DataHammer/scimrc huggingface dataset:
Question: What network is used to capture the long-term dependencies from the whole sentence.?
Evidence: Quality estimation (QE) refers to the task of measuring the quality of machine translation (MT) system outputs without reference to the gold translations BIBREF0 , BIBREF1 . QE research has grown increasingly popular due to the improved quality of MT systems, and potential for reductions in post-editing time and the corresponding savings in labor costs BIBREF2 , BIBREF3 . QE can be performed on multiple granularities, including at word level, sentence level, or document level. In this paper, we focus on quality estimation at word level, which is framed as the task of performing binary classification of translated tokens, assigning “OK” or “BAD” labels.
Early work on this problem mainly focused on hand-crafted features with simple regression/classification models BIBREF4 , BIBREF5 . Recent papers have demonstrated that utilizing recurrent neural networks (RNN) can result in large gains in QE performance BIBREF6 . However, these approaches encode the context of the target word by merely concatenating its left and right context words, giving them limited ability to control the interaction between the local context and the target word.
In this paper, we propose a neural architecture, Context Encoding Quality Estimation (CEQE), for better encoding of context in word-level QE. Specifically, we leverage the power of both (1) convolution modules that automatically learn local patterns of surrounding words, and (2) hand-crafted features that allow the model to make more robust predictions in the face of a paucity of labeled data. Moreover, we further utilize stacked recurrent neural networks to capture the long-term dependencies and global context information from the whole sentence.
We tested our model on the official benchmark of the WMT18 word-level QE task. On this task, it achieved highly competitive results, with the best performance over other competitors on English-Czech, English-Latvian (NMT) and English-Latvian (SMT) word-level QE task, and ranking second place on English-German (NMT) and German-English word-level QE task.
Moreover, we further utilize stacked recurrent neural networks to capture the long-term dependencies and global context information from the whole sentence.
Answer: Stacked recurrent neural networks.
Question: Name four Ivy League schools.
Answer: Four Ivy League schools are Dartmouth, Brown, Princeton, and Yale.
Question: How many World Series have the Detroit Tigers won?
Answer: The Detroit Tigers are an American professional baseball team based in Detroit that plays in the American League (AL). The Tigers have won four World Series titles (1935, 1945, 1968, 1984) and 11 AL pennants.
Question: What are the co-founders of Tesla Motors?
Answer: Eberhard, Tarpenning, Wright, Elon Musk, and J.B. Straubel
Question: What are five totally distinct, creative ways to describe the usefulness of a broken stapler.
Answer: One can put a broken stapler to good use as a 

- paper weight
- hammer
- home decor 
- ruler
- weapon
Question: What is Easter?
Answer: Easter is Christian holiday that reminds us of Jesus getting raised from the dead or the resurrection of Jesus.  This  is described in the Bible in the New Testament happening on the third day after his crucifixion.  The Romans were the one's responsible for the Crucifixion.  Easter is commemorated with fasting, and an Easter Egg hunt for the kids on Easter Sunday.
Question: How is the shallow syntactic information obtained?
Evidence: The NLP community is revisiting the role of linguistic structure in applications with the advent of contextual word representations (cwrs) derived from pretraining language models on large corpora BIBREF2, BIBREF3, BIBREF4, BIBREF5. Recent work has shown that downstream task performance may benefit from explicitly injecting a syntactic inductive bias into model architectures BIBREF6, even when cwrs are also used BIBREF7. However, high quality linguistic structure annotation at a large scale remains expensive—a trade-off needs to be made between the quality of the annotations and the computational expense of obtaining them. Shallow syntactic structures (BIBREF8; also called chunk sequences) offer a viable middle ground, by providing a flat, non-hierarchical approximation to phrase-syntactic trees (see Fig. FIGREF1 for an example). These structures can be obtained efficiently, and with high accuracy, using sequence labelers. In this paper we consider shallow syntax to be a proxy for linguistic structure.
While shallow syntactic chunks are almost as ubiquitous as part-of-speech tags in standard NLP pipelines BIBREF9, their relative merits in the presence of cwrs remain unclear. We investigate the role of these structures using two methods. First, we enhance the ELMo architecture BIBREF0 to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context (§SECREF2). Our second method involves classical addition of chunk features to cwr-infused architectures for four different downstream tasks (§SECREF3). Shallow syntactic information is obtained automatically using a highly accurate model (97% $F_1$ on standard benchmarks). In both settings, we observe only modest gains on three of the four downstream tasks relative to ELMo-only baselines (§SECREF4).
Recent work has probed the knowledge encoded in cwrs and found they capture a surprisingly large amount of syntax BIBREF10, BIBREF1, BIBREF11. We further examine the contextual embeddings obtained from the enhanced architecture and a shallow syntactic context, using black-box probes from BIBREF1. Our analysis indicates that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings (§SECREF18).
Overall, our findings show that while shallow syntax can be somewhat useful, ELMo-style pretraining discovers representations which make additional awareness of shallow syntax largely redundant.
Shallow syntactic information is obtained automatically using a highly accurate model (97% F1 on standard benchmarks).
Answer: It is obtained automatically using a highly accurate model (97% F1 on stan dard benchmarks).
Question: Who where the San Diego Stingrays
Answer: The San Diego Stingrays were a charter member of International Basketball League (IBL) and played at the San Diego Sports Arena and was owned by online sports gaming entrepreneur Scott Atkins. The following season, the team returned to San Diego under the name, San Diego Wildfire, along with new ownership and management.

In June 1999, San Diego hired former San Diego State Aztecs men's basketball head coach Smokey Gaines to helm the team. Former National Basketball Association (NBA) player Jeff Malone was hired as Gains' assistant coach in July 1999. Bill Tosheff was the director of basketball operations for the Stingrays. Rapper Master P, who had previously played in the NBA pre-season, signed with the Stingrays in November 1999.

The Stingrays first home game in 1999 was attended by 9,786 people—an IBL best. The San Diego Stingrays had a dance team as well that participated in several community events. They were led by director Demilo Young and Sheila Christensen.
Question: What is XGBoost Algorithm?
Answer: XGBoost is a robust machine-learning algorithm that can help you understand your data and make better decisions. XGBoost is an implementation of gradient-boosting decision trees. It has been used by data scientists and researchers worldwide to optimize their machine-learning models. LightGBM is another common, related library for Gradient Boosting algorithm training. Both XGBoost and LightGBM are open-source and are used in a variety of Machine Learning tasks. LightGBM is generally faster for training on large datasets.
Question: Given this paragraph about Platylesches Robustus, what type of insect is it and how many countries can it be found?
Evidence: Platylesches robustus, the robust hopper or large hopper, is a butterfly of the family Hesperiidae. It is found in Zululand, Eswatini, Transvaal, Mozambique and Zimbabwe. The habitat consists of moist, frost-free savanna and the transitional zone between forest and Guinea savanna.
Answer: The Platylesches robustus is a butterfly and it can be found in five different countries.
Question: Tell me whether these words are English or Spanish: dog, cat, perro, gato, house, casa
Answer: Dog: English
Cat: English
Perro: Spanish
Gato: Spanish
House: English
Casa: Spanish
Question: Why do humans like stories?
Answer: Stories allow conveying important thoughts and sharing experiences from one person to another in an easily understandable and relatable way, ultimately leading to knowledge transfer between humans. They also spark imagination and give readers or listeners the opportunity to come up with their own depiction of the plot and of the characters.
Question: What do redshifts tell astronomers?
Answer: Edwin Hubble discovered in the 1920s that the spectral lines of light reaching us from the universe's most distant galaxies were redshifting to areas of the electromagnetic spectrum with lower energy intensity. This allows us to confirm that the various objects in the universe are moving away from us and from one another. The faster a galaxy moves away from the observer, the further it is from the observer. This phenomenon is interpreted as proof of the universe's expansion and the Big Bang.
Question: What are some popular tequila drinks that I should try?
Answer: Paolo Maldini, Tequila Sunrise, Strawberry Basil Jalapeño Margarita , The Paloma, Ranch Water
Question: Please provide some resources that will help me think through the process of creating an online course.
Answer: First, determine what type of course you want to teach. A common technique that you might use to identify a suitable course is: Find a topic you are knowledgeable about, find a topic that you are passionate about, and find a topic that others are interested in and willing to pay for. If you have a topic that overlaps all three areas, then you have a good topic for a course.

Once you have a topic identified, there are numerous resources you can consult. Course-hosting platforms like Thinkific, Kajabi, and Podia have helpful information about creating an online course. Other ways to learn are podcasts like My First Million, Creator Science, and the Online Course Show.
Question: Name three countries where Arabic is the official language.
Answer: Arabic is the official language of Egypt, Lebanon, and Jordan.
Question: Where is the planet mars?
Answer: Mars is the fourth planet from the sun.  It is also the seconds smallest planet in the Milky Way Solar System.  The name "mars" means the Roman God of War in the English language.  Mars is also called the "red planet" because of how red the surface looks when viewing it through a telescope from Earth.  You may have also heard the name "Mars" used as an analog "Men are from Mars and Women are from Venus."
Question: What is Scottsdale, Arizona USA known for?
Answer: Scottsdale is famous for it's sunshine, beautiful desert scenery, dozens of golf courses, resorts, nightlife, spring training baseball, Phoenix open golf tournament, Barrett-Jackson car auction, and more.
Question: How many instances does their dataset have?
Evidence: For each speaker, we divide all available utterances into disjoint train, development, and test sets. Using the force-aligned phone boundaries, we extract the mid-phone frame for each example across the four classes, which leads to a data imbalance. Therefore, for all utterances in the training set, we randomly sample additional examples within a window of 5 frames around the center phone, to at least 50 training examples per class per speaker. It is not always possible to reach the target of 50 examples, however, if no more data is available to sample from. This process gives a total of INLINEFORM0 10700 training examples with roughly 2000 to 3000 examples per class, with each speaker having an average of 185 examples. Because the amount of data varies per speaker, we compute a sampling score, which denotes the proportion of sampled examples to the speaker's total training examples. We expect speakers with high sampling scores (less unique data overall) to underperform when compared with speakers with more varied training examples.
This process gives a total of INLINEFORM0 10700 training examples with roughly 2000 to 3000 examples per class, with each speaker having an average of 185 examples.
Answer: The answers are shown as follows:
* 10700
Question: If I am traveling from Berkeley to San Francisco, what are the public transit options?
Answer: To go to San Francisco from Berkeley on public transit, you can take Bart, Transbay Bus, or the Oakland & Alameda Ferry.
Question: What embeddings do they use?
Evidence: Sentiment analysis has recently been one of the hottest topics in natural language processing (NLP). It is used to identify and categorise opinions expressed by reviewers on a topic or an entity. Sentiment analysis can be leveraged in marketing, social media analysis, and customer service. Although many studies have been conducted for sentiment analysis in widely spoken languages, this topic is still immature for Turkish and many other languages.
Neural networks outperform the conventional machine learning algorithms in most classification tasks, including sentiment analysis BIBREF0. In these networks, word embedding vectors are fed as input to overcome the data sparsity problem and make the representations of words more “meaningful” and robust. Those embeddings indicate how close the words are to each other in the vector space model (VSM).
Most of the studies utilise embeddings, such as word2vec BIBREF1, which take into account the syntactic and semantic representations of the words only. Discarding the sentimental aspects of words may lead to words of different polarities being close to each other in the VSM, if they share similar semantic and syntactic features.
For Turkish, there are only a few studies which leverage sentimental information in generating the word and document embeddings. Unlike the studies conducted for English and other widely-spoken languages, in this paper, we use the official dictionaries for this language and combine the unsupervised and supervised scores to generate a unified score for each dimension of the word embeddings in this task.
Our main contribution is to create original and effective word vectors that capture syntactic, semantic, and sentimental characteristics of words, and use all of this knowledge in generating embeddings. We also utilise the word2vec embeddings trained on a large corpus. Besides using these word embeddings, we also generate hand-crafted features on a review basis and create document vectors. We evaluate those embeddings on two datasets. The results show that we outperform the approaches which do not take into account the sentimental information. We also had better performances than other studies carried out on sentiment analysis in Turkish media. We also evaluated our novel embedding approaches on two English corpora of different genres. We outperformed the baseline approaches for this language as well. The source code and datasets are publicly available.
The paper is organised as follows. In Section 2, we present the existing works on sentiment classification. In Section 3, we describe the methods proposed in this work. The experimental results are shown and the main contributions of our proposed approach are discussed in Section 4. In Section 5, we conclude the paper.
We also utilise the word2vec embeddings trained on a large corpus. Besides using these word embeddings, we also generate hand-crafted features on a review-basis and create document vectors.
Answer: The word2vec embeddings trained on a large corpus.
Question: Is Hainanese chicken rice a national dish of Singapore or Malaysia?
Evidence: Hainanese chicken rice is a dish of poached chicken and seasoned rice, served with chilli sauce and usually with cucumber garnishes. It was created by immigrants from Hainan in southern China and adapted from the Hainanese dish Wenchang chicken. It is considered one of the national dishes of Singapore and is most commonly associated with Singaporean cuisine, being widely available in most food courts and hawker centres around the country. Variants of the dish can also be seen throughout Southeast Asia, particularly in Malaysia and Thailand, where it remains a culinary staple. In a debate that stretches back decades to 1965, when the two countries split, Malaysia and Singapore have both laid claim to inventing the dish. In 2009, then Malaysian Tourism Minister Ng Yen Yen said that Hainanese chicken rice was "uniquely Malaysian" and had been "hijacked" by other countries. Ng later clarified that she was misquoted on her intention to patent the foods, and that a study on the origins of the foods would be conducted "and an apology conveyed if it was wrongly claimed." In 2018, then Malaysian Finance Minister Lim Guan Eng joked that Singapore claimed "chicken rice is theirs (and) if we’re not careful, ‘char koay teow‘ will become theirs" one day. The debate has been described as an example of gastronationalism.
Answer: The answer might differ depending on who is answering your question. Hainanese chicken rice is considered one of the national dishes of Singapore being widely available in most food courts and hawker centres around the country. Variants of the dish can also be seen throughout Southeast Asia, particularly in Malaysia where it remains a culinary staple.
Question: Please summarize Sinclair's argument in this paragraph for why longer lifespans will not lead to overpopulation.
Evidence: A review for Boston Magazine called Sinclair “one of science’s most controversial figures” and said many in the scientific community were skeptical of claims he made about human longevity. University of Alabama biology professor Steven N. Austad said, “David is a good friend, but I do think he’s been guilty of making excessive claims.” Writing in Archives of Gerontology and Geriatrics, Charles Brenner summarized that Lifespan has "become an influential source of misinformation on longevity, featuring counterfactual claims about longevity genes being conserved between yeast and humans, the existence of supposed activators of these genes, and claimed successful age reversal in mice based on partial reprogramming." In a 2019 interview, Sinclair dismissed the idea that longer lifespans could lead to overpopulation. "Population growth will level off within the next couple of decades, and healthier people are having fewer children," Sinclair said. "The global population is already stabilizing, and in many advanced countries going down, so people’s fear that the world will be overpopulated with frail old people is completely wrong."
Answer: Based on this paragraph, Sinclair is not concerned with the risk of overpopulation even if lifespans become longer. His rationale is the healthier people are having fewer children and so the population is going down in many advanced countries.
Question: What is investment banking?
Answer: Investment banking pertains to certain activities of a financial services company or a corporate division that consist in advisory-based financial transactions on behalf of individuals, corporations, and governments. Traditionally associated with corporate finance, such a bank might assist in raising financial capital by underwriting or acting as the client's agent in the issuance of debt or equity securities. An investment bank may also assist companies involved in mergers and acquisitions (M&A) and provide ancillary services such as market making, trading of derivatives and equity securities, FICC services (fixed income instruments, currencies, and commodities) or research (macroeconomic, credit or equity research). Most investment banks maintain prime brokerage and asset management departments in conjunction with their investment research businesses. As an industry, it is broken up into the Bulge Bracket (upper tier), Middle Market (mid-level businesses), and boutique market (specialized businesses).

Unlike commercial banks and retail banks, investment banks do not take deposits.
Question: Can you write a post advertising a social event for the end of the year?
Answer: Are you ready to kickstart this incredible Spring?
Come join us in celebrating. Bring your friends, family and whomever else to enjoy fun activities and treats from the city’s finest dessert vendors. 

In addition to this great event, make sure to see all of the other incredible programming as we head toward the end of the year!
Question: What system does the author introduce?
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.
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.
Answer: They 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.
Question: What is the capital of Finland and when was it founded?
Answer: The capital of Finland is Helsinki and it was founded on the 12th June, 1550. However Helsinki only became the Finnish capital in 1812. Before that the Finnish capital was Turku.
Question: How many island does Hawaii have?
Answer: Hawaii has 8 islands in total,
Question: What are The Loxurini
Answer: The Loxurini are a small tribe of butterflies in the family Lycaenidae.
Question: how is white rice made?
Answer: White rice is produced from brown rice.  Around the outside of the brown rice is a bran layer which is removed, and then a germ is removed.  What is left behind is a starchy composition.  During this process most of the vitamins and minerals are removed.  This is why brown rice is usually preferred as a healthier alternative to white rice.  White rice is has no nutritional value because the nutrition has been stripped from it during the production process of making white rice.