DataHammer/scimrc huggingface dataset:
Question: What aim do the authors have by improving Wiki(GOLD) results?
Evidence: The results for each class type are shown in Table TABREF19 , with some specific examples shown in Figure FIGREF18 . For the Wiki(gold) we quote the micro-averaged F-1 scores for the entire top level entity category. The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. It is worth noting that one could improve Wiki(gold) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset. The results in Table TABREF19 (OntoNotes) only show the main 7 categories in OntoNotes which map to Wiki(gold) for clarity. The other categories (date, time, norp, language, ordinal, cardinal, quantity, percent, money, law) have F-1 scores between 80-90%, with the exception of time (65%)
It is worth noting that one could improve Wiki(GOLD) results by training directly using this dataset. However, the aim is not to tune our model specifically on this class hierarchy. We instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset.
Answer: The aim is not to tune their model specifically on this class hierarchy. They instead aim to present a framework which can be modified easily to any domain hierarchy and has acceptable out-of-the-box performances to any fine-grained dataset.
Question: Classify each of the following as either characters from books by Anne Rice or Stephanie Meyer: Lestat de Lioncourt, Memnoch, Lasher, Armand, Yvette, Bella Swan, Edward Cullen, Jacob Black, Melanie Stryder, The Seeker
Answer: Characters from Anne Rice books: Lestat de Lioncourt, Memnoch, Lasher, Armand, Yvette
Characters from Stephanie Meyer books: Bella Swan, Edward Cullen, Jacob Black, Melanie Stryder, The Seeker
Question: Given this paragraph about the M26, tell me how many times Hunt finished in the top 3, and explain your reasoning.
Evidence: McLaren relied on the M23 during the rest of the season and for the first few races of 1977 until it was deemed necessary that the M26 was to race in light of the wilting performance of its predecessor. The redesigned car made its debut at the Spanish Grand Prix, where it proved to be a solid, if unspectacular performer. James Hunt, who initially hated the car knuckled down to improve the race pace and reliability of the M26, and throughout the season the car's performance improved noticeably. Hunt won three times during the course of the season, and scored two other podium finishes in the second half of the season. Two other potential victories were lost in Austria and Canada through reliability issues when Hunt was leading easily. At season's end, McLaren had scored 69 points and were third in the constructors' championship.
Answer: He placed in the top 3 a total of 5 times, because he won three times and had an additional two podium finishes.
Question: Who is Pia Olsen Dyhr
Answer: Pia Olsen Dyhr (born 30 November 1971 in Vallensbæk, Copenhagen) is a Danish politician who has been a member of the Folketing for the Socialist People's Party since the 2007 general elections. Dyhr has served as Minister for Trade and Investment and later Minister of Transport in the first Helle Thorning-Schmidt Cabinet. Following her party's resignation from the cabinet, Dyhr was elected as chairman for the Socialist People's Party.
Question: How is module that analyzes behavioral state trained?
Evidence: The behavior model was implemented using an RNN with LSTM units and trained with the Couples Therapy Corpus. Out of the 33 behavioral codes included in the corpus we applied the behaviors Acceptance, Blame, Negativity, Positivity, and Sadness to train our models. This is motivated from previous works which showed good separability in these behaviors as well as being easy to interpret. The behavior model is pre-trained to identify the presence of each behavior from a sequence of words using a multi-label classification scheme. The pre-trained portion of the behavior model was implemented using a single layer RNN with LSTM units with dimension size 50.
The behavior model was implemented using an RNN with LSTM units and trained with the Couples Therapy Corpus. Out of the 33 behavioral codes included in the corpus we applied the behaviors Acceptance, Blame, Negativity, Positivity, and Sadness to train our models. This is motivated from previous works which showed good separability in these behaviors as well as being easy to interpret. The behavior model is pre-trained to identify the presence of each behavior from a sequence of words using a multi-label classification scheme. The pre-trained portion of the behavior model was implemented using a single layer RNN with LSTM units with dimension size 50.
Answer: pre-trained to identify the presence of behavior from a sequence of word using the Couples Therapy Corpus
Question: Given this paragraph about full distance ironman competition, how far is each discipline
Evidence: A number of non-WTC full distance triathlons have been held since the mid-1990s. The limited number of WTC-sanctioned events, and the limited number of entries available per race, have combined with a growth in the sport that has created demand for these non-trademarked events. Many of them share the 2.4-mile (3.9 km), 112-mile (180 km), 26.2-mile (42.2 km) format with the Ironman triathlon. Originally, many used the Ironman name. Due to aggressive trademark protection, most of these races no longer use the word "Ironman".
Answer: The distances for a full ironman competition are 2.4 miles (3.9km) for the swim, 112 miles (180km) for the bike and 26.2 miles (42.2km) for the run.
Question: According to this paragraph, what records did Stevens break as a basketball coach?
Evidence: In 2010, his third year as head coach, Stevens broke the NCAA record for most wins in a coach's first three years, exceeding the previous record by eight wins. In the NCAA Tournament, Stevens coached Butler to the first Final Four in school history, while also becoming the second-youngest head coach to make an NCAA National Championship game, losing 61–59 to Duke. With the following season's team also making the Final Four, Stevens became the youngest coach to go to two Final Fours. Stevens coached the Bulldogs in their second consecutive national championship game on April 4, 2011, where the team again lost, this time to the Connecticut Huskies. Stevens was regularly named a finalist for Horizon League Coach of the Year award, winning twice, and was also a nominee for both the Hugh Durham Award and Jim Phelan Award in every year of his college career.
https://en.wikipedia.org/wiki/Brad_Stevens
Answer: According to this paragraph, Stevens has the most wins as an NCAA coach in his first three years and took Butler to its first Final Four in the history of the school. He was also the youngest men's college basketball coach ever to take his team to the NCAA championship game and to two consecutive Final Fours.
Question: What are their future work plans?
Evidence: Our next step is to address the classification of rarer depressive symptoms suggestive of major depressive disorder from our dataset and hierarchy including inappropriate guilt, difficulty concentrating, psychomotor agitation or retardation, weight loss or gain, and anhedonia BIBREF15 , BIBREF16 . We are developing a population-level monitoring framework designed to estimate the prevalence of depression (and depression-related symptoms and psycho-social stressors) over millions of United States-geocoded tweets. Identifying the most discriminating feature sets and natural language processing classifiers for each depression symptom is vital for this goal.
Our next step is to address the classification of rarer depressive symptoms suggestive of major depressive disorder from our dataset and hierarchy including inappropriate guilt, difficulty concentrating, psychomotor agitation or retardation, weight loss or gain, and anhedonia.
Answer: To address the classification of rarer depressive symptoms suggestive of major depressive disorder from their dataset and hierarchy including inappropriate guilt, difficulty concentrating, psychomotor agitation or retardation, weight loss or gain, and anhedonia.
Question: Which languages are evaluated?
Evidence: We evaluate the proposed methods in 8 languages, showing a significant ability to learn from partial data. We additionally experiment with initializing CBL with domain-specific instance-weighting schemes, showing mixed results. In the process, we use weighted variants of popular NER models, showing strong performance in both non-neural and neural settings. Finally, we show experiments in a real-world setting, by employing non-speakers to manually annotate romanized Bengali text. We show that a small amount of non-speaker annotation combined with our method can outperform previous methods.
We experiment on 8 languages. Four languages – English, German, Spanish, Dutch – come from the CoNLL 2002/2003 shared tasks BIBREF21, BIBREF22. These are taken from newswire text, and have labelset of Person, Organization, Location, Miscellaneous.
The remaining four languages come from the LORELEI project BIBREF23. These languages are: Amharic (amh: LDC2016E87), Arabic (ara: LDC2016E89), Hindi (hin: LDC2017E62), and Somali (som: LDC2016E91). These come from a variety of sources including discussion forums, newswire, and social media. The labelset is Person, Organization, Location, Geo-political entity. We define train/development/test splits, taking care to keep a similar distribution of genres in each split. Data statistics for all languages are shown in Table TABREF25.
We evaluate the proposed methods in 8 languages, showing a significant ability to learn from partial data. We additionally experiment with initializing CBL with domain-specific instance-weighting schemes, showing mixed results. In the process, we use weighted variants of popular NER models, showing strong performance in both non-neural and neural settings. Finally, we show experiments in a real-world setting, by employing non-speakers to manually annotate romanized Bengali text.
We experiment on 8 languages. Four languages – English, German, Spanish, Dutch – come from the CoNLL 2002/2003 shared tasks BIBREF21, BIBREF22. These are taken from newswire text, and have labelset of Person, Organization, Location, Miscellaneous.

The remaining four languages come from the LORELEI project BIBREF23. These languages are: Amharic (amh: LDC2016E87), Arabic (ara: LDC2016E89), Hindi (hin: LDC2017E62), and Somali (som: LDC2016E91). These come from a variety of sources including discussion forums, newswire, and social media. 
Answer: The answers are shown as follows:
* Bengali
* English, German, Spanish, Dutch
* Amharic
* Arabic
* Hindi
* Somali
Question: What kind of Information does the preceding tokens in the decoder provide?
Evidence: The encoder-decoder based framework BIBREF0, BIBREF1, BIBREF2 is the dominant approach for neural machine translation (NMT) BIBREF3, BIBREF4. Although the encoder and decoder usually adopt the same model structure (RNN BIBREF5, CNN BIBREF6 or self-attention BIBREF3, BIBREF7) and the same number of layers, they perform different functionalities: the encoder extracts the hidden representations of the source sentence, and the decoder generates target tokens conditioned on the source hidden representations as well as the previous generated tokens.
While most existing works focus on the design and improvement of encoder-decoder framework for NMT BIBREF8, BIBREF6, BIBREF3, BIBREF9 as well as its detailed analyses BIBREF10, BIBREF11, BIBREF12, BIBREF13, few works concentrate on the characteristics and functionalities of the encoder and the decoder, which are valuable to understand this popular framework and improve its performance in NMT. Therefore, in this paper, we conduct a study and aim to understand the characteristics of the encoder and the decoder in NMT. We observe some interesting phenomena:
The decoder handles an easier task than the encoder. 1) We find that adding more layers to the encoder achieves larger improvements than adding more layers to the decoder. 2) We also compare the training time of the encoder and decoder by fixing the parameters of a well-trained decoder (encoder), and just update the parameters of the encoder (decoder). We found that the decoder converges faster than the encoder. These two results suggest that the decoder handles an easier task than the encoder in NMT.
The decoder is more sensitive to the input noise than the encoder. We randomly add different level of noise to the input of the encoder and decoder respectively during inference, and find that adding noise to the input of the decoder leads to better accuracy drop than that of the encoder.
We further analyze why the decoder is more sensitive by masking the previous tokens, and comparing autoregressive NMT with the non-autoregressive counterpart. We find that the preceding tokens in the decoder provide strong conditional information, which partially explain the previous two observations on the decoder.
We believe our studies on the different characteristics of the encoder and decoder will inspire the following research on the encoder-decoder framework as well as improve the performance on NMT and other encoder-decoder based tasks.
We find that the preceding tokens in the decoder provide strong conditional information, which partially explain the previous two observations on the decoder. 
Answer: Strong conditional information.
Question: Extract the names of the original songs Sandler performed on Saturday Night Live and list them in bullet format.
Evidence: In 1987, Sandler played Theo Huxtable's friend Smitty on The Cosby Show and the Stud Boy or Trivia Delinquent on the MTV game show Remote Control. After his film debut in Going Overboard in 1989, Sandler performed in comedy clubs, having first taken the stage at his brother's urging when he was 17. He was discovered by comedian Dennis Miller, who caught Sandler's act in Los Angeles and recommended him to Saturday Night Live producer Lorne Michaels. Sandler was hired as a writer for SNL in 1990, and became a featured player the following year. He made a name for himself by performing amusing original songs on the show, including "The Thanksgiving Song" and "The Chanukah Song". Sandler told Conan O'Brien on The Tonight Show that NBC fired him and Chris Farley from the show in 1995. Sandler used his firing as part of his monologue when he returned in 2019 to host the show.
Answer: -The Thanksgiving Song
-The Chanukah Song
Question: What's the best place to visit China?
Answer: The most popular places to visit china are Beijing, Xian, Shanghai and Nanjing. You can find plenty historical places to visit in those city. There are also many mountains to visit. Speaking of long Chinese history, religious culture and distinctive natural scenery, the Five Great Mountains (Wu Yue) are the best destinations to discovery once for all. Despite the lower height than those high mountains like Mount Emei (3099m) and Mount Wutai (3058m), the five in different directions are known far and wide as the great figure of Chinese nation, through the combination of worshipping tradition of ancient mount gods, theory of five Taoist elements (Gold, Wood, Water, Fire and Soil) and mountain apotheosis from emperors. As the popular saying goes, trips to China's five great mountains belittle trips to other mountains unnecessary(五岳归来不看山). Moreover, the starring five famous mountains in China are still major ashram of Chinese Taoism, where each own typical characteristics with both scenic and cultural significance.
Question: How do you push start a car with a manual transmission?
Answer: 1. Get the car to the top of a stretch of clear downhill road, or get people who will push you there.

2. Fully depress the clutch and put the car in second gear (first gear if you’ll be rolling slowly; choose the gear that you would normally use at that speed, and faster is better).

3. Turn the ignition to on.

4. Leaving the clutch fully depressed, take your foot off the brake and start rolling down the hill (or get people to start pushing you).

5. Coast down the hill until you reach 5+ miles per hour (faster is better).

6. Release the clutch quickly while giving some gas; the engine should turn and start. You may need to feather the clutch/gas to keep the engine running at first.

7. If it doesn’t start the first time, depress the clutch and release it again.
Question: Give me a list of 7 continents in the world in alphabetical order.
Answer: There are seven different continents in the world. Here is the list of them in alphabetical order:

Africa
Antarctica
Asia
Australia
Europe
North America
South America
Question: When did WWII begin?
Evidence: World War II or the Second World War, often abbreviated as WWII or WW2, was a global conflict that lasted from 1939 to 1945. The vast majority of the world's countries, including all of the great powers, fought as part of two opposing military alliances: the Allies and the Axis. Many participants threw their economic, industrial, and scientific capabilities behind this total war, blurring the distinction between civilian and military resources. Aircraft played a major role, enabling the strategic bombing of population centres and the delivery of the only two nuclear weapons ever used in war.
Answer: WWII began in 1939 and lasted until 1945.
Question: What system is used in the experiments?
Evidence: We evaluate our model through two types of metrics. The BLEU score BIBREF34 aims at measuring to what extent the generated descriptions are literally closed to the ground truth. The second category designed by BIBREF10 is more qualitative.
The BLEU score BIBREF34 is commonly used as an evaluation metric in text generation tasks. It estimates the correspondence between a machine output and that of a human by computing the number of co-occurrences for ngrams ($n \in {1, 2, 3, 4}$) between the generated candidate and the ground truth. We use the implementation code released by BIBREF35.
These metrics estimate the ability of our model to integrate elements from the table in its descriptions. Particularly, they compare the gold and generated descriptions and measure to what extent the extracted relations are aligned or differ. To do so, we follow the protocol presented in BIBREF10. First, we apply an information extraction (IE) system trained on labeled relations from the gold descriptions of the RotoWire train dataset. Entity-value pairs are extracted from the descriptions. For example, in the sentence Isaiah Thomas led the team in scoring, totaling 23 points [...]., an IE tool will extract the pair (Isaiah Thomas, 23, PTS). Second, we compute three metrics on the extracted information:
$\bullet $ Relation Generation (RG) estimates how well the system is able to generate text containing factual (i.e., correct) records. We measure the precision and absolute number (denoted respectively RG-P% and RG-#) of unique relations $r$ extracted from $\hat{y}_{1:T}$ that also appear in $s$.
$\bullet $ Content Selection (CS) measures how well the generated document matches the gold document in terms of mentioned records. We measure the precision and recall (denoted respectively CS-P% and CS-R%) of unique relations $r$ extracted from $\hat{y}_{1:T}$ that are also extracted from $y_{1:T}$.
$\bullet $ Content Ordering (CO) analyzes how well the system orders the records discussed in the description. We measure the normalized Damerau-Levenshtein distance BIBREF36 between the sequences of records extracted from $\hat{y}_{1:T}$ that are also extracted from $y_{1:T}$.
CS primarily targets the “what to say" aspect of evaluation, CO targets the “how to say it" aspect, and RG targets both. Note that for CS, CO, RG-% and BLEU metrics, higher is better; which is not true for RG-#. The IE system used in the experiments is able to extract an average of 17 factual records from gold descriptions. In order to mimic a human expert, a generative system should approach this number and not overload generation with brute facts.
The IE system used in the experiments is able to extract an average of 17 factual records from gold descriptions.
Answer: IE system.
Question: Classify the following habits as healthy or unhealthy. 
1) Smoke
2) Exercise
3) Take Breaks
4) Drink Alcohol
Answer: Smoking and drinking alcohol can be very dangerous to your health so it is a good example of unhealthy habits. Exercise and take breaks during the day can be considered healthy to your body.
Question: Can you tell me about the history of sports in Philadelphia?
Evidence: Philadelphia is one of thirteen cities that hosts teams in the "Big Four" major sports leagues in North America, and Philadelphia is one of just four cities in which one team from every league plays within city limits. These major sports teams are the Philadelphia Phillies of Major League Baseball (MLB), the Philadelphia Eagles of the National Football League (NFL), the Philadelphia 76ers of the National Basketball Association (NBA) and the Philadelphia Flyers of the National Hockey League (NHL). Each team has played in Philadelphia since at least the 1960s, and each team has won at least two championships. Since 2010, the Greater Philadelphia area also has been the home of the Philadelphia Union of Major League Soccer (MLS), making the Philadelphia market one of only nine cities that hosts a team in the five major sports leagues. Prior to the 1980s, Philadelphia was home to several other notable professional franchises, including the Philadelphia Athletics, the Frankford Yellow Jackets, the Philadelphia Warriors, the Philadelphia Quakers, Philadelphia Atoms, and the Philadelphia Field Club.
Answer: Philadelphia host teams in the 4 major sport leagues. Over the years, there has been varying success from the teams. In some years, there have been teams at the bottom of the league. Most years, teams are within reach of the league championship. Philadelphia Phillies and Philadelphia Eagles have been teams with the most success with both teams ending as runner up to the league championship. The Philadelphia Sixers has been a top tier team but has not been able to get past the second round of playoffs in the last few years. Philadelphia sports fans are notoriously loyal, demanding of their players and aggressive against fans of opposing team fans.
Question: What do they call Remembrance Day in the United States
Evidence: Remembrance Day (also known as Poppy Day owing to the tradition of wearing a remembrance poppy) is a memorial day observed in Commonwealth member states since the end of the First World War in 1919 to honour armed forces members who have died in the line of duty. The day is also marked by war remembrances in several other non-Commonwealth countries. In most countries, Remembrance Day is observed on 11 November to recall the end of First World War hostilities. Hostilities formally ended "at the 11th hour of the 11th day of the 11th month" of 1918, in accordance with the armistice signed by representatives of Germany and the Entente between 5:12 and 5:20 that morning. ("At the 11th hour" refers to the passing of the 11th hour, or 11:00 am.) The First World War officially ended with the signing of the Treaty of Versailles on 28 June 1919.

The tradition of Remembrance Day evolved out of Armistice Day. The initial Armistice Day was observed at Buckingham Palace, commencing with King George V hosting a "Banquet in Honour of the President of the French Republic" during the evening hours of 10 November 1919. The first official Armistice Day was subsequently held on the grounds of Buckingham Palace the following morning. During the Second World War, many countries changed the name of the holiday. Member states of the Commonwealth of Nations adopted Remembrance Day, while the US chose Veterans Day.
Answer: In the United States, Remembrance Day, which is a memorial day observing the end of the First World War, is known as Veterans Day.