DataHammer/scimrc huggingface dataset:
Question: How does the authors' framework work?
Evidence: Our framework combines text-embedding, geometric graph construction and multi-resolution community detection to identify, rather than impose, content-based clusters from free, unstructured text in an unsupervised manner.
Figure 1 shows a summary of our pipeline. First, we pre-process each document to transform text into consecutive word tokens, where words are in their most normalised forms, and some words are removed if they have no distinctive meaning when used out of context BIBREF5 , BIBREF6 . We then train a paragraph vector model using the Document to Vector (Doc2Vec) framework BIBREF7 on the whole set (13 million) of preprocessed text records, although training on smaller sets (1 million) also produces good results. This training step is only done once. This Doc2Vec model is subsequently used to infer high-dimensional vector descriptions for the text of each of the 3229 documents in our target analysis set. We then compute a matrix containing pairwise similarities between any pair of document vectors, as inferred with Doc2Vec. This matrix can be thought of as a full, weighted graph with documents as nodes and edges weighted by their similarity. We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF8 , a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The derived MST-kNN graph is analysed with Markov Stability BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , a multi-resolution dynamics-based graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. MS uses a diffusive process on the graph to reveal the multiscale organisation at different resolutions without the need for choosing a priori the number of clusters, scale or organisation. To analyse a posteriori the different partitions across levels of resolution, we use both visualisations and quantitative scores. The visualisations include word clouds to summarise the main content, graph layouts, as well as Sankey diagrams and contingency tables that capture the correspondences across levels of resolution and relationships to the hand-coded classifications. The partitions are also evaluated quantitatively to score: (i) their intrinsic topic coherence (using pairwise mutual information BIBREF13 , BIBREF14 ), and (ii) their similarity to the operator hand-coded categories (using normalised mutual information BIBREF15 ). We now expand on the steps of the computational framework.
Our framework combines text-embedding, geometric graph construction and multi-resolution community detection to identify, rather than impose, content-based clusters from free, unstructured text in an unsupervised manner.
Answer: Their framework combines text-embedding, geometric graph construction and multi-resolution community detection to identify, rather than impose, content-based clusters from free, unstructured text in an unsupervised manner.
Question: Ideas to keep kids occupied for Spring break?
Answer: 1. Take them to a movie 
2. Go to the mountains
3. Practice maths
Question: What chinese like drinking hot water?
Answer: Chinese like drinking hot water when they get sick or feel under the weather. The reason is that they believe most diseases are caused by getting cold, and hot water can balance cold and humidity and help circulate the blood and release toxins.
Question: If we wanted to play sports that involve a ball, which of these would we play: soccer, football, swimming, snowboard, skiing, baseball.
Answer: Soccer, football and baseball are sports that require a ball whereas swimming, snowboarding and skiing do not.
Question: Do they repot results only on English data?
Evidence: We evaluate the proposed architecture on two publicly available datasets: the Adverse Drug Events (ADE) dataset BIBREF6 and the CoNLL04 dataset BIBREF7. We show that our architecture is able to outperform the current state-of-the-art (SOTA) results on both the NER and RE tasks in the case of ADE. In the case of CoNLL04, our proposed architecture achieves SOTA performance on the NER task and achieves near SOTA performance on the RE task. On both datasets, our results are SOTA when averaging performance across both tasks. Moreover, we achieve these results using an order of magnitude fewer trainable parameters than the current SOTA architecture.
We evaluate the proposed architecture on two publicly available datasets: the Adverse Drug Events (ADE) dataset BIBREF6 and the CoNLL04 dataset BIBREF7. We show that our architecture is able to outperform the current state-of-the-art (SOTA) results on both the NER and RE tasks in the case of ADE. In the case of CoNLL04, our proposed architecture achieves SOTA performance on the NER task and achieves near SOTA performance on the RE task. On both datasets, our results are SOTA when averaging performance across both tasks.
Answer: Yes.
Question: Why is soccer so popular?
Answer: Soccer, or football, is played in every corner of the globe, from world-class pitches to back streets. All one needs to play is a round ball and some markers to signify the goals. The same cannot be said for other sports which commonly require specialized equipment or venues. Due to this accessibility, the world has seen many great footballing talents come from diverse socio-economic backgrounds and nations. 

It is a team-based sport that is incredibly unpredictable. Ironically, underdogs have frequently overcome defying odds to win. It is a beautiful sport to watch as moments of individual skill or team play can completely change the outcome of a game. 

For these reasons, among many others, is why football is the world's most popular sport and is commonly referred to as "the beautiful game."
Question: Think of five words that rhyme with kits
Answer: Bits, nits, slits, brits, ditz
Question: What are some ways to prepare for a new career?
Answer: There are many ways to prepare for a new career and it often depends on your starting point and your goal.  That said, there are many general practices that can help to prepare for a new career:

1. Research - learn as much as you can about the new career or careers
2. Create a plan - create steps to help you organize your path from now to your new career
3. Interview - find and reach out to professionals who are in that career and ask them for advice
4. Job Postings - look up job postings for the career and note the requirements for the career.  Look at entry level, mid-level and senior roles to get an understanding of the career path
5. Education - for some careers it may be helpful to have a related degree, accreditation, certification or simply a conversational understanding of the career
6. Experience - it is often helpful to have related experience to a career.  First, think about your own experiences and how they could be of interest to potential hiring managers.  If required, next think about any jobs you could take on as a step to getting closer to that career that would help in your next career search
7. Network - ask your contacts if they know of anyone looking to hire in that career that you could talk to about potential opportunities in the field or gather additional advice
Question: How is the performance on the task evaluated?
Evidence: Neural network models, and specifically sequence-to-sequence models, have pushed the state of the art for morphological inflection – the task of learning a mapping from lemmata to their inflected forms – in the last years BIBREF6. Thus, in this work, we experiment on such models, asking not what they learn, but, motivated by the respective research on human subjects, the related question of how what they learn depends on their prior knowledge. We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages. We aim at finding answers to two main questions: (i) Do errors systematically differ between source languages? (ii) Do these differences seem explainable, given the properties of the source and target languages? In other words, we are interested in exploring if and how L2 acquisition of morphological inflection depends on the L1, i.e., the "native language", in neural network models.
For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison. As we can see, the results are similar to the test set results for all language combinations. We manually annotate the outputs for the first 75 development examples for each source–target language combination. All found errors are categorized as belonging to one of the following categories.
We manually investigate the errors made by artificial neural networks for morphological inflection in a target language after pretraining on different source languages.
For our qualitative analysis, we make use of the validation set. Therefore, we show validation set accuracies in Table TABREF19 for comparison.
We manually annotate the outputs for the first 75 development examples for each source–target language combination. All found errors are categorized as belonging to one of the following categories.
Answer: Comparison of test accuracies of neural network models on an inflection task and qualitative analysis of the errors
Question: How many Children Trump has?
Evidence: In 1977, Trump married Czech model Ivana Zelníčková. They had three children: Donald Jr. (born 1977), Ivanka (born 1981), and Eric (born 1984). Ivana became a naturalized United States citizen in 1988. The couple divorced in 1990, following Trump's affair with actress Marla Maples. Trump and Maples married in 1993 and divorced in 1999. They have one daughter, Tiffany (born 1993), who was raised by Marla in California. In 2005, Trump married Slovenian model Melania Knauss. They have one son, Barron (born 2006). Melania gained U.S. citizenship in 2006.
Answer: From his marriages he has 5 children from three marriages.
Question: How food insecurity can lead to obesity. How can teachers help alleviate food insecurity if they identify this problem in children’s families?
Answer: Food insecurity can lead to less expensive, less healthy food choices that are limited in variety. In addition, children who do not have a consistent source of food may overeat when food is available. Low-income families often must choose high-fat, energy-dense, inexpensive food to maximize their calories per dollar spent. They also face additional risk factors for obesity, such as limited access to healthy and affordable foods and a lack of safe and accessible recreational facilities, parks, and walking trails that support physical activity.
Question: Give me a few ways that I could travel from Munich to Rome and list the advantages and disadvantages for each.
Answer: Here are several ways that you could travel from Munich to Rome:

1. By air - Plane travel is probably the most common and fastest way to travel from Munich to Rome. Depending on when you fly, your ticket class, and how close to your departure date that you buy your tickets, pricing can vary from less than $100 (91 Euros) to over $500 (455 Euros). Be sure to plan your trip and purchase tickets in advance to ensure that you save money. Also, while Munich has one major airport (MUC), Rome has two large international airports (FCO and CIA). It will take roughly 90 minutes by plane to travel from Munich to Rome, not accounting for time in the airport, security lines and customs.

2. By train - Traveling by train is another common way to get from Munich to Rome. While it will take longer than by air, tickets are very inexpensive and can be purchased the day of travel. A one way train ticket from Munich to Rome will cost roughly $35 (32 Euros), and take between 9-12 hours, depending on the route and number of stops.

3. By car - Traveling by car from Munich to Rome is also an option. With limited stops, it will take roughly 10 hours to drive from Munich to Rome, but having a car will allow you flexibility to stop along the way at any cities or sites that you want to see. 

Depending on your specific needs, all three of these options could be viable ways to travel from Munich to Rome.
Question: What is the biggest fish in the ocean?
Answer: Whale Sharks are the biggest fish in the ocean, growing up to 23 feet long. Surprisingly, they're not at all threatening and actually very approachable. There are many places around the world where you can swim (or dive) with whale sharks, from the Philippines to Mexico.
Question: What language do the agents talk in?
Evidence: Tourist: I can't go straight any further.
Guide: ok. turn so that the theater is on your right.
Guide: then go straight
Tourist: That would be going back the way I came
Guide: yeah. I was looking at the wrong bank
Tourist: I'll notify when I am back at the brooks brothers, and the bank.
Tourist: ACTION:TURNRIGHT ACTION:TURNRIGHT
Guide: make a right when the bank is on your left
Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNRIGHT
Tourist: Making the right at the bank.
Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT ACTION:TURNLEFT
Tourist: I can't go that way.
Tourist: ACTION:TURNLEFT ACTION:TURNLEFT ACTION:TURNLEFT
Tourist: Bank is ahead of me on the right
Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT
Guide: turn around on that intersection
Tourist: I can only go to the left or back the way I just came.
Guide: you're in the right place. do you see shops on the corners?
Guide: If you're on the corner with the bank, cross the street
Tourist: I'm back where I started by the shop and the bank.
Tourist: I can't go straight any further.

Guide: ok. turn so that the theater is on your right.

Guide: then go straight

Tourist: That would be going back the way I came

Guide: yeah. I was looking at the wrong bank

Tourist: I'll notify when I am back at the brooks brothers, and the bank.

Tourist: ACTION:TURNRIGHT

Guide: make a right when the bank is on your left

Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNRIGHT

Tourist: Making the right at the bank.

Tourist: ACTION:FORWARD ACTION:FORWARD

Tourist: I can't go that way.

Tourist: ACTION:TURNLEFT

Tourist: Bank is ahead of me on the right

Tourist: ACTION:FORWARD ACTION:FORWARD ACTION:TURNLEFT

Guide: turn around on that intersection

Tourist: I can only go to the left or back the way I just came.

Tourist: ACTION:TURNLEFT

Guide: you're in the right place. do you see shops on the corners?

Guide: If you're on the corner with the bank, cross the street

Tourist: I'm back where I started by the shop and the bank.

Tourist: ACTION:TURNRIGHT
Answer: English
Question: How is the lexicon of trafficking flags expanded?
Evidence: If we re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones, assisting anti-trafficking experts in expanding the lexicon of trafficking flags. This approach also works for acronyms and deliberate typos.
If we re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones, assisting anti-trafficking experts in expanding the lexicon of trafficking flags. This approach also works for acronyms and deliberate typos.
Answer: The answers are shown as follows:
* re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones
Question: Where is the best place in the Bahamas to go on holiday?
Answer: Given that there are direct flights to Nassau, this tends to be the focal point for US and British families to congregate for Spring break and the holiday periods. Nassau has 2 major resorts - the famous Atlantis, and the newer Baha Mar. Baha Mar allows you to use all 3 properties amenities regardless of where you are staying so is probably the best bet offering the most experiences and restaurants. It's also got it's own water park - Baha Bay. 
For those looking to get off the beaten track - you cannot beat Eluthera, Harbour Island and the Exumas. Andros is the largest of the islands, and offers a gorgeous resort Kamalame Key. Top tip is to hire a boat captain to see some of the outer islands- Exumas, Rose Island, Green Key etc.
Question: Which of the following numbers is a real number: -200, -1.5, -1, 0, 1, 1.5, 200, pi, e, sqrt(-1)?
Answer: The real numbers among those are -200, -1.5, -1, 0, 1, 1.5, 200, pi, e
Question: In the field of economics, what is considered a monopoly?
Answer: A monopoly is when a single company or individual has a dominant position in a market and can freely raise prices without risk of losing customers to competing products. Monopolies are generally discouraged as they reduce competition.
Question: Please summarize the timeline of the Chinese Exclusion Act.
Evidence: The Chinese Exclusion Act was a United States federal law signed by President Chester A. Arthur on May 6, 1882, prohibiting all immigration of Chinese laborers for 10 years. The law excluded merchants, teachers, students, travelers, and diplomats. The Chinese Exclusion Act was the first and only major U.S. law ever implemented to prevent all members of a specific national group from immigrating to the United States.

Passage of the law was preceded by growing anti-Chinese sentiment and anti-Chinese violence, as well as various policies targeting Chinese migrants. The act followed the Angell Treaty of 1880, a set of revisions to the U.S.–China Burlingame Treaty of 1868 that allowed the U.S. to suspend Chinese immigration. The act was initially intended to last for 10 years, but was renewed and strengthened in 1892 with the Geary Act and made permanent in 1902. These laws attempted to stop all Chinese immigration into the United States for ten years, with exceptions for diplomats, teachers, students, merchants, and travelers. They were widely evaded.

The law remained in force until the passage of the Magnuson Act in 1943, which repealed the exclusion and allowed 105 Chinese immigrants to enter the United States each year. Chinese immigration later increased with the passage of the Immigration and Nationality Act of 1952, which abolished direct racial barriers, and later by the Immigration and Nationality Act of 1965, which abolished the National Origins Formula.
Answer: The Chinese Exclusion Act was signed on May 6, 1882. The act was initially intended to last for 10 years, but was renewed and strengthened in 1892 with the Geary Act and made permanent in 1902. The law remained in force until the passage of the Magnuson Act in 1943, which repealed the exclusion.
Question: In their novel gazetteer embedding approach what is used to build enhanced gazetteer representation?
Evidence: Named-entity recognition (NER) is the task of tagging relevant entities such as person, location and organization in unstructured text. Modern NER has been dominated by neural models BIBREF0, BIBREF1 combined with contextual embeddings from language models (LMs) BIBREF2, BIBREF3, BIBREF4. The LMs are pre-trained on large amounts of unlabeled text which allows the NER model to use the syntactic and semantic information captured by the LM embeddings. On the popular benchmark datasets CoNLL-03 BIBREF5 and Ontonotes 5 BIBREF6, neural models with LMs achieved state-of-the-art results without gazetteers features, unlike earlier approaches that heavily relied on them BIBREF7. Gazetteers are lists that contain entities such as cities, countries, and person names. The gazetteers are matched against unstructured text to provide additional features to the model. Data for building gazetteers is available for multiple language from structured data resources such as Wikipedia, DBpedia BIBREF8 and Wikidata BIBREF9.
In this paper, we propose GazSelfAttn, a novel gazetteer embedding approach that uses self-attention and match span encoding to build enhanced gazetteer representation. GazSelfAttn embeddings are concatenated with the input to a LSTM BIBREF10 or CNN BIBREF11 sequence layer and are trained end-to-end with the model. In addition, we show how to extract general gazetteers from the Wikidata, a structured knowledge-base which is part of the Wikipedia project.
Our contributions are the following:
[topsep=1pt, leftmargin=15pt, itemsep=-1pt]
We propose novel gazetteer embeddings that use self-attention combined with match span encoding.
We enhance gazetteer matching with multi-token and single-token matches in the same representation.
We demonstrate how to use Wikidata with entity popularity filtering as a resource for building gazetteers.
GazSelfAttn evaluations on CoNLL-03 and Ontonotes 5 datasets show F$_1$ score improvement over baseline model from 92.34 to 92.86 and from 89.11 to 89.32 respectively. Moreover, we perform ablation experiments to study the contribution of the different model components.
We propose novel gazetteer embeddings that use self-attention combined with match span encoding.
Answer: Self-attention combined with match span encoding.
Question: Why is Kanye sometimes regarded the GOAT (Greatest of All Time)?
Answer: Kanye Omari West ("Ye") is debated as being the GOAT by many in the hip hop scene due to his prolific and arguably genius utilization of samples in his production. Starting off as merely a producer for Roc a Fella records, he quickly rose to international acclaim with his debut record "The College Dropout". His freshman album topped the Billboard charts for a long stretch of time and featured absolutely iconic and inspired tracks such as "Jesus Walks", "Family Business", and of course "Through The Wire", the infamous track where Kanye provided vocals following a car crash that forced his jaw to be wired shut as a result.

Following his initial success, Kanye released banger after banger with "Late Registration" and his stadium album "Graduation". He followed on to release what many consider to be his magnum opus with "My Beautiful Dark Twisted Fantasy" which received critical acclaim despite mixed reviews from popular internet music nerd Anthony Fantano.

Not to be stopped, Kanye was on the track to certified Godhood, dropping "808s and Heartbreak", a track that laid the foundation for artists like Juice WRLD, Frank Ocean, and The Weekend to discover their own sounds. Modern R&B really does have Kanye to thank.

His discography continues with the controversial "Yeezus", "The Life of Pablo", "Ye", and "Kids See Ghosts". The latter of which even internet music melon Anthony Fantano awarded an unprecedented 10. Despite a decline in the quality of his recent releases with "Jesus is King" and "Donda", the insane influence Kanye has had upon the music industry and the hip hop / production scene at large can not be understated. His resume alone speaks for itself and is a jarring indictment of him being in contention for the title of GOAT.

Despite heated debate and a multitude of haters, ultimately, many fans simply suffice to speak the undeniable truth "He made Graduation".
Question: Given this series of navigation instructions, determine whether one would end up back at the starting point: Walk south 5 miles. Walk east 5 miles. Walk north 5 miles.
Answer: One would not end up back at the starting location, as they would end up 5 miles west of where they started. However, in the case of someone starting at the north pole, they would end up back at where they started, owing to the curvature of the Earth.
Question: Where is Laskowice?
Evidence: Laskowice [laskɔˈvit͡sɛ] (also known as Laskowice Pomorskie; German: Laskowitz) is a village in the administrative district of Gmina Jeżewo, within Świecie County, Kuyavian-Pomeranian Voivodeship, in north-central Poland. It lies approximately 5 kilometres (3 mi) south-west of Jeżewo, 8 km (5 mi) north of Świecie, 51 km (32 mi) north-east of Bydgoszcz, and 52 km (32 mi) north of Toruń.

The village has a population of 2,500. It is a major rail junction, where the Bydgoszcz-Gdynia line merges with the connection to Warsaw, via Grudziądz.
Answer: Laskowice is a village in north-central Poland