DataHammer/scimrc huggingface dataset:
Question: Is it true that Bayes Opt performance is worse than \mathcal{D} (best)?
Evidence: Figure 9: Optimization behavior of Firefly optimizer (Online Evolutionary). Observe that the optimization procedure converges and plateaus very quickly (at least 1000 iterations in advance) and hence we stop at 8000 iterations. In the case of t-RNN Enc and t-RNN Dec, we find that the evolutionary algorithm performs poorly and we suspect this is because it saturates quite quickly to a suboptimal solution and is unable to escape. This is also evident from Figures 9h and 9i, where we observe that online optimization plateaus the fastest for these RNN applications.
Answer: Please note that the dataset \mathcal{D} used in our experiments is collected via random sampling of the accelerator design space, and this dataset is not used by online methods, such as BayesOpt, since they continually query the simulator with design points of their choice. To understand what happens to online optimization methods, we plotted the convergence plot for the evolutionary method (which we found performs generally better than BayesOpt) in Figure 9, and we find that these curves very quickly (within 1000 iterations) converge to their final solution for t-RNN Enc and t-RNN Dec. We suspect that this denotes getting stuck in a suboptimal region of the accelerator landscape and being unable to recover, while random-sampling may find a design better than the solution found by these methods.
Question: Write me an introductory email to a lawyer to represent me in court for a red light ticket I did not deserve
Answer: Hello,

I recently received a ticket for disregarding a red light (see attached ticket). I sincerely believe I was already in the intersection and needed to keep going, though the police officer says I was not yet at the crossing lines. Could you please share your rates with me?

Thank you
Question: What previously proposed rules for predicting binoial ordering are used?
Evidence: Basic Features. We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.
We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. 
Answer: The answers are shown as follows:
* word length, number of phonemes, number of syllables, alphabetical order, and frequency
Question: Write a stern email to your dog asking him not to chew on the couch leg anymore.
Answer: Dear Mr. Cody,

I was hoping this issue would be taken care of by now, but it seems you still haven't learned your lesson. You can imagine how disappointed I was to come home yesterday and see new bite marks on the couch legs. 

If this was the first time maybe I wouldn't need to write this email, but this has been an ongoing problem ever since we brought home the new couch. I feel like I can't trust you anymore, which is a terrible feeling for me.

Consider this your final warning before more drastic action is taken (e.g. kenneling, the cone). I have taken pictures of every angle of the couch legs and I'll be able to tell if you even put one tooth into that wood.

On the other hand, if you show me that I can trust you, I might be able to pick up some more Beggin' Strips next time at the store.

I appreciate your cooperation and I hope we can get past this.

Regards,

Your Owner
Question: What is the limitation of the Hidden Markov Model?
Evidence: In Markov chain model is represented as a chain of tokens which are observations, and word taggers are represented as predicted labels. Many researchers applied Hidden Markov model to solve Vietnamese word segmentation such as in BIBREF8 , BIBREF20 and so on.
N-gram language modeling applied to estimate probabilities for each word segmentation solution BIBREF21 . The result of this method depends on copora and is based maximal matching strategy. So, they do not solve missing word issue. Let INLINEFORM0 is a product of probabilities of words created from sentence s (1) with length INLINEFORM1 : DISPLAYFORM0 
Each conditional probability of word is based on the last n-1 words (n-gram) in the sentence s. It is estimated by Markov chain model for word w from position i-n+1 to i-1 with probability (2) DISPLAYFORM0 
We have equation (3) DISPLAYFORM0 
In Markov chain model is represented as a chain of tokens which are observations, and word taggers are represented as predicted labels. Many researchers applied Hidden Markov model to solve Vietnamese word segmentation such as in [9],[21]and so on. N-gram language modeling applied to estimate probabilities for each word segmentation solution [22]. The result of this method depends on copora and is based maximal matching strategy. So, they do not solve missing word issue.
Answer: It does not solve missing word issue.
Question: What is the average interest rate for a 30 year mortgage in the United States?
Answer: The average interest rate for a 30 year mortgage in the United States as of Tuesday, March 28th is 7.9%. The interest rate that you'll actually for a mortgage get depends on your lender, credit score, and debt to income ratio, among other things.
Question: What is the Monument to the Victims of the Holocaust?
Evidence: The Monument to the Victims of the Holocaust (Spanish: Monumento a las víctimas del Holocausto) is a monument in Madrid, Spain, in memory of the victims of the Holocaust during World War II. It is located in the Three Cultures Garden (Spanish: Jardín de las Tres Culturas) in Juan Carlos I Park. Inaugurated in 2007, the monument was the first Holocaust memorial in Spain.
Answer: The Monument to the Victims of the Holocaust honors the memory of the victims of the Holocaust during World War II and is located in Madrid, Spain.
Question: Are any experiments performed to try this approach to word embeddings?
Evidence: In the literature, two main types of datasets are used for machine translation: Word-aligned data and Sentence-aligned data. The first one is basically a dictionary between the two languages, where there is a direct relation between same words in different languages. The second one has the relation between corresponding sentences in the two languages. We decided to start with the sentence aligned corpus, since it was more interesting to infer dependency from contexts among words. For our experiment we decided to use the Europarl dataset, using the data from the WMT11 .The Europarl parallel corpus is extracted from the proceedings of the European Parliament. It includes versions in 21 European languages: Romanic (French, Italian, Spanish, Portuguese, Romanian), Germanic (English, Dutch, German, Danish, Swedish), Slavik (Bulgarian, Czech, Polish, Slovak, Slovene), Finni-Ugric (Finnish, Hungarian, Estonian), Baltic (Latvian, Lithuanian), and Greek. For this experience, we used the English-French parallel corpus, which contains 2,007,723 sentences and the English-Italian corpus, that contains 1,909,115 sentences.
For our experiment we decided to use the Europarl dataset, using the data from the WMT11 .
Answer: Yes.
Question: Using the passage below, in Machine Learning, what is unsupervised learning and is it different from supervised learning?
Evidence: Unsupervised learning is a type of algorithm that learns patterns from untagged data. The goal is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.

In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged.
Answer: Unsupervised learning can be understood in contrast to supervised learning. The latter requires a data set tagged by an expert to train the machine learning model. The former learns patterns directly from the (unlabeled or untagged) data.
Question: Based on the following passage make a list of 6 conventional dried fruits.
Evidence: Dried fruit is fruit from which the majority of the original water content has been removed either naturally, through sun drying, or through the use of specialized dryers or dehydrators. Dried fruit has a long tradition of use dating back to the fourth millennium BC in Mesopotamia, and is prized because of its sweet taste, nutritive value and long shelf life.
Today, dried fruit consumption is widespread. Nearly half of the dried fruits sold are raisins, followed by dates, prunes, figs, apricots, peaches, apples, and pears. These are referred to as "conventional" or "traditional" dried fruits: fruits that have been dried in the sun or in heated wind tunnel dryers. Many fruits such as cranberries, blueberries, cherries, strawberries, and mango are infused with a sweetener (e.g. sucrose syrup) prior to drying. Some products sold as dried fruit, like papaya, kiwifruit and pineapple, are most often candied fruit.
Dried fruits retain most of the nutritional value of fresh fruits. The specific nutrient content of the different dried fruits reflects their fresh counterpart and the processing method.
Answer: 1. Raisins
2. Dates
3. Prunes 
4. Figs
5. Apricots 
6. Peaches
Question: Why was revenge of the jedi renamed to return of the jedi?
Answer: George Lucas changed the title as he realized that true Jedi wouldn't seek revenge.
Question: Who scored first in Austin FC's first victory in a home game in the 2021 MLS season?
Evidence: Austin FC played their first MLS match against LAFC at Banc of California Stadium on April 17, 2021, losing 0–2. The club secured its first victory the following week against the Colorado Rapids, winning 3–1. Diego Fagúndez scored the club's first goal and the following two were scored by the club's first Designated Player, Cecilio Dominguez. Their home opener was held on June 19, 2021, against the San Jose Earthquakes, a match which ended in a scoreless draw. The team secured its first ever home victory on July 1, 2021, defeating the Portland Timbers 4–1. Jon Gallagher scored the team's first home goal and the first MLS goal at Q2 Stadium.
Answer: Jon Gallagher scored the first goal in Austin FC's first win at home, over the Portland Timbers.
Question: Which land mammal has the best hearing?
Answer: Bats have the best hearing among land mammals
Question: Which countries do the authors collect posts from to create the HispaBlogs corpus?
Evidence: In this section, we describe the corpus and the alternative representations that we employ in this work.
We have created the HispaBlogs dataset by collecting posts from Spanish blogs from five different countries: Argentina, Chile, Mexico, Peru and Spain. For each country, there are 450 and 200 blogs respectively for training and test, ensuring that each author appears only in one set.
Answer: Argentina, Chile, Mexico, Peru and Spain.
Question: Is the glass ceiling real?
Evidence: A glass ceiling is a metaphor usually applied to women, used to represent an invisible barrier that prevents a given demographic from rising beyond a certain level in a hierarchy. No matter how invisible the glass ceiling is expressed, it is actually a difficult obstacle to overcome. The metaphor was first used by feminists in reference to barriers in the careers of high-achieving women. It was coined by Marilyn Loden during a speech in 1978. In the United States, the concept is sometimes extended to refer to racial inequality in the United States. Minority women in white-majority countries often find the most difficulty in "breaking the glass ceiling" because they lie at the intersection of two historically marginalized groups: women and people of color. East Asian and East Asian American news outlets have coined the term "bamboo ceiling" to refer to the obstacles that all East Asian Americans face in advancing their careers. Similarly, a multitude of barriers that refugees and asylum seekers face in their search for meaningful employment is referred to as canvas ceiling.
Answer: While it is not real as a physical object, it is considered real metaphorically, as it represents an invisible barrier that hinder women from rising beyond a certain organizational or societal hierarchy.
Question: Describe the quantitative easing in 3 bullet points or less.
Evidence: Quantitative easing (QE) is a monetary policy action where a central bank purchases predetermined amounts of government bonds or other financial assets in order to stimulate economic activity. Quantitative easing is a novel form of monetary policy that came into wide application after the financial crisis of 2007-2008. It is intended to mitigate an economic recession when inflation is very low or negative, making standard monetary policy ineffective. Quantitative tightening (QT) does the opposite, where for monetary policy reasons, a central bank sells off some portion of its holdings of government bonds or other financial assets.

Similar to conventional open-market operations used to implement monetary policy, a central bank implements quantitative easing by buying financial assets from commercial banks and other financial institutions, thus raising the prices of those financial assets and lowering their yield, while simultaneously increasing the money supply. However, in contrast to normal policy, quantitative easing usually involves the purchase of riskier or longer-term assets (rather than short-term government bonds) of predetermined amounts at a large scale, over a pre-committed period of time.

Central banks usually resort to quantitative easing when their nominal interest rate target approaches or reaches zero. Very low interest rates induce a liquidity trap, a situation where people prefer to hold cash or very liquid assets, given the low returns on other financial assets. This makes it difficult for interest rates to go below zero; monetary authorities may then use quantitative easing to further stimulate the economy rather than trying to lower the interest rate further.

Quantitative easing can help bring the economy out of recession and help ensure that inflation does not fall below the central bank's inflation target. However QE programmes are also criticized for their side-effects and risks, which include the policy being more effective than intended in acting against deflation (leading to higher inflation in the longer term), or not being effective enough if banks remain reluctant to lend and potential borrowers are unwilling to borrow. Quantitative easing has also been criticized for raising financial asset prices, contributing to inequality. Quantitative easing was undertaken by some major central banks worldwide following the global financial crisis of 2007–08, and again in response to the COVID-19 pandemic.
Answer: 1- Quantitative easing (QE) is a monetary policy action in which the government buys bonds and other assets to keep long term interest rate low to stimulate the economy.

2- It was introduced after the great financial crisis (GFC) in 2008.

3- It contributes to inequality by raising financial assets.
Question: Why is the ocean blue?
Answer: The ocean is blue because water absorbs colors in the red part of the light spectrum. Like a filter, this leaves behind colors in the blue part of the light spectrum for us to see. The ocean may also take on green, red, or other hues as light bounces off of floating sediments and particles in the water.
Question: How long do hump back whales live?
Answer: Hump back whales range is size from 14 - 17 meters, and can live 45 - 50 years.  They weight a staggering 40 metric tons or about 55,000 - 65,000 pounds.  They get their name hump back whale because of the shape of their back.  They are also a very friendly and non aggressive, and there is a very low chance of a human ever getting harmed who comes into contact with one.
Question: What benefits are offered by introducing a simple two-stage solution?
Evidence: To tackle character-level adversarial attacks, we introduce a simple two-stage solution, placing a word recognition model ( $W$ ) before the downstream classifier ( $C$ ). Under this scheme, all inputs are classified by the composed model $C \circ W$ . This modular approach, with $W$ and $C$ trained separately, offers several benefits: (i) we can deploy the same word recognition model for multiple downstream classification tasks/models; and (ii) we can train the word recognition model with larger unlabeled corpora.
Against adversarial mistakes, two important factors govern the robustness of this combined model: $W$ 's accuracy in recognizing misspelled words and $W$ 's sensitivity to adversarial perturbations on the same input. We discuss these aspects in detail below.
To tackle character-level adversarial attacks, we introduce a simple two-stage solution, placing a word recognition model (W) before the downstream classifier (C). Under this scheme, all inputs are classified by the composed model C ◦W. This modular approach, with W and C trained separately, offers several benefits: (i) we can deploy the same word recognition model for multiple downstream classification tasks/models; and (ii) we can train the word recognition model with larger unlabeled corpora.
Answer: They can deploy the same word recognition model for multiple downstream classification tasks or models and train the word recognition model with larger unlabeled corpora.
Question: What is the great depression?
Answer: The great depression was 10 years long from 1929 to 1939.  It was a great economic shock that impact countries all over the world.  A major plunge in the stock market and stock market prices is what caused the start of the Great Depression.  The Great Depression also led to political instability all over the world, and gave rise to dictatorial regimes like Hitler's rise to fame in Germany.
Question: What is the goal of the team in this research?
Evidence: We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.
With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph.
Answer: Their goal is to improve implicit discourse relation classification.
Question: Why might someone choose to move to Seattle?
Answer: Seattle is an appealing place to move for a variety of reasons. It is lively city with plenty of urban activities while also featuring the natural beauty of the Puget Sound and the Cascade Mountains.
Question: Tell me whether these are electric or gas powered vehicles
Answer: Volkswagen Jetta, Toyota Corolla, Tesla Model 3, Hyundai Ioniq 5, Kia EV6, Honda Civic
Question: What is the economy size of Tamil Nadu?
Evidence: As the most urbanised state of India, Tamil Nadu boasts an economy with gross state domestic product (GSDP) of ₹24.85 lakh crore (US$310 billion), marking the second-largest economy amongst the 28 states of India. It has the country's 9th-highest GSDP per capita of ₹225,106 (US$2,800), and ranks 11th in human development index. Tamil Nadu is also one of the most industrialised states, with the manufacturing sector accounting for more than one-third of the state's GDP. Home to a number of ancient relics, historic buildings, religious pilgrimage spots, hill stations, forts, and three World Heritage Sites, Tamil Nadu's tourism industry is the largest among the Indian states. 15% of Tamil Nadu's forests are protected areas, hosting diverse wildlife. The Tamil film industry, nicknamed as Kollywood, plays an influential role in the state's popular culture.
Answer: Tamil Nadu is one of the most industrialized and second-largest economy among all states in India with gross state domestic product (GSDP) of US$310 billion.
Question: How to become good friends with your neighbor?
Answer: Regardless you live in a house or an apartment, it is important to build a friendly relationship with your neighbor. You may begin by introducing yourself to your neighbor when you come across with them in the hall way or at the yard. You can try to invite them to your personal events, such as birthday parties or potluck. You can also offer help for your neighbor, such as take care of their pets when they are traveling etc. It takes time to get to know someone, the same here for your neighbor. Overall, just be kind and be considerate, your neighbor will eventually and naturally become your friend.
Question: Which dataset(s) do they evaluate on?
Evidence: Datasets
We conduct experiments on two Chinese question answering datasets from NLPCC-2016 evaluation task BIBREF13 .
DBQA is a document based question answering dataset. There are 8.8k questions with 182k question-sentence pairs for training and 6k questions with 123k question-sentence pairs in the test set. In average, each question has 20.6 candidate sentences and 1.04 golden answers. The average length for questions is 15.9 characters, and each candidate sentence has averagely 38.4 characters. Both questions and sentences are natural language sentences, possibly sharing more similar word choices and expressions compared to the KBQA case. But the candidate sentences are extracted from web pages, and are often much longer than the questions, with many irrelevant clauses.
KBRE is a knowledge based relation extraction dataset. We follow the same preprocess as BIBREF14 to clean the dataset and replace entity mentions in questions to a special token. There are 14.3k questions with 273k question-predicate pairs in the training set and 9.4k questions with 156k question-predicate pairs for testing. Each question contains only one golden predicate. Each question averagely has 18.1 candidate predicates and 8.1 characters in length, while a KB predicate is only 3.4 characters long on average. Note that a KB predicate is usually a concise phrase, with quite different word choices compared to the natural language questions, which poses different challenges to solve.
Datasets
We conduct experiments on two Chinese question answering datasets from NLPCC-2016 evaluation task BIBREF13 .
DBQA is a document based question answering dataset. 
KBRE is a knowledge based relation extraction dataset.
Answer: The answers are shown as follows:
* DBQA
* KBRE
Question: Are the OpenIE extractions all triples?
Evidence: We create an additional table in TableILP with all the tuples in $T$ . Since TableILP uses fixed-length $(subject; predicate; object)$ triples, we need to map tuples with multiple objects to this format. For each object, $O_i$ in the input Open IE tuple $(S; P; O_1; O_2 \ldots )$ , we add a triple $(S; P; O_i)$ to this table.
For each object, $O_i$ in the input Open IE tuple $(S; P; O_1; O_2 \ldots )$ , we add a triple $(S; P; O_i)$ to this table.
Answer: No.