DataHammer/scimrc huggingface dataset:
Question: What is the new labeling strategy?
Evidence: We address the above issues with a new methodology. First, we introduce a two-stage labeling strategy for sentiment texts. In the first stage, annotators are invited to label a large number of short texts with relatively pure sentiment orientations. Each sample is labeled by only one annotator. In the second stage, a relatively small number of text samples with mixed sentiment orientations are annotated, and each sample is labeled by multiple annotators. Second, we propose a two-level long short-term memory (LSTM) BIBREF4 network to achieve two-level feature representation and classify the sentiment orientations of a text sample to utilize two labeled data sets. Lastly, in the proposed two-level LSTM network, lexicon embedding is leveraged to incorporate linguistic features used in lexicon-based methods.
First, we introduce a two-stage labeling strategy for sentiment texts. In the first stage, annotators are invited to label a large number of short texts with relatively pure sentiment orientations. Each sample is labeled by only one annotator. In the second stage, a relatively small number of text samples with mixed sentiment orientations are annotated, and each sample is labeled by multiple annotators. 
Answer: They use a two-stage labeling strategy where in the first stage single annotators label a large number of short texts with relatively pure sentiment orientations and in the second stage multiple annotators label few text samples with mixed sentiment orientations
Question: Identify which instrument is string or percussion: Dhaa, Tremoloa
Answer: Tremoloa is string, Dhaa is percussion.
Question: why is hydration so important?
Answer: Water is very good for you because it helps to keep you hydrated.  Your body needs water for nearly every function.  When you are well hydrated you will look younger because your skin has better elasticity.  You can think more clearly because your brain is hydrated.  You muscles will recover faster, and you will have better digestion.  Coffee and sugar drinks will dehydrate you so if you have some of these make sure you replenish your fluids with more water to get hydrated.
Question: What are the data sources for learning NMT and RAT jointly?
Evidence: We train NMT with RAT to achieve better query translations. We improve a recently proposed NMT baseline, Transformer, that achieves state-of-the-art results for sentence pairs in some languages BIBREF8 . We discuss Transformer, RAT, and our multi-task learning architecture that achieves balanced translation.
We consider two data sources for learning NMT and RAT jointly. The first one is a sentence-level parallel corpus, which we refer to as translation corpus, T C = {(si , ti);i = 1, 2, . . . m}. The second one is the retrieval corpus, which is a collection of k documents RC = {D1, D2, . . . Dk} in the same language as ti .
Answer: The first one is a sentence-level parallel corpus and the second one is the retrieval corpus.
Question: Is a golden doodle a good pet for a family with young children?
Answer: Golden doodles, which are part golden retriever and part poodle, are very popular and make good pets, especially for families with children.  

While some golden doodles may be outliers, generally they have a very good temperament, and are loving and loyal pets.  A young child can yank on their tails or fur, and parents don’t need to be concerned that their child will get bitten (although the golden doodle will likely nudge the child to stop that behavior).  

They also are generally intelligent dogs, and learn easily.  More importantly, they are easy to house train, which is important for a family with young children, since there aren’t additional messes to clean up after. They don’t shed like long-haired dogs, where the hairs can get on your clothes.  They shed in clumps of hair, which are easier to see and clean up. 

Last, golden doodles are very cute dogs, which is another element that has contributed to their popularity. 

However, because they are part poodle, they can be excitable, in particular when they are puppies, and they need to get a lot of exercise so that they can expend their energy. As a result, they should be with an active family so that they can get plenty of activity.
Question: What are a few things to do when visiting Northern Vietnam?
Answer: Here are some to do when visiting Northern Vietnam: 
1. Go hiking through the picturesque rice terraces in Sa Pa. This is a great way to experience the beauty and local culture of Northern Vietnam.
2. Take a cruise on Ha Long Bay. This Unesco world heritage site is a great place to relax and take in the views as you sail through thousands of limestone islands.
3. Visit Hanoi and go on a food tour. This is one of the best ways to experience the local culture of Vietnam through authentic Vietnamese cuisine. 
4. Spend a day exploring the stunning landscapes of Ninh Binh. Home to the ancient capital of Vietnam and the Unesco World Heritage site of Trang An, Ninh Binh is a popular destination in Northern Vietnam.
Question: Give me a list of the best French rugby players ever
Answer: Antoine Dupont, Damian Penaud, Vincent Clerc, Fabien Pelous, Thierry Dusautoir, Serge Betsen, Fabien Galthié, Romain N'tamack, Cyril Baille, François Cros, Aurélien Rougerie, Nicolas Mas, William Servat
Question: Who has hosted Saturday Night Live the most in the history of the show?
Answer: Actor Alec Baldwin has hosted Saturday Night Live 17 times since 1990.
Question: what processing was done on the speeches before being parsed?
Evidence: To estimate speakers' position we use Wordscore BIBREF1 – a version of the Naive Bayes classifier that is deployed for text categorization problems BIBREF22 . In a similar application, BIBREF1 have already demonstrated that Wordscore can be effectively used to derive estimates of TDs policy positions. As in the example above, we pre-process documents by removing all numbers and interjections.
As in the example above, we pre-process documents by removing all numbers and interjections.
Answer: Remove numbers and interjections
Question: How do I make good pour over coffee?
Answer: Start with a good pour over filter holder, preferably one made of borosilicate glass, and filters especially designed for pour over coffee, such as cone filters. Use a medium-coarse grind of coffee beans, and use filtered water instead of regular tap water. The recommended ratio for coffee to water is 1:15, which means for every 1 gram of coffee, use 15 grams of water. Pour the water over the ground coffee in a circular motion, with a first pour just wetting the coffee grounds, and a second pour slow and steady until to reach the correct ratio. Sip and enjoy.
Question: What is the key challenge in handling document streams?
Evidence: Following developing news stories is imperative to making real-time decisions on important political and public safety matters. Given the abundance of media providers and languages, this endeavor is an extremely difficult task. As such, there is a strong demand for automatic clustering of news streams, so that they can be organized into stories or themes for further processing. Performing this task in an online and efficient manner is a challenging problem, not only for newswire, but also for scientific articles, online reviews, forum posts, blogs, and microblogs.
A key challenge in handling document streams is that the story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream. In this paper, we provide a treatment to the problem of online document clustering, i.e. the task of clustering a stream of documents into themes. For example, for news articles, we would want to cluster them into related news stories.
To this end, we introduce a system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. Our clustering approach is part of a larger media monitoring project to solve the problem of monitoring massive text and TV/Radio streams (speech-to-text). In particular, media monitors write intelligence reports about the most relevant events, and being able to search, visualize and explore news clusters assists in gathering more insight about a particular story. Since relevant events may be spawned from any part of the world (and from many multilingual sources), it becomes imperative to cluster news across different languages.
In terms of granularity, the type of story clusters we are interested in are the group of articles which, for example : (i) Narrate recent air-strikes in Eastern Ghouta (Syria); (ii) Describe the recent launch of Space X's Falcon Heavy rocket.
A key challenge in handling document streams is that the story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream.
Answer: The story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream.
Question: Is tap water safe to drink in Japan?
Answer: Yes, tap water is safe to drink in Japan.
Question: Do they use a crowdsourcing platform for annotation?
Evidence: Reddit is popular website that allows registered users (without identity verification) to participate in fora grouped by topic or interest. Participation consists of posting stories that can be seen by other users, voting stories and comments, and comments in the story's comment section, in the form of a forum. The forums are arranged in the form of a tree, allowing nested conversations, where the replies to a comment are its direct responses. We collected all comments in the stories' conversation in Reddit that were posted in August 2015. Since it is infeasible to manually annotate all of the comments, we process this dataset with the goal of extracting threads that involve suspected trolling attempts and the direct responses to them. To do so, we used Lucene to create an inverted index from the comments and queried it for comments containing the word “troll” with an edit distance of 1 in order to include close variations of this word, hypothesizing that such comments would be reasonable candidates of real trolling attempts. We did observe, however, that sometimes people use the word troll to point out that another user is trolling. Other times, people use the term to express their frustration about a particular user, but there is no trolling attempt. Yet other times people simply discuss trolling and trolls without actually observing one. Nonetheless, we found that this search produced a dataset in which 44.3% of the comments are real trolling attempts. Moreover, it is possible for commenters to believe that they are witnessing a trolling attempt and respond accordingly even where there is none due to misunderstanding. Therefore, the inclusion of comments that do not involve trolling would allow us to learn what triggers a user's interpretation of trolling when it is not present and what kind of response strategies are used.
We had two human annotators who were trained on snippets (i.e., (suspected trolling attempt, responses) pairs) taken from 200 conversations and were allowed to discuss their findings. After this training stage, we asked them to independently label the four aspects for each snippet. We recognize that this limited amount of information is not always sufficient to recover the four aspects we are interested in, so we give the annotators the option to discard instances for which they couldn't determine the labels confidently. The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens. The distribution over the classes per trolling aspect is shown in the table TABREF19 in the column “Size”.
Since it is infeasible to manually annotate all of the comments, we process this dataset with the goal of extracting threads that involve suspected trolling attempts and the direct responses to them. 
We had two human annotators who were trained on snippets (i.e., (suspected trolling attempt, responses) pairs) taken from 200 conversations and were allowed to discuss their findings. After this training stage, we asked them to independently label the four aspects for each snippet. 
Answer: No.
Question: In what four Chinese review datasets does LCF-ATEPC achieves state of the art?
Evidence: To comprehensive evaluate the performance of the proposed model, the experiments were conducted in three most commonly used ABSA datasets, the Laptops and Restaurant datasets of SemEval-2014 Task4 subtask2 BIBREF0 and an ACL Twitter social dataset BIBREF34. To evaluate our model capability with processing the Chinese language, we also tested the performance of LCF-ATEPC on four Chinese comment datasets BIBREF35, BIBREF36, BIBREF29 (Car, Phone, Notebook, Camera). We preprocessed the seven datasets. We reformatted the origin dataset and annotated each sample with the IOB labels for ATE task and polarity labels for APC tasks, respectively. The polarity of each aspect on the Laptops, Restaurants and datasets may be positive, neutral, and negative, and the conflicting labels of polarity are not considered. The reviews in the four Chinese datasets have been purged, with each aspect may be positive or negative binary polarity. To verify the effectiveness and performance of LCF-ATEPC models on multilingual datasets, we built a multilingual dataset by mixing the 7 datasets. We adopt this dataset to conduct multilingual-oriented ATE and APC experiments.
To evaluate our model capability with processing the Chinese language, we also tested the performance of LCF-ATEPC on four Chinese comment datasets BIBREF35, BIBREF36, BIBREF29 (Car, Phone, Notebook, Camera).
Answer: The answers are shown as follows:
* Car, Phone, Notebook, Camera
Question: Who was boxer John Baldwin
Evidence: Known as "The Mad" Baldwin, he turned pro in 1970 and lost a decision to Marvin Hagler in 1975. In 1977 he took on Rocky Mosley Jr in the ill-fated U.S. Championship Tournament, but lost via K.O. In 1978 he lost a decision to Marvin Johnson, and retired a year later.
Answer: Born in Detroit, John Baldwin was a professional US boxer.  He fought in the Light-Middleweight category and won a bronze medal in the 1968 Olympics.   He turned pro in 1970, and lost a decision to Marvin Hagler in 1975.  He retired in 1979
Question: Should I buy a gravel bike or a down country mountain bike?
Answer: Although there is reasonable overlap in what you can use gravel and downcountry bikes for, the answer her largely depends on what you plan to do the most of and if trail riding will be important for you. Gravel bikes are great for covering offroad miles more efficiently and long days in the saddle both on road or offroad. However, the lack of any meaningful suspension means that or but a small amount of time off paths and more established trails will be hard work. Downcountry bikes will enable you to go on a wider range of terrain, inlcuding the ability to do trail riding (be very careful before trying to navigate downhill trails on a gravel bike!). However, downcountry bikes are usually heavier but the full suspension and geometry makes them less efficient compared to a gravel bike.
Question: When was the Sydney Opera House formally opened
Evidence: The Sydney Opera House is a multi-venue performing arts centre in Sydney. Located on the foreshore of Sydney Harbour, it is widely regarded as one of the world's most famous and distinctive buildings and a masterpiece of 20th-century architecture.

Designed by Danish architect Jørn Utzon, but completed by an Australian architectural team headed by Peter Hall, the building was formally opened by Queen Elizabeth II on 20 October 1973 after a gestation beginning with Utzon's 1957 selection as winner of an international design competition. The Government of New South Wales, led by the premier, Joseph Cahill, authorised work to begin in 1958 with Utzon directing construction. The government's decision to build Utzon's design is often overshadowed by circumstances that followed, including cost and scheduling overruns as well as the architect's ultimate resignation.

The building and its surrounds occupy the whole of Bennelong Point on Sydney Harbour, between Sydney Cove and Farm Cove, adjacent to the Sydney central business district and the Royal Botanic Gardens, and near to the Sydney Harbour Bridge.
Answer: The Sydney Opera House is a multi-venue performing arts centre in Sydney. Located on the foreshore of Sydney Harbour, it is widely regarded as one of the world's most famous and distinctive buildings and a masterpiece of 20th-century architecture.

Designed by Danish architect Jorn Utzon, but completed by an Australian architectural team headed by Peter Hall, the building was formally opened by Queen Elizabeth II on 20 October 1973 after a gestation beginning with Utzon's 1957 selection as winner of an international design competition. The Government of New South Wales, led by the premier, Joseph Cahill, authorised work to begin in 1958 with Utzon directing construction. The government's decision to build Utzon's design is often overshadowed by circumstances that followed, including cost and scheduling overruns as well as the architect's ultimate resignation.

The building and its surrounds occupy the whole of Bennelong Point on Sydney Harbour, between Sydney Cove and Farm Cove, adjacent to the Sydney central business district and the Royal Botanic Gardens, and near to the Sydney Harbour Bridge.
Question: Why is the Saguaro cactus important to Arizona?
Answer: The Saguaro cactus (Saguaro Cacti) is a very rare type of cacti that only grows in the Sonoran Desert, which southern Arizona is part of.  The Saguaro, can easily live a few hundred years as it takes about 50-75 years just to grow one arm.  Due to new construction in Arizona, a Saguaro interfering with the location of a building must be transplanted and cannot be destroyed.  The destruction of a Saguaro cactus is a class-4 felony.
Question: What was selected for the dataset?
Evidence: Legal documents are a rather heterogeneous class, which also manifests in their linguistic properties, including the use of named entities and references. Their type and frequency varies significantly, depending on the text type. Texts belonging to specific text type, which are to be selected for inclusion in a corpus must contain enough different named entities and references and they need to be freely available. When comparing legal documents such as laws, court decisions or administrative regulations, decisions are the best option. In laws and administrative regulations, the frequencies of person, location and organization are not high enough for NER experiments. Court decisions, on the other hand, include person, location, organization, references to law, other decision and regulation.
Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection. The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
From the table of contents, 107 documents from each court were selected (see Table ). The data was collected from the XML documents, i. e., it was extracted from the XML elements Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel. The metadata at the beginning of the documents (name of court, date of decision, file number, European Case Law Identifier, document type, laws) and those that belonged to previous legal proceedings was deleted. Paragraph numbers were removed. The extracted data was split into sentences, tokenised using SoMaJo BIBREF16 and manually annotated in WebAnno BIBREF17.
The annotated documents are available in CoNNL-2002. The information originally represented by and through the XML markup was lost in the conversion process. We decided to use CoNNL-2002 because our primary focus was on the NER task and experiments. CoNNL is one of the best practice formats for NER datasets. All relevant tools support CoNNL, including WebAnno for manual annotation. Nevertheless, it is possible, of course, to re-insert the annotated information back into the XML documents.
Court decisions from 2017 and 2018 were selected for the dataset, published online by the Federal Ministry of Justice and Consumer Protection.
Answer: Court decisions from 2017 and 2018 were selected for the datase.
Question: What is passive solar building design
Evidence: In passive solar building design, windows, walls, and floors are made to collect, store, reflect, and distribute solar energy, in the form of heat in the winter and reject solar heat in the summer. This is called passive solar design because, unlike active solar heating systems, it does not involve the use of mechanical and electrical devices.

The key to designing a passive solar building is to best take advantage of the local climate performing an accurate site analysis. Elements to be considered include window placement and size, and glazing type, thermal insulation, thermal mass, and shading. Passive solar design techniques can be applied most easily to new buildings, but existing buildings can be adapted or "retrofitted".
Answer: In passive solar building design, windows, walls, and floors are made to collect, store, reflect, and distribute solar energy, in the form of heat in the winter and reject solar heat in the summer. This is called passive solar design because, unlike active solar heating systems, it does not involve the use of mechanical and electrical devices.

The key to designing a passive solar building is to best take advantage of the local climate performing an accurate site analysis. Elements to be considered include window placement and size, and glazing type, thermal insulation, thermal mass, and shading. Passive solar design techniques can be applied most easily to new buildings, but existing buildings can be adapted or "retrofitted".