DataHammer/scimrc huggingface dataset:
Question: What domain is the news generation system used for?
Evidence: Automated, or robotic, journalism aims at news generation from structured data sources, either as the final product or as a draft for subsequent post-editing. At present, automated journalism typically focuses on domains such as sports, finance and similar statistics-based reporting, where there is a commercial product potential due to the high volume of news, combined with the expectation of a relatively straightforward task.
News generation systems—especially those deployed in practice—tend to be based on intricate template filling, aiming to give the users the full control of the generated facts, while maintaining a reasonable variability of the resulting text. This comes at the price of having to develop the templates and specify their control logic, neither of which are tasks naturally fitting journalists' work. Further, this development needs to be repeated for every domain, as the templates are not easily transferred across domains. Examples of the template-based news generation systems for Finnish are Voitto by the Finnish Public Service Broadcasting Company (YLE) used for sports news generation, as well as Vaalibotti BIBREF0, a hybrid machine learning and template-based system used for election news.
BIBREF1 suggested a neural template generation, which jointly models latent templates and text generation. Such a system increases interpretability and controllability of the generation, however, recent sequence-to-sequence systems represent the state-of-the-art in data-to-text generation. BIBREF2
In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods. In order to train such a system, we compile a corpus of news based on over 2000 game reports from the Finnish News Agency STT. While developing this corpus into a form suitable for training of end-to-end systems naturally requires manual effort, we argue that compiling and refining a set of text examples is a more natural way for journalists to interact with the system, in order for them to codify their knowledge and to adapt it for new domains.
Our aim is to generate reports that give an overview of a game based on information inferrable from the statistics. Such reports can be used either as a basis for further post-editing by a journalist imprinting own insights and background information, or even used directly as a news stream labelled as machine-generated.
In the following, we will introduce the news dataset and the process of its creation, introduce an end-to-end model for news generation, and evaluate its output respective to the abovementioned objectives.
In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods.
Answer: The news generation system is used for the Finnish ice hockey news domain, based on sequence-to-sequence methods
Question: What is the source of the user interaction data? 
Evidence: The recent advancements in deep neural networks, specifically for image analysis task, can lead to determining demographic features such as age and gender BIBREF13 . We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.
Answer: Sociability from ego-network on Twitter
Question: Why are most plants green?
Answer: Plants and algae are green due to a vital energy producing pigment in their leaves called chlorophyll. Chlorophyll molecules absorb light energy across the electromagnetic spectrum of sunshine but reflect light in the green part of the spectrum. As a result, plants appear to be green to human eyes. As an interesting side note, many animals and insects can see beyond the visible light spectrum that human's can see so that they may not view plants as being what we call 'green'.
Question: How big are this dataset and catalogue?
Evidence: The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude. Differences in size partly reflect different annotation approaches. The largest datasets are from proprietary data sharing agreements with platforms. Smaller datasets tend to be carefully collected and then manually annotated. There are no established guidelines for how large an abusive language training dataset needs to be. However, smaller datasets are problematic because they contain too little linguistic variation and increase the likelihood of overfitting. Rizoiu et al.BIBREF61 train detection models on only a proportion of the Davidson et al. and Waseem training datasets and show that this leads to worse performance, with a lower F1-Score, particularly for `data hungry' deep learning approaches BIBREF61. At the same time, `big' datasets alone are not a panacea for the challenges of abusive content classification. Large training datasets which have been poorly sampled, annotated with theoretically problematic categories or inexpertly and unthoughtfully annotated, could still lead to the development of poor classification systems.
The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude.
Answer: The answers are shown as follows:
*  from 469 posts to 17 million
Question: What cause toe fungus?
Answer: One common way that people can get nail fungus on their toes is walking barefoot in moist areas like locker room or public shower. You can also get a fungal infection by skin-to-skin contact, or by sharing an infected nail clipper or towel.
Question: What causes leaves to change color in fall
Answer: Leaves are green due to the presence of chlorophyll. During the spring and summer months when trees are growing, there's an abundance of chlorophyll pigments in the leaves. 

Chlorophyll serves the vital function of capturing sun rays and converting it to energy for the plants i.e, plants food & source of nourishment. In late summer, early fall as the days shorten there's less solar energy and thus Chlorophyll starts to breakdown giving way for other pigments such as Carotenoids to become more obvious and there orange-yellow-red colors to surface giving the trees the famous "fall color" look
Question: When do grasshoppers become locusts?
Answer: Grasshoppers are solitary, but under certain circumstances, when food supplies become rare, they interact with other grasshoppers, changing their behaviour and becoming gregarious. Under suitable conditions of drought followed by rapid vegetation growth, serotonin in their brains triggers dramatic changes: they start to breed abundantly, becoming gregarious and nomadic when their populations become dense enough. They form bands of wingless nymphs that later become swarms of winged adults.
Question: Why Bangalore climate is pleasant
Evidence: Bangalore is situated in the southeast of the South Indian state of Karnataka. It is positioned at 12.97° N 77.56° E and covers an area of 2,190 square kilometres (850 sq mi). A landlocked city, Bangalore is located in the heart of the Mysore Plateau (a region of the larger Deccan Plateau) at an average elevation of 920 metres (3,020 ft). Bangalore district borders with Kolar and Chikkaballapur in the northeast, Tumkur in the northwest, and Mandya and Ramanagaram in the southeast.
Answer: Bangalore city is positioned at a 920 mt high altitude from the sea level. Bangalore is nearest to both Arabian Sea & Bay of Bengal. Hence it receives plenty of rainfall & makes the place cooler as compared to rest of the cities in India. Along with this the city is full of big & old trees which adds up to the environment and makes more cooler with greenery.
Question: Classify the following as an animal or plant. Polar bear, Oak Tree, Venus Flytrap, Shire Horse, Aloe vera, Golden retriever
Answer: The following are animals:
Polar bear
Shire horse
Golden retriever

The following are plants: 
Oak Tree
Venus Flytrap
Aloe Vera
Question: List top 5 people with most silver medals in the olympics
Answer: 1. Larisa Latynina - 5 medals
2. Nikolai Andrianov - 5 medals
3. Edoardo Mangiarotti - 5 medals
4. Ireen Wust - 5 medals
5. Isabell Werth - 5 medals
Question: Provide a number of different ways that someone can decorate a party.
Answer: There are many different decorations that can be used for a party. Different color balloons are very popular for parties, and they can either be manually inflated or alternatively they can be filled with helium so they float to the ceiling in a room. Color streamers are also festive and can be hung between furniture or tied to balloons as tassels. Also, flowers are popular for decorating, especially more formal parties. They can be either fresh flowers, or more recently dried flowers are becoming more popular to use for decoration.
Question: For whom is Daenerys Targaryen's dragon, Drogon, named for?
Answer: She named the dragon after her late husband, Khal Drogo
Question: How many soccer teams are in the English Premier League?
Answer: The English Premier League consists of 20 teams.
Question: How do I decorate my new home?
Answer: To decorate your new home, its good to get a strong sense of your own personal style and to seek inspiration from things you like. There are certain platforms like Pinterest where you can save images that you like to get inspiration from others, and see patterns in what type of decor speaks to you. Its good to invest in certain pieces that you will use a lot, like a mattress or couch, and then search for bargains on other pieces on secondhand marketplaces like Craigslist or Facebook marketplace. If you feel totally lost and overwhelmed, there are also interior design services available online to do remotely or you can hire a personal interior designer. Sometimes it is good to live in your space for a bit before making big purchases since your ideas on how to utilize the space may evolve as you spend more time in it.
Question: What teams currently play in the English Premier League?
Answer: AFC Bournemouth, Arsenal, Aston Villa, Brentford, Brighton & Hove Albion, Chelsea, Crystal Palace, Everton, Fulham, Leeds United, Leicester City, Liverpool, Manchester City, Manchester United, Newcastle United, Nottingham Forest, Southampton, Tottenham Hotspur, West Ham United, Wolverhampton Wanderers
Question: Tell me whether each of these wine regions is in California or Italy: Napa, Sonoma, Healdsburg, Tuscany, Veneto
Answer: Napa: California
Sonoma: California
Healdsburg: California
Tuscany: Italy
Veneto: Italy
Question: What do the authors do to overcome the size issue of the student reflection dataset?
Evidence: To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PG-net)BIBREF0. To experiment with domain transfer, the model was pretrained using the CNN/DM dataset, then fine tuned using the student reflection dataset (see the Experiments section). A second approach we explore to overcome the lack of reflection data is data synthesis. We first propose a template model for synthesizing new data, then investigate the performance impact of using this data when training the summarization model. The proposed model makes use of the nature of datasets such as ours, where the reference summaries tend to be close in structure: humans try to find the major points that students raise, then present the points in a way that marks their relative importance (recall the CS example in Table TABREF4). Our third explored approach is to combine domain transfer with data synthesis.
To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PGnet)(See, Liu, and Manning 2017)4 . To experiment with domain transfer, the model was pretrained using the CNN/DM dataset, then fine tuned using the student reflection dataset (see the Experiments section). A second approach we explore to overcome the lack of reflection data is data synthesis. We first propose a template model for synthesizing new data, then investigate the performance impact of using this data when training the summarization model. The proposed model makes use of the nature of datasets such as ours, where the reference summaries tend to be close in structure: humans try to find the major points that students raise, then present the points in a way that marks their relative importance (recall the CS example in Table 1). Our third explored approach is to combine domain transfer with data synthesis.
Answer: Firstly, the authors explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PGnet). Secondly, the authors explore to overcome the lack of reflection data is data synthesis. Thirdly, the explored approach is to combine domain transfer with data synthesis.
Question: How many linguistic features do the authors extract from the transcripts?
Evidence: 
Feature Extraction: We extract 297 linguistic features from the transcripts and 183 acoustic features from the associated audio files, all task-independent. Linguistic features encompass syntactic features (e.g. syntactic complexity BIBREF9 ), lexical features (e.g. occurrence of production rules). Acoustic features include Mel-frequency Cepstral Coefficients (MFCCs) & pause-related features (e.g., mean pause duration). We also use sentiment lexical norms BIBREF10 , local, and global coherence features BIBREF11 .
Feature Predicates as Anchors for Prediction: Given a black box classifier INLINEFORM0 with interpretable input representation, Ribeiro et al. BIBREF6 define anchors INLINEFORM1 as a set of input rules such that when conditions in the rule are met, humans can confidently predict the behavior of a model with high precision. Since the inputs to the classifier are engineered features with finite ranges,we can obtain sufficient conditions for the prediction INLINEFORM2 in terms of interpretable feature thresholds for an unseen instance INLINEFORM3 . Anchors are found by maximizing the metric of coverage, defined as the probability of anchors holding true to samples in the data distribution INLINEFORM4 , in BIBREF6 . Hence, INLINEFORM5 is maximized, where INLINEFORM6 .
We show in Sec. SECREF13 that anchors identified from a model trained on multiple tasks have more coverage over the data distribution than those obtained from a model trained on a single task. Such a scenario is possible when task-independant, clinically relevant speech features are selected as anchors (e.g., fraction of filled pauses in speech BIBREF12 , acoustic features BIBREF13 etc. ). Additionally, such selected anchors must also be associated with thresholds applicable across multiple types of speech.

We extract 297 linguistic features from the transcripts and 183 acoustic features from the associated audio files, all task-independent. Linguistic features encompass syntactic features (e.g. syntactic complexity [12]), lexical features (e.g. occurrence of production rules).
Answer: 297 linguistic features.
Question: Why do humans like dogs?
Answer: Humans like dogs because dogs love humans and want to be with them. Humans and dogs share a close emotional connection that is unmatched by other species, including – at times – other humans. There is evidence of a humans and dogs interacting going back 18,000 years. Domesticated dogs will quickly bond to a humans, often showing affection in presence of their human owners and degrees of separation anxiety when their human owners aren’t present. Dogs will seek comfort from their human when they feel anxious or are in physical distress. Dogs often seek approval from their human by performing specific tasks in which they have been trained, or by exhibiting character traits or behaviors that they have been selectively bred for. Dogs love humans and interact with them in complex ways. Humans, in turn, interact with dogs and receive emotional satisfaction.
Question: What is the only supervision in training?
Evidence: Neural language models BIBREF0 , BIBREF1 , BIBREF2 have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning. They have also become a common benchmarking application in machine learning research on recurrent neural networks (RNN), because producing an accurate probabilistic model of human language is a very challenging task which requires all levels of linguistic analysis, from pragmatics to phonology, to be taken into account.
A typical language model is trained on text in a single language, and if one needs to model multiple languages the standard solution is to train a separate model for each language. This presupposes large quantities of monolingual data in each of the languages that needs to be covered and each model with its parameters is completely independent of any of the other models.
We propose instead to use a single model with real-valued vectors to indicate the language used, and to train this model with a large number of languages. We thus get a language model whose predictive distribution INLINEFORM0 is a continuous function of the language vector INLINEFORM1 , a property that is trivially extended to other neural NLP models. In this paper, we explore the “language space” containing these vectors, and in particular explore what happens when we move beyond the points representing the languages of the training corpus.
The motivation of combining languages into one single model is at least two-fold: First of all, languages are related and share many features and properties, a fact that is ignored when using independent models. The second motivation is data sparseness, an issue that heavily influences the reliability of data-driven models. Resources are scarce for most languages in the world (and also for most domains in otherwise well-supported languages), which makes it hard to train reasonable parameters. By combining data from many languages, we hope to mitigate this issue.
In contrast to related work, we focus on massively multilingual data sets to cover for the first time a substantial amount of the linguistic diversity in the world in a project related to data-driven language modeling. We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding. From that and the actual training examples, the system learns dense vector representations for each language included in our data set along with the character-level RNN parameters of the language model itself.
The only supervision that is giving during training is a language identifier as a one-hot encoding.
Answer: A language identifier as a one-hot encoding.
Question: Name the top scorer in NBA history.
Answer: In 2023, Lebron James passed Kareem Abdul- Jabbar for most career points in NBA history.
Question: Which are main characters in Harry potter
Answer: Harry Potter
Hermoine Gringer
Ron Wesley
Lord Voldemort
Albus Dumbeldore
Remus Lupin
Luna Lovegood
ginny wesley
Draco Malfoy
Hagrid
Snape
Lilly
james
Dursleys
Question: What are the cutest animals on the planet?
Answer: - baby puppies
- small sloths
- lemurs
- baby chicks
- kittens
- bunnies
- baby otters
- arctic foxes
Question: What are five totally distinct, creative ways to describe the usefulness of a broken stapler.
Answer: Even though it may be broken, a stapler may still retain its usefulness. Consider these alternative uses for a broken stapler:
1. Hammer - the solid metal frame is sturdy enough to pound a nail.
2. Paper weight - even if you can't use staples to hold your papers together, the stapler can keep them on your desk.
3. Weapon - Whip it back and forth as a flail or throw it to dent someone's skull.
4. Door stop - hold the door open by wedging it between the door and frame.
5. Compass - magnetize the frame and never get lost again!
Question: What is used to predict the rise and fall in stock prices in the paper?
Evidence: The most well-known publication in this area is by Bollen BIBREF10 . They investigated whether the collective mood states of public (Happy, calm, Anxiety) derived from twitter feeds are correlated to the value of the Dow Jones Industrial Index. They used a Fuzzy neural network for their prediction. Their results show that public mood states in twitter are strongly correlated with Dow Jones Industrial Index. Chen and Lazer BIBREF11 derived investment strategies by observing and classifying the twitter feeds. Bing et al. BIBREF12 studied the tweets and concluded the predictability of stock prices based on the type of industry like Finance, IT etc. Zhang BIBREF13 found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Recently, Brian et al. BIBREF14 investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stocks. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation. Sentiment analyzer is used to classify the sentiments in tweets extracted.The human annotated dataset in our work is also exhaustive. We have shown that a strong correlation exists between twitter sentiments and the next day stock prices in the results section. We did so by considering the tweets and stock opening and closing prices of Microsoft over a year.
In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation.
Answer: A sentiment analyzer.