DataHammer/scimrc huggingface dataset:
Question: Given this reference text about the Koala, tell me what makes up most of their diet, how long joeys spend in their mothers' pouches and what are some of the pathogens they are threatened by.
Evidence: The koala or, inaccurately, koala bear (Phascolarctos cinereus), is an arboreal herbivorous marsupial native to Australia. It is the only extant representative of the family Phascolarctidae and its closest living relatives are the wombats. The koala is found in coastal areas of the mainland's eastern and southern regions, inhabiting Queensland, New South Wales, Victoria, and South Australia. It is easily recognisable by its stout, tailless body and large head with round, fluffy ears and large, spoon-shaped nose. The koala has a body length of 60–85 cm (24–33 in) and weighs 4–15 kg (9–33 lb). Fur colour ranges from silver grey to chocolate brown. Koalas from the northern populations are typically smaller and lighter in colour than their counterparts further south. These populations possibly are separate subspecies, but this is disputed.

Koalas typically inhabit open Eucalyptus woodland, as the leaves of these trees make up most of their diet. This eucalypt diet has low nutritional and caloric content and contains toxins that deter most other mammals from feeding on it. Koalas are largely sedentary and sleep up to twenty hours a day. They are asocial animals, and bonding exists only between mothers and dependent offspring. Adult males communicate with loud bellows that intimidate rivals and attract mates. Males mark their presence with secretions from scent glands located on their chests. Being marsupials, koalas give birth to underdeveloped young that crawl into their mothers' pouches, where they stay for the first six to seven months of their lives. These young koalas, known as joeys, are fully weaned around a year old. Koalas have few natural predators and parasites, but are threatened by various pathogens, such as Chlamydiaceae bacteria and koala retrovirus.
Answer: Eucalyptus makes up most of their diet. Joeys spend the first six to seven months of their lives in their mothers' pouches. They are threatened by pathogens like Chlamydiaceae and koala retrovirus.
Question: What does current consist of?
Evidence: The movement of electric charge is known as an electric current, the intensity of which is usually measured in amperes. Current can consist of any moving charged particles; most commonly these are electrons, but any charge in motion constitutes a current. Electric current can flow through some things, electrical conductors, but will not flow through an electrical insulator.
By historical convention, a positive current is defined as having the same direction of flow as any positive charge it contains, or to flow from the most positive part of a circuit to the most negative part. Current defined in this manner is called conventional current. The motion of negatively charged electrons around an electric circuit, one of the most familiar forms of current, is thus deemed positive in the opposite direction to that of the electrons. However, depending on the conditions, an electric current can consist of a flow of charged particles in either direction, or even in both directions at once. The positive-to-negative convention is widely used to simplify this situation.
The process by which electric current passes through a material is termed electrical conduction, and its nature varies with that of the charged particles and the material through which they are travelling. Examples of electric currents include metallic conduction, where electrons flow through a conductor such as metal, and electrolysis, where ions (charged atoms) flow through liquids, or through plasmas such as electrical sparks. While the particles themselves can move quite slowly, sometimes with an average drift velocity only fractions of a millimetre per second, the electric field that drives them itself propagates at close to the speed of light, enabling electrical signals to pass rapidly along wires.
In engineering or household applications, current is often described as being either direct current (DC) or alternating current (AC). These terms refer to how the current varies in time. Direct current, as produced by example from a battery and required by most electronic devices, is a unidirectional flow from the positive part of a circuit to the negative.  If, as is most common, this flow is carried by electrons, they will be travelling in the opposite direction. Alternating current is any current that reverses direction repeatedly; almost always this takes the form of a sine wave. Alternating current thus pulses back and forth within a conductor without the charge moving any net distance over time. The time-averaged value of an alternating current is zero, but it delivers energy in first one direction, and then the reverse. Alternating current is affected by electrical properties that are not observed under steady state direct current, such as inductance and capacitance. These properties however can become important when circuitry is subjected to transients, such as when first energised.
Answer: Current can consist of any moving charged particles; most commonly these are electrons, but any charge in motion constitutes a current.
Question: List five titles by George R.R. Martin.
Answer: A Game of Thrones, Dying of the Light, The Armageddon Rag, A Storm of Swords, A Song for Lya
Question: Why is finding the most frequently occurring word pairs in a text a common task in NLP?
Evidence: A common task in natural language processing is to find the most frequently occurring word pairs in a text(s) in the expectation that these pairs will shed some light on the main ideas of the text, or offer insight into the structure of the language. One might be interested in pairings of adjacent words, but in some cases one is also interested in pairs of words in some small neighborhood. The neighborhood is usually refered to as a window, and to illustrate the concept consider the following text and bigram set:
Text: “I like kitties and doggies”
Window: 2
Bigrams: {(I like), (like kitties), (kitties and), (and doggies)} and this one:
Text: “I like kitties and doggies”
Window: 4
Bigrams: {(I like), (I kitties), (I and), (like kitties), (like and), (like doggies), (kitties and), (kitties doggies), (and doggies)}.
A common task in natural language processing is to find the most frequently occurring word pairs in a text(s) in the expectation that these pairs will shed some light on the main ideas of the text, or offer insight into the structure of the language.
Answer: These pairs will shed some light on the main ideas of the text, or offer insight into the structure of the language
Question: How are the ties resolved while ranking for knowledge graph completion task?
Evidence: We use the filtered setting during evaluation on the standard training-validation-test split, randomly break ties for triplets with the same score (Sun et al., 2019b) and report Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hit@K (K=1,3,10).
Answer: We randomly break ties for triplets with the same score (denoted as RANDOM in [1]). We have clarified this in Section 5.1.
[1] A Re-evaluation of Knowledge Graph Completion Methods. Sun et al., ACL 2020.
Question: Name some of the characters from DC Comics.
Evidence: The Flash (Bartholomew Henry "Barry" Allen) is a superhero appearing in American comic books published by DC Comics. He is the second character known as the Flash, following Jay Garrick. The character first appeared in Showcase #4 (October 1956), created by writer Robert Kanigher and penciler Carmine Infantino.

Like other heroes who go by The Flash, Barry is a "speedster", with powers that derive mainly from his superhuman speed. He wears a distinct red and gold costume treated to resist friction and wind resistance, traditionally storing the costume compressed inside a ring. Originally created as a reimagining of the popular 1940s superhero The Flash (Jay Garrick), the success of the Barry Allen's Flash comic book helped to bring about the Silver Age of Comic Books, and contributed to a large growth in DC Comics' stable of science fiction comics and characters. During popular early volumes as the Flash, Barry established his own Rogues Gallery of colourful villains and sci-fi concepts like the Speed Force. Through crossovers with popular characters like Superman, Wonder Woman, and Batman, Barry Allen's Flash also helped establish DC's flagship Justice League title, whose success would define its publishing strategy for decades to come.
Answer: Superman, Wonder Woman, Batman and Flash.
Question: The t-SNE visualization of Figures 3 and 5 for KTH and the Radar Echo dataset would be interesting. Are the modes as clearly separated?
Evidence: t-SNE. As shown in Figure 13(a), we visualize the memory state of ConvLSTM using t-SNE (Van der Maaten & Hinton, 2008). It is observed that the learned cell states by ConvLSTM are entangled among different action groups. The t-SNE visualization result matches the PhyDNet visualization on the RoboNet dataset shown in Figure 3(a). Thus, these results verify that the STMC also exists under the real-world human motion dataset. While in ModeRNN, we further visualize the learned features of the slot bus in Figure 13(b), which shows 2 clusters with clear boundaries, corresponding to two action groups in the KTH dataset. According to these t-SNE results, we can find that directly training the previous methods on the mixed dynamics will lead to severe STMC in representation learning, shown as the entanglement of hidden representations. These entangled representations will make the model provide a poor ambiguous prediction. In contrast, ModeRNN can effectively overcome the STMC by learning an accurate decoupling for mixed dynamics.****Figure 13: (a, c) Illustration of STMC on the existing ConvLSTM model on KTH and radar echo dataset of Guangzhou (GZ). (b, d) The slot bus of ModeRNN shows discriminative representations on different groups of video dynamics. The two groups in KTH respectively correspond to subtle hand motion (e.g., hand-waving, hand-clapping, and boxing) and more global body motion (e.g., running, walking, and jogging). The two groups in Radar are divided by different seasons.
Answer: We added these t-SNE results in Figure 13 in the Appendix. As shown: - STMC still exists for KTH and Radar Echo. - The slot bus of ModeRNN learns discriminative representations on different groups of video dynamics (KTH: body motion vs. hand motion; Radar Echo: winter vs. summer).
Question: What are different ways of data collection?
Answer: Gathering data can be accomplished through a primary source (the researcher is the first person to obtain the data) or a secondary source (the researcher obtains the data that has already been collected by other sources, such as data disseminated in a scientific journal). Data analysis methodologies vary and include data triangulation and data percolation. The latter offers an articulate method of collecting, classifying, and analyzing data using five possible angles of analysis (at least three) to maximize the research's objectivity and permit an understanding of the phenomena under investigation as complete as possible: qualitative and quantitative methods, literature reviews (including scholarly articles), interviews with experts, and
Question: Classify the following hikes in terms of difficulty: Half dome in Yosemite, Mt Everest in Nepal, Mission Peak in California
Answer: Mt Everest is the toughest among the list based on scaling height of 28k feet and requires multiple days of hike, acclimatization at different altitudes, requires carrying oxygen tanks and heavy gear.

Half-dome is also considered as a difficult hike but less tougher than Mt Everest. Half-dome can be done as a day-hike and is at 4k elevation gain from the Yosemite valley and with moderate fitness & stamina its possible to do.

Mission Peak is considered a medium difficulty hike. It takes between 2-3 hours to scale Mission peak, it can be strenuous for people with health difficulties and less stamina.
Question: Does multitasking make someone more productive at work?
Answer: It is a commonly held belief that multitasking is a desirable attribute for an employee to possess. This is based on the idea that multitasking results in a higher volume of work product. However, the contrary view is that multitasking diminishes the quality of work. In other words, multitasking increases productivity if the goal is simply to produce more work product; but at the same time, it decreases the quality of each individual outcome.
Question: Give me a list of 10 nematode species
Answer: Caenorhabditis elegans,
Ascaris lumbricoides,
Pristionchus pacificus,
Trichuris suis,
Necator americanus,
Ancylostoma duodenale,
Heligmosomoides polygyrus,
Nippostrongylus brasiliensis,
Lumbricus terrestris,
Ostertagia ostertagi
Question: Give me a title of one of the "Harry Potter" novels.
Answer: Harry Potter and the Chamber of Secrets
Question: Tell me if these are countries in the Americas or in Europe: Spain, Guyana, Peru, Netherlands, Belgium, Canada, French Guiana
Answer: Spain: Europe, Guyana: Americas, Peru: Americas, Netherlands: Europe, Belgium: Europe, Canada: Americas, French Guiana: Americas
Question: What does the joint Bayesian model for unsupervised semantic role induction in multiple languages consist of?
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.
The model consists of individual Bayesian models for each language (Garg and Henderson, 2012), and crosslingual latent variables to incorporate soft role agreement between aligned constituents.
Answer: Individual Bayesian models for each language (Garg and Henderson, 2012), and crosslingual latent variables.
Question: Why is it challenging to analyze the metadata of tweets?
Evidence: Twitter, a micro-blogging and social networking site has emerged as a platform where people express themselves and react to events in real-time. It is estimated that nearly 500 million tweets are sent per day . Twitter data is particularly interesting because of its peculiar nature where people convey messages in short sentences using hashtags, emoticons, emojis etc. In addition, each tweet has meta data like location and language used by the sender. It's challenging to analyze this data because the tweets might not be grammatically correct and the users tend to use informal and slang words all the time. Hence, this poses an interesting problem for NLP researchers. Any advances in using this abundant and diverse data can help understand and analyze information about a person, an event, a product, an organization or a country as a whole. Many notable use cases of the twitter can be found here.
Along the similar lines, The Task 1 of WASSA-2017 BIBREF0 poses a problem of finding emotion intensity of four emotions namely anger, fear, joy, sadness from tweets. In this paper, we describe our approach and experiments to solve this problem. The rest of the paper is laid out as follows: Section 2 describes the system architecture, Section 3 reports results and inference from different experiments, while Section 4 points to ways that the problem can be further explored.
It’s challenging to analyze this data because the tweets might not be grammatically correct and the users tend to use informal and slang words all the time. Hence, this poses an interesting problem for NLP researchers. Any advances in using this abundant and diverse data can help understand and analyze information about a person, an event, a product, an organization or a country as a whole.
Answer: It’s challenging to analyze this data because the tweets might not be grammatically correct and the users tend to use informal and slang words all the time.
Question: What is the motto of the United States Marine Corps?
Answer: Adopted in 1883, Semper Fidelis: Latin for "Always Faithful" is the motto of the Marine Corps.  Semper Fidelis symbolizes the lifelong commitment held by every Marine for the Corps and America, a promise reciprocated by the Corps to all Marines.
Question: which side of the road you can drive in the UK?
Answer: In the UK, you are driving on the left side of the road, however, the driver is sitting on the right side of the car.
Question: What is the capital of Tamilnadu?
Evidence: Tamil Nadu (/ˌtæmɪl ˈnɑːduː/; Tamil: [ˈtamiɻ ˈnaːɽɯ] (listen), abbr. TN) is the southern-most state of India. The tenth largest Indian state by area and the sixth largest by population, Tamil Nadu is the home of the Tamil people, whose Tamil language—one of the longest surviving classical languages in the world—is widely spoken in the state and serves as its official language. The capital and largest city is Chennai.
Answer: Chennai
Question: How do the authors evaluate the proposed scheme?
Evidence: We evaluate our model on two publicly available datasets. The statistics for both are shown in Table TABREF3 . The details of these datasets are as follows:
OntoNotes: OntoNotes 5.0 BIBREF16 includes texts from five different text genres: broadcast conversation (200k), broadcast news (200k), magazine (120k), newswire (625k), and web data (300k). This dataset is annotated with 18 categories.
Wiki(gold): The training data consists of Wikipedia sentences and was automatically generated using a distant supervision method, mapping hyperlinks in Wikipedia articles to Freebase, which we do not use in this study. The test data, mainly consisting of sentences from news reports, was manually annotated as described in BIBREF8 . The class hierarchy is shown in Figure FIGREF2 . This dataset is annotated with 7 main categories (bold text in Figure FIGREF2 ), which maps directly to OntoNotes. The miscellaneous category in Figure FIGREF2 does not have direct mappings, so future work may include redefining these categories so the mappings are more meaningful.
We evaluate our model on two publicly available datasets.
Answer: They evaluate their model on two publicly available datasets.
Question: How to collect datasets?
Evidence: The proliferation of the online social media has lately resulted in the democratization of online content sharing. Among other media, Twitter is very popular for research and application purposes due to its scale, representativeness and ease of public access to its content. However, tweets, that are short messages of up to 140 characters, pose several challenges to traditional Natural Language Processing (NLP) systems due to the creative use of characters and punctuation symbols, abbreviations ans slung language.
Named Entity Recognition (NER) is a fundamental step for most of the information extraction pipelines. Importantly, the terse and difficult text style of tweets presents serious challenges to NER systems, which are usually trained using more formal text sources such as newswire articles or Wikipedia entries that follow particular morpho-syntactic rules. As a result, off-the-self tools trained on such data perform poorly BIBREF0 . The problem becomes more intense as the number of entities to be identified increases, moving from the traditional setting of very few entities (persons, organization, time, location) to problems with more. Furthermore, most of the resources (e.g., software tools) and benchmarks for NER are for text written in English. As the multilingual content online increases, and English may not be anymore the lingua franca of the Web. Therefore, having resources and benchmarks in other languages is crucial for enabling information access worldwide.
In this paper, we propose a new benchmark for the problem of NER for tweets written in French. The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities. The annotators were native speakers of French and had previous experience in the task of NER. Overall, the generated datasets consists of INLINEFORM0 tweets, split in training and test parts.
The paper is organized in two parts. In the first, we discuss the data preparation steps (collection, annotation) and we describe the proposed dataset. The dataset was first released in the framework of the CAp 2017 challenge, where 8 systems participated. Following, the second part of the paper presents an overview of baseline systems and the approaches employed by the systems that participated. We conclude with a discussion of the performance of Twitter NER systems and remarks for future work.
The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities.
Answer: The dataset was collected using the publicly available Twitter API.
Question: What are some weird ways I can cut my grass?
Answer: If you aren't using a lawnmower to cut your grass, here are some weird ideas to keep your grass trimmed:

- Buy a goat and leave it in your yard
- Spend all day cutting each blade with some craft scissors
- Flamethrower
- Dress up like the grim reaper and use a scythe
Question: Why does sand get firmer when it is wet?
Answer: Sand is made up of tiny grain particles that are largely oval or flat. When they get wet, the particles can more easily move around and align, which packs them more tightly together.
Question: What is the indigenous name for Dunk Island?
Evidence: Dunk Island, known as Coonanglebah in the Warrgamay and Dyirbal languages, is an island within the locality of Dunk in the Cassowary Coast Region, Queensland, Australia. It lies 4 kilometres (2.5 mi) off the Australian east coast, opposite the town of Mission Beach. The island forms part of the Family Islands National Park and is in the larger Great Barrier Reef World Heritage Area.
Answer: Dunk Island is known as Coonanglebah in the Warrgamay and Dyirbal languages.