DataHammer/scimrc huggingface dataset:
Question: Based on the given text, do beavers eat wood or do they only chop down trees for dams?
Evidence: Beavers (genus Castor) are large, semiaquatic rodents of the Northern Hemisphere. There are two existing species: the North American beaver (Castor canadensis) and the Eurasian beaver (C. fiber). Beavers are the second-largest living rodents, after capybaras, weighing up to 50 kg (110 lb). They have stout bodies with large heads, long chisel-like incisors, brown or gray fur, hand-like front feet, webbed back feet, and tails that are flat and scaly. The two species differ in skull and tail shape and fur color. Beavers can be found in a number of freshwater habitats, such as rivers, streams, lakes and ponds. They are herbivorous, consuming tree bark, aquatic plants, grasses and sedges.

Beavers build dams and lodges using tree branches, vegetation, rocks and mud; they chew down trees for building material. Dams restrict water flow, and lodges serve as shelters. Their infrastructure creates wetlands used by many other species, and because of their effect on other organisms in the ecosystem, beavers are considered a keystone species.
Answer: Beavers are herbivorous and are known to consume tree bark as part of their diet. However, beavers often build dams and lodges using fallen tree branches and other vegetation that they chew with their teeth in order to make materials for restricting water flow and shelter.
Question: What is the architecture of the siamese neural network?
Evidence: To further distinguish speech from different Arabic dialects, while making speech from the same dialect more similar, we adopted a Siamese neural network architecture BIBREF24 based on an i-vector feature space. The Siamese neural network has two parallel convolutional networks, INLINEFORM0 , that share the same set of weights, INLINEFORM1 , as shown in Figure FIGREF5 (a). Let INLINEFORM2 and INLINEFORM3 be a pair of i-vectors for which we wish to compute a distance. Let INLINEFORM4 be the label for the pair, where INLINEFORM5 = 1 if the i-vectors INLINEFORM6 and INLINEFORM7 belong to same dialect, and INLINEFORM8 otherwise. To optimize the network, we use a Euclidean distance loss function between the label and the cosine distance, INLINEFORM9 , where INLINEFORM10
The Siamese neural network has two parallel convolutional networks, INLINEFORM0 , that share the same set of weights, INLINEFORM1 , as shown in Figure FIGREF5 (a).
Answer: The answers are shown as follows:
* two parallel convolutional networks, INLINEFORM0 , that share the same set of weights
Question: For following Apps in Apple Store, help me classify them into different categories, categories include Business, Developer Tools, Education, Games, Music. 
Here is the App list: Slack, Microsoft Excel, WPS Office, Apple Developer, Xcode, CodeRunner 4, Termius -SSH&SFTP client, Kahoot, Duolingo, Complete Anatomy 23, SimCity, Civilization VI, NBA 2k23, Angry Birds, GarageBand, Spotify, QQ Music
Answer: Here is the classification result based on the provided categories:

For Business: Slack, Microsoft Excel, WPS Office
For Developer Tools: Apple Developer, Xcode, CodeRunner 4, Termius -SSH&SFTP client
For Education: Kahoot, Duolingo, Complete Anatomy 23
For Games: SimCity, Civilization VI, NBA 2k23, Angry Birds
For Music: GarageBand, Spotify, QQ Music
Question: How do they condition the output to a given target-source class?
Evidence: Methods ::: Length Token Method
Our first approach to control the length is inspired by target forcing in multilingual NMT BIBREF15, BIBREF16. We first split the training sentence pairs into three groups according to the target/source length ratio (in terms of characters). Ideally, we want a group where the target is shorter than the source (short), one where they are equally-sized (normal) and a last group where the target is longer than the source (long). In practice, we select two thresholds $t_\text{min}$ and $t_\text{max}$ according to the length ratio distribution. All the sentence pairs with length ratio between $t_\text{min}$ and $t_\text{max}$ are in the normal group, the ones with ratio below $t_\text{min}$ in short and the remaining in long. At training time we prepend a length token to each source sentence according to its group ($<$short$>$, $<$normal$>$, or $<$long$>$), in order to let a single network to discriminate between the groups (see Figure FIGREF2). At inference time, the length token is used to bias the network to generate a translation that belongs to the desired length group.
Methods ::: Length Token Method
Our first approach to control the length is inspired by target forcing in multilingual NMT BIBREF15, BIBREF16. We first split the training sentence pairs into three groups according to the target/source length ratio (in terms of characters). Ideally, we want a group where the target is shorter than the source (short), one where they are equally-sized (normal) and a last group where the target is longer than the source (long). In practice, we select two thresholds $t_\text{min}$ and $t_\text{max}$ according to the length ratio distribution. All the sentence pairs with length ratio between $t_\text{min}$ and $t_\text{max}$ are in the normal group, the ones with ratio below $t_\text{min}$ in short and the remaining in long. At training time we prepend a length token to each source sentence according to its group ($<$short$>$, $<$normal$>$, or $<$long$>$), in order to let a single network to discriminate between the groups (see Figure FIGREF2). At inference time, the length token is used to bias the network to generate a translation that belongs to the desired length group.
Answer: They use three groups short/normal/long translation classes to learn length token, which is in inference used to bias network to generate desired length group.
Question: Have you ever seen the Milky Way?
Answer: I have seen the Milky Way on a few occasions.  Its usually on a clear, moonless night when I am in a remote location.  For some reason, it is brighter when the air is colder. I don't get to see the MIlky Way often, but when I do, I am always amazed by its beauty.
Question: Who was the first woman to finish the Boston Marathon?
Answer: Roberta Gibbs ran the 1966 Boston Marathon and was the first woman to finish it. At the time, women were not qualified to run in marathons, so she dressed up as a man and waited in the sidelines until about half the runners had already started then jumped in to join the pack.
Question: How was annotation done?
Evidence: We provide a hand-curated collection of complete inflection tables for 198 lemmata. The morphological tags follow the guidelines of the UniMorph schema BIBREF6, BIBREF7, in order to allow for the potential of cross-lingual transfer learning, and they are tagged with respect to:
Person: first (1), second (2), and third (3)
Number: singular (SG) ad plural (PL)
Inclusivity (only applicable to first person plural verbs: inclusive (INCL) and exclusive (EXCL)
Aspect/mood: completive (CPL), progressive (PROG), potential (POT), and habitual (HAB).
We provide a hand-curated collection of complete inflection tables for 198 lemmata. The morphological tags follow the guidelines of the UniMorph schema BIBREF6, BIBREF7, in order to allow for the potential of cross-lingual transfer learning, and they are tagged with respect to:

Person: first (1), second (2), and third (3)

Number: singular (SG) ad plural (PL)

Inclusivity (only applicable to first person plural verbs: inclusive (INCL) and exclusive (EXCL)

Aspect/mood: completive (CPL), progressive (PROG), potential (POT), and habitual (HAB).
Answer: The answers are shown as follows:
*  hand-curated collection of complete inflection tables for 198 lemmata
Question: Where is the city of Nasimerice
Evidence: Našiměřice (German: Aschmeritz) is a municipality and village in Znojmo District in the South Moravian Region of the Czech Republic. It has about 200 inhabitants.

Našiměřice lies approximately 28 kilometres (17 mi) north-east of Znojmo, 32 km (20 mi) south-west of Brno, and 189 km (117 mi) south-east of Prague.
Answer: Nasimerice (German: Aschmeritz) is a municipality and village in Znojmo District in the South Moravian Region of the Czech Republic. It has about 200 inhabitants.

Nasimerice lies approximately 28 kilometres (17 mi) north-east of Znojmo, 32 km (20 mi) south-west of Brno, and 189 km (117 mi) south-east of Prague.
Question: Why do people read books?
Answer: People may read books for a number of reasons.  Most commonly, people either read books for entertainment, or for information.  These types of books are generally separated into fiction and non-fiction books.  A fiction book may craft a story for the reader to consume, exciting their mind by the premise of this new world the author paints for them.  A non-fiction book can provide useful information to the reader, and help them learn a new subject.
Question: What do they use the blog collection to do?
Evidence: Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0 .
For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community. In particular, during May-July 2015, we gathered the profile information for all the users that have self-reported their location in the U.S., along with a number of posts for all their associated blogs. We utilize this blog collection to generate maps of the U.S. that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes. We believe that these maps can provide valuable insights and partial verification of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5 .
We utilize this blog collection to generate maps of the U.S. that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes.
Answer: To generate maps of the U.S. that reflect user demographics, language use, and distributions of psycholinguistic and semantic word classes.
Question: Identify from the following list characters from The X-Files who are bald or balding: Walter Skinner, John Fitzgerald Byers, Dana Scully, Melvin Frohike, Darius Michaud, Peter Watts, Conrad Strughold, Queequeg
Answer: Walter Skinner, Melvin Frohike, Darius Michaud, Peter Watts, Conrad Strughold
Question: What is a bond
Answer: A bond is a fixed-income instrument that represents a loan made by an investor to a borrower (typically corporate or governmental). A bond could be thought of as an I.O.U. between the lender and borrower that includes the details of the loan and its payments. Bonds are used by companies, municipalities, states, and sovereign governments to finance projects and operations. Owners of bonds are debtholders, or creditors, of the issuer.

Bond details include the end date when the principal of the loan is due to be paid to the bond owner and usually include the terms for variable or fixed interest payments made by the borrower.
Question: What is an optimisation problem?
Answer: In mathematics and computer science, an optimisation problem is typically a search problem in which the best solution is to locate among all feasible solutions. An optimisation problem can be categorised as continuous or discrete depending on the type of their variables. In a discrete optimisation problem, variables are discrete and countable. In contrast, continuous variables include infinitely uncountable values, for example, a variable over a non-empty range of real numbers. 

According to the number of objective functions, an optimisation problem is classified as single or multi-objective. A single-objective optimisation problem only involves one objective function to optimise. On the other hand, a multi-objective one considers two or more objectives simultaneously, which means the interaction between objectives has to be considered.
Question: How does morphological analysis differ from morphological inflection?
Evidence: Inflectional realization defines the inflected forms of a lexeme/lemma. As a computational task, often referred to as simply “morphological inflection," inflectional realization is framed as a mapping from the pairing of a lemma with a set of morphological tags to the corresponding word form. For example, the inflectional realization of SJQ Chatino verb forms entails a mapping of the pairing of the lemma lyu1 `fall' with the tag-set 1;SG;PROG to the word form nlyon32.
Morphological analysis is the task of creating a morphosyntactic description for a given word. It can be framed in a context-agnostic manner (as in our case) or within a given context, as for instance for the SIGMORPHON 2019 second shared task BIBREF11. We trained an encoder-decoder model that receives the form as character-level input, encodes it with a BiLSTM encoder, and then an attention enhanced decoder BIBREF14 outputs the corresponding sequence of morphological tags, implemented in DyNet. The baseline results are shown in Table . The exact-match accuracy of 67% is lower than the average accuracy that context-aware systems can achieve, and it highlights the challenge that the complexity of the tonal system of SJQ Chatino can pose.
Inflectional realization defines the inflected forms of a lexeme/lemma. As a computational task, often referred to as simply “morphological inflection," inflectional realization is framed as a mapping from the pairing of a lemma with a set of morphological tags to the corresponding word form. For example, the inflectional realization of SJQ Chatino verb forms entails a mapping of the pairing of the lemma lyu1 `fall' with the tag-set 1;SG;PROG to the word form nlyon32.
Morphological analysis is the task of creating a morphosyntactic description for a given word. It can be framed in a context-agnostic manner (as in our case) or within a given context, as for instance for the SIGMORPHON 2019 second shared task BIBREF11.
Answer: The answers are shown as follows:
* Morphological analysis is the task of creating a morphosyntactic description for a given word
*  inflectional realization is framed as a mapping from the pairing of a lemma with a set of morphological tags to the corresponding word form
Question: Name some countries that don't share a border with any other countries.
Answer: New Zealand, Australia and Japan do not share a border with any other country.
Question: How can a football training session be planned?
Answer: First of all, the team has to change and put on the football clothes. Depending on the weather conditions, the right football shoes also have to be put on. Rubber nubs or steel studs can be used. It is better to wear indoor shoes in the hall and short nubs on the hard court. The first step is to warm up during training. Then stretch to prepare the muscles for fast movements. Afterwards, passing and shooting exercises are rehearsed. At the end, a small game should take place to keep it fun. Then stretch again and run out.
Question: What is the plot of Jojo's Bizarre Adventures?
Evidence: The universe of JoJo's Bizarre Adventure is a reflection of the real world with the added existence of supernatural forces and beings. In this setting, some people are capable of transforming their inner spiritual power into a Stand (スタンド, Sutando); another significant form of energy is Hamon (波紋, "Ripple"), a martial arts technique that allows its user to focus bodily energy into sunlight via controlled breathing. The narrative of JoJo's Bizarre Adventure is split into parts with independent stories and different characters. Each of the series' protagonists is a member of the Joestar family, whose mainline descendants possess a star-shaped birthmark above their left shoulder blade and a name that can be abbreviated to the titular "JoJo". The first six parts take place within a single continuity whose generational conflict stems from the rivalry between Jonathan Joestar and Dio Brando, while the latter three parts take place in an alternate universe where the Joestar family tree is heavily altered.
Answer: JoJo's Bizarre Adventure is a manga and anime about a family called the Joestars with a name that can usually be abbreviated to Jojo. The story takes place in a world similar to the real world but with added supernatural forces such as inner energy personifying into reality called Hamon. In the later parts, a new force emerges called Stands that give each user an additional power. The story is split into parts following a descendant of the Joestar family and the antagonist is usually Dio Brando or an associate.
Question: Tell me whether companies keep each of these confidential or publish publicly: acquisition targets, customer data, marketing materials, blog posts, product instructions
Answer: Acquisition Targets: Confidential
Customer Data: Confidential
Marketing Materials: Publish Publicly
Blog Posts: Publish Publicly
Product Instructions: Publish Publicly
Question: What was the record for University of Miami Hurricanes in 1980?
Evidence: The 1980 Miami Hurricanes football team represented the University of Miami as an independent during the 1980 NCAA Division I-A football season. Led by second-year head coach Howard Schnellenberger, the Hurricanes played their home games at the Miami Orange Bowl in Miami, Florida. Miami finished the season with a record of 9–3. They were invited to the Peach Bowl, where they defeated Virginia Tech, 20–10.
Answer: The Hurricanes won 9 games and lost 3 games in 1980
Question: What is data-to-text?
Evidence: Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as "data-to-text". These structures generally regroup multiple elements, as well as their attributes. Most attempts rely on translation encoder-decoder methods which linearize elements into a sequence. This however loses most of the structure contained in the data. In this work, we propose to overpass this limitation with a hierarchical model that encodes the data-structure at the element-level and the structure level. Evaluations on RotoWire show the effectiveness of our model w.r.t. qualitative and quantitative metrics.
Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as “data-to-text”.
Answer: Transcribing structured data into natural language descriptions.
Question: What future work will they consider?
Evidence: We hope that this research paper establishes a first attempt at using generative machine learning models for the purposes of marketing on peer-to-peer platforms. As the use of these markets becomes more widespread, the use of self-branding and marketing tools will grow in importance. The development of tools like the DMK loss in combination with GANs demonstrates the enormous potential these frameworks can have in solving problems that inevitably arise on peer-to-peer platforms. Certainly, however, more works remains to be done in this field, and recent developments in unsupervised generative models already promise possible extensions to our analysis.
For example, since the introduction of GANs, research groups have studied how variational autoencoders can be incorporated into these models, to increase the clarity and accuracy of the models’ outputs. Wang et al. in particular demonstrate how using a variational autoencoder in the generator model can increase the accuracy of generated text samples [11]. Most promisingly, the data used by the group was a large data set of Amazon customer reviews, which in many ways parallels the tone and semantic structure of Airbnb listing descriptions. More generally, Bowman et al. demonstrate how the use of variational autoencoders presents a more accurate model for text generation, as compared to standard recurrent neural network language models [1].
For future work, we may also consider experimenting with different forms of word embeddings, such as improved word vectors (IWV) which were suggested by Rezaeinia et al [10]. These IMV word embeddings are trained in a similar fashion to GloVe vectors, but also encode additional information about each word, like the word’s part of speech. For our model, we may consider encoding similar information in order for the generator to more easily learn common semantic patterns inherent to the marketing data being analyzed.
For future work, we may also consider experimenting with different forms of word embeddings, such as improved word vectors (IWV) which were suggested by Rezaeinia et al [10]. These IMV word embeddings are trained in a similar fashion to GloVe vectors, but also encode additional information about each word, like the words part of speech. For our model, we may consider encoding similar information in order for the generator to more easily learn common semantic patterns inherent to the marketing data being analyzed.
Answer: They may research experiments with different forms of word embeddings and encoding similar information.
Question: Who is Romain Iannetta
Evidence: Romain Iannetta (born 27 November 1979 in Villecresnes) is a French racing driver who last competed in the GT4 European Series, driving the No. 77 Audi R8 LMS GT4 Evo for Full Motorsport. He also competes part-time in the NASCAR Whelen Euro Series as the driver of the No. 46 Chevrolet Camaro for Marko Stipp Motorsport in the EuroNASCAR PRO class.

He is the nephew of driver Alain Iannetta who participated in 24 Hours of Le Mans three times (1988, 1989 and 1990).

In parallel to his passion, Romain is an instructor at the flight school PRO'PULSION (Dreux and Issoire) and driving Ferrari 360 Modena for JMB Racing. He also worked for many auto manufacturers at the launch of new vehicles on the market (Volkswagen, Audi, Citroën, Renault, Alfa Romeo...).

It began in 2000 in the trade of automotive stunt team CINE CASCADE directed by Jean-Claude Lagnes. It revolves mainly around feature films such as: Le Boulet, Ripoux 3, San Antonio, Hush!, Taxi, 36 Quai des Orfèvres, The Bourne Identity and The Mental Love your father.

He was awarded the World Stunt Awards in the United States for the best sequence on a vehicle for the year 2003 for the film "The Bourne Identity".
Answer: Romain Iannetta (born 27 November 1979 in Villecresnes) is a French racing driver who last competed in the GT4 European Series, driving the No. 77 Audi R8 LMS GT4 Evo for Full Motorsport. He also competes part-time in the NASCAR Whelen Euro Series as the driver of the No. 46 Chevrolet Camaro for Marko Stipp Motorsport in the EuroNASCAR PRO class.

He is the nephew of driver Alain Iannetta who participated in 24 Hours of Le Mans three times (1988, 1989 and 1990).

In parallel to his passion, Romain is an instructor at the flight school PRO'PULSION (Dreux and Issoire) and driving Ferrari 360 Modena for JMB Racing. He also worked for many auto manufacturers at the launch of new vehicles on the market (Volkswagen, Audi, Citroen, Renault, Alfa Romeo...).

It began in 2000 in the trade of automotive stunt team CINE CASCADE directed by Jean-Claude Lagnes. It revolves mainly around feature films such as: Le Boulet, Ripoux 3, San Antonio, Hush!, Taxi, 36 Quai des Orfèvres, The Bourne Identity and The Mental Love your father.

He was awarded the World Stunt Awards in the United States for the best sequence on a vehicle for the year 2003 for the film The Bourne Identity.