DataHammer/scimrc huggingface dataset:
Question: What are the three datasets used?
Evidence: We use three different datasets for training the models. We use the Dialogue State Tracking Competition 2 (DSTC2) dataset BIBREF27 which is the most widely used dataset for research on task-oriented chatbots. We also used two other datasets recently open-sourced by Google Research BIBREF28 which are M2M-sim-M (dataset in movie domain) and M2M-sim-R (dataset in restaurant domain). M2M stands for Machines Talking to Machines which refers to the framework with which these two datasets were created. In this framework, dialogues are created via dialogue self-play and later augmented via crowdsourcing. We trained on our models on different datasets in order to make sure the results are not corpus-biased. Table TABREF12 shows the statistics of these three datasets which we will use to train and evaluate the models.
We use the Dialogue State Tracking Competition 2 (DSTC2) dataset BIBREF27 which is the most widely used dataset for research on task-oriented chatbots.
 We also used two other datasets recently open-sourced by Google Research BIBREF28 which are M2M-sim-M (dataset in movie domain) and M2M-sim-R (dataset in restaurant domain). 
Answer: The answers are shown as follows:
* DSTC2
* M2M-sim-M
* M2M-sim-R
Question: Identify which animal species is alive or extinct: Giant Panda, Oviraptor
Answer: Oviraptor is extinct, Giant Panda is alive.
Question: I want to plan a vacation in summer. How do I know where I can go?
Answer: There are multiple steps to deciding what the best destination is. First, you need to think about how long you want to go on vacation. This will determine how much travel time will be adequate. Second, you need to think about what type of vacation you would like and when you want to go. Should it be a relaxing vacation with warm weather? Or do you want to go sightseeing in a city with mild temperature? Do you want to go hiking? Or skiing? Climate zones are very different, therefore it will depend on what kind of vacation you want to have. Third, you need to think about getting there. Do you want to be able to drive there by car or take the train? Or do you want to take a plane? Importantly, you also have to think about the budget for the overall vacation. Some destinations are more pricey than others. 
Following these steps will narrow down your list of potential vacation destinations.
Question: Why f1 drivers earn so much
Answer: Because of the risk factor and popularity of this sport
Question: Where can you find Lygodactylus gutturalis?
Evidence: Lygodactylus gutturalis, also known as the Uganda dwarf gecko or chevron-throated dwarf gecko, is a species of gecko. It is widely distributed in Sub-Saharan Africa from near the Equator northward. Subspecies Lygodactylus gutturalis dysmicus is endemic to Cameroon, and subspecies Lygodactylus gutturalis paurospilus to Tanzania.
Answer: Lygodactylus gutturalis is a species of gecko commonly found in Sub-Saharan Africa from near the Equator northward.
Question: What does the author do to eliminate class imbalance?
Evidence: The training datasets had a major problem of class imbalance with posts marked as bullying in the minority. As a result, all models were biased towards labeling the posts as non-bullying. To remove this bias, we oversampled the data from bullying class thrice. That is, we replicated bullying posts thrice in the training data. This significantly improved the performance of all DNN models with major leap in all three evaluation measures. Table TABREF17 shows the effect of oversampling for a variety of word embedding methods with BLSTM Attention as the detection model. Results for other models are similar BIBREF15 . We can notice that oversampled datasets (F+, T+, W+) have far better performance than their counterparts (F, T, W respectively). Oversampling particularly helps the smallest dataset Formspring where number of training instances for bullying class is quite small (825) as compared to other two datasets (about 5K and 13K). We also experimented with varying the replication rate for bullying posts BIBREF15 . However, we observed that for bullying posts, replication rate of three is good enough.
The training datasets had a major problem of class imbalance with posts marked as bullying in the minority. As a result, all models were biased towards labeling the posts as non-bullying. To remove this bias, we oversampled the data from bullying class thrice. That is, we replicated bullying posts thrice in the training data.
Answer: They oversampled the data from bullying class thrice. That is, they replicated bullying posts thrice in the training data.
Question: Are resolution mode variables hand crafted?
Evidence: According to previous work BIBREF17 , BIBREF18 , BIBREF1 , antecedents are resolved by different categories of information for different mentions. For example, the Stanford system BIBREF1 uses string-matching sieves to link two mentions with similar text and precise-construct sieve to link two mentions which satisfy special syntactic or semantic relations such as apposition or acronym. Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n\rbrace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr\rbrace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes — string-matching (str), precise-construct (prec), and attribute-matching (attr) — and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:
$\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .
$\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.
$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions.
Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n\rbrace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr\rbrace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes — string-matching (str), precise-construct (prec), and attribute-matching (attr) — and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:

$\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .

$\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.

$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions.
Answer: No.
Question: What are some great comets named after people?
Answer: Great comets named after people: 
- Aristotle's Comet
- Caesar's Comet
- Donati's Comet
- Halley's Comet
- Comet Skjellerup–Maristany
- Comet Arend–Roland
- Comet Mrkos
- Comet Ikeya–Seki
- Comet Bennett
- Comet Kohoutek
- Comet West
- Comet Hyakutake
- Comet Hale–Bopp
- Comet McNaught
- Comet Lovejoy
Question: Who adopted Superman as per the passage?
Evidence: Superman was born on the fictional planet Krypton and was named Kal-El. When he was a baby, his parents sent him to Earth in a small spaceship moments before Krypton was destroyed in a natural cataclysm. His ship landed in the American countryside, near the fictional town of Smallville. He was found and adopted by farmers Jonathan and Martha Kent, who named him Clark Kent. Clark developed various superhuman abilities, such as incredible strength and impervious skin. His adoptive parents advised him to use his abilities for the benefit of humanity, and he decided to fight crime. To protect his personal life, he changes into a colorful costume and uses the alias "Superman" when fighting crime. Clark resides in the fictional American city of Metropolis, where he works as a journalist for the Daily Planet. Superman's supporting characters include his love interest and fellow journalist Lois Lane, Daily Planet photographer Jimmy Olsen and editor-in-chief Perry White, and enemies such as Brainiac, General Zod, and his archenemy Lex Luthor.
Answer: Jonathan and Martha Kent
Question: Give me a bulleted list of the five biggest cities in Germany
Answer: Here is a list of the largest cities by population

1. Berlin: This is the capital of Germany and one of the prominent artists and cultural hubs of Europe.
2. Hamburg: This port city, situated on the North Sea, is one of the largest non-capital cities in the EU.
3. Munich: It is the capital of the famous state of Bavaria and home to the famous BMW and Oktoberfest.
4. Cologne: Famous for the cathedral, it is the research hub of various aerospace industries.
5. Frankfurt: It is also referred to as the financial capital of Germany and a prominent banking hub not only in Germany but for the rest of the world.
Question: What woodwind instruments are popular to learn?
Answer: Flute, Clarinet and Saxophone are popular woodwind instruments. Oboe and Bassoon are interesting but less popular.
Question: Do transferring hurt the performance is the corpora are not related?
Evidence: We also performe another experiment to examine INIT and MULT method for original WikiQA. The F1-score for this dataset is equal to 33.73; however, the average INIT result for both SQuAD and SelQA as initializers is 30.50. In addition, the average results for MULT and ISS-MULT are 31.76 and 32.65, respectively. The result on original WikiQA indicates that all three transfer learning methods not only do not improve the results but also hurt the F1-score. Therefore, SelQA and SQuAD could not estimate a proper initial point for gradient based optimization method. Moreover, these corpora could not refine the error surface of the original WikiQA dataset during optimization for MULT and ISS-MULT method.
These are because other datasets could not add new information to the original dataset or they apparently add some redundant information which are dissimilar to the target dataset. Although ISS-MULT tries to remove this effect and consequently the result is improved, this method is on top of MULT method, and the result is significantly based on the effectiveness of this method.
The result on original WikiQA indicates that all three transfer learning methods not only do not improve the results but also hurt the F1-score.
These are because other datasets could not add new information to the original dataset or they apparently add some redundant information which are dissimilar to the target dataset.
Answer: Yes.
Question: What is a database schema?
Evidence: The database schema is the structure of a database described in a formal language supported by the database management system (DBMS). The term "schema" refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases). The formal definition of a database schema is a set of formulas (sentences) called integrity constraints imposed on a database.[citation needed] These integrity constraints ensure compatibility between parts of the schema. All constraints are expressible in the same language. A database can be considered a structure in realization of the database language. The states of a created conceptual schema are transformed into an explicit mapping, the database schema. This describes how real-world entities are modeled in the database.
Answer: It is the structure of a database -- as described in a formal language supported by the database management system. It refers to the organization of data as a blueprint for how the database is built (eg comprised of tables in the case of relational databases).
Question: Is the method described in this work a clustering-based method?
Evidence: The task of Word Sense Induction (WSI) can be seen as an unsupervised version of WSD. WSI aims at clustering word senses and does not require to map each cluster to a predefined sense. Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense of a word. WSI approaches fall into three main groups: context clustering, word ego-network clustering and synonyms (or substitute) clustering.
We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such “anti-edges”. Thus, our algorithm is based on graph-based word sense induction, but it also relies on vector-based operations between word embeddings to perform filtering of graph nodes. Analogously to the work of Pelevina:16, we construct a semantic relatedness graph from a list of nearest neighbours, but we filter this list using the following procedure:
The task of Word Sense Induction (WSI) can be seen as an unsupervised version of WSD. WSI aims at clustering word senses and does not require to map each cluster to a predefined sense. Instead of that, word sense inventories are induced automatically from the clusters, treating each cluster as a single sense of a word.
We suggest a more advanced procedure of graph construction that uses the interpretability of vector addition and subtraction operations in word embedding space BIBREF6 while the previous algorithm only relies on the list of nearest neighbours in word embedding space. The key innovation of our algorithm is the use of vector subtraction to find pairs of most dissimilar graph nodes and construct the graph only from the nodes included in such “anti-edges”.
Answer: Yes.
Question: Explain what is April Fools' Day?
Answer: - In the Western history, April Fools’ Day occurs on April 1 every year. It has been celebrated for more than 400 years
- The exact origins of the Day remains a mystery, though many historians attribute the origin of April Fool's Day to medieval festivals during spring
- The traditions of the Day include playing pranks on others or telling audience jokes followed by yelling "April Fools" at the end to reveal to the audience that they were tricked
Question: What metric do they use for every evaluated scheme?
Evidence: To produce results in a setup comparable with the current state of the art BIBREF15 , we performed 10-fold cross validation and calculated the Precision,Recall and F-Score for every evaluated scheme. We randomly split each training fold into 15% validation and 85% training, while performance is evaluated over the remaining fold of unseen data. The model was implemented using Keras. We used categorical cross-entropy as learning objective, and selected the ADAM optimization algorithm BIBREF18 . Furthermore, the vocabulary size was set to 25000, and the batch-size during training was set to 500.
To avoid over-fitting, the model training was allowed to run for a maximum number of 100 epochs, out of which the optimally trained state was chosen for the model evaluation. An optimal epoch was identified so, such that the validation accuracy was maximized, while at the same time the error remained within INLINEFORM0 of the lowest ever figure within the current fold. Throughout the experiment we observed that the optimal epochs typically occurred after between the 30 and 40 epochs.
To achieve stability in the results produced, we ran every single classifier for 15 times and the output values were aggregated. In addition, the output from each single classifier run was combined with the output from another two single classifiers to build the input of an ensemble, producing INLINEFORM0 combinations. For the case of the ensemble that incorporates all five classifiers we restricted to using the input by only the first five runs of the single classifiers ( INLINEFORM1 combinations). That was due to the prohibitively very large number of combinations that were required.
To produce results in a setup comparable with the current state of the art (Badjatiya et al., 2017), we performed 10-fold cross validation and calculated the Precision,Recall and F-Score for every evaluated scheme.
Answer: Precision,Recall and F-Score.
Question: How is the domain knowledge transfer represented as knowledge graph?
Evidence: The agent also has access to all actions accepted by the game's parser, following BIBREF2. For general interactive fiction environments, we develop our own method to extract this information. This is done by extracting a set of templates accepted by the parser, with the objects or noun phrases in the actions replaces with a OBJ tag. An example of such a template is "place OBJ in OBJ". These OBJ tags are then filled in by looking at all possible objects in the given vocabulary for the game. This action space is of the order of $A=\mathcal {O}(|V| \times |O|^2)$ where $V$ is the number of action verbs, and $O$ is the number of distinct objects in the world that the agent can interact with. As this is too large a space for a RL agent to effectively explore, the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7
This action space is of the order of $A=\mathcal {O}(|V| \times |O|^2)$ where $V$ is the number of action verbs, and $O$ is the number of distinct objects in the world that the agent can interact with. As this is too large a space for a RL agent to effectively explore, the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7
Answer: The answers are shown as follows:
* the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7
Question: Given a reference text about the blackbuck, tell me how big the males are.
Evidence: The blackbuck (Antilope cervicapra), also known as the Indian antelope, is an antelope native to India and Nepal. It inhabits grassy plains and lightly forested areas with perennial water sources. It stands up to 74 to 84 cm (29 to 33 in) high at the shoulder. Males weigh 20–57 kg (44–126 lb), with an average of 38 kg (84 lb). Females are lighter, weighing 20–33 kg (44–73 lb) or 27 kg (60 lb) on average. Males have 35–75 cm (14–30 in) long corkscrew horns, and females occasionally develop horns, as well. The white fur on the chin and around the eyes is in sharp contrast with the black stripes on the face. Both sexes’ coats feature a two-tone colouration; in males, the majority of the body is dark brown to black, with white circles around the eyes, white ears and tail, and the belly, lower jaw, and inner legs also white. Females and juveniles are yellowish-fawn to tan and display the same white areas, only with more of a beige tone than the males. Females also feature a more pronounced horizontal white side-stripe, starting around the shoulder and ending at the rump. The blackbuck is the sole living member of the genus Antilope and was scientifically described by Carl Linnaeus in 1758. Two subspecies are recognized.
Answer: Male blackbuck range from 44 to 126 pounds, are 29 to 33 inches tall, and have 14 to 30 inch long corkscrew horns.
Question: What kind of data is used for low resource languages?
Evidence: Our approach to learn the initial foreign word embeddings $_f \in ^{|V_f| \times d}$ is based on the idea of mapping the trained English word embeddings $_e \in ^{|V_e| \times d}$ to $_f$ such that if a foreign word and an English word are similar in meaning then their embeddings are similar. Borrowing the idea of universal lexical sharing from BIBREF11, we represent each foreign word embedding $_f[i] \in ^d$ as a linear combination of English word embeddings $_e[j] \in ^d$
where $_i\in ^{|V_e|}$ is a sparse vector and $\sum _j^{|V_e|} \alpha _{ij} = 1$.
In this step of initializing foreign embeddings, having a good estimation of $$ could speed of the convergence when tuning the foreign model and enable zero-shot transfer (§SECREF5). In the following, we discuss how to estimate $_i\;\forall i\in \lbrace 1,2, \dots , |V_f|\rbrace $ under two scenarios: (i) we have parallel data of English-foreign, and (ii) we only rely on English and foreign monolingual data.
Given an English-foreign parallel corpus, we can estimate word translation probability $p(e\,|\,f)$ for any (English-foreign) pair $(e, f)$ using popular word-alignment BIBREF12 toolkits such as fast-align BIBREF13. We then assign:
Since $_i$ is estimated from word alignment, it is a sparse vector.
For low resource languages, parallel data may not be available. In this case, we rely only on monolingual data (e.g., Wikipedias). We estimate word translation probabilities from word embeddings of the two languages. Word vectors of these languages can be learned using fastText BIBREF14 and then are aligned into a shared space with English BIBREF15, BIBREF16. Unlike learning contextualized representations, learning word vectors is fast and computationally cheap. Given the aligned vectors $\bar{}_f$ of foreign and $\bar{}_e$ of English, we calculate the word translation matrix $\in ^{|V_f|\times |V_e|}$ as
Here, we use $\mathrm {sparsemax}$ BIBREF17 instead of softmax. Sparsemax is a sparse version of softmax and it puts zero probabilities on most of the word in the English vocabulary except few English words that are similar to a given foreign word. This property is desirable in our approach since it leads to a better initialization of the foreign embeddings.
For low resource languages, parallel data may not be available. In this case, we rely only on monolingual data (e.g., Wikipedias). We estimate word translation probabilities from word embeddings of the two languages.
Answer: Monolingual data (e.g., Wikipedias).