DataHammer/scimrc huggingface dataset:
Question: What model do we propose to correct inconsistencies between sentence-level translations in context-free machine translation systems?
Evidence: We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations of a context-agnostic MT system. It does not use any states of a trained MT model whose outputs it corrects and therefore can in principle be trained to correct translations from any black-box MT system.
The DocRepair model requires only monolingual document-level data in the target language. It is a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. Consistent groups come from monolingual document-level data. To obtain inconsistent groups, each sentence in a group is replaced with its round-trip translation produced in isolation from context. More formally, forming a training minibatch for the DocRepair model involves the following steps (see also Figure FIGREF9):
sample several groups of sentences from the monolingual data;
for each sentence in a group, (i) translate it using a target-to-source MT model, (ii) sample a translation of this back-translated sentence in the source language using a source-to-target MT model;
using these round-trip translations of isolated sentences, form an inconsistent version of the initial groups;
use inconsistent groups as input for the DocRepair model, consistent ones as output.
At test time, the process of getting document-level translations is two-step (Figure FIGREF10):
produce translations of isolated sentences using a context-agnostic MT model;
apply the DocRepair model to a sequence of context-agnostic translations to correct inconsistencies between translations.
In the scope of the current work, the DocRepair model is the standard sequence-to-sequence Transformer. Sentences in a group are concatenated using a reserved token-separator between sentences. The Transformer is trained to correct these long inconsistent pseudo-sentences into consistent ones. The token-separator is then removed from corrected translations.
We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations of a context-agnostic MT system.
Answer: Monolingual DocRepair model.
Question: Do they use crowdsourcing to collect human judgements?
Evidence: We collect human judgments on Amazon Mechanical Turk via ParlAI BIBREF18. We present summaries one sentence at a time, along with the entire article. For each summary sentence, the annotator makes a binary decision as to whether the sentence is factually consistent with the article. Workers are instructed to mark non-grammatical sentences as not consistent, and copies of article sentences as consistent. Workers are paid $1 per full summary annotated. See Appendix SECREF10 for further details.
We collect human judgments on Amazon Mechanical Turk via ParlAI BIBREF18.
Answer: Yes.
Question: Which is a species of fish? Walu or Whitehead
Answer: Walu
Question: When was The Palpable Leprosy of Pollution released?
Evidence: Infant Annihilator are an English deathcore band formed in Hull, East Riding of Yorkshire in 2012 by drummer Aaron Kitcher and guitarist Eddie Pickard. The band are known for their technical, eclectic and extreme musical style; parodistic and satirically graphic lyrical content and shock humour; and music videos that feature ribald themes.

Their debut album The Palpable Leprosy of Pollution, which features the American vocalist Dan Watson, was released in late 2012. After replacing their vocalist with Massachusetts-native Dickie Allen, their second album The Elysian Grandeval Galèriarch was recorded and mixed by Jesse Kirkbride at his home studio Kirkbride Recordings and was released in 2016. Their third album, The Battle of Yaldabaoth, was released on 11 September 2019.

Infant Annihilator were described by the Hysteria Magazine as an internet band and even though they have stated that touring is a possibility, they have performed only as a studio project so far.
Answer: The album The Palpable Leprosy of Pollution was released in late 2012 featuring the American vocalist Dan Watson.
Question: What do the authors use to draw texts paired with gold keyphrases?
Evidence: Keyphrases are single or multi-word lexical units that best summarise a document BIBREF0. As such, they are of great importance for indexing, categorising and browsing digital libraries BIBREF1. Yet, very few documents have keyphrases assigned, thus raising the need for automatic keyphrase generation systems. This task falls under the task of automatic keyphrase extraction which can also be the subtask of finding keyphrases that only appear in the input document. Generating keyphrases can be seen as a particular instantiation of text summarization, where the goal is not to produce a well-formed piece of text, but a coherent set of phrases that convey the most salient information. Those phrases may or may not appear in the document, the latter requiring some form of abstraction to be generated. State-of-the-art systems for this task rely on recurrent neural networks BIBREF2, BIBREF3, BIBREF4, and hence require large amounts of annotated training data to achieve good performance. As gold annotated data is expensive and difficult to obtain BIBREF5, previous works focused on readily available scientific abstracts and used author-assigned keyphrases as a proxy for expert annotations. However, this poses two major issues: 1) neural models for keyphrase generation do not generalize well across domains, thus limiting their use in practice; 2) author-assigned keyphrases exhibit strong consistency issues that negatively impacts the model's performance. There is therefore a great need for annotated data from different sources, that is both sufficiently large to support the training of neural-based models and that comprises gold-standard labels provided by experts. In this study, we address this need by providing KPTimes, a dataset made of 279 923 news articles that comes with editor-assigned keyphrases.
Online news are particularly relevant to keyphrase generation since they are a natural fit for faceted navigation BIBREF6 or topic detection and tracking BIBREF7. Also, and not less importantly, they are available in large quantities and are sometimes accompanied by metadata containing human-assigned keyphrases initially intended for search engines. Here, we divert these annotations from their primary purpose, and use them as gold-standard labels to automatically build our dataset. More precisely, we collect data by crawling selected news websites and use heuristics to draw texts paired with gold keyphrases. We then explore the resulting dataset to better understand how editors tag documents, and how these expert annotations differ from author-assigned keyphrases found in scholarly documents. Finally, we analyse the performance of state-of-the-art keyphrase generation models and investigate their transferability to the news domain and the impact of domain shift.
More precisely, we collect data by crawling selected news websites and use heuristics to draw texts paired with gold keyphrases.
Answer: Heuristics.
Question: Provide a list of ten countries that don't have any ocean borders, in alphabetical order.
Answer: The following countries are landlocked and have no coastal borders: Afghanistan, Austra, Bhutan, Bolivia, Hungary, Laos, Nepal, Serbia, Switzerland and Zimbabwe.
Question: What feedback labels are used?
Evidence: The proposed models use binary classifiers to perform point-wise ranking, and therefore require a labelled dataset. To generate it, we explore two implicit user-feedback labelling strategies: five-minute reuse and one-day return. Online A/B testing is used to determine if these labelling strategies are suited to optimize the desired user-satisfaction metrics, and offline data to evaluated and compared the system's performance.
To generate it, we explore two implicit user-feedback labelling strategies: five-minute reuse and one-day return. 
Answer: The answers are shown as follows:
* five-minute reuse and one-day return
Question: What is the research field of their paper?
Evidence: In this article, we describe some discursive segmentation methods as well as a preliminary evaluation of the segmentation quality. Although our experiment were carried for documents in French, we have developed three discursive segmentation models solely based on resources simultaneously available in several languages: marker lists and a statistic POS labeling. We have also carried out automatic evaluations of these systems against the Annodis corpus, which is a manually annotated reference. The results obtained are very encouraging.
In this article, we describe some discursive segmentation methods as well as an evaluation of the segmentation quality.
Answer: Discursive segmentation.
Question: What factors will influence the results in the case that the training data are translated?
Evidence: Here we discuss the case that the training data are translated. We consider each result is affected by at least three factors: (1) training corpus, (2) data size, (3) whether the source corpus is translated into the target language. To study the effect of data-size, we conducted an extra experiment where we down-sampled the size of English data to be the same as Chinese corpus, and used the down-sampled corpus to train. Then We carried out one-way ANOVA test and found out the significance of the three factors are ranked as below: (1) > (2) >> (3). The analysis supports that the characteristics of training data is more important than translated into target language or not. Therefore, although translation degrades the performance, whether translating the corpus into the target language is not critical.
Here we discuss the case that the training data are translated. We consider each result is affected by at least three factors: (1) training corpus, (2) data size, (3) whether the source corpus is translated into the target language.
Answer: (1) training corpus, (2) data size, (3) whether the source corpus is translated into the target language.
Question: Give me a list of 5 cool starter projects a student new to computer programming could have fun making.
Answer: Here are some great starter projects for those learning to code:
1. Conway's Game of Life
2. Rock Paper Scissors
3. Connect 4
4. Choose Your Own Adventure style text-based game
5. Blackjack Simulator
Question: Current industry trends and how to capitalize on them?
Answer: 1. Increase focus on sustainability
2. Cutting unnecessary costs
3. Emphasis on digital transformation
Question: Is this a list of universities or companies: Alphabet, Microsoft, Dell, John Deere, and JP Morgan Chase?
Answer: That is a list of companies.
Question: What are the 3 largest oil companies?
Answer: The 3 largest oil companies are Saudi Aramco, ExxonMobil, and Chevron.
Question: Extract some details about the company 'Bath & Body Works' from the given passage
Evidence: Bath & Body Works was founded in 1990 in New Albany, Ohio. The company's first store opened in a Cambridge, Massachusetts mall in September 1990. In 1997, a secondary brand called Bath & Body Works at Home was launched. Two years later the company launched the White Barn Candle Company, a division specializing in the sale of scented candles.
Answer: 1.	In 1990, New Albany, Ohio, Bath & Body Works was founded.
2.	In the same year Sept 1990 , the company's first store was opened in a Cambridge, Massachusetts mall.
3.	In year 1997, a secondary brand called Bath & Body Works at Home was also launched.
4.	Two years after the lauch, they launched another company named the White Barn Candle Company, a division specializing in the sale of scented candles.
Question: Where is a good place to go on holiday in Cornwall, England?
Answer: Without a doubt, the best place in Cornwall is Rock. Situated in close proximity to both Daymer Bay and Polzeath beach, you have easy access to great beaches and breathtaking clliff top walks. In addition, the Camel estuary is close by which is great for dinghy sailing and there is a frequent water taxi that will take you over the estuary from Rock to Padstow (which is full of good pubs and restaurants).

If one is partial golf, St Enedoc's golf course can be found in Rock which boasts one of the most beautiful Links in the South West.  On the Links one will find St Enedoc's Church, an enigmatic church sunken into the dunes and also the burial place of Sir John Betjeman CBE, who was Poet Laureate from 1972 until his death on 19 May 1984.
Question: What does the Gini coefficient measure?
Evidence: In economics, the Gini coefficient, also known as the Gini index or Gini ratio, is a measure of statistical dispersion intended to represent the income inequality or the wealth inequality or the consumption inequality within a nation or a social group. It was developed by statistician and sociologist Corrado Gini.
The Gini coefficient measures the inequality among values of a frequency distribution, such as levels of income. A Gini coefficient of 0 reflects perfect equality, where all income or wealth values are the same, while a Gini coefficient of 1 (or 100%) reflects maximal inequality among values. For example, if everyone has the same income, the Gini coefficient will be 0. In contrast, a Gini coefficient of 1 indicates that within a group of people, a single individual has all the income or consumption, while all others have none.
The Gini coefficient was proposed by Corrado Gini as a measure of inequality of income or wealth.  For OECD countries, in the late 20th century, considering the effect of taxes and transfer payments, the income Gini coefficient ranged between 0.24 and 0.49, with Slovenia being the lowest and Mexico the highest. African countries had the highest pre-tax Gini coefficients in 2008–2009, with South Africa having the world's highest, estimated to be 0.63 to 0.7, although this figure drops to 0.52 after social assistance is taken into account, and drops again to 0.47 after taxation. The global income Gini coefficient in 2005 has been estimated to be between 0.61 and 0.68 by various sources.
Answer: The Gini coefficient measures the inequality among values of a frequency distribution, such as levels of income.
Question: Create a beginner-intermediate push-pull-legs workout routine that I can do 6 times a week.
Answer: Day 1 (push):
5x5 Bench Press
3x8 Incline Bench Press
3x8 Overhead Press
3x8-12 Triceps Pushdown
3x8-12 Triceps Extension superset with 3x8-12 Lateral Raise

Day 2 (pull):
1x5 Deadlift
3x8 Lat Pulldown
3x8 Seated Row
3x12-15 Face Pull
4x8-12 Incline Curl
4x8-12 Hammer Curl

Day 3 (legs):
2x5 Squat
3x8 Romanian Deadlift
3x8 Leg Press
3x8-12 Seated Leg Curl
3x8-12 Standing Calf Raise

Day 4: (push)
5x5 Overhead Press
3x8 Incline Bench Press
3x8 Bench Press
3x8-12 Triceps Pushdown
3x8-12 Triceps Extension superset with 3x8-12 Lateral Raise

Day 5 (pull):
5x5 Bent Over Row
3x8 Lat Pulldown
3x8 Seated Row
3x12-15 Face Pull
4x8-12 Incline Curl
4x8-12 Hammer Curl

Day 6 (legs):
2x5 Squat
3x8 Romanian Deadlift
3x8 Leg Press
3x8-12 Seated Leg Curl
3x8-12 Standing Calf Raise
Question: What corpus did the authors define for each language?
Evidence: There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions.
The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification.
For each language, we define a train, development and test corpus.
Answer: The authors define a train, development and test corpus for each language.
Question: What are the different ways to score in American football?
Answer: The most common way to score in American football is a touchdown, which is worth 6 points. After each touchdown, you have the option of scoring 1 or 2 points during the "extra point". Many teams elect to kick field goals when they can't get close enough to score a touchdown. Field goals are worth 3 points each. Defensive players can also score a safety, which is worth 2 points.
Question: From the passage identify the areas where cytokines can help the host immune responses. Display the results in a comma separated format.
Evidence: Cytokines are a broad and loose category of small proteins (~5–25 kDa) important in cell signaling. Cytokines are peptides and cannot cross the lipid bilayer of cells to enter the cytoplasm. Cytokines have been shown to be involved in autocrine, paracrine and endocrine signaling as immunomodulating agents.Cytokines include chemokines, interferons, interleukins, lymphokines, and tumour necrosis factors, but generally not hormones or growth factors (despite some overlap in the terminology). Cytokines are produced by a broad range of cells, including immune cells like macrophages, B lymphocytes, T lymphocytes and mast cells, as well as endothelial cells, fibroblasts, and various stromal cells; a given cytokine may be produced by more than one type of cell. They act through cell surface receptors and are especially important in the immune system; cytokines modulate the balance between humoral and cell-based immune responses, and they regulate the maturation, growth, and responsiveness of particular cell populations. Some cytokines enhance or inhibit the action of other cytokines in complex ways. They are different from hormones, which are also important cell signaling molecules. Hormones circulate in higher concentrations, and tend to be made by specific kinds of cells. Cytokines are important in health and disease, specifically in host immune responses to infection, inflammation, trauma, sepsis, cancer, and reproduction.The word comes from the ancient Greek language: cyto, from Greek κύτος, kytos, 'cavity, cell' + kines, from Greek κίνησις, kinēsis, 'movement'.
Answer: infection, inflammation, trauma, sepsis, cancer, reproduction
Question: What is the largest state by land size in the United States of America?
Answer: The largest state by land size in the United States of America is Alaska.
Question: What are some tools that every homeowner should have?
Answer: There is a tool designed for almost every challenge a homeowner will face, but purchasing every tool could prove to be cost prohibitive, especially if they are highly specialized for tasks that may occur infrequently.  It's best to have a general set of tools that have a wide variety of uses, including a hammer, a set of screwdrivers both flat and philips, a tape measure, a level, a stud finder, and a utility knife. A battery powered drill and impact driver can also save time, and make mounting projects easier.
Question: What syntactic structure is used to model tones?
Evidence: Based on the above considerations, in this paper, we present CSSMCM, a sentence-level Chinese Mandarin lip reading network, which contains three sub-networks. Same as BIBREF5 , in the first sub-network, pinyin sequence is predicted from the video. Different from BIBREF5 , which predicts pinyin characters from video, pinyin is taken as a whole in CSSMCM, also known as syllables. As we know, Mandarin Chinese is a syllable-based language and syllables are their logical unit of pronunciation. Compared with pinyin characters, syllables are a longer linguistic unit, and can reduce the difficulty of syllable choices in the decoder by sequence-to-sequence attention-based models BIBREF6 . Chen et al. BIBREF7 find that there might be a relationship between the production of lexical tones and the visible movements of the neck, head, and mouth. Motivated by this observation, in the second sub-network, both video and pinyin sequence is used as input to predict tone. Then in the third sub-network, video, pinyin, and tone sequence work together to predict the Chinese character sequence. At last, three sub-networks are jointly finetuned to improve overall performance.
Same as BIBREF5 , in the first sub-network, pinyin sequence is predicted from the video. Different from BIBREF5 , which predicts pinyin characters from video, pinyin is taken as a whole in CSSMCM, also known as syllables. As we know, Mandarin Chinese is a syllable-based language and syllables are their logical unit of pronunciation.
Answer: The answers are shown as follows:
* syllables
Question: Are there companies that build a plane end to end?
Evidence: Few companies produce planes on a large scale. However, the production of a plane for one company is a process that actually involves dozens, or even hundreds, of other companies and plants, that produce the parts that go into the plane. For example, one company can be responsible for the production of the landing gear, while another one is responsible for the radar. The production of such parts is not limited to the same city or country; in the case of large plane manufacturing companies, such parts can come from all over the world.

The parts are sent to the main plant of the plane company, where the production line is located. In the case of large planes, production lines dedicated to the assembly of certain parts of the plane can exist, especially the wings and the fuselage
Answer: A plane is typically not created by a single entity, but instead is a collaboration between numerous parts manufacturers and assemblers.
Question: What is "Scandoval?"
Answer: "Scandoval" is a term coined to describe the interpersonal relationship drama between multiple cast members on the reality show Vanderpump Rules (VPR). It combines the word "scandal" with the surname of the main perpetrator, Tom Sandoval. Tom Sandoval, a sometime bartender and longtime cast member on VPR, had an affair on his girlfriend of 8 years, fellow bartender and cast member Ariana Maddox, with Raquel Leviss. Leviss is a VPR cast member, former beauty queen, and most notably, one of Ariana's closest friends. The scandal shocked cast members and VPR fans alike, primarily due to the unforeseen nature of the affair, as well as Tom and Raquel's lack of remorse for their behavior. The drama became so nationally amplified that even celebrities - from model Chrissy Teigen to SNL's Aidy Bryant - and fashion brands - including bag brand Beis - made public comments regarding it.