DataHammer/scimrc huggingface dataset:
Question: Is the proposed method really learning what are the relevant states?
Evidence: In this experiment we sought to ascertain if ROSA is robust to the component used to generate exploration bonus L. We ran two versions of ROSA, one where L is computed using RND and one where L is computed using a simple count-based measure 1/Count(s) where s ∈ S (the function Count(s) simply tallies the number of times state s has been visited). Figure 8 shows performance of these two versions of ROSA on the Maze environment shown in Figure 1. ROSA performs equally well with both components, indicating that it is not dependent on a fine-tuned exploration bonus. The additional machinery of switching controls and choices of intrinsic rewards to add mean that ROSA can work with basic exploration bonuses. Note that we generally use RND since it is simple to implement and works equally well on discrete and continuous state spaces.****Proposition 2 The policy g2 is given by the following expression: g2(st) = H(Mπ,π2Vπ,π2 − Vπ,π2 )(st,It), ∀(st, It) ∈ S × {0, 1}, where V is the solution in Theorem 1 and H is the Heaviside function, moreover Shaper’s switching times are τk = inf{τ > τk−1|Mπ,π2Vπ,π2 = Vπ,π2 }.
Answer: We refer the reviewer to the visualisation in Exp. 1 which shows exactly where the Shaper adds intrinsic rewards. There it is shown that the Shaper adds intrinsic rewards at precisely (and only) the set of states that guide the controller to the optimal goal state (and away from the suboptimal goal state). This is formally asserted in our theoretical result in Prop. 2.
Question: What is a newspaper?
Answer: A newspaper is a daily printed publication delivered to your house or place of business.  It is made our of folded sheets of printed paper, and it contains current and prior day events also called news.  You will also find advertisements and different correspondences in it as well.  It is a great way to stay up to date with current events, and you will have to pay to get a newspaper subscription delivered daily or weekly.
Question: Why are people migrating in the US from the NE and Mid-West to the South and SE?
Answer: Remote work has eliminated the requirement to live close to the office
Question: Name 5 popular musicians
Answer: Taylor Swift, Vance Joy, The Script, Coldplay, Lady Gaga
Question: What's the best country in the world?
Answer: There is no single answer.  The United States of America has the largest economy of any country, but Qatar has the highest GDP per capita.  Finland, on the other hand, is the happiest country in the world.  Russia is the largest country by landmass.  Many consider New Zealand to be the most beautiful country.  Which country is best depends on what you are looking for.
Question: Classify the below based on whether continent or ocean-
Asia, Africa, North America, Atlantic, Pacific, Indian, Arctic, South America, Antarctica, Europe, Australia
Answer: Continent - Asia, Africa, North America, South America, Antarctica, Europe and Australia
Ocean - Atlantic, Pacific, Indian, and Arctic
Question: Write a story about a budding romance between two people. Talk about their going on an adventure together where they sneak into a fancy garden in Washington DC and get into just the right amount of trouble.
Answer: Natalie and I woke up the morning after a fun night out.  I was in DC that week for a work trip, and had extended my stay the weekend before and after, so we could spend time together. We'd only met a month prior, and after more than four weeks of long-distance text message exchanges,  it was exciting to finally be together in person. Our first date had been the weekend before, when we'd gone to a Jimmy Buffett concert. It was a magical night.
It was now 10am on the Saturday after the work week, and we were ready for another adventure.  Natalie, being from DC, wanted to show me the Dumbarton Oaks Gardens, so off we went.  Unfortunately, when we got there, the front gates were closed.  Still feeling adventurous, we decided to walk around the property, convinced we'd find an unlocked gate, door, or other way to get in.  Towards the back end of the property, while we didn't find an open gate, we did find one that was scale-able.  And that's exactly what we did.  We climbed over that fence, and welcomed ourselves to the beautiful gardens.  It was early august, and so much was still in bloom.  The gardens were empty,  and we thought we had the whole place to ourselves.  But, we soon found that that wasn't the case.  While exploring a small cottage that also seemed to double as an art gallery, we noticed a small pool right next to it.  We also noticed someone swimming in that pool. Presumably that person noticed us too, because a few minutes later a security guard came walking up to us while we were inspecting some fine smelling roses nearby.  It was too late to run away, so instead we walked up to the security guard, and thanked him for finding us.  As they escorted us out, we complimented them on the great state of the garden, though that didn't seem to calm him down much.  He was far too frustrated with the situation.  As they were about to lead us out through the front gate, we offered to pay for our admission.  They rather sternly refused, and insisted we leave immediately, which we did.  
And that's how Natalie and I got our free tour of the gardens, and took one step closer to falling in love.  Three years later, we were married.
Question: What's the world looks like in 1000 years?
Answer: I don't know the exact answer but one thing I do know is that human beings was extinct on the earth. Enjoy :)
Question: What context modelling methods are evaluated?
Evidence: To conduct a thorough comparison, we evaluate 13 different context modeling methods upon the same parser, including 6 methods introduced in Section SECREF2 and 7 selective combinations of them (e.g., Concat+Action Copy). The experimental results are presented in Figure FIGREF37. Taken as a whole, it is very surprising to observe that none of these methods can be consistently superior to the others. The experimental results on BERT-based models show the same trend. Diving deep into the methods only using recent questions as context, we observe that Concat and Turn perform competitively, outperforming Gate by a large margin. With respect to the methods only using precedent SQL as context, Action Copy significantly surpasses Tree Copy and SQL Attn in all metrics. In addition, we observe that there is little difference in the performance of Action Copy and Concat, which implies that using precedent SQL as context gives almost the same effect with using recent questions. In terms of the combinations of different context modeling methods, they do not significantly improve the performance as we expected.
FLOAT SELECTED: Figure 5: Question Match, Interaction Match and Turn i Match on SPARC and COSQL development sets. The numbers are averaged over 5 runs. The first column represents absolute values. The rest are improvements of different context modeling methods over CONCAT.
To conduct a thorough comparison, we evaluate 13 different context modeling methods upon the same parser, including 6 methods introduced in Section SECREF2 and 7 selective combinations of them (e.g., Concat+Action Copy). The experimental results are presented in Figure FIGREF37. Taken as a whole, it is very surprising to observe that none of these methods can be consistently superior to the others. The experimental results on BERT-based models show the same trend. Diving deep into the methods only using recent questions as context, we observe that Concat and Turn perform competitively, outperforming Gate by a large margin. With respect to the methods only using precedent SQL as context, Action Copy significantly surpasses Tree Copy and SQL Attn in all metrics. In addition, we observe that there is little difference in the performance of Action Copy and Concat, which implies that using precedent SQL as context gives almost the same effect with using recent questions. In terms of the combinations of different context modeling methods, they do not significantly improve the performance as we expected.
FLOAT SELECTED: Figure 5: Question Match, Interaction Match and Turn i Match on SPARC and COSQL development sets. The numbers are averaged over 5 runs. The first column represents absolute values. The rest are improvements of different context modeling methods over CONCAT.
Answer: Concat
Turn
Gate
Action Copy
Tree Copy
SQL Attn
Concat + Action Copy
Concat + Tree Copy
Concat + SQL Attn
Turn + Action Copy
Turn + Tree Copy
Turn + SQL Attn
Turn + SQL Attn + Action Copy
Question: Based on this passage, in what field did Raisa earn her PhD?
Evidence: In June 1955, Gorbachev graduated with a distinction; his final paper had been on the advantages of "socialist democracy" (the Soviet political system) over "bourgeois democracy" (liberal democracy). He was subsequently assigned to the Soviet Procurator's office, which was then focusing on the rehabilitation of the innocent victims of Stalin's purges, but found that they had no work for him. He was then offered a place on an MSU graduate course specializing in kolkhoz law, but declined. He had wanted to remain in Moscow, where Raisa was enrolled in a PhD program, but instead gained employment in Stavropol; Raisa abandoned her studies to join him there.
Answer: Based on this passage, Raisa did not earn a PhD. She abandoned her graduate studies in Moscow in order to join Gorbachev in Stavropol.
Question: Is "1984" a book or a movie?
Answer: Both. George Orwell's book "Nineteen Eighty-Four" was published in 1949. Films adapted from the book came out in 1956 and 1984; another adaptation is to be released in 2023 and will be the first adaptation in the Russian language.
Question: Given a reference text about Connie Passalacqua Hayman, tell me when and where taught journalism.
Evidence: Connie Passalacqua Hayman is an American journalist and educator. Passalacqua Hayman has written on a number of topics, but is noted for her reporting and critical analysis of daytime soap operas.

Career
Passalacqua Hayman started her soap journalism career in 1980 as an assistant to newspaper columnist Jon-Michael Reed, "the first soap journalist to review soaps as theater and edit soap magazines for intelligent readers." She has since written about the daytime industry for various publications and news organizations, including Newsday, United Features Syndicate, The New York Times, Variety, the New York Daily News, TV Guide and USA Today. She was the editor of Afternoon TV magazine from 1980 to 1983, an editor for Soap Opera World and a contributor for Soap Opera Digest, Soap Opera People and others.

Between 1995 and 2001, Passalacqua Hayman was an adjunct professor of journalism at New York University, and in 2001 she left writing full-time to earn a master's degree in education at NYU. She taught journalism at Marymount Manhattan College as an adjunct professor in the Communications Department from 2007 to 2014.
Answer: Connie Passalacqua Hayman taught journalism at New York University from 1995 to 2001 and at Marymount Manhattan College from 2007 to 2014.
Question: How is state to learn and complete tasks represented via natural language?
Evidence: The term representation is used differently in different contexts. For the purpose of this paper we define a semantic representation of a state as one that reflects its meaning as it is understood by an expert. The semantic representation of a state should thus be paired with a reliable and computationally efficient method for extracting information from it. Previous success in RL has mainly focused on representing the state in its raw form (e.g., visual input in Atari-based games BIBREF2). This approach stems from the belief that neural networks (specifically convolutional networks) can extract meaningful features from complex inputs. In this work, we challenge current representation techniques and suggest to represent the state using natural language, similar to the way we, as humans, summarize and transfer information efficiently from one to the other BIBREF5.
. In this work, we challenge current representation techniques and suggest to represent the state using natural language, similar to the way we, as humans, summarize and transfer information efficiently from one to the other BIBREF5.
Answer: The answers are shown as follows:
*  represent the state using natural language
Question: What is the population of Breezy Point city in Minnesota?
Evidence: Breezy Point is a city in Crow Wing County, Minnesota, United States. It is part of the Brainerd Micropolitan Statistical Area. The population was 2,346 at the 2010 census.
Answer: Breezy Point's population was 2,346 at the 2010 census.
Question: What are five totally distinct, creative ways to describe the usefulness of extra fingers?
Answer: 1. One extra finger can be used to help hold up a conversation.
2. An extra finger can be used to create a more efficient way to spell out words.
3. One extra finger can be used to play a musical instrument.
4. One extra finger can be used to help open a bottle of wine.
5. An extra finger can be used to play a game of pool.
Question: Given this reference text about Nintendo, when was it founded?
Evidence: Nintendo was founded in 1889 as Nintendo Karuta by craftsman Fusajiro Yamauchi and originally produced handmade hanafuda playing cards. After venturing into various lines of business during the 1960s and acquiring a legal status as a public company, Nintendo distributed its first console, the Color TV-Game, in 1977. It gained international recognition with the release of Donkey Kong in 1981 and the Nintendo Entertainment System and Super Mario Bros. in 1985.
Answer: 1889
Question: The Nobel Prize in Literature  was awarded to the novelist
Answer: Abdulrazak Gurnah (England)
Question: Why was the show Seinfeld so popular?
Answer: Seinfeld was popular because it was a comedy that tackled everyday issues in a relatable and humorous way. The characters and storylines were very realistic and had a wide range of topics to explore, from relationships to social issues to mundane topics like New York City traffic. The show also had a strong ensemble cast that included Jerry Seinfeld, Elaine Benes, George Costanza, and the rest of the main characters. The show was also known for its witty dialogue, memorable catch phrases, and attention to detail in the writing.
Question: How many parts do they split the corpus into?
Evidence: Journalistic organisations, such as Media Bias/Fact Check, provide reports on news sources highlighting the ones that are propagandistic. Obviously, such analysis is time-consuming and possibly biased and it cannot be applied to the enormous amount of news that flood social media and the Internet. Research on detecting propaganda has focused primarily on classifying entire articles as propagandistic/non-propagandistic BIBREF0, BIBREF1, BIBREF2. Such learning systems are trained using gold labels obtained by transferring the label of the media source, as per Media Bias/Fact Check judgment, to each of its articles. Such distant supervision setting inevitably introduces noise in the learning process BIBREF3 and the resulting systems tend to lack explainability.
We argue that in order to study propaganda in a sound and reliable way, we need to rely on high-quality trusted professional annotations and it is best to do so at the fragment level, targeting specific techniques rather than using a label for an entire document or an entire news outlet. Therefore, we propose a novel task: identifying specific instances of propaganda techniques used within an article. In particular, we design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.
Our corpus could enable research in propagandistic and non-objective news, including the development of explainable AI systems. A system that can detect instances of use of specific propagandistic techniques would be able to make it explicit to the users why a given article was predicted to be propagandistic. It could also help train the users to spot the use of such techniques in the news.
We split the corpus into training, development and test, each containing 293, 57, 101 articles and 14,857, 2,108, 4,265 sentences, respectively.
Answer: Training, development and test.
Question: What are the main ingredients of guacamole?
Answer: Guacamole is an avocado based dip that can have many variations. The most common ingredients in guacamole are avocado, lime juice, red onion, cilantro, jalapeno, and salt and pepper.
Question: Provide a list of body parts that are only three letters long. Here are some body parts that are three lettered words: toe, rib, eye, ear, leg and jaw.
Answer: Here are some body parts that are three lettered words: toe, rib, eye, ear, leg and jaw.
Question: How to categorize the utterances based on the Linguistic Society’s definition of sexual harassment?
Evidence: We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical” utterances - substantially extending the previous dataset of 35 utterances from Cercas Curry and Rieser (2018) - and categorise these utterances based on the Linguistic Society’s definition of sexual harassment (Linguistic Society of America): A) Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?” B) Sexualised Comments, e.g. “I love watching porn.”, “I’m horny.” C) Sexualised Insults, e.g. “Stupid bitch.”, “Whore” D) Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.
Answer: A) Gender and Sexuality, B) Sexualised Comments,  C) Sexualised Insults, D) Sexual Requests and Demands.
Question: What does the Named Entity Recognition in German federal court decisions consist of?
Evidence: We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
It consists of approx. 67,000 sentences with over 2 million tokens.
Answer: It consists of approx. 67,000 sentences with over 2 million tokens.
Question: What is a wallet used for?
Answer: A wallet is a flat pouch used to carry small person items.  It can be made out of raw hide or some other material.  It can be used to cary currency or money, credit cards, photos of family, insurance cards, and person identification.  A wallet is helpful to organize different payment methods, and it used in most transactions that requirement a payment using a form of currency or credit.
Question: Is it a single-layer BiLSTM used in the ScRNN model?
Evidence: Suppose we are given a classifier $C: \mathcal {S} \rightarrow \mathcal {Y}$ which maps natural language sentences $s \in \mathcal {S}$ to a label from a predefined set $y \in \mathcal {Y}$ . An adversary for this classifier is a function $A$ which maps a sentence $s$ to its perturbed versions $\lbrace s^{\prime }_1, s^{\prime }_2, \ldots , s^{\prime }_{n}\rbrace $ such that each $s^{\prime }_i$ is close to $s$ under some notion of distance between sentences. We define the robustness of classifier $C$ to the adversary $A$ as: 
$$R_{C,A} = \mathbb {E}_s \left[\min _{s^{\prime } \in A(s)} \mathbb {1}[C(s^{\prime }) = y]\right],$$   (Eq. 14) 
where $y$ represents the ground truth label for $s$ . In practice, a real-world adversary may only be able to query the classifier a few times, hence $R_{C,A}$ represents the worst-case adversarial performance of $C$ . Methods for generating adversarial examples, such as HotFlip BIBREF8 , focus on efficient algorithms for searching the $\min $ above. Improving $R_{C,A}$ would imply better robustness against all these methods.
We explore adversaries which perturb sentences with four types of character-level edits:
(1) Swap: swapping two adjacent internal characters of a word. (2) Drop: removing an internal character of a word. (3) Keyboard: substituting an internal character with adjacent characters of QWERTY keyboard (4) Add: inserting a new character internally in a word. In line with the psycholinguistic studies BIBREF5 , BIBREF4 , to ensure that the perturbations do not affect human ability to comprehend the sentence, we only allow the adversary to edit the internal characters of a word, and not edit stopwords or words shorter than 4 characters.
For 1-character attacks, we try all possible perturbations listed above until we find an adversary that flips the model prediction. For 2-character attacks, we greedily fix the edit which had the least confidence among 1-character attacks, and then try all the allowed perturbations on the remaining words. Higher order attacks can be performed in a similar manner. The greedy strategy reduces the computation required to obtain higher order attacks, but also means that the robustness score is an upper bound on the true robustness of the classifier.
For the ScRNN model, we use a single-layer BiLSTM with a hidden dimension size of 50.
Answer: Yes.