DataHammer/scimrc huggingface dataset:
Question: List the club sports teams that Neumann University has
Evidence: Neumann University is a member of the Atlantic East Conference as well as the United Collegiate Hockey Conference (UCHC). It competes in the National Collegiate Athletic Association (NCAA), Division III. In 2009 Neumann University's men's ice hockey team won the NCAA Division III National Championship. In the summer of 2012, Neumann University constructed the Mirenda Center, a 72,000 square foot athletic complex which houses an indoor track, NCAA Division III regulation basketball court, dance studios, student lounges, a classroom, weight and cardio rooms as well as the Center for Sport Spirituality and Character Development.

Neumann sponsors 12 women's athletic teams and 10 men's teams. In addition the 22 varsity teams, Neumann University also has a very active club sports roster which includes baseball, basketball, cheer leading, a dance team, ice hockey, roller hockey as well as men's and women's rugby. Most recently, Neumann University won back to back national championships (2015 and 2016) as part of the National Collegiate Roller Hockey Association (NCHRA).
Answer: Based off of the shared information, Neumann University has the following club sports teams:

* baseball
* basketball
* cheer leading
* a dance team
* ice hockey
* roller hockey
* men's rugby
* women's rugby

Note that this may not be a fully exhaustive list.
Question: How do I put a bouquet of fresh flowers in a vase?
Answer: In order to put fresh flowers in a vase, follow these steps:

1. Find a vase that it is the right size for the flowers. You can use water glasses here in a pinch. 
2. Fill the vase to about two-thirds full with water. You can add a dash of bleach in the water as well, but it is not necessary. 
3. Now, trim your flowers to the size of the vase. Cut the end of each flower in a diagonal cut. 
4. Remove the leaves from the flowers. This will help to grow the flower without putting resources towards the leaves. Be sure to remove any leaves under the water line, since they can cause infections. 
5. Arrange the flowers in the vase. You can adjust the positions until you are happy with the arrangement.
6. Place your vase in a shady spot and you're done!
Question: How they show that mBERT representations can be split into a language-specific component and a language-neutral component?
Evidence: Following BIBREF3, we hypothesize that a sentence representation in mBERT is composed of a language-specific component, which identifies the language of the sentence, and a language-neutral component, which captures the meaning of the sentence in a language-independent way. We assume that the language-specific component is similar across all sentences in the language.
We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space. We do this by estimating the language centroid as the mean of the mBERT representations for a set of sentences in that language and subtracting the language centroid from the contextual embeddings.
We then analyze the semantic properties of both the original and the centered representations using a range of probing tasks. For all tasks, we test all layers of the model. For tasks utilizing a single-vector sentence representation, we test both the vector corresponding to the [cls] token and mean-pooled states.
Following BIBREF3, we hypothesize that a sentence representation in mBERT is composed of a language-specific component, which identifies the language of the sentence, and a language-neutral component, which captures the meaning of the sentence in a language-independent way. We assume that the language-specific component is similar across all sentences in the language.

We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space.
We then analyze the semantic properties of both the original and the centered representations using a range of probing tasks.
Answer: The answers are shown as follows:
* We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space.
Question: Measured in feet, what was the length of the gun barrel produced by Philip Webley & Son of Birmingham discussed in the above passage?
Evidence: The British Bull Dog was a popular type of solid-frame pocket revolver introduced by Philip Webley & Son of Birmingham, England, in 1872, and subsequently copied by gunmakers in continental Europe and the United States. It featured a 2.5-inch (64 mm) barrel and was chambered for .442 Webley or .450 Adams cartridges, with a five-round cylinder. Webley produced smaller scaled .320 Revolver and .380 calibre versions later, but did not mark them with the British Bull Dog name.
Answer: 0.208 feet
Question: What is the terminal outbound station of the D line in the Boston MBTA?
Answer: Riverside Station is the last station headed outbound on the D line of Boston's MBTA.
Question: How they measure robustness in experiments?
Evidence: The log-likelihood of a Plackett-Luce model is not a strict upper bound of the BLEU score, however, it correlates with BLEU well in the case of rich features. The concept of “rich” is actually qualitative, and obscure to define in different applications. We empirically provide a formula to measure the richness in the scenario of machine translation.
The greater, the richer. In practice, we find a rough threshold of r is 5.
The log-likelihood of a Plackett-Luce model is not a strict upper bound of the BLEU score, however, it correlates with BLEU well in the case of rich features. The concept of “rich” is actually qualitative, and obscure to define in different applications. We empirically provide a formula to measure the richness in the scenario of machine translation.
The greater, the richer. In practice, we find a rough threshold of r is 5
Answer: The answers are shown as follows:
* We empirically provide a formula to measure the richness in the scenario of machine translation.
Question: What are the best travel destinations in 2023.
Answer: With the world opens up after long impact of Covid, a lot of people resume traveling again and started planning for their next trips! Globally, popular travel destinations often have less restrictive entry requirements for visitors, safe social and health environment, and of course, beautiful sceneries and profound culture backgrounds. Here are some recommendations: Singapore, Seoul, Hawaii, Amsterdam and Toronto.
Question: Can you share a response of politely informing the customer that we dont support a feature
Answer: Yes, we understand that we don't support that feature and apologize for the inconvenience. We will take your feedback into consideration and look into alternative options.
Question: Could a nuclear explosion change the Earth's rotation?
Answer: Nuclear explosions release more energy than anything else that humans do. Luckily, the energy released by a nuclear blast is only about one trillionth of the power of the Earth spinning. Scientists compare it to trying to slow the speed of a truck by crashing it into a mosquito.
Question: Who is the first chairman of Chine?
Evidence: Mao Zedong (26 December 1893 – 9 September 1976), also known as Chairman Mao, was a Chinese communist revolutionary who was the founder of the People's Republic of China (PRC), which he led as the chairman of the Chinese Communist Party from the establishment of the PRC in 1949 until his death in 1976. Ideologically a Marxist–Leninist, his theories, military strategies, and political policies are collectively known as Maoism.
Answer: Mao Zedong is the first chairman of the People's Republic of China, he is also known as Chairman Mao, was a Chinese communist revolutionary and the founder of the People's Republic of China. He served as the chairman of the Chinese Communist Party from the establishment of the PRC in 1949 until his death in 1976
Question: How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work?
Evidence: Results on WNLaMPro rare and medium are shown in Table TABREF34, where the mean reciprocal rank (MRR) is reported for BERT, Attentive Mimicking and Bertram. As can be seen, supplementing BERT with any of the proposed relearning methods results in noticeable improvements for the rare subset, with add clearly outperforming replace. Moreover, the add and add-gated variants of Bertram perform surprisingly well for more frequent words, improving the score for WNLaMPro-medium by 50% compared to BERT$_\text{base}$ and 31% compared to Attentive Mimicking. This makes sense considering that compared to Attentive Mimicking, the key enhancement of Bertram lies in improving context representations and interconnection of form and context; naturally, the more contexts are given, the more this comes into play. Noticeably, despite being both based on and integrated into a BERT$_\text{base}$ model, our architecture even outperforms a standalone BERT$_\text{large}$ model by a large margin.
Moreover, the add and add-gated variants of Bertram perform surprisingly well for more frequent words, improving the score for WNLaMPro-medium by 50% compared to BERT$_\text{base}$ and 31% compared to Attentive Mimicking.
Answer: The answers are shown as follows:
* improving the score for WNLaMPro-medium by 50% compared to BERT$_\text{base}$ and 31% compared to Attentive Mimicking
Question: Classify each country as being in the Caribbean or Mediterranean: Haiti, Cuba, Jamaica, Greece, Italy, France
Answer: Haiti: Caribbean
Cuba: Caribbean
Jamaica: Caribbean
Greece: Mediterranean
Italy: Mediterranean
France: Mediterranean
Question: Give a single-sentence explanation of this paragraph without quoting from the text.
Evidence: The type of soil would make a difference to the design of the motte, as clay soils could support a steeper motte, whilst sandier soils meant that a motte would need a more gentle incline. Where available, layers of different sorts of earth, such as clay, gravel and chalk, would be used alternatively to build in strength to the design. Layers of turf could also be added to stabilise the motte as it was built up, or a core of stones placed as the heart of the structure to provide strength. Similar issues applied to the defensive ditches, where designers found that the wider the ditch was dug, the deeper and steeper the sides of the scarp could be, making it more defensive. Although militarily a motte was, as Norman Pounds describes it, "almost indestructible", they required frequent maintenance. Soil wash was a problem, particularly with steeper mounds, and mottes could be clad with wood or stone slabs to protect them. Over time, some mottes suffered from subsidence or damage from flooding, requiring repairs and stabilisation work.
Answer: Soil type and composition play a significant role in the kind of mound that can be built to support a motte and the maintenance required to upkeep it.
Question: Does the Agent ask for a value of a variable using natural language generated text?
Evidence: FLOAT SELECTED: Figure 1. The User-Agent Interaction
In Example UID13 , illustrating a successful interaction, the Agent asks for the value of $V0 and the User responds with the answer (Silvia) as well as an explanation indicating that it was correct (helpful) and why. Specifically, in this instance it was helpful because it enabled an inference which reduced the possible answer set (and reduced the set of relevant variables). On the other hand, in Example UID30 , we see an example of a bad query and corresponding critical explanation.
FLOAT SELECTED: Figure 1. The User-Agent Interaction
In Example UID13 , illustrating a successful interaction, the Agent asks for the value of $V0 and the User responds with the answer (Silvia) as well as an explanation indicating that it was correct (helpful) and why.
Answer: Yes.
Question: did they use other pretrained language models besides bert?
Evidence: Our Recurrent +ELMo model uses the language model from BIBREF9 to provide contextualized embeddings to the baseline model outlined above, as recommended by the authors.
Our OpenAI GPT model fine-tunes the 12 layer 768 dimensional uni-directional transformer from BIBREF27 , which has been pre-trained as a language model on the Books corpus BIBREF36 .
Our Recurrent +ELMo model uses the language model from BIBREF9 to provide contextualized embeddings to the baseline model outlined above, as recommended by the authors.

Our OpenAI GPT model fine-tunes the 12 layer 768 dimensional uni-directional transformer from BIBREF27 , which has been pre-trained as a language model on the Books corpus BIBREF36 .
Answer: Yes.
Question: Given the paragraph above paragraph, what is the PMP and who established the exam.
Evidence: Project Management Professional (PMP) is an internationally recognized professional designation offered by the Project Management Institute (PMI). As of 31 July 2020, there are 1,036,368 active PMP-certified individuals and 314 chartered chapters across 214 countries and territories worldwide.

The exam is one of eight credentials offered by PMI and is based on the ECO PMP Examination Content Outline. Most of the questions reference the Exam Content Outline PMP Examination Content Outline (also known as the E.C.O ).
Answer: PMP stands for Project Management Professional, and it was established by the Project Management Institute (PMI). The PMP is a professional certification for project managers that is recognized internationally. The PMP is based on the ECO PMP Examination Content Outline.
Question: What vector do they use to represent each spoken document after obtaining the tokenizations of speech by either UTD or AUD?
Evidence: After we obtain the tokenizations of speech by either UTD or AUD, each spoken document is represented by a vector of unigram occurrence counts over discovered terms, or a vector of INLINEFORM0 -gram counts over acoustic units, respectively. Each feature vector can be further scaled by inverse document frequency (IDF), producing a TF-IDF feature.
After we obtain the tokenizations of speech by either UTD or AUD, each spoken document is represented by a vector of unigram occurrence counts over discovered terms, or a vector of ngram counts over acoustic units, respectively. Each feature vector can be further scaled by inverse document frequency (IDF), producing a TF-IDF feature.
Answer: A vector of unigram occurrence counts over discovered terms, or a vector of ngram counts over acoustic units, respectively.
Question: Which of the following movies is not from the Harry Potter Franchise? 
-  Order of the Phoenix.
- Goblet of Fire.
- Sorcerer's Stone.
- Chamber of Secrets.
- The Fellowship of the rings.
Answer: "The Fellowship of the rings" is not part of the Harry Potter Franchise, it is in-fact a part of the Lord of the Rings Franchise. The canonical Harry Potter movies in chronological order are:
- Harry Potter and the Philosopher's Stone.
- Harry Potter and the Chamber of Secrets.
- Harry Potter and the Prisoner of Azkaban.
- Harry Potter and the Goblet of Fire.
- Harry Potter and the Order of the Phoenix.
- Harry Potter and the Half-Blood Prince.
- Harry Potter and the Deathly Hallows – Part 1.
- Harry Potter and the Deathly Hallows – Part 2.
Question: What supervised methods are used?
Evidence: We first experiment with a suite of non-neural models, including Support Vector Machines (SVMs), logistic regression, Naïve Bayes, Perceptron, and decision trees. We tune the parameters for these models using grid search and 10-fold cross-validation, and obtain results for different combinations of input and features.
We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., BIBREF22, BIBREF23). Because neural models require large amounts of data, we do not cull the data by annotator agreement for these experiments and use all the labeled data we have. We experiment with training embeddings with random initialization as well as initializing with our domain-specific Word2Vec embeddings, and we also concatenate the best feature set from our non-neural experiments onto the representations after the recurrent and convolutional/pooling layers respectively.
We first experiment with a suite of non-neural models, including Support Vector Machines (SVMs), logistic regression, Naïve Bayes, Perceptron, and decision trees. 
We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., BIBREF22, BIBREF23). 
Answer: The answers are shown as follows:
* Support Vector Machines (SVMs), logistic regression, Naïve Bayes, Perceptron, and decision trees
* a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21)
Question: How are seed dictionaries obtained by fully unsupervised methods?
Evidence: The landscape of CLWE methods has recently been dominated by the so-called projection-based methods BIBREF15 , BIBREF16 , BIBREF17 . They align two monolingual embedding spaces by learning a projection/mapping based on a training dictionary of translation pairs. Besides their simple conceptual design and competitive performance, their popularity originates from the fact that they rely on rather weak cross-lingual supervision. Originally, the seed dictionaries typically spanned several thousand word pairs BIBREF15 , BIBREF18 , BIBREF19 , but more recent work has shown that CLWEs can be induced with even weaker supervision from small dictionaries spanning several hundred pairs BIBREF20 , identical strings BIBREF21 , or even only shared numerals BIBREF22 .
Originally, the seed dictionaries typically spanned several thousand word pairs BIBREF15 , BIBREF18 , BIBREF19 , but more recent work has shown that CLWEs can be induced with even weaker supervision from small dictionaries spanning several hundred pairs BIBREF20 , identical strings BIBREF21 , or even only shared numerals BIBREF22 .
Answer: The answers are shown as follows:
* the latest CLWE developments almost exclusively focus on fully unsupervised approaches BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 : they fully abandon any source of (even weak) supervision and extract the initial seed dictionary by exploiting topological similarities between pre-trained monolingual embedding spaces
Question: What is Milky Way?
Evidence: The Milky Way is the galaxy that includes the Solar System, with the name describing the galaxy's appearance from Earth: a hazy band of light seen in the night sky formed from stars that cannot be individually distinguished by the naked eye. The term Milky Way is a translation of the Latin via lactea, from the Greek γαλακτικὸς κύκλος (galaktikòs kýklos), meaning "milky circle". From Earth, the Milky Way appears as a band because its disk-shaped structure is viewed from within. Galileo Galilei first resolved the band of light into individual stars with his telescope in 1610. Until the early 1920s, most astronomers thought that the Milky Way contained all the stars in the Universe. Following the 1920 Great Debate between the astronomers Harlow Shapley and Heber Doust Curtis, observations by Edwin Hubble showed that the Milky Way is just one of many galaxies.
Answer: Milky Way is the galaxy that includes Solar System in which our planet Earth is located.
Question: Of the following measurements, which units relate to mass? Kilogram, microgram, pound, litre, year, kilometre and ounce.
Answer: Kilogram (kg), microgram (μg), pound (lb), and ounce (oz) are all units of mass. Kilogram and microgram are units from the metric (SI) system. Pound and ounce are units from the imperial system. Litre is a unit of volume, year is a unit of time and kilometre is a unit of distance.
Question: Which deep learning model performed better?
Evidence: In the literature, deep learning based automated feature extraction has been shown to outperform state-of-the-art manual feature engineering based classifiers such as Support Vector Machine (SVM), Naive Bayes (NB) or Multilayer Perceptron (MLP) etc. One of the important techniques in deep learning is the autoencoder that generally involves reducing the number of feature dimensions under consideration. The aim of dimensionality reduction is to obtain a set of principal variables to improve the performance of the approach. Similarly, CNNs have been proven to be very effective in sentiment analysis. However, little work has been carried out to exploit deep learning based feature representation for Persian sentiment analysis BIBREF8 BIBREF9 . In this paper, we present two deep learning models (deep autoencoders and CNNs) for Persian sentiment analysis. The obtained deep learning results are compared with MLP.
To evaluate the performance of the proposed approach, precision (1), recall (2), f-Measure (3), and prediction accuracy (4) have been used as a performance matrices. The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. DISPLAYFORM0 DISPLAYFORM1
In this paper, we present two deep learning models (deep autoencoders and CNNs) for Persian sentiment analysis. 
To evaluate the performance of the proposed approach, precision (1), recall (2), f-Measure (3), and prediction accuracy (4) have been used as a performance matrices.
The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%.
The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%.
Answer: The answers are shown as follows:
* CNN
Question: Should I buy a tesla model 3 or tesla model y?
Answer: It comes down to what your family needs are at end of the day! Model Y has more leg space, more height and good luggage space compared to model 3. Riding comfort is also relatively better on model Y considering it is an SUV. Ultimately it is up-to your personal choice to go with 3 or Y.
Question: Which is a species of fish? Rohu or Naked Mole
Answer: Rohu
Question: Classify these as things you would want to wear for a rainy day or a very hot day: umbrella, ball cap, rain jacket, t-shirt, sunglasses, rain boots, flip-flops
Answer: umbrella: rain, ball cap: hot day, rain jacket: rainy day, t-shirt: hot day, sunglasses: hot day, rain boots: rainy day, flip-flops: hot day