some of the oldest board games. Senet (Egypt), The Royal Game of Ur (Iraq), The Lewis Chessmen (Scotland), Mahjong (China), Game of Goose (Scotland) and Pachisi (India).
Question: List the hyper-scale cloud companies.
Answer: Amazon Web Services (AWS, Microsoft Azure (Azure) and Google Cloud Platform (GCP) are the three hyper-scale cloud providers.
Question: Who is the finest Indian cricketer right now?
Evidence: Virat Kohli (Hindi pronunciation:  born 5 November 1988) is an Indian international cricketer and former captain of the Indian national cricket team. He now plays as a right-handed batter for Royal Challengers Bangalore in the IPL and for Delhi in Indian domestic cricket. He is widely recognised as one of the best batsman in cricket history. He is the second most prolific international century batsman in cricket history. The International Cricket Council elected him the male cricketer of the decade despite holding the record for most runs in T20 internationals and the IPL. Kohli has also contributed to a number of India's victories, including the 2011 World Cup and the 2013 Champions Trophy.
Answer: Virat Kohli (Hindi pronunciation:  born 5 November 1988) is an Indian international cricketer and former captain of the Indian national cricket team. He now plays as a right-handed batter for Royal Challengers Bangalore in the IPL and for Delhi in Indian domestic cricket. He is widely recognised as one of the best batsman in cricket history. He is the second most prolific international century batsman in cricket history. The International Cricket Council elected him the male cricketer of the decade despite holding the record for most runs in T20 internationals and the IPL. Kohli has also contributed to a number of India's victories, including the 2011 World Cup and the 2013 Champions Trophy.

Kohli was born and raised in New Delhi, where he attended the West Delhi Cricket Academy and began his junior career with the Delhi Under-15 team. He made his international debut in 2008 and soon established himself as a prominent member of the ODI team, eventually making his Test debut in 2011. For the first time, Kohli topped the ICC ODI batting rankings in 2013. During the 2014 T20 World Cup, he set a competition record for the most runs scored. In 2018, he became the world's top-ranked Test batsman, making him the only Indian cricketer to hold the number one place in all three versions of the game. 
In 2019, he became the first player to score 20,000 international runs in a single decade. Following the T20 World Cup in 2021, Kohli decided to step down as captain of the Indian national team for T20Is, and he stood down as captain of the Test team in early 2022.


He has garnered numerous awards for his achievements on the cricket pitch. He was named the ICC One-Day International Player of the Year in 2012 and has twice won the Sir Garfield Sobers Trophy, which is awarded to the ICC Cricketer of the Year, in 2017 and 2018. Kohli was the best run scorer in the 2012 Asia Cup, scoring 357 runs. Kohli was also named ICC Test Player of the Year and ICC ODI Player of the Year in 2018, making him the first player to get both honours in the same year. In addition, from 2016 to 2018, he was crowned the Wisden Top Cricketer in the World for three years in a row. Kohli received the Arjuna Award in 2013, the Padma Shri in the sports category in 2017, and the Rajiv Gandhi Khel Ratna medal, India's highest sporting honour, in 2018.
Question: What is John Ellis water and the bond angle of water?
Answer: Traditional tap and purified water has a hydrogen bond angle of 104 - 104.5 degrees.  John Ellis water takes standard tap or purified water and changes the hydrogen bond angle of it from 104 degrees to 113 - 114 degrees.  Changing the bond angle is done using multi-pass distillation by rapidly heating and cooling the water inside the boiling chamber.

Water consumed with a bond angle above 108° degrees eliminates the effect of potential pathogens in our food supply (e.g. bacterium, viruses, or other microorganisms that can cause disease) from affecting our bodies because more oxygen from the water mixes with the superior blood flow to all the extremities of our body.  The larger bond angle enables our bodies to use more of the oxygen present in the water.
Question: Is language our greatest invention  ?
Answer: General consensus is that language is regarded as one of the greatest inventions,  as it helped improve our communication of ideas and emotions. This also allowed us to create long distance trading outputs, design more advanced weapons and negotiate with other settlements. These collective advantages helped us to defeat the neanderthals to become the dominant primate species.
Question: What is the English Premier League?
Answer: The English Premier League is the top flight of English football.
Question: What is a Balance sheet?
Answer: A balance sheet is a summary of an organisation's financial position. It lists the values, in the books of account on a particular date, of all the organisation's assets and liabilities. The assets and liabilities are grouped in categories, to paint a picture of the organisation's strengths and weaknesses.
Question: Extract comma separated list of the names of the tenants of the Willis Tower from the text.
Evidence: The Willis Tower (originally the Sears Tower) is a 110-story, 1,451-foot (442.3 m) skyscraper in the Loop community area of Chicago in Illinois, United States. Designed by architect Bruce Graham and engineer Fazlur Rahman Khan of Skidmore, Owings & Merrill (SOM), it opened in 1973 as the world's tallest building, a title that it held for nearly 25 years. It is the third-tallest building in the Western Hemisphere, as well as the 23rd-tallest in the world. Each year, more than 1.7 million people visit the Skydeck observation deck, the highest in the United States, making it one of Chicago's most popular tourist destinations. As of April 2018, the building's largest tenant is United Airlines, which occupies around 20 floors. Other major tenants include the building's namesake Willis Towers Watson and law firms Schiff Hardin and Seyfarth Shaw. Morgan Stanley became the building's fourth-largest tenant in 2017.
Answer: United Airlines, Willis Towers Watson, Schiff Hardin, Seyfarth Shaw, Morgan Stanley
Question: Why is cold water coming out of my water heater?
Answer: If your gas water heater is not working, first check to see if:

 - Is the water heater leaking? If so, turn off the water and gas to the heater, and contact a plumber to replace the heater.
 - Do you smell gas around your heater? If so, you likely have a gas leak--turn off the gas line to the heater and contact a plumber to replace the heater.
 
If neither of these are issues, check to see if the pilot light for you heater is lit. This is likely hidden behind a panel or door on the bottom of the front of the heater.  If the pilot light is not lit, follow the instructions to try re-lighting it.

If the pilot light does not light or continues to go out after heating the water, the thermal coupling on the water heater likely needs to be replaced. Contact a plumber to have this replaced.

Generally speaking, traditional tank-based, gas water heaters have a lifetime of 10-15 years. If the age water heater is within this range, you may want to prepare for replacing the water heater soon.
Question: What has their approach achieved?
Evidence: This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
Answer: Their scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
Question: What's Jiang's theoretical contribution?
Evidence: Jiang Zemin (17 August 1926 – 30 November 2022) was a Chinese politician who served as general secretary of the Chinese Communist Party (CCP) from 1989 to 2002, as chairman of the Central Military Commission from 1989 to 2004, and as president of China from 1993 to 2003. Jiang was paramount leader of China from 1989 to 2002. He was the core leader of the third generation of Chinese leadership, one of four core leaders alongside Mao Zedong, Deng Xiaoping and Xi Jinping.

Born in Yangzhou, Jiangsu, Jiang joined the CCP while he was in college. After the establishment of the People's Republic of China in 1949, he received training at the Stalin Automobile Works in Moscow in the 1950s, later returning to Shanghai in 1962 to serve in various institutes, later being sent between 1970-1972 to Romania as part of an expert team to establish machinery manufacturing plants in the country. After 1979, he was appointed as the vice chair of two commissions by vice premier Gu Mu to oversee the newly-established special economic zones (SEZs). He became the vice minister of the newly-established Ministry of Electronics Industry and a member of the CCP Central Committee in 1982.

Jiang was appointed as the mayor of Shanghai in 1985, later being promoted to its Communist Party secretary, as well as a member of the CCP Politburo, in 1987. Jiang came to power unexpectedly as a compromise candidate following the 1989 Tiananmen Square protests and massacre, when he replaced Zhao Ziyang as CCP general secretary after Zhao was ousted for his support for the student movement. As the involvement of the "Eight Elders" in Chinese politics steadily declined, Jiang consolidated his hold on power to become the "paramount leader" in the country during the 1990s. Urged by Deng Xiaoping's southern tour in 1992, Jiang officially introduced the term "socialist market economy" in his speech during the 14th CCP National Congress held later that year, which accelerated "opening up and reform".

Under Jiang's leadership, China experienced substantial economic growth with the continuation of market reforms. The returning of Hong Kong from the United Kingdom in 1997 and of Macau from Portugal in 1999, and entry into the World Trade Organisation in 2001, were landmark moments of his era. China also witnessed improved relations with the outside world, while the Communist Party maintained its tight control over the state. Jiang faced criticism over human rights abuses, including the crackdown on the Falun Gong movement. His contributions to party doctrine, known as the "Three Represents", were written into the CCP constitution in 2002. Jiang gradually vacated his official leadership titles from 2002 to 2005, being succeeded in these roles by Hu Jintao, although he and his political faction continued to influence affairs until much later. On 30 November 2022, Jiang died from leukemia and multiple organ failure in Shanghai.
Answer: The Three Represents
Question: Provide legal ways to pay less taxes in the USA.
Answer: The following are some legal ways to pay less taxes in the USA:
1. Make less money
2. Earn more money from capital gains than earned income
3. Invest more in tax advantaged accounts
4. Marry someone with significantly lower pay
5. Move to a location with less state and local taxes
Question: What are the numbers of examples collected by the authors?
Evidence: The dataset statistics can be found in Table TABREF7. The number of examples we collected increases per round, starting with approximately 19k examples for Round 1, to around 47k examples for Round 2, to over 103k examples for Round 3. We collected more data for later rounds not only because that data is likely to be more interesting, but also simply because the base model is better and so annotation took longer to collect good, verified correct examples of model vulnerabilities.
For each round, we report the model error rate, both on verified and unverified examples. The unverified model error rate captures the percentage of examples where the model disagreed with the writer's target label, but where we are not (yet) sure if the example is correct. The verified model error rate is the percentage of model errors from example pairs that other annotators were able to confirm the correct label for. Note that this error rate represents a straightforward way to evaluate model quality: the lower the model error rate—assuming constant annotator quality and context-difficulty—the better the model.
We observe that model error rates decrease as we progress through rounds. In Round 3, where we included a more diverse range of contexts from various domains, the overall error rate went slightly up compared to the preceding round, but for Wikipedia contexts the error rate decreased substantially. While for the first round roughly 1 in every 5 examples were verified model errors, this quickly dropped over consecutive rounds, and the overall model error rate is less than 1 in 10. On the one hand, this is impressive, and shows how far we have come with just three rounds. On the other hand, it shows that we still have a long way to go if even untrained annotators can fool ensembles of state-of-the-art models with relative ease.
Table TABREF7 also reports the average number of “tries”, i.e., attempts made for each context until a model error was found (or the number of possible tries is exceeded), and the average time this took (in seconds). Again, these metrics represent a useful way to evaluate model quality. We observe that the average tries and average time per verified error both go up as we progress through the rounds. The numbers clearly demonstrate that the rounds are getting increasingly more difficult.
The number of examples we collected increases per round, starting with approximately 19k examples for Round 1, to around 47k examples for Round 2, to over 103k examples for Round 3.   The number of examples we collected
Answer: The number of examples the authors collected increases per round, starting with approximately 19k examples for Round 1, to around 47k examples for Round 2, to over 103k examples for Round 3.
Question: What problems did the author solve in this project?
Evidence: In this project, we tackle the problem of extracting clinical concepts from user queries on medical search engines. By training a BiLSTM-CRF model on a hybrid data consisting of synthesized user queries and sentences from clinical note, we adopt a CE framework for clinical user queries with minimal effort spent on annotating user queries. We find that the hybrid data enables the NER model perform better on both tagging the user queries and the clinical note sentences. Furthermore, our framework is built on an easy-to-use deep learning NLP Python library, which lends it more prospective value to various online medical applications that employ medical search engines.
In this project, we tackle the problem of extracting clinical concepts from user queries on medical search engines. By training a BiLSTM-CRF model on a hybrid data consisting of synthesized user queries and sentences from clinical note, we adopt a CE framework for clinical user queries with minimal effort spent on annotating user queries. We find that the hybrid data enables the NER model perform better on both tagging the user queries and the clinical note sentences.
Answer: They tackle the problem of extracting clinical concepts from user queries on medical search engines.
Question: What is a word confusion network?
Evidence: We take a first step towards integrating ASR in an end-to-end SDS by passing on a richer hypothesis space to subsequent components. Specifically, we investigate how the richer ASR hypothesis space can improve DST. We focus on these two components because they are at the beginning of the processing pipeline and provide vital information for the subsequent SDS components. Typically, ASR systems output the best hypothesis or an n-best list, which the majority of DST approaches so far uses BIBREF11 , BIBREF8 , BIBREF7 , BIBREF12 . However, n-best lists can only represent a very limited amount of hypotheses. Internally, the ASR system maintains a rich hypothesis space in the form of speech lattices or confusion networks (cnets).
Internally, the ASR system maintains a rich hypothesis space in the form of speech lattices or confusion networks (cnets).
Answer: It is a network used to encode speech lattices to maintain a rich hypothesis space.