DataHammer/scimrc huggingface dataset:
Question: What is the state of the art?
Evidence: We will compare to the more recent cross-lingual language model XLM BIBREF12, as well as the state-of-the-art CoNLL 2018 shared task results with predicted tokenisation and segmentation in an updated version of the paper.
In French, no extensive work has been done due to the limited availability of NER corpora. We compare our model with the strong baselines settled by BIBREF49, who trained both CRF and BiLSTM-CRF architectures on the FTB and enhanced them using heuristics and pretrained word embeddings.
In the TRANSLATE-TRAIN setting, we report the best scores from previous literature along with ours. BiLSTM-max is the best model in the original XNLI paper, mBERT which has been reported in French in BIBREF52 and XLM (MLM+TLM) is the best-presented model from BIBREF50.
We will compare to the more recent cross-lingual language model XLM BIBREF12, as well as the state-of-the-art CoNLL 2018 shared task results with predicted tokenisation and segmentation in an updated version of the paper.
In French, no extensive work has been done due to the limited availability of NER corpora. We compare our model with the strong baselines settled by BIBREF49, who trained both CRF and BiLSTM-CRF architectures on the FTB and enhanced them using heuristics and pretrained word embeddings.
In the TRANSLATE-TRAIN setting, we report the best scores from previous literature along with ours. BiLSTM-max is the best model in the original XNLI paper, mBERT which has been reported in French in BIBREF52 and XLM (MLM+TLM) is the best-presented model from BIBREF50.
Answer: POS and DP task: CONLL 2018
NER task: (no extensive work) Strong baselines CRF and BiLSTM-CRF
NLI task: mBERT or XLM (not clear from text)
Question: Based on this text, where was Susan Eaton a professor?
Evidence: Susan Catharine Eaton (July 9, 1957 – December 30, 2003) was an American political scientist and workers' rights activist. Eaton was an assistant professor of public policy at Harvard Kennedy School, who became a nursing home researcher at Harvard and workers' activist. She wrote about health care management, women's role in union leadership and work-family issues and gender equity in the workplace.
Answer: Based on this text, Susan Eaton was a professor at Harvard Kennedy School.
Question: Give me a bulleted list of tech billionaires.
Answer: Here is a bulleted list of tech billionaires:
- Elon Musk
- Bill Gates
- Jeff Bezos
- Larry Ellison
- Steve Ballmer
- Larry Page
- Zhang Yiming
- Michael Dell
- Mark Zuckerberg
Question: What is the difference between pursue and persuade?
Answer: Pursue is to go after something to achieve a means. Persuade is to convince someone to do something.
Question: As it is known that retrieval performance highly depends on the training datasets, Maybe it would be better to compare on other datasets like YouCook2, TVR, ActivityNet Captions, and more?
Evidence: We report additional details on the benchmark datasets used in the paper. ActivityNet ActivityNet Fabian Caba Heilbron & Niebles (2015) is a video dataset with 200 classes, 100 videos per class and 1.5 activity instances per video. The dataset contains 648h of video. There are 10,024 training videos (15,410 instances) and 4,926 validation videos (7,654 instances). The dataset itself has no license and is provided as an annotated list of youtube links. The copyright of the individual videos remains with their creators.****LSMDC Large Scale Movie Description Challenge (LSMDC) Rohrbach et al. (2017) consists of 118K video clips from 202 movies. We evaluate on a test set of 1,000 videos from movies disjoint from the train and val sets, as done in (Bain et al., 2021). The dataset itself has no license and the copyright of the individual videos remains with their creators and the dataset is solely to be used for research purposes.
Answer: We actually do provide more experiments where we evaluate our method on other benchmarks such as LSMDC and ActivityNet in the paper (see Tables 7-8).
Question: What discourse features are used?
Evidence: CNN2-PV. This model (Figure FIGREF10 , left+center) featurizes discourse information into a vector of relation probabilities. In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). The GR features are represented as grammatical relation transitions derived from the entity grid, e.g., INLINEFORM0 . The RST features are represented as RST discourse relations with their nuclearity, e.g., INLINEFORM1 . The probability vectors are then distributions over relation types. For GR, the vector is a distribution over all the entity role transitions, i.e., INLINEFORM2 (see Table TABREF2 ). For RST, the vector is a distribution over all the RST discourse relations, i.e., INLINEFORM3 Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer.
In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST). 
Answer: Entity grid with grammatical relations and RST discourse relations.
Question: What are the main challenges to  solar panel adoption ?
Answer: Based on our collective understanding , main barriers to residential solar panel adoption are Economic, Policy and legal, and technical. For example, the initial cost of homeowner to invest in solar, is substantial and the break even on the investment is over 20 years. In addition many utilities don’t have policies to sufficiently compensate for storage and reselling of the excess energy produced.
Question: Write an e-mail to the office asking employees to clean their coffee mugs rather than leave them in the sink. It should be polite, but express some annoyance.
Answer: Hello fellow coffee-drinkers,

I love drinking coffee like you, but, I also don't like seeing a bunch of dirty mugs piling up in the sink. We do not have cleaning staff to clean them up during the day. Your coworkers have been cleaning up after you. Please rinse any used coffee mugs and put them in the dishwasher like you do at home! Thank you!
Question: Does the paper consider the use of perplexity in order to identify text anomalies?
Evidence: PERPLEXITY
Answer: No.
Question: Do they give generalizations of patterns?
Evidence: In the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. In fact, online harassment becomes very common in Twitter and there have been a lot of critics that Twitter has become the platform for many racists, misogynists and hate groups which can express themselves openly. Online harassment is usually in the form of verbal or graphical formats and is considered harassment, because it is neither invited nor has the consent of the receipt. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter. The main reason is because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. The basic goal of this automatic classification is that it will significantly improve the process of detecting these types of hate speech on social media by reducing the time and effort required by human beings.
Previous studies have been focused on collecting data about sexism and racism in very broad terms or have proposed two categories of sexism as benevolent or hostile sexism BIBREF0, which undermines other types of online harassment. However, there is no much study focusing on different types online harassment alone attracting natural language processing techniques.
In this paper we present our work, which is a part of the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference. The topic of the competition is the classification of different types of harassment and it is divided in two tasks. The first one is the classification of the tweets in harassment and non-harassment categories, while the second one is the classification in specific harassment categories like indirect harassment, physical and sexual harassment as well. We are using the dataset of the competition, which includes text from tweets having the aforementioned categories. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classiﬁcation specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach like multi-attention and single attention models. The next Section includes a short description of the related work, while the third Section includes a description of the dataset. After that, we describe our methodology. Finally, we describe the experiments and we present the results and our conclusion.
The next Section includes a short description of the related work, while the third Section includes a description of the dataset. After that, we describe our methodology. Finally, we describe the experiments and we present the results and our conclusion.
Answer: Yes.
Question: What's the difference between the weather in California compared to New York
Answer: The weather in New York has four distinct seasons, spring, summer, fall and winter. During those seasons the temperature can vary from zero degrees in the winter, and up to 100+ degrees in the summer. There is also regular rain in the spring and there can be snow in the winter. The summer can also be quite humid. In most of California the weather is temperate and consistent throughout the year with very little variation. Summer months may be hotter by 10-20 degrees than the colder months but otherwise, the temperature remains relatively the same. There is not much humidity and there is sunshine most days out of the year.
Question: How many feet are in 1 mile?
Answer: 5,280 feet
Question: What is the difference between NAFTA and NATO?
Answer: NAFTA was the North American Free Trade Agreement, an agreement that created a trade bloc in North America between Canada, Mexico and the United States that was in force from 1994 to 2020.  NATO is the North Atlantic Treaty Organization, a military alliance with 30 member states created in 1949 after World War 2.
Question: how is kirkland central park tennis center
Answer: Kirkland Central Park Tennis Center is a great place to play tennis. It has 18 courts (12 indoor & 6 outdoor), a pro shop, and a mini-kitchen. Overall the courts are in good condition and the staff is friendly and helpful. The center offers a variety of programs and clinics for all levels of players from adults to young kids. The center is located in a convenient location.
Something you may want to know when you plan a visit to the center: the center can be busy during peak hours or during tournaments, it's not open all hours in a day and the membership fee is not cheap.
Question: What are some of the best Sports Movies of all time?
Answer: 1) Hoosiers
2) Rocky
3) Any Given Sunday
4) Miracle
5) Remember the Titans
6) Hoop Dreams
7) Raging Bull
8) Friday Night Lights
9) The Natural
10) Chariots of Fire
Question: Given that the current year is 2023, how old is Chevron?
Evidence: Chevron traces its history back to the 1870s. The company grew quickly after the breakup of Standard Oil by acquiring companies and partnering with others, eventually becoming one of the Seven Sisters that dominated the global petroleum industry from the mid-1940s to the 1970s. In 1985, Socal merged with the Pittsburgh-based Gulf Oil and rebranded as Chevron; the newly merged company later merged with Texaco in 2001. Today, Chevron manufactures and sells fuels, lubricants, additives, and petrochemicals, primarily in Western North America, the U.S. Gulf Coast, Southeast Asia, South Korea and Australia. In 2018, the company produced an average of 791,000 barrels of net oil-equivalent per day in United States.
Answer: Because the current year is 2023 and Chevron traces its history back to the 1870s, Chevron is roughly between 143 and 153 years old.
Question: Classify each of the following animals as ectothermic or endothermic: alligator, snake, frog, goat, human, gorilla
Answer: Alligator: Ectothermic
Snake: Ectothermic
Frog: Ectothermic
Goat: Endothermic
Human: Endothermic
Gorilla: Endothermic
Question: What are the other examples of Colour Revolutions?
Answer: Orange Revolution(in Ukraine between 2004 & 2005, Tulip Revolution(in Kyrgyzstan in 2005), Jasmine Revolution(in Tunisia in 2010-11)
Question: What articles of clothes are often worn by people in summer months?
Answer: In summer, when the weather is warmer, it is typical for people to wear shorts or skirts, light shirts, and sandals. People can also wear swimming suits with cover ups if they are somewhere where swimming is likely, such as a beach, pool, or water park.
Question: What types of Barbecue (BBQ) are there?
Answer: In America there are five distinct types of regional BBQ: Texas, Memphis, Kansas City, North Carolina, and South Carolina. Memphis and the Carolinas are known for the pork, Texas for their beef, and Kansas City for a variety of both.
Question: Does adding more layers to the encoder achieve larger improvements than adding more layers to the decoder?
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 adding more layers to the encoder achieves larger improvements than adding more layers to the decoder.
Answer: Yes.
Question: Identify which instrument is string or woodwind: Chapey, Cromorne
Answer: Chapey is string, Cromorne is woodwind.
Question: Why is Falmouth, Massachusetts a great place to visit for vacation?
Answer: Falmouth, Massachusetts is located on Cape Cod, approximately 90 minutes south of Boston.  Falmouth offers many activities for adults and children, including beaches, bike trails, shopping, dining, minor league baseball, maritime activities, fairs and ferries to Martha's Vineyard and Nantucket.
Question: Who is Germain Damar?
Evidence: Germaine Damar (born August 31, 1929) is a Luxembourger actress and dancer. Sometimes she used the stage name Ria Poncelet. She started her career as an acrobat and played in nearly 30 German films, including three films in which she was the partner of Peter Alexander. In 1964, Damar retired and two years later her son Roman Martin Toporow was born. Her husband died in 1993, and Damar has since then lived with her son in Fort Lauderdale, Florida.

In June 2011 the documentary Germaine Damar – Der tanzende Stern (Germaine Damar, the dancing star) by Michael Wenk was presented at the Luxembourg cinema Ciné Utopia. The former dancing star herself was present and even sang one of her old songs. The audience gave her a standing ovation.
Answer: Germaine Damar was born on August 31, 1929 and is a Luxembourger actress and dancer. She's been in nearly 30 German films and started her career as an acrobat. In 1964, she retired from the film industry.
Question: I'm hosting a breakfast party - what should I serve?
Answer: Depending on how many people you're hosting and how much cooking you want to do the morning of, there are several options.

If you're hosting a big group, it may be best to get the prep done ahead of time so you can enjoy the party. This could be achieved by setting up a bagel bar. Lay out platforms of assorted bagels, cream cheeses, and toppings. Some common topping examples are Lox, tomatoes, cucumbers, red onion and capers. Guest can then toast their own bagels and make a plate according to their preferences. 

Another option would be to prep and assemble breakfast casseroles the night before and pop them into the oven before you're due to eat. This way you can serve a hot breakfast without needing to do all the cooking the morning of. Here is a highly rated breakfast casserole recipe: 

Ingredients:
- 1 x Bag Frozen Shredded Potatoes
- 1 lb breakfast sausage meat
- 1 chopped onion
- 1 chopped pepper
- Dozen Eggs
- Shredded cheddar cheese

Instructions
- Cook chopped onion and pepper in olive oil for 5 minutes until soft. 
- Add in breakfast sausage into the pan and cook through. Be sure to break up sausage a you cook to avoid having big chunks.
- In another pan try the shredded potato until crispy.
- To assemble, layer the potato with the sausage mixture into a baking dish.
- Scramble 12 eggs and a cup of shredded cheese - pour mixture over the sausage and potatoes. 
- Top with cheese and bake for 45 minutes at 350 degrees until the eggs are cooked through.
Question: What does the machine need to do when it is given a spoken document?
Evidence: In SQA, each sample is a triple, INLINEFORM0 , where INLINEFORM1 is a question in either spoken or text form, INLINEFORM2 is a multi-sentence spoken-form document, and INLINEFORM3 is the answer in text from. The task of this work is extractive SQA; that means INLINEFORM4 is a word span from the reference transcription of INLINEFORM5 . An overview framework of SQA is shown in Figure FIGREF1 . In this paper, we frame the source domain as reference transcriptions and the target domain as ASR hypotheses. Hence, we can collect source domain data more easily, and adapt the model to the target domain.
In this task, when the machine is given a spoken document, it needs to find the answer of a question from the spoken document. SQA can be solved by the concatenation of an ASR module and a question answering module. Given the ASR hypotheses of a spoken document and a question, the question answering module can output a text answer.
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer. We used the standard evaluation script from SQuAD BIBREF1 to evaluate the performance.
In this task, when the machine is given a spoken document, it needs to find the answer of a question from the spoken document.
Answer: It needs to find the answer of a question from the spoken document.
Question: How long was the Nazi German battleship Bismarck active during WWII?
Evidence: In the course of the warship's eight-month career, Bismarck conducted only one offensive operation that lasted 8 days in May 1941, codenamed Rheinübung. The ship, along with the heavy cruiser Prinz Eugen, was to break into the Atlantic Ocean and raid Allied shipping from North America to Great Britain. The two ships were detected several times off Scandinavia, and British naval units were deployed to block their route. At the Battle of the Denmark Strait, the battlecruiser HMS Hood initially engaged Prinz Eugen, probably by mistake, while HMS Prince of Wales engaged Bismarck. In the ensuing battle Hood was destroyed by the combined fire of Bismarck and Prinz Eugen, which then damaged Prince of Wales and forced her retreat. Bismarck suffered sufficient damage from three hits by Prince of Wales to force an end to the raiding mission.
Answer: The Bismarck was active for only 8 months of World WarII. The Bismarck and an accompanying heavy crusier, the Prinz Eugen engaged with two British battlecruisers, the Hood and the Prince of Wales off the Denmark Strait in May 1941.  During the engagement, the Hood was destroyed but the Bismarck suffered damage from fire from the Price of Wales that forced the Nazi battleship to effectively end its operations.