DataHammer/scimrc huggingface dataset:
Question: Is annotation done manually?
Evidence: The biggest difference between discourse parsing for well-written document and dialogues is that discourse relations can exist on two nonadjacent utterances in dialogues. When we annotate dialogues, we should read dialogues from begin to the end. For each utterance, we should find its one parent node at least from all its previous utterances. We assume that the discourse structure is a connected graph and no utterance is isolated.
We propose three questions for eache dialogue and annotate the span of answers in the input dialogue. As we know, our dataset is the first corpus for multi-party dialogues reading comprehension.
We construct following questions and answers for the dialogue in Example 1:
Q1: When does Bdale leave?
A1: Fri morning
Q2: How to get people love Mark in Mjg59's opinion.
A2: Hire people to work on reverse-engineering closed drivers.
On the other hand, to improve the difficulty of the task, we propose $ \frac{1}{6}$ to $ \frac{1}{3}$ unanswerable questions in our dataset. We annotate unanswerable questions and their plausible answers (PA). Each plausible answer comes from the input dialogue, but is not the answer for the plausible question.
Q1: Whis is the email of daniels?
PA: +61 403 505 896
When we annotate dialogues, we should read dialogues from begin to the end. For each utterance, we should find its one parent node at least from all its previous utterances. We assume that the discourse structure is a connected graph and no utterance is isolated.
We propose three questions for eache dialogue and annotate the span of answers in the input dialogue. As we know, our dataset is the first corpus for multi-party dialogues reading comprehension.

We construct following questions and answers for the dialogue in Example 1:

Q1: When does Bdale leave?

A1: Fri morning

Q2: How to get people love Mark in Mjg59's opinion.

A2: Hire people to work on reverse-engineering closed drivers.

On the other hand, to improve the difficulty of the task, we propose $ \frac{1}{6}$ to $ \frac{1}{3}$ unanswerable questions in our dataset. We annotate unanswerable questions and their plausible answers (PA). Each plausible answer comes from the input dialogue, but is not the answer for the plausible question.

Q1: Whis is the email of daniels?

PA: +61 403 505 896
Answer: Yes.
Question: What is Materialized View?
Evidence: In computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function.
Answer: In Database, a materialized view (MV) is a database object that stores the results of a query. It can be a copy of data from a remote location, a subset of rows and/or columns from a table or join result, or a summary using an aggregate function. MV is normally used for frequent queries of based tables.
Question: What is the best watch in the world ?
Answer: No one watch qualifies as best. It depends on an individual's preferences. Broadly watches can be classified into Digital and Mechanical watches. Mechanical watches can be further categorized into automatic and non-automatic. 

Digital watches typically use an electronic circuit to keep track of time. They generally are mass-produced and are inexpensive but highly accurate in keeping time. 

On the other hand, Mechanical Automatic watches typically use a rotor, or metal weight, to power the timepiece and keep time. The rotor is powered by the movement of the watch wearer's hand. Premium watches are handmade and can be pretty expensive. They also are the least accurate in keeping time. However, watch enthusiasts still love and collect these watches. 

Mechanical non-automatic watches typically use a quartz crystal to keep track of time. These are inexpensive and very accurate at keeping time. 

So the best watch for you depends on what you are looking for. If you have a tight budget and care about accurate timing, then digital or non-automatic watches might work well. On the other hand, if you are a watch enthusiast, then Automatic watches are a great buy.
Question: Write a friendly, convincing e-mail to my landlord asking whether I can get a dog.
Answer: Dear landlord, 

I hope you've had a great start to your day and all is well! I've really enjoyed living in your building and amenities. In my time residing here, I've been a clean and respectful tenant. I'm very interested in getting a dog and would like to stay in your building however, I wanted to check with you first if the building is dog friendly. If so, are there any restrictions such as size or breed? Additionally are there any additional costs or changes to my lease I can expect? 

I look forward to hearing from you and I hope we can make this work! 

Best,
Tenant
Question: Based on the reference text, what were the main reasons homeless people from outside of San Francisco went to San Francisco?
Evidence: Homelessness in the San Francisco Bay Area

One of the primary causes of homelessness in the Bay Area is an insufficient supply of affordable housing. Mass homelessness has several contributing factors, including: "Economic Dislocation", "Reduced Social Safety Nets", "Failed Housing Policy", "Mass Incarceration", "Family Instability", and other "Individual Causes" including mental health and physical wellness. Reasons cited for homelessness in the 2019 survey commissioned by the City of San Francisco include job loss (26%), alcohol/drug use (18%), eviction (13%), argument/asked to leave by friend/family (12%), mental health issues (8%), and divorce/separation (5%). 70% of people homeless in San Francisco in 2019 reported most recently becoming homeless while living in San Francisco: 22% came from another county within California, and 8% came from another state. However, of the 70% who had become homeless while living in San Francisco, 45% had only been in San Francisco for ten years or less, and 6% had only been in San Francisco for one year or less. Reasons for coming from outside San Francisco at the time of homelessness include seeking a job (25%), LGBTQ acceptance (11%), accessing homeless services (22%), was visiting and decided to stay (17%), accessing VA services or clinic (5%), and family/friends are here (13%).
Answer: The reasons homeless people not from San Francisco went to San Francisco include:
1. Seeking a job
2. LGBTQ acceptance
3. Accessing homeless services
4. Was visiting and decided to stay
5. Family or friends live in San Francisco
6. Accessing VA services or clinic
Question: Can you please show some details regarding the comedy film Bjorck Family using given text as a reference
Evidence: The Bjorck Family (Swedish: Familjen Björck) is a 1940 Swedish comedy film directed by Anders Henrikson and starring Olof Winnerstrand, Frida Winnerstrand and Birgitta Arman. It was shot at the Centrumateljéerna Studios in Stockholm. The film's sets were designed by the art director Arthur Spjuth. It was based on a radio show of the same name that ran from 1936 to 1943.
Answer: 1. The 1940 Swedish comedy film The Bjorck Family, also known as Familjen Björck, was directed by Anders Henrikson and starred Olof Winnerstrand, Frida Winnerstrand, and Birgitta Arman.
2. It was filmed in Stockholm at Centrumateljéerna Studios.
3. Arthur Spjuth, the film's art director, created the sets.
4. It was based on the 1936–1943 radio programme of the same name.
Question: What is the main difference between ORC and Parquet?
Answer: Both file formats are columnar with similar support for encoding and compression. Their key difference is in how nested data is represented. In Parquet, the nested data is fully shredded into columns with additional metadata to reconstruct the nesting structure. In ORC, nested structures are not decomposed into columns. Rather the nested data value is stored as a regular column value.
Question: Given this reference text about the history of statistics, what was the earliest publican of writing on statistics?
Evidence: Formal discussions on inference date back to Arab mathematicians and cryptographers, during the Islamic Golden Age between the 8th and 13th centuries. Al-Khalil (717–786) wrote the Book of Cryptographic Messages, which contains one of the first uses of permutations and combinations, to list all possible Arabic words with and without vowels. Al-Kindi's Manuscript on Deciphering Cryptographic Messages gave a detailed description of how to use frequency analysis to decipher encrypted messages, providing an early example of statistical inference for decoding. Ibn Adlan (1187–1268) later made an important contribution on the use of sample size in frequency analysis.

The earliest writing containing statistics in Europe dates back to 1663, with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt. Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences.
Answer: The earliest publication containing statistics is Natural and Political Observations upon the Bills or Mortality by John Graunt.
Question: Provide a bullet point list of all primary matter phase transitions, with the name of the process following by the matter states involved  Use the format: {process name}: {initial state} -> {final state}
Answer: - melting: solid -> liquid
- sublimation: solid -> gas
- freezing: liquid -> solid
- vaporization: liquid -> gas
- ionization: gas -> plasma
- condensation: gas -> liquid
- deposition: gas -> solid
- deionization: plasma -> gas
Question: What are some of the best places to eat in Boston?
Answer: There are many types of restaurants in Boston.  Boston is well known for its local seafood, lobster, scollops, shrimp, and many types of fish.  Boston is also a waterfront city, so there are many dinning options available on the water.
Question: What's the function of the strong neural baseline?
Evidence: The task of interpreting and following natural language (NL) navigation instructions involves interleaving different signals, at the very least the linguistic utterance and the representation of the world. For example, in turn right on the first intersection, the instruction needs to be interpreted, and a specific object in the world (the intersection) needs to be located in order to execute the instruction. In NL navigation studies, the representation of the world may be provided via visual sensors BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 or as a symbolic world representation. This work focuses on navigation based on a symbolic world representation (referred to as a map).
Previous datasets for NL navigation based on a symbolic world representation, HCRC BIBREF5, BIBREF6, BIBREF7 and SAIL BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15 present relatively simple worlds, with a small fixed set of entities known to the navigator in advance. Such representations bypass the great complexity of real urban navigation, which consists of long paths and an abundance of previously unseen entities of different types.
In this work we introduce Realistic Urban Navigation (RUN), where we aim to interpret navigation instructions relative to a rich symbolic representation of the world, given by a real dense urban map. To address RUN, we designed and collected a new dataset based on OpenStreetMap, in which we align NL instructions to their corresponding routes. Using Amazon Mechanical Turk, we collected 2515 instructions over 3 regions of Manhattan, all specified (and verified) by (respective) sets of humans workers. This task raises several challenges. First of all, we assume a large world, providing long routes, vulnerable to error propagation; secondly, we assume a rich environment, with entities of various different types, most of which are unseen during training and are not known in advance; finally, we evaluate on the full route intended, rather than on last-position only.
We then propose a strong neural baseline for RUN where we augment a standard encoder-decoder architecture with an entity abstraction layer, attention over words and worlds, and a constantly updating world-state. Our experimental results and ablation study show that this architecture is indeed better-equipped to treat grounding in realistic urban settings than standard sequence-to-sequence architectures. Given this RUN benchmark, empirical results, and evaluation procedure, we hope to encourage further investigation into the topic of interpreting NL instructions in realistic and previously unseen urban domains.
[1]The task defined by BIBREF6 is of moving between entities only.
We then propose a strong neural baseline for RUN where we augment a standard encoder-decoder architecture with an entity abstraction layer, attention over words and worlds, and a constantly updating world-state.
Answer: The authors augment a standard encoder-decoder architecture with an entity abstraction layer, attention over words and worlds, and a constantly updating world-state.
Question: Who create Ben & Jerry's icecream?
Answer: Ben & Jerry's ice cream was founded by Ben Cohen and Jerry Greenfield in South Burlington, Vermont.  The ice cream grew in popularity largely due to its exotic flavors which included large chunks of ingredients mixed in with regular ice cream.  This was due to Cohen's server anosmia, a lack of sense and smell, who relied on the textures when creating new flavors.
Question: Identify which animal species is alive or extinct: Archelon Turtle, Bornean Orangutan
Answer: Archelon Turtle is extinct, Bornean Orangutan is alive.
Question: Given this paragraph about Jiu-Jitsu, who taught Carlos Gracie.
Evidence: Brazilian jiu-jitsu was initially developed in 1925 by Brazilian brothers Carlos, Oswaldo, Gastão Jr., George, and Hélio Gracie, after Carlos was taught jiu-jitsu by a travelling Japanese judoka, Mitsuyo Maeda, who himself mastered his ground fighting while interacting with Taro Miyake (Tanabe student), Sadakazu Uyenishi (Handa, Tanabe) and Yukio Tani (Tenjin Shinyo-ryu) and catch wrestlers in Europe.
Answer: Mitsuyo Maeda
Question: Is it easy to construct high quality training data?
Evidence: The major advantage of our system is that we only leverage NLTK tokenization and a POS tagger to preprocess our training dataset. When implementing our neural network based clinical information extraction system, we found it is not easy to construct high quality training data due to the noisy format of clinical notes. Choosing the proper tokenizer is quite important for span identification. After several experiments, we found "RegexpTokenizer" can match our needs. This tokenizer can generate spans for each token via sophisticated regular expression like below,
The major advantage of our system is that we only leverage NLTK2 tokenization and a POS tagger to preprocess our training dataset. When implementing our neural network based clinical information extraction system, we found it is not easy to construct high quality training data due to the noisy format of clinical notes.
Answer: No, it isn't.
Question: List the ingredients and step by step instructions to make a glass of AMF.
Answer: Ingredients:
3/4 oz vodka
3/4 oz rum
3/4 oz tequila
3/4 oz gin
3/4 oz blue curaçao
3/4 oz simple syrup
3/4 oz lemon juice
Sprite or 7up
Preserved cherry

Steps:
1. Mix the vodka, rum, tequila, gin, blue curaçao, simple syrup, and lemon juice in a glass filled with ice.
2. Top the glass off with the Sprite or 7up and stir.
3. Garnish with the preserved cherry.
Question: What evaluation metrics did they look at?
Evidence: Los resultados de la evaluación se presentan en la Tabla TABREF42, en la forma de promedios normalizados entre [0,1] y de su desviación estándar $\sigma $.
Los resultados de la evaluación se presentan en la Tabla TABREF42, en la forma de promedios normalizados entre [0,1] y de su desviación estándar $\sigma $.
Answer: accuracy with standard deviation
Question: Identify which instrument is string or percussion: Tubular bells, Cuataro
Answer: Cuataro is string, Tubular bells is percussion.
Question: What is Project Mosaic?
Answer: Mosaic is an extension to the Apache Spark framework that allows easy and fast processing of very large geospatial datasets.
Mosaic provides:
-easy conversion between common spatial data encodings (WKT, WKB and GeoJSON);
-constructors to easily generate new geometries from Spark native data types;
-many of the OGC SQL standard ST_ functions implemented as Spark Expressions for transforming, aggregating and joining spatial datasets;
-high performance through implementation of Spark code generation within the core Mosaic functions;
-optimisations for performing point-in-polygon joins using an approach we co-developed with Ordnance Survey (blog post); and
-the choice of a Scala, SQL and Python API.
Question: What are state of the art methods authors compare their work with? 
Evidence: Table TABREF21 shows the performance of non-static capsule network for fake news detection in comparison to other methods. The accuracy of our model is 7.8% higher than the best result achieved by LSVM.
As mentioned in Section SECREF13, the LIAR dataset is a multi-label dataset with short news statements. In comparison to the ISOT dataset, the classification task for this dataset is more challenging. We evaluate the proposed model while using different metadata, which is considered as speaker profiles. Table TABREF30 shows the performance of the capsule network for fake news detection by adding every metadata. The best result of the model is achieved by using history as metadata. The results show that this model can perform better than state-of-the-art baselines including hybrid CNN BIBREF15 and LSTM with attention BIBREF16 by 3.1% on the validation set and 1% on the test set.
The accuracy of our model is 7.8% higher than the best result achieved by LSVM.
The results show that this model can perform better than state-of-the-art baselines including hybrid CNN BIBREF15 and LSTM with attention BIBREF16 by 3.1% on the validation set and 1% on the test set.
Answer: ISOT dataset: LLVM
Liar dataset: Hybrid CNN and LSTM with attention
Question: Which of these sports leagues are considered part of Big 4 Sports leagues in the United States: MLS, NFL, La Liga, IPL, NHL
Answer: The sports leagues considered part of Big 4 Sports leagues in the United States are the NFL and the NHL
Question: Which paired corpora did they use in the other experiment?
Evidence: We select a large-scale Chinese dataset BIBREF0 with millions of real comments and a human-annotated test set to evaluate our model. The dataset is collected from Tencent News, which is one of the most popular Chinese websites for news and opinion articles. The dataset consists of 198,112 news articles. Each piece of news contains a title, the content of the article, and a list of the users' comments. Following the previous work BIBREF0 , we tokenize all text with the popular python package Jieba, and filter out short articles with less than 30 words in content and those with less than 20 comments. The dataset is split into training/validation/test sets, and they contain 191,502/5,000/1,610 pieces of news, respectively. The whole dataset has a vocabulary size of 1,858,452. The average lengths of the article titles and content are 15 and 554 Chinese words. The average comment length is 17 words.
We analyze the performance of the proposed method under the semi-supervised setting. We train the supervised IR model with different numbers of paired data. Figure FIGREF39 shows the curve (blue) of the recall1 score. As expected, the performance grows as the paired dataset becomes larger. We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M). It shows that IR+Proposed can outperform the supervised IR model given the same paired dataset. It concludes that the proposed model can exploit the unpaired data to further improve the performance of the supervised model.
We select a large-scale Chinese dataset BIBREF0 with millions of real comments and a human-annotated test set to evaluate our model.
We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M).
Answer: The answers are shown as follows:
* Chinese dataset BIBREF0
Question: What type of chair would I want to design to give an option to lean all the way back?
Evidence: A chair is a type of seat, typically designed for one person and consisting of one or more legs, a flat or slightly angled seat and a back-rest. They may be made of wood, metal, or synthetic materials, and may be padded or upholstered in various colors and fabrics. Chairs vary in design. An armchair has armrests fixed to the seat; a recliner is upholstered and features a mechanism that lowers the chair's back and raises into place a footrest; a rocking chair has legs fixed to two long curved slats; and a wheelchair has wheels fixed to an axis under the seat.
Answer: As the name suggests, a recliner features a mechanism that lowers the chair’s back and raises a footrest into place
Question: What are Gummi Bears called in England?
Answer: Jelly Babies are what people call Gummi Bears in England
Question: Why practicing sport is important ?
Answer: Studies show that practicing sport regularly can reduce the risk of cancer and improve your overall health. You can practice what we call "neat", meaning you don't have to go for a swim or a run 1h a day, you can already be physically active by walking 30 minutes or 1 hour per day, by taking the stairs instead of the elevator, going shopping or house cleaning and so on.
Question: How are sub-lets traditionally they handled?
Answer: Many properties try to be as hands-off as possible when dealing with sub-lets and others go as far as to deter and make sub-letting as difficult as possible. No follow-up calls to replacement tenants, massive sub-let fees, and even trying to persuade replacement tenants to sign a new lease, with move-in specials. While all this may help your property in the short-run with maintaining and driving your current occupancy up; mishandling sub-lets can be detrimental to your property in the long run. Reputation and brand management are the most important factors in generating new leads and driving traffic to your property. Word-of-mouth reputation, online reviews, and social media platforms all suffer from the hands-off/no sub-letting mindset. No one wants to lease at an apartment that will try to keep him or her in their lease when they need to move back home to tend to a family emergency or transfer schools to better their education. Sub-lets will ALWAYS be a part of your property, regardless of how great your customer service may be.
Question: What's the best career advise you could give a new graduate entering into a technical or analytics field?
Answer: A key feature of any technical or analytics job is that you will be constantly challenged with new trying something new.  You will need to learn to learn, i.e. to teach yourself new techniques and new technology, and to have the courage to apply these things to complex challenges where it's not always clear that you have the right answer (and where very few if any people can fully validate your work).  For many people, the uncertainty and risk of failure associated with these situations tends to push them towards safe and known opportunities with well-tread paths for them to follow.  If you crave a fulfilling career, have the courage to pursue the paths less traveled, acknowledge and learn from your failures and surround yourself with others similarly willing to stretch and grow. You will find such choices lead to a more satisfying career over the longer term.