DataHammer/scimrc huggingface dataset:
Question: What does additional linguistic and contextual information use 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 further analysis.
Question: What models do the authors emphasize?
Evidence: If the translation of a pronoun requires context beyond the current sentence (see the example in Figure FIGREF1 ), a natural extension of sentence-level NMT models is to condition the model prediction on this necessary context. In the following, we describe a number of existing approaches to making models “aware” of additional context.
The simplest possible extension is to translate units larger than sentences. BIBREF5 concatenate each sentence with the sentence that precedes it, for the source side of the corpus or both sides. All of their models are standard sequence-to-sequence models built with recurrent neural networks (RNNs), since the method does not require any architectural change. BIBREF11 use the same concatenation technique with a Transformer architecture BIBREF2 , and experiment with wider context.
A number of works do propose changes to the NMT architecture. A common technique is to extend a standard encoder-decoder model by additional encoders for the context sentence(s), with a modified attention mechanism BIBREF6 , BIBREF9 , BIBREF8 . One aspect that differs between these works is the architecture of the encoder and attention. While BIBREF6 , BIBREF9 extend an RNN encoder-decoder with a second encoder that the decoder attends to, BIBREF8 extend the Transformer architecture with an encoder that is attended to by the main encoder. BIBREF8 also introduce parameter sharing between the main encoder and the context encoder, but do not empirically demonstrate its importance.
While the number of encoded sentences in the previous work is fixed, BIBREF7 , BIBREF10 explore the integration of variable-size context through a hierarchical architecture, where a first-level RNN reads in words to produce sentence vectors, which are then fed into a second-level RNN to produce a document summary.
Apart from differences in the architectures, related work varies in whether it considers source context, target context, or both (see Table TABREF9 for an overview of language arcs and context types). Some work considers only source context, but for pronoun translation, target-side context is intuitively important for disambiguation, especially if the antecedent itself is ambiguous. In our evaluation, we therefore emphasize models that take into account both source and target context.
Our experiments are based on models from BIBREF9 , who have released their source code. We extend their models with parameter sharing, which was shown to be beneficial by BIBREF8 . Additionally, we consider a concatenative baseline, similar to BIBREF5 , and Transformer-based models BIBREF8 .
This section describes several context-aware NMT models that we use in our experiments. They fall into two major categories: models based on RNNs and models based on the Transformer architecture BIBREF2 . We experiment with additional context on the source side and target side.
In our evaluation, we therefore emphasize models that take into account both source and target context.
Answer: The authors emphasize models that take into account both source and target context.
Question: What do the authors propose in this paper?
Evidence: Neural machine translation (NMT) emerged in the last few years as a very successful paradigm BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . While NMT is generally more fluent than previous statistical systems, adequacy is still a major concern BIBREF4 : common mistakes include dropping source words and repeating words in the generated translation.
Previous work has attempted to mitigate this problem in various ways. BIBREF5 incorporate coverage and length penalties during beam search—a simple yet limited solution, since it only affects the scores of translation hypotheses that are already in the beam. Other approaches involve architectural changes: providing coverage vectors to track the attention history BIBREF6 , BIBREF7 , using gating architectures and adaptive attention to control the amount of source context provided BIBREF8 , BIBREF9 , or adding a reconstruction loss BIBREF10 . BIBREF11 also use the notion of fertility implicitly in their proposed model. Their “fertility conditioned decoder” uses a coverage vector and an “extract gate” which are incorporated in the decoding recurrent unit, increasing the number of parameters.
In this paper, we propose a different solution that does not change the overall architecture, but only the attention transformation. Namely, we replace the traditional softmax by other recently proposed transformations that either promote attention sparsity BIBREF12 or upper bound the amount of attention a word can receive BIBREF13 . The bounds are determined by the fertility values of the source words. While these transformations have given encouraging results in various NLP problems, they have never been applied to NMT, to the best of our knowledge. Furthermore, we combine these two ideas and propose a novel attention transformation, constrained sparsemax, which produces both sparse and bounded attention weights, yielding a compact and interpretable set of alignments. While being in-between soft and hard alignments (Figure FIGREF20 ), the constrained sparsemax transformation is end-to-end differentiable, hence amenable for training with gradient backpropagation.
To sum up, our contributions are as follows:
Furthermore, we combine these two ideas and propose a novel attention transformation, constrained sparsemax, which produces both sparse and bounded attention weights, yielding a compact and interpretable set of alignments.
Answer: A novel attention transformation, constrained sparsemax, which produces both sparse and bounded attention weights, yielding a compact and interpretable set of alignments.
Question: Summarize what the network Bravo is about
Evidence: Bravo is an American basic cable television network, launched on December 8, 1980. It is owned by the NBCUniversal Television and Streaming division of Comcast's NBCUniversal. The channel originally focused on programming related to fine arts and film. It currently mainly focuses on lifestyle reality television series targeted at 25-to-54-year-old women as well as the LGBTQIA+ community.

As of January 2016, approximately 89,824,000 American households (77% of households with TV) receive Bravo.
Answer: Bravo is a television network that show reality TV shows which are targeted at 25-54 year old women as well as the LGBTQIA+ community. Approximately 77% of households with a TV have access to Bravo.
Question: How can I initiate conversation with a stranger?
Answer: First impression is one thing which leaves an everlasting impact on people we meet. So, meeting and greeting everyone with a smile is the key to leave that pleasant effect. 

Next, talking about general topics based on where you are meeting interests the person to talk to you. Make sure to ask open ended questions so that the person have the opportunity to speak a bit more.

Listening is the key to have a good conversation. During initial general conversation, try to find common interests to take the conversation forward. Make sure to show interest in the person you are speaking to.

Body postures also is an important part of being a pro at conversation striker. Positive gestures and body postures makes one take interest in what you are speaking and also, make anyone feel you are interested in listening and making friends.

Finally, practice makes a man perfect. If you are a beginner, try setting goal for yourself. For example, determine yourself to talk to at least three strangers in a week and try increasing the goal and one day you would be able to initiate a conversation effortlessly.
Question: Given this paragraph about the tour de france, has the race ever not ran
Evidence: The race was first organized in 1903 to increase sales for the newspaper L'Auto and is currently run by the Amaury Sport Organisation. The race has been held annually since its first edition in 1903 except when it was stopped for the two World Wars. As the Tour gained prominence and popularity, the race was lengthened and its reach began to extend around the globe. Participation expanded from a primarily French field as more riders from all over the world began to participate in the race each year. The Tour is a UCI World Tour event, which means that the teams that compete in the race are mostly UCI WorldTeams, with the exception of the teams that the organizers invite. It has become "the world's biggest annual sporting event."
Answer: The tour de france has ran since 1903, with the only noted cancellations happening for the two world wars
Question: What is game theory?
Evidence: Game theory is the study of mathematical models of strategic interactions among rational agents. It has applications in all fields of social science, as well as in logic, systems science and computer science. Originally, it addressed two-person zero-sum games, in which each participant's gains or losses are exactly balanced by those of other participants. In the 21st century, game theory applies to a wide range of behavioral relations; it is now an umbrella term for the science of logical decision making in humans, animals, as well as computers.
Answer: Game theory is a scientific study of mathematical models and strategies. It has wide applications in all aspects of social science, system, and computer science that include zero-sum games and behavioral relations. Currently Game Theory is an umbrella term for decision making in humans, animals, and computers.
Question: What are the words of House Graceford?
Answer: "Work Her Will"
Question: Classify each of the following as either a citrus fruit or non citrus-fruit: apples, pears, watermelons, lemons, clementines, mandarines
Answer: Apples are not a citrus fruit.
Pears are not a citrus fruit.
Watermelons are not a citrus fruit.
Lemons are a citrus fruit.
Clementines are a citrus fruit.
Mandarines are a citrus fruit.
Question: Given the reference article, Please give marriage date of indian actor Shahrukh Khan
Evidence: Although Khan was given the birth name Shahrukh Khan, he prefers his name to be written as Shah Rukh Khan, and is commonly referred to by the initialism SRK. He married Gauri Chibber, a Punjabi Hindu, in a traditional Hindu wedding ceremony on 25 October 1991, after a six-year courtship. They have a son Aryan (born 1997) and a daughter Suhana (born 2000). In 2013, they became parents of a third child, a son named AbRam, who was born through a surrogate mother. Both his elder children have expressed interest in entering the entertainment industry; Khan has stated that Aryan, who studied filmmaking at the USC School of Cinematic Arts in California, aspires to become a writer-director, while Suhana, who served as assistant director for Khan's film Zero (2018), is studying drama and acting at NYU's Tisch School of the Arts for higher education. Suhana made her acting debut in November 2019, in a short film titled "The Grey Part of Blue." According to Khan, while he strongly believes in Islam, he also values his wife's religion. His children follow both religions; at his home the Qur'an is situated next to the murti of Hindu deities.
Answer: Shahrukh Khan married Gauri in a traditional Hindu wedding ceremony on 25 October 1991
Question: What are the primary grapes in a red Bordeaux?
Evidence: Red Bordeaux is generally made from a blend of grapes. Permitted grapes are Cabernet Sauvignon, Cabernet Franc, Merlot, Petit Verdot, Malbec and Carménère. Today Carménère is rarely used, with Château Clerc Milon, a fifth growth Bordeaux, being one of the few to still retain Carménère vines. As of July 2019, Bordeaux wineries authorized the use of four new red grapes to combat effects of climate change on viticulture in Bordeaux. These newly approved grapes are Marselan, Touriga Nacional, Castets, and Arinarnoa.
Answer: Cabernet Sauvignon, Cabernet Franc, Merlot, Petit Verdot, Malbec
Question: In total how long was Winston Churchill the Prime Minister of the United Kingdom.
Evidence: Sir Winston Leonard Spencer Churchill (30 November 1874 – 24 January 1965) was a British statesman, soldier, and writer who served as Prime Minister of the United Kingdom twice, from 1940 to 1945 during the Second World War, and again from 1951 to 1955. Apart from two years between 1922 and 1924, he was a Member of Parliament (MP) from 1900 to 1964 and represented a total of five constituencies. Ideologically an economic liberal and imperialist, he was for most of his career a member of the Conservative Party, which he led from 1940 to 1955. He was a member of the Liberal Party from 1904 to 1924.
Answer: A total of 9 years in 2 spells between 1940 to 1945 and 1951 to 1955.
Question: What task do they evaluate the CEQE model on?
Evidence: We evaluate our CEQE model on the WMT2018 Quality Estimation Shared Task for word-level English-German, German-English, English-Czech, and English-Latvian QE. Words in all languages are lowercased. The evaluation metric is the multiplication of F1-scores for the “OK” and “BAD” classes against the true labels. F1-score is the harmonic mean of precision and recall. In Table TABREF15 , our model achieves the best performance on three out of six test sets in the WMT 2018 word-level QE shared task.
We evaluate our CEQE model on the WMT2018 Quality Estimation Shared Task3 for wordlevel English-German, German-English, EnglishCzech, and English-Latvian QE. Words in all languages are lowercased.
Answer: WMT2018 Quality Estimation Shared Task3
Question: Where is the training data from?
Evidence: Base phrase chunking is a cheap sequence-labeling–based alternative to full syntactic parsing, where the sequence consists of non-overlapping labeled segments (Fig. FIGREF1 includes an example.) Full syntactic trees can be converted into such shallow syntactic chunk sequences using a deterministic procedure BIBREF9. BIBREF12 offered a rule-based transformation deriving non-overlapping chunks from phrase-structure trees as found in the Penn Treebank BIBREF13. The procedure percolates some syntactic phrase nodes from a phrase-syntactic tree to the phrase in the leaves of the tree. All overlapping embedded phrases are then removed, and the remainder of the phrase gets the percolated label—this usually corresponds to the head word of the phrase.
In order to obtain shallow syntactic annotations on a large corpus, we train a BiLSTM-CRF model BIBREF14, BIBREF15, which achieves 97% $F_1$ on the CoNLL 2000 benchmark test set. The training data is obtained from the CoNLL 2000 shared task BIBREF12, as well as the remaining sections (except §23 and §20) of the Penn Treebank, using the official script for chunk generation. The standard task definition from the shared task includes eleven chunk labels, as shown in Table TABREF4.
The size of the shallow syntactic feature embedding was 50 across all experiments, initialized uniform randomly.
All model implementations are based on the AllenNLP library BIBREF33.
The training data is obtained from the CoNLL 2000 shared task (Tjong Kim Sang and Buchholz, 2000), as well as the remaining sections (except §23 and §20) of the Penn Treebank, using the official script for chunk generation.
Answer: It obtained from the CoNLL 2000 shared task (Tjong Kim Sang and Buchholz, 2000), as well as the remaining sections (except §23 and §20) of the Penn Treebank.
Question: Given this paragraph about the tour de france, how many riders are in the tour
Evidence: There are usually between 20 and 22 teams, with eight riders in each. All of the stages are timed to the finish; the riders' times are compounded with their previous stage times. The rider with the lowest cumulative finishing times is the leader of the race and wears the yellow jersey. While the general classification garners the most attention, there are other contests held within the Tour: the points classification for the sprinters, the mountains classification for the climbers, young rider classification for riders under the age of 26, and the team classification, based on the first three finishers from each team on each stage. Achieving a stage win also provides prestige, often accomplished by a team's sprint specialist or a rider taking part in a breakaway.
Answer: The are approximately between 160 and 176 riders start the tour de france. Each team has 8 member riders with between 20 and 22 teams participating.
Question: Please summarize the challenges faced by the factory workers in The Matchgirls musical.
Evidence: The Matchgirls is a musical by Bill Owen and Tony Russell about the London matchgirls strike of 1888. It premiered at the Globe Theatre, London, on 1 March 1966, directed and choreographed by Gillian Lynne.

The musical focuses on the lifestyle of the match cutters at the Bryant and May factory in Bow, London, with strong references to the condition Phossy jaw and the political climate of the era.

Production history
After out-of-town tryouts in Leatherhead, Surrey, the show opened on the West End in March 1966. The show closed about three months later.

A cast recording was made of the 1966 Globe Theatre London production. The musical was published by Samuel French Ltd in 1979.

Synopsis
The central character of the musical is Kate, a tenement girl and factory worker, who writes to Annie Besant to ask for help in seeking reform at the Bryant and May factory. The story follows Kate and Annie's attempts to rally the girls, leading Kate to become a reckless strike-leader and a key player in the creation and recognition of the union. With much of the action set in the incongruously named, but fictional, 'Hope Court', the musical portrays Bryant and May as callous and uncaring employers, with factory foreman 'Mr Mynel' representing the threatening and imposing regime in which the girls were forced to work.

There is also a sub plot in which Kate's involvement in the strike puts strain on her relationship with docker Joe.

Despite the subject matter of the musical, a strong emphasis is placed on the positive mentality and natural ebullience of the so-called 'cockney sparrows', this leading to a number of cheerful and entertaining vocal numbers and dance routines.
Answer: The Bryant and May factory was a popular employer in the 1960's and 70's in the fictional town of Hope Court. This factory hired many semi-skilled workers to produce their goods.  The story focuses on Kate who felt that the working conditions needed reform and put together a union to demand better working conditions for her and her peers. The musical follows the highs and lows of Kate and peers as they fight for better working conditions.
Question: Classify the below companies based on their market capitalization.
Reliance, Adani Enterprise, Tanla, Globus Spirits, Apollo Hospitals, NELCO, Zee Entertainment Enterprises Ltd
Answer: Small Cap - Tanla, Globus Spirits, NELCO
Mid Cap - Zee Entertainment Enterprises Ltd
Large Cap - Reliance, Adani Enterprise, Apollo Hospitals
Question: What are the tax boxes in the Netherlands?
Answer: There are three tax boxes in the Netherlands:
- Box One: This box includes income from employment or primary residence
- Box Two: This box includes income from a large stake in a business
- Box Three: This box includes income from savings and investments
Question: Is it acceptable to sign a cappella (without musical accompaniment) at a community musical theater audition?
Answer: No, this is usually not considered acceptable. Auditioners for local, community musical theater are usually expected to provide sheet music -- clearly marked with their specific cut of the material -- for the audition accompanist. This may be a physical, printed copy of the music, or an electronic version provided on a tablet computer such as an iPad. In some cases, the company holding the auditions may also allow you to play recorded accompaniment at the audition. However, this should also be avoided unless the audition instructions specifically state that this is acceptable.
Question: How was the saxophone named?
Answer: after the inventor, Adolphie Sax
Question: What does bpa-free mean?
Answer: BPA-Free refers to any plastics or other materials that do not contain any traces of Bisphenol-A.  Most plastics including bottled waters use BPA-Free plastic because it has been shown when BPA plastics are exposed to heat that the BPA chemicals can leach into liquids and into food which people then ingest.