DataHammer/scimrc huggingface dataset:
Question: what are the advantages of the proposed model?
Evidence: For all the experiments the hyper-parameters INLINEFORM0 , INLINEFORM1 and INLINEFORM2 were set using a simple grid search in the collection INLINEFORM3 . The same approach was used to optimize the hyper-parameters of the all the baselines. For the svi algorithm, different mini-batch sizes and forgetting rates INLINEFORM4 were tested. For the 20-Newsgroup dataset, the best results were obtained with a mini-batch size of 500 and INLINEFORM5 . The INLINEFORM6 was kept at 1. The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best.
In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.
The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best.
In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.
Answer: The answers are shown as follows:
* he proposed model outperforms all the baselines, being the svi version the one that performs best.
* the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.
Question: What is the EXPERIMENTS part about?
Evidence: To evaluate the proposed approach for multilingual sentiment analysis task, we conducted experiments. This section first presents the corpora used in this study followed by experimental results.
Throughout our experiments, we use SAS Deep Learning Toolkit. For machine translation, Google translation API is used.
To evaluate the proposed approach for multilingual sentiment analysis task, we conducted experiments. This section first presents the corpora used in this study followed by experimental results.
Answer: This section first presents the corpora used in this study followed by experimental results.
Question: Help me plan a trip to Nova Scotia
Answer: Day 1:
- Start the day with a walking tour of Old Town (2 miles / 3.2 km)
- Visit the National Museum (1 mile / 1.6 km)
- Attend a performance at the National Theater (1 mile / 1.6 km)

Day 2:
- Take a day trip to a nearby national park for hiking and wildlife viewing (60 miles / 96.5 km)
- Return to the city for dinner and drinks in the evening

Day 3:
- Visit the iconic city palace and gardens (2.5 miles / 4 km)
- Explore local markets and street food vendors (1 mile / 1.6 km)

Day 4:
- Take a scenic drive to a nearby coastal city (80 miles / 128.7 km)
- Spend the day at the beach and exploring the city's sights and cuisine

Day 5:
- Boat tour of the city's waterways (7 miles / 11.2 km)
- Visit a traditional fishing village and have a seafood lunch (15 miles / 24.1 km)

Day 6:
- Take a day trip to a nearby hill station for hiking and nature walks (40 miles / 64.4 km)
- Stop at a tea plantation for a tour and tasting (20 miles / 32.2 km)

Day 7:
- Visit a nearby UNESCO World Heritage site (50 miles / 80.5 km)
- Enjoy a cultural performance and local meal in the evening

Day 8:
- Take a leisurely bike ride along a scenic route (10 miles / 16.1 km)
- Visit a local winery for a tasting and tour (5 miles / 8 km)

Day 9:
- Visit a local art museum (2 miles / 3.2 km)
- Take a cooking class and learn to make some local dishes

Day 10:
- Spend the day exploring the city at your own pace, shopping for souvenirs, and enjoying local cuisine before departing in the evening.
Question: What are some ways that I can reduce the latency of my API?
Answer: To reduce the latency of your API, you could consider caching any database operations you may have, reducing the number of network requests required, or scaling up the number of instances of your server.
Question: What is the official bird of the State of Hawaii?
Evidence: The nene (Branta sandvicensis), also known as the nēnē or the Hawaiian goose, is a species of bird endemic to the Hawaiian Islands. The nene is exclusively found in the wild on the islands of Oahu, Maui, Kauaʻi, Molokai, and Hawaiʻi. In 1957, it was designated as the official state bird of the state of Hawaiʻi.
Answer: The nene, also know as the Hawaiian goose is the official bird of the state of Hawaii.
Question: Where is the data of their datasets from?
Evidence: We construct three datasets based on IMDB reviews and Yelp reviews. The IMDB dataset is binarised and split into a training and test set, each with 25K reviews (2K reviews from the training set are reserved for development). We filter out any review that has more than 400 tokens, producing the final dataset (imdb400). For Yelp, we binarise the ratings, and create 2 datasets, where we keep only reviews with $\le $ 50 tokens (yelp50) and $\le $200 tokens (yelp200). We randomly partition both datasets into train/dev/test sets (90/5/5 for yelp50; 99/0.5/0.5 for yelp200). For all datasets, we use spaCy for tokenisation. We train and tune target classifiers (see Section SECREF8) using the training and development sets; and evaluate their performance on the original examples in the test sets as well as the adversarial examples generated by attacking methods for the test sets. Note that AutoEncoder also involves a training process, for which we train and tune AutoEncoder using the training and development sets in yelp50, yelp200 and imdb400. Statistics of the three datasets are presented in Table TABREF22. These datasets present a variation in the text lengths (e.g. the average number of words for yelp50, yelp200 and imdb400 is 34, 82 and 195 words respectively), training data size (e.g. the number of training examples for target classifiers for imdb400, yelp50 and yelp200 are 18K, 407K and 2M, respectively) and input domain (e.g. restaurant vs. movie reviews).
We construct three datasets based on the Yelp reviews4 and the sentence-level Rotten Tomato (RT) movie reviews5.
Answer: Yelp reviews and the sentence-level Rotten Tomato (RT) movie reviews.
Question: Write a short poem about spring
Answer: Spring has sprung and I am here
To take in all the joyful cheer
To bask in sunshine and feel the breeze
And watch the butterflies and the bees
The skies are blue, the days are long
The flowers bloom, birds sing their song
I'll spend many hours here on my deck
OW! Something bit me, what the heck?
The bug spray doesn't work, it lied!
That's it, I'm going back inside.
Question: What has been shown by the experiments in the paper?
Evidence: The baseline experiments confirm that M-Bert can achieve a reasonable accuracy on x-stance.
To put the supervised score into context we list scores that variants of Bert have achieved on other stance detection datasets in Table TABREF46. It seems that the supervised part of x-stance has a similar difficulty as the SemEval-2016 BIBREF0 or MPCHI BIBREF22 datasets on which Bert has previously been evaluated.
On the other hand, in the cross-lingual and cross-target settings, the mean score drops by 6–8 percentage points compared to the supervised setting; while zero-shot transfer is possible to a degree, it can still be improved.
The additional experiments (Table TABREF39) validate the results and show that the sequence-pair classification approach to stance detection is justified.
It is interesting to see what errors the M-Bert model makes. Table TABREF47 presents instances where it predicts the wrong label with a high confidence. These examples indicate that many comments express their stance only on a very implicit level, and thus hint at a potential weakness of the dataset. Because on the voting advice platform the label is explicitly shown to readers in addition to the comments, the comments do not need to express the stance explicitly. Manual annotation could eliminate very implicit samples in a future version of the dataset. However, the sheer size and breadth of the dataset could not realistically be achieved with manual annotation, and, in our view, largely compensates for the implicitness of the texts.
Our experiments show that M-BERT achieves a reasonable accuracy on X-stance, outperforming majority class baselines and a fastText classifier.
Answer: M-BERT achieves a reasonable accuracy on X-stance, outperforming majority class baselines and a fastText classifier.
Question: In what year was the Oakland Ski Club founded?
Answer: OSC was founded in 1941.
Question: What corpora do the authors make both intrinsic and extrinsic analyses?
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WIKIQA, SELQA, SQUAD, and INFOBOXQA.
Answer: Four latest corpora based on Wikipedia, WIKIQA, SELQA, SQUAD, and INFOBOXQA.
Question: Why is the 1980 gold medal match in hockey called the 'Miracle on Ice'?
Answer: The US Olympic hockey team was a heavy underdog against the Soviets in 1980, and their victory over the Soviets was a surprise to nearly everyone. As the game concluded, Al Michaels, the play-by-play announcer for the game, exclaimed "Do you believe in miracles? Yes!', and because many people around the world watched the game on television, the game became known as the 'Miracle on Ice'.
Question: Who are the most famous athletes in Seattle sports history?
Answer: Seattle sports are relatively young with Seattle only gaining franchises in the 1970s in the NBA, NFL and MLB.  The most famous athletes include Ken Griffey Jr (MLB), Ichiro Suzuki (MLB), Russell Wilson, Marshan Lynch & Steve Largent (NFL) and Gary Payton, Shawn Kemp and Kevin Durant (NBA)
Question: When do avalanches occur?
Answer: Avalanches are primarily caused by unstable snow conditions. Although a mountainside covered in snow might appear as a uniform surface, it is actually made up of many layers of snow from different storms, each of which might have very different compositions. For example, a "dry" snow from a very cold storm might land directly on top of a sun-baked layer which turned to ice. The point at which these layers meet is called an interface, and is where slipping or sliding of one layer on top of another is most likely to occur. This occurrence is what can trigger an avalanche. Snow conditions on a slope can also evolve over time with wind and sun and other weather patterns, so even a once-safe slope might increase in risk under certain conditions. People recreating in the outdoors in the winter can benefit from learning about these situations from avalanche professionals.
Question: Classify the following foods as Indian or Japanese: Sushi, Sashimi, Ramen, Naan, Chicken Tikka Masala, Samosa
Answer: Sushi: Japanese
Sashimi: Japanese
Ramen: Japanese
Naan: Indian
Chicken Tikka Masala: Indian
Samosa: Indian
Question: What is a fixed-rate mortgage (FRM)?
Answer: A fixed-rate mortgage (FRM) is a mortgage loan where the interest rate on the note remains the same through the term of the loan, as opposed to loans where the interest rate may adjust or "float". As a result, payment amounts and the duration of the loan are fixed and the person who is responsible for paying back the loan benefits from a consistent, single payment and the ability to plan a budget based on this fixed cost.
Question: What is black tie dress code?
Evidence: Black tie is a semi-formal Western dress code for evening events, originating in British and American conventions for attire in the 19th century. In British English, the dress code is often referred to synecdochically by its principal element for men, the dinner suit or dinner jacket. In American English, the equivalent term tuxedo (or tux) is common. The dinner suit is a black, midnight blue or white two- or three-piece suit, distinguished by satin or grosgrain jacket lapels and similar stripes along the outseam of the trousers. It is worn with a white dress shirt with standing or turndown collar and link cuffs, a black bow tie, typically an evening waistcoat or a cummerbund, and black patent leather dress shoes or court pumps. Accessories may include a semi-formal homburg, bowler, or boater hat. For women, an evening gown or other fashionable evening attire may be worn.
Answer: Black tie is a dress code for events, typically weddings and other formal ceremonies. The dress code roughly translates to a tuxedo or dinner suit for men and an evening gown for women.
Question: What are options for materials to build a house?
Answer: Depending on your geographic location, different materials will be available to you. Typical construction materials include brick, cement, concrete, gravel, steel and lumber. In the United States, it is very common to see houses built with lumber since this material is readily available, however in other regions in the world such as Europe and Latin America, it is very common to see use of brick, concrete and steel for a typical house.
Question: Group these font types based on their style of typefaces: Times New Roman, Monaco, Garamond, Helvetica, Courier,  Futura
Answer: These fonts can be categories as follow;
Serif fonts: Times New Roman, Garamond
San-serif fonts: Helvetica,  Futura
Monospaced fonts: Monaco, Courier
Question: Identify which instrument is string or woodwind: Cimboa, Sipsi
Answer: Sipsi is woodwind, Cimboa is string.
Question: Why is the hierarchical structure of documents still needed for MPAD?
Evidence: Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words $\rightarrow $ bigrams $\rightarrow $ compositions of bigrams, etc.). However, it might be beneficial to explicitly capture document hierarchy. Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation BIBREF24, BIBREF25, sentiment and topic classification BIBREF26, BIBREF27, and spoken language understanding BIBREF28, BIBREF29. Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows. In all of them, we represent each sentence in the document as a word co-occurrence network, and obtain an embedding for it by applying MPAD as previously described.
MPAD-sentence-att. Here, the sentence embeddings are simply combined through self-attention.
MPAD-clique. In this variant, we build a complete graph where each node represents a sentence. We then feed that graph to MPAD, where the feature vectors of the nodes are initialized with the sentence embeddings previously obtained.
MPAD-path. This variant is similar to the clique one, except that instead of a complete graph, we build a path according to the natural flow of the text. That is, two nodes are linked by a directed edge if the two sentences they represent follow each other in the document.
Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words t bigrams t compositions of bigrams, etc.). However, it might be beneficial to explicitly capture document hierarchy. Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation (Lin et al. 2015; Li, Luong, and Jurafsky 2015), sentiment and topic classification (Tang, Qin, and Liu 2015;Yang et al. 2016), and spoken language understanding (Ra- heja and Tetreault 2019; Shang et al. 2019). Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows.
Answer: It is because capturing document hierarchy benefits the performance of many NLP tasks and they thought it may work for MPAD as well.
Question: By what other name does Bunnie DeFord go by?
Evidence: Jelly Roll is married to Bunnie DeFord aka Bunnie XO and has two children from a previous relationship.
Answer: Bunnie XO
Question: How is the ground truth of gang membership established in this dataset?
Evidence: In our previous work BIBREF9 , we curated what may be the largest set of gang member profiles to study how gang member Twitter profiles can be automatically identified based on the content they share online. A data collection process involving location neutral keywords used by gang members, with an expanded search of their retweet, friends and follower networks, led to identifying 400 authentic gang member profiles on Twitter. Our study discovered that the text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles. While a very promising INLINEFORM0 measure with low false positive rate was achieved, we hypothesize that the diverse kinds and the multitude of features employed (e.g. unigrams of tweet text) could be amenable to an improved representation for classification. We thus explore the possibility of mapping these features into a considerably smaller feature space through the use of word embeddings.
A data collection process involving location neutral keywords used by gang members, with an expanded search of their retweet, friends and follower networks, led to identifying 400 authentic gang member profiles on Twitter. Our study discovered that the text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles.
Answer: The answers are shown as follows:
*  text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles
Question: How long is the Georgia State Route 49
Evidence: State Route 49 (SR 49) is a 122.8-mile-long (197.6 km) state highway that travels southwest-to-northeast through portions of Terrell, Sumter, Macon, Peach, Houston, Bibb, Jones, and Baldwin counties, mainly in the central part of the U.S. state of Georgia. The highway connects SR 45 north of Dawson to SR 22/SR 24 in Milledgeville.

The segment from Fort Valley to Byron is part of the Fall Line Freeway, a highway that connects Columbia to Augusta. It may also be incorporated into the proposed eastern extension of Interstate 14 (I-14), which is currently entirely in Central Texas, and may be extended into Augusta.
Answer: The Georgia State Route 49 (SR 49) is a 122.8-mile-long (197.6 km) state highway that travels southwest-to-northeast through portions of Terrell, Sumter, Macon, Peach, Houston, Bibb, Jones, and Baldwin counties, mainly in the central part of the U.S. state of Georgia. The highway connects SR 45 north of Dawson to SR 22/SR 24 in Milledgeville.

The segment from Fort Valley to Byron is part of the Fall Line Freeway, a highway that connects Columbia to Augusta. It may also be incorporated into the proposed eastern extension of Interstate 14 (I-14), which is currently entirely in Central Texas, and may be extended into Augusta.
Question: What is the baseline machine learning prediction approach?
Evidence: To evaluate if the sentiment feature improves the prediction accuracy, a baseline model is defined. The baseline applies linear logistic regression to a set of stock technical signals to predict the following day’s stock return sign (+/‐). No sentiment features are provided to the baseline model.
The baseline applies linear logistic regression to a set of stock technical signals to predict the following day’s stock return sign (+/‐). No sentiment features are provided to the baseline model.
Answer: The answers are shown as follows:
* linear logistic regression to a set of stock technical signals
Question: Best Airline in the world
Answer: 1. Quatar airway
2. Singapore Airlines
3. Emirates
Question: Which of the following are considered golf clubs: woods, irons, jellies, sliders, hybrids, wedges, driver, squeaker, putters
Answer: woods, irons, hybrids, wedges, driver, putters
Question: Why is my electrical outlet not working?
Answer: Assuming that outlet has been working fine and there were not recent catastrophic events or construction that has taken place, you likely need to reset the circuit.

First check to see if there is an ongoing regional power outage. An easy way to check for this is to see if the power is working elsewhere in your building or neighboring buildings.

If the outage is localized to a portion of your building, you may want to check the following:

If the outlet is a GFCI, you will need to find the main outlet in the circuit and push the reset button on it.  GFCI (or GFI) outlets are common in locations where water is present, for example in kitchens and bathrooms.

If the outlet is not a GFCI outlet or if resetting the GFCI outlet had no effect, locate the circuit breaker for your building and check if any of the circuits have been tripped.  You can identify a tripped circuit when you open the panel by looking for the one of the circuits in the panel that is not in the same orientation as the others. Fix it by flipping the switch to match the others.