DataHammer/scimrc huggingface dataset:
Question: Why are the Green Bay Packers called the Packers?
Evidence: Needing to outfit his new squad, team founder Curly Lambeau solicited funds from his employer, the Indian Packing Company. He was given $500 for uniforms and equipment in return for the team being named for its sponsor. An early newspaper article referred to the fledglings as "the Indians", but by the time they played their first game "Packers" had taken hold.

Indian Packing was purchased in 1920 by the Acme Packing Company. Acme continued to support the team, which played its first NFL season with "ACME PACKERS" emblazoned on its jerseys.
Answer: The Green Bay Packers are called the Packers because in 1920, the team founder, Curly Lambeau asked his employer, the Indian Packing Company for money to help buy uniforms and equipement.  In return for the funding, the team was named after the sponser.
Question: How Should I train my dog?
Answer: 10 years ago the very common approach to dog training was the "Alpha" approach, also known as dominance training.  This approach focuses on building a pack structure in the home where the humans are seen as higher in the pecking order than the dogs in the home.  Examples include leading at all times (ie through doorways and walks), and only allowing your dog to eat after you’ve finished dinner and using training aids like a shock/vibration collar to enforce behaviors. 

In recent years, many experts have recommended a positive reinforcement based training approach.  Although there are a lot of nuances, at its core this method uses rewards to guide your dog towards desired behaviors. Positive reinforcement training typically means using a signal (like a word or a noise) combined with training treats, loved toys or verbal praise to reinforce good behaviors or actions. Other benefits include strengthening the relationship between owner and dog and building a happy training environment.
Question: Who is Linda Hargreaves
Answer: Linda Hargreaves is a British actress who has appeared on both stage and screen. She made her television debut in the BBC soap opera Doctors and has since returned to the soap on three separate occasions in different roles, most recently in the recurring role of Constance Buchanan. She has also appeared in various television series including Containment and Call the Midwife.
Question: Who invented the first electric car?
Answer: The first electric car is attributed to Robert Anderson, a Scottish inventor whose crude "electric carriage" is believed to have been built sometime in the 1830s.
Question: What is a grocery store?
Answer: A grocery store is a place that offers food and beverages to people for purchase.  You can purchase raw ingredients for cooking, drinks for hydration, meat for barbecuing, raw fruits and vegetables, and pre-packaged foods like pizza, candy and ice cream.  The food purchased at a grocery store is used to keep people alive, sustained, and healthy.  Grocery stores may also be referred to as a supermarkets.
Question: What are legos?
Answer: Legos are small plastic toy blocks that stack together, and can be used to build a variety of structures.
Question: How to build language-independent semantic representations?
Evidence: We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus. Our data is a collection of Russian and Ukrainian academic texts, for which topics are their academic fields. In order to build language-independent semantic representations of these documents, we train neural distributional models on monolingual corpora and learn the optimal linear transformation of vectors from one language to another. The resulting vectors are then used to produce `semantic fingerprints' of documents, serving as input to a clustering algorithm. The presented method is compared to several baselines including `orthographic translation' with Levenshtein edit distance and outperforms them by a large margin. We also show that language-independent `semantic fingerprints' are superior to multi-lingual clustering algorithms proposed in the previous work, at the same time requiring less linguistic resources.
In order to build language-independent semantic representations of these documents, we train neural distributional models on monolingual corpora and learn the optimal linear transformation of vectors from one language to another.
Answer: Train neural distributional models on monolingual corpora and learn the optimal linear transformation of vectors from one language to another.
Question: Who won the 63rd staging of the Limerick Senior Hurling Championship?
Evidence: On 29 September 1957, Claughaun won the championship after a 7-07 to 3-02 defeat of St. Patrick's in the final.
Answer: The 1957 Limerick Senior Hurling Championship was the 63rd staging of the Limerick Senior Hurling Championship since its establishment by the Limerick County Board in 1887.

Cappamore were the defending champions, however, they were defeated by St. Patrick's.

On 29 September 1957, Claughaun won the championship after a 7-07 to 3-02 defeat of St. Patrick's in the final. It was their sixth championship title overall and their first championship title since 1926.
Question: How is it determined that a fact is easy-to-guess?
Evidence: Filter 1: The string match filter deletes all KB triples where the correct answer (e.g., Apple) is a case-insensitive substring of the subject entity name (e.g., Apple Watch). This simple heuristic deletes up to 81% of triples from individual relations (see Appendix for statistics and examples).
Filter 2: Of course, entity names can be revealing in ways that are more subtle. As illustrated by our French actor example, a person's name can be a useful prior for guessing their native language and by extension, their nationality, place of birth, etc. Our person name filter uses cloze-style questions to elicit name associations inherent in BERT, and deletes KB triples that correlate with them. Consider our previous example (Jean_Marais, native-language, French). We whitespace-tokenize the subject name into Jean and Marais. If BERT considers either name to be a common French name, then a correct answer is insufficient evidence for factual knowledge about the entity Jean_Marais. On the other hand, if neither Jean nor Marais are considered French, but a correct answer is given nonetheless, then we consider this sufficient evidence for factual knowledge.
Filter 1: The string match filter deletes all KB triples where the correct answer (e.g., Apple) is a case-insensitive substring of the subject entity name (e.g., Apple Watch).
Filter 2: Of course, entity names can be revealing in ways that are more subtle. As illustrated by our French actor example, a person's name can be a useful prior for guessing their native language and by extension, their nationality, place of birth, etc. Our person name filter uses cloze-style questions to elicit name associations inherent in BERT, and deletes KB triples that correlate with them.
Answer: The answers are shown as follows:
*  filter deletes all KB triples where the correct answer (e.g., Apple) is a case-insensitive substring of the subject entity name (e.g., Apple Watch)
* person name filter uses cloze-style questions to elicit name associations inherent in BERT, and deletes KB triples that correlate with them
Question: What is cholesterol and bad cholesterol and why would I want to reduce my levels?
Answer: Cholesterol is a waxy, fat-like substance found in the cells in your body. Your liver makes cholesterol and it is also found in some foods like meat and dairy products. Your body needs certain levels of cholesterol to function, but too much raises the risk of  cardiovascular disease. 

LDL, low-density lipoprotein, is also known as bad cholesterol. It gets it's "bad" name because too high of levels of LDL can lead to a build up of cholesterol in you arteries.  The extra amount of LDL forms plaque and the build up of this plaque in your arteries leads to a condition called, artherosclerosis.  Coronary artery disease happens when the plaque buildup is in the arteries of your heart. It causes the arteries to become hardened and narrowed , which slows down or blocks the blood flow to your heart.  Because your blood carries oxygen to your heart, this means that your heart may not be able to get enough oxygen. This can cause angina or chest pains and may lead to a heat attack if blood flow is completely blocked.
Question: Which is a species of fish? Tope or Rope
Answer: Tope
Question: Complete the joke 'A Large Language Model enters a bar'
Answer: A Large Language Model enters a bar and asks the bartender. "Hey ho buddy. How are you?" Bartender smirks and says, "75 kilos". LLM: "What do you mean?" Bartender: "Well, every LLM worth his weight in booze keeps asking me to share my weights."
Question: Do the authors' approaches significantly outperforms several strong baseline systems?
Evidence: In this paper, we propose a cross-lingual pretraining based transfer approach for the challenging zero-shot translation task, in which source and target languages have no parallel data, while they both have parallel data with a high resource pivot language. With the aim of building the language invariant representation between source and pivot languages for smooth transfer of the parent model of pivot$\rightarrow $target direction to the child model of source$\rightarrow $target direction, we introduce one monolingual pretraining method and two bilingual pretraining methods to construct an universal encoder for the source and pivot languages. Experiments on public datasets show that our approaches significantly outperforms several strong baseline systems, and manifest the language invariance characteristics in both sentence level and word level neural representations.
Experiments on public datasets show that our approaches significantly outperforms several strong baseline systems, and manifest the language invariance characteristics in both sentence level and word level neural representations.
Answer: Yes.
Question: What benefits can be brought by using open-source NLP software and an abundance of online news content?
Evidence: Automated coding of political event data, or the record of who-did-what-to-whom within the context of political actions, has existed for roughly two decades. The approach has remained largely the same during this time, with the underlying coding procedures not updating to reflect changes in natural language processing (NLP) technology. These NLP technologies have now advanced to such a level, and with accompanying open-source software implementations, that their inclusion in the event-data coding process comes as an obvious advancement. When combined with changes in how news content is obtained, the ability to store and process large amounts of text, and enhancements based on two decades worth of event-data experience, it becomes clear that political event data is ready for a next generation dataset.
In this chapter, I provide the technical details for creating such a next-generation dataset. The technical details lead to a pipeline for the production of the Phoenix event dataset. The Phoenix dataset is a daily updated, near-real-time political event dataset. The coding process makes use of open-source NLP software, an abundance of online news content, and other technical advances made possible by open-source software. This enables a dataset that is transparent and replicable, while providing a more accurate coding process than previously possible. Additionally, the dataset's near-real-time nature also enables many applications that were previously impossible with batch-updated datasets, such as monitoring of ongoing events. Thus, this dataset provides a significant improvement over previous event data generation efforts.
In the following sections I briefly outline the history of computer-generated political event data to this point in history. I then outline what the “next generation” of event data should look like. Following this, I discuss the many facets of creating a real-time political event dataset, mainly from a technological and infrastructure standpoint. Finally, the paper concludes with a brief empirical view of the Phoenix event dataset, which is the output of the previously-discussed technological pipeline.
The coding process makes use of open-source NLP software, an abundance of online news content, and other technical advances made possible by open-source software. This enables a dataset that is transparent and replicable, while providing a more accurate coding process than previously possible.
Answer: This enables a dataset that is transparent and replicable, while providing a more accurate coding process than previously possible.
Question: How can I learn to snowboard?
Answer: Buying or renting a snowboard, snowboard boots, bindings, snowboard pants, and jacket at your favorite snowboard shop is the first step. Next, check the snow conditions and availability of lessons at the ski resort of your choice. Lift tickets and lessons can be purchased for the day online.  Lastly, drive to the ski resort with your gear, and register to find your snowboard instructor, who will start with lesson basics on how to strap in, stand, stop, and carve your snowboard.
Question: Extract the airlines mentioned in the text and separate them with a comma.  Which one operated the shortest flight?
Evidence: In June 2017 after 30 months in commercial operation, 80 A350s were in service with 12 operators, the largest being Qatar Airways with 17 and 13 each at Cathay Pacific and Singapore Airlines (SIA). The fleet average block time (time between pushback and destination gate arrival) was 7.2 hours with 53% below 3,000 nmi (5,556 km), 16% over 5,000 nmi (9,260 km), and 31% in between. LATAM Airlines had the longest average sector at 10.7 hours, and Asiana had the shortest at 3.8 hours. Singapore Airlines operated the longest leg, Singapore to San Francisco 7,340 nmi (13,594 km), and the shortest leg, Singapore to Kuala Lumpur 160 nmi (296 km). Seating varied from 253 for Singapore Airlines to 389 for Air Caraïbes, with most between 280 and 320.
Answer: Qatar Airways, Cathay Pacific, Singapore Airlines, LATAM Airlines, Air Caraïbes.  Singapore Airlines operated the shortest flight.
Question: What is the model accuracy?
Evidence: We adopt the leave-one-out evaluation protocol to evaluate the performance of our model and baselines. The leave-one-out evaluation protocol has been widely used in top-K recommendation tasks. In particular, we held the latest interaction of each user as the test set and used the remaining interactions for training. Each testing instance was paired with 99 randomly sampled negative instances. Each recommendation model ranks the 100 instances according to its predicted results. The ranked list is judged by Hit Ratio (HR) BIBREF49 and Normalized Discount Cumulative Gain (NDCG) BIBREF50 at the position 10. HR@10 is a recall-based metric, measuring the percentage of the testing item being correctly recommended in the top-10 position. NDCG@10 is a ranked evaluation metric which considers the position of the correct hit in the ranked result. Since both modules in our framework introduce randomness, we repeat each experiment 5 times with different weight initialization and randomly selecting neighbors. We report the average score of the best performance in each training process for both metrics to ensure the robustness of our framework.
Overall, our AMRAN outperforms all baselines, achieving 0.657 HR@10 and 0.410 NDCG@10. It improves HR@10 by 5.3% and NDCG@10 by 3% over the best baseline (i.e., $NAIS_{concat}$).
Each testing instance was paired with 99 randomly sampled negative instances. Each recommendation model ranks the 100 instances according to its predicted results. The ranked list is judged by Hit Ratio (HR) BIBREF49 and Normalized Discount Cumulative Gain (NDCG) BIBREF50 at the position 10. HR@10 is a recall-based metric, measuring the percentage of the testing item being correctly recommended in the top-10 position. NDCG@10 is a ranked evaluation metric which considers the position of the correct hit in the ranked result.
Overall, our AMRAN outperforms all baselines, achieving 0.657 HR@10 and 0.410 NDCG@10. It improves HR@10 by 5.3% and NDCG@10 by 3% over the best baseline (i.e., $NAIS_{concat}$).
Answer: The answers are shown as follows:
* Overall, our AMRAN outperforms all baselines, achieving 0.657 HR@10 and 0.410 NDCG@10.
Question: What are the contrastive translation variants used for?
Evidence: Even though machine translation has improved considerably with the advent of neural machine translation (NMT) BIBREF0 , BIBREF1 , the translation of pronouns remains a major issue. They are notoriously hard to translate since they often require context outside the current sentence.
As an example, consider the sentences in Figure FIGREF1 . In both languages, there is a pronoun in the second sentence that refers to the European Central Bank. When the second sentence is translated from English to German, the translation of the pronoun it is ambiguous. This ambiguity can only be resolved with context awareness: if a translation system has access to the previous English sentence, the previous German translation, or both, it can determine the antecedent the pronoun refers to. In this German sentence, the antecedent Europäische Zentralbank dictates the feminine gender of the pronoun sie.
It is unfortunate, then, that current NMT systems generally operate on the sentence level BIBREF2 , BIBREF3 , BIBREF4 . Documents are translated sentence-by-sentence for practical reasons, such as line-based processing in a pipeline and reduced computational complexity. Furthermore, improvements of larger-context models over baselines in terms of document-level metrics such as BLEU or RIBES have been moderate, so that their computational overhead does not seem justified, and so that it is hard to develop more effective context-aware architectures and empirically validate them.
To address this issue, we present an alternative way of evaluating larger-context models on a test set that allows to specifically measure a model's capability to correctly translate pronouns. The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis). The resource is freely available. Additionally, we evaluate several context-aware models that have recently been proposed in the literature on this test set, and extend existing models with parameter tying.
The main contributions of our paper are:
Section SECREF2 explains how our paper relates to existing work on context-aware models and the evaluation of pronoun translation. Section SECREF3 describes our test suite. The context-aware models we use in our experiments are detailed in Section SECREF4 . We discuss our experiments in Section SECREF5 and the results in Section SECREF6 .
The test suite consists of pairs of source and target sentences, in combination with contrastive translation variants (for evaluation by model scoring) and additional linguistic and contextual information (for further analysis).
Answer: It is used for evaluation by model scoring.
Question: Why does their model do better than prior models?
Evidence: Without ELMo (the same setting as 4-th row in Table 3 ), our data settings is the same as qin-EtAl:2017:Long whose performance was state-of-the-art and will be compared directly. We see that even without the pre-trained ELMo encoder, our performance is better, which is mostly attributed to our better sentence pair representations.
Without ELMo (the same setting as 4-th row in Table 3 ), our data settings is the same as qin-EtAl:2017:Long whose performance was state-of-the-art and will be compared directly. We see that even without the pre-trained ELMo encoder, our performance is better, which is mostly attributed to our better sentence pair representations.
Answer: The answers are shown as follows:
* better sentence pair representations
Question: Which is a species of fish? Bombay duck or Bombay Sapphire
Answer: Bombay duck
Question: What work does the author do by crosslingual latent variable approach?
Evidence: Semantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and other NLP tasks. SRL is the problem of finding predicate-argument structure in a sentence, as illustrated below:
 INLINEFORM0 
Here, the predicate WRITE has two arguments: `Mike' as A0 or the writer, and `a book' as A1 or the thing written. The labels A0 and A1 correspond to the PropBank annotations BIBREF0 .
As the need for SRL arises in different domains and languages, the existing manually annotated corpora become insufficient to build supervised systems. This has motivated work on unsupervised SRL BIBREF1 , BIBREF2 , BIBREF3 . Previous work has indicated that unsupervised systems could benefit from the word alignment information in parallel text in two or more languages BIBREF4 , BIBREF5 , BIBREF6 . For example, consider the German translation of sentence INLINEFORM0 :
 INLINEFORM0 
If sentences INLINEFORM0 and INLINEFORM1 have the word alignments: Mike-Mike, written-geschrieben, and book-Buch, the system might be able to predict A1 for Buch, even if there is insufficient information in the monolingual German data to learn this assignment. Thus, in languages where the resources are sparse or not good enough, or the distributions are not informative, SRL systems could be made more accurate by using parallel data with resource rich or more amenable languages.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages. The model consists of individual Bayesian models for each language BIBREF3 , and crosslingual latent variables to incorporate soft role agreement between aligned constituents. This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs BIBREF4 . We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
We begin by briefly describing the unsupervised SRL pipeline and the monolingual semantic role induction model we use, and then describe our multilingual model.
We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
Answer: presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
Question: What countries are regarded as Nordic?
Answer: Nordic countries can be found in Northern Europe. It includes Denmark, Sweden, Norway, Finland and Iceland.
Question: What’s the function of the decoder block's additional sublayer?
Evidence: Based on the models described in Section we experiment with eight variants: (a) baseline transformer model (base); (b) base with AIC (base+sum); (c) base with AIF using spacial (base+att) or object based (base+obj) image features; (d) standard deliberation model (del); (e) deliberation models enriched with image information: del+sum, del+att and del+obj.
The decoder block also comprises of 6 layers.  It contains an additional sublayer which performs multi-head attention over the outputs of the encoder block.
Answer: Performs multi-head attention over the outputs of the encoder block.
Question: Think of a lot of different ways you could tell your roommate about something you need to buy at the store.
Answer: You could send your roommate a text, leave them a post-it note, add it to the grocery list, tell your roommate in person or give them a phone call