DataHammer/scimrc huggingface dataset:
Question: What has been addressed by the authors?
Evidence: Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3 . Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4 . Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios.
However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child). On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs. However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En). Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9 .
In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. Furthermore, the amount of indirect in-domain parallel corpora, i.e., Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small. As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9 .
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows:
To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT)
Answer: Two important research questions (RQs) in the context of extremely low-resource machine translation (MT)
Question: Identify which instrument is string or percussion: Cantaro, Gudok
Answer: Gudok is string, Cantaro is percussion.
Question: What are some of the best fantasy novels of all time?
Answer: 1) The Lord of the Rings - JRR Tolkien
2) The Name of the Wind - Patrick Rothfuss
3) A Game of Thrones - George RR Martin
4) Harry Potter and the Philosopher's Stone - JK Rowling
5) The Way of Kings - Brandon Sanderson
6) The Lion The Witch and The Wardrobe - CS Lewis
7) Kushiel's Dart - Jaqueline Carey
8) Bloodsong - Anthon Ryan
9) The Sword of Shannara -Terry Brooks
10) The Lies of Locke Lomora - Scott Lynch
Question: Are the authors the first to computationally study language cues in the early phases of negotiation that direct the final outcomes?
Evidence: Negotiations, either between individuals or entities, are ubiquitous in everyday human interactions ranging from sales to legal proceedings. Being a good negotiator is a complex skill, requiring the ability to understand the partner's motives, ability to reason and to communicate effectively, making it a challenging task for an automated system. While research in building automatically negotiating agents has primarily focused on agent-agent negotiations BIBREF0, BIBREF1, there is a recent interest in agent-human negotiations BIBREF2 as well. Such agents may act as mediators or can be helpful for pedagogical purposes BIBREF3.
Efforts in agent-human negotiations involving free-form natural language as a means of communication are rather sparse. Researchers BIBREF4 recently studied natural language negotiations in buyer-seller bargaining setup, which is comparatively less restricted than previously studied game environments BIBREF5, BIBREF6. Lack of a well-defined structure in such negotiations allows humans or agents to express themselves more freely, which better emulates a realistic scenario. Interestingly, this also provides an exciting research opportunity: how can an agent leverage the behavioral cues in natural language to direct its negotiation strategies? Understanding the impact of natural language on negotiation outcomes through a data-driven neural framework is the primary objective of this work.
We focus on buyer-seller negotiations BIBREF4 where two individuals negotiate the price of a given product. Leveraging the recent advancements BIBREF7, BIBREF8 in pre-trained language encoders, we attempt to predict negotiation outcomes early on in the conversation, in a completely data-driven manner (Figure FIGREF3). Early prediction of outcomes is essential for effective planning of an automatically negotiating agent. Although there have been attempts to gain insights into negotiations BIBREF9, BIBREF10, to the best of our knowledge, we are the first to study early natural language cues through a data-driven neural system (Section SECREF3). Our evaluations show that natural language allows the models to make better predictions by looking at only a fraction of the negotiation. Rather than just realizing the strategy in natural language, our empirical results suggest that language can be crucial in the planning as well. We provide a sample negotiation from the test set BIBREF4 along with our model predictions in Table TABREF1.
However, to the best of our knowledge, we are the first to computationally study language cues in the early phases of negotiation that direct the final outcomes.
Answer: Yes, they are.
Question: Can you suggest 5 ways to use a Katana other than a weapon ?
Answer: 1. The Katana in its' sheath can be used as a walking stick
2. Used to removed overgrown foliage. 
3. Can presented as a gift 
4. Decorative purposes for your office, house or vehicle    
5. Fancy dress events
Question: What are the songs on the album At Basin Street?
Answer: The songs on the album At Basin Street, also known as Clifford Brown and Max Roach at Basin Street, are as follows:
"What Is This Thing Called Love?" (Cole Porter) – 7:33
"Love Is a Many-Splendored Thing" (Sammy Fain, Paul Francis Webster) – 4:13
"I'll Remember April" (Gene de Paul, Patricia Johnston, Don Raye) – 9:13
"Powell's Prances" (Richie Powell) – 3:28
"Time" (Richie Powell) – 5:03
"The Scene Is Clean" (Tadd Dameron, arr. Dameron) – 6:04
"Gertrude's Bounce" (Richie Powell) – 4:09
Bonus tracks included on the 2002 CD release:
"Step Lightly (Junior's Arrival)" (Benny Golson) – 3:33
"Flossie Lou" (Dameron) – 3:55
"What Is This Thing Called Love? (alternate take)" – 8:18
"Love Is a Many-Splendored Thing (breakdown)" – 0:45
"Love Is a Many-Splendored Thing (alternate take)" – 3:53
"I'll Remember April (breakdown)" – 1:25
"I'll Remember April (alternate take)" – 9:42
"Flossie Lou (alternate take)" – 4:00
Question: What are the contributions of this paper？
Evidence: It is well-established that gender bias exists in language – for example, we see evidence of this given the prevalence of sexism in abusive language datasets BIBREF0, BIBREF1. However, these are extreme cases of gender norms in language, and only encompass a small proportion of speakers or texts.
Less studied in NLP is how gender norms manifest in everyday language – do people talk about women and men in different ways? These types of differences are far subtler than abusive language, but they can provide valuable insight into the roots of more extreme acts of discrimination. Subtle differences are difficult to observe because each case on its own could be attributed to circumstance, a passing comment or an accidental word. However, at the level of hundreds of thousands of data points, these patterns, if they do exist, become undeniable. Thus, in this work, we introduce new datasets and methods so that we can study subtle gender associations in language at the large-scale.
Our contributions include:
Two datasets for studying language and gender, each consisting of over 300K sentences.
Methods to infer gender-associated words and labeled clusters in any domain.
Novel findings that demonstrate in both domains that people do talk about women and men in different ways.
Each contribution brings us closer to modeling how gender associations appear in everyday language. In the remainder of the paper, we present related work, our data collection, methods and findings, and human evaluations of our system.
Our contributions include: Two datasets for studying language and gender, each consisting of over 300K sentences. Methods to infer gender-associated words and labeled clusters in any domain. Novel findings that demonstrate in both domains that people do talk about women and men in different ways.
Answer: Two datasets for studying language and gender, each consisting of over 300K sentences. Methods to infer gender-associated words and labeled clusters in any domain. Novel findings that demonstrate in both domains that people do talk about women and men in different ways.
Question: Give me an idea of a simple yet unique Mocktail made up of Mango
Answer: Here is a Mocktail named "Mango Mule". All you need is a 4-5 slices of cucumber, some simple syrup, 50gm of mango puree, 50gm of fresh lime juice, small can of ginger beer (non-alcoholic), and some mint, and lime for extra garnishing touch. It is quite easy to make this Mocktail. Mix cucumber slices and simple syrup in cocktail shaker, followed by addition of mango puree and lime juice and crushed ice. Pour it into a glass and add the ginger beer with a final good old stir. Garnish it as per your needs.
Question: Tell me what I should do in London, England.
Answer: If you're visiting London, it may feel daunting attempting to take advantage of everything this cosmopolitan city has to offer. But that's part of its magic — there's something for everyone! A few ideas:

Enjoy the food. I highly recommend wandering through the vendor stands at Borough Market in Southwark — it's only open for lunch and you should bring cash because most vendors don't take cards! If you're looking for a high-end restaurant, try Chiltern Firehouse in Marylebone. Another popular option is Dishoom: it's an upscale Indian restaurant with multiple locations and worth the hype. And for lighter fare, indulge in the English custom of afternoon tea! You could try Sketch in Mayfair or The Savoy in Covent Garden for completely different experiences — Sketch is definitely a millennial’s take on tea and it’s very trendy, whereas The Savoy is more traditional.

Grab a drink. You might think of dark pubs when you think of London, but it is also home to some of the best cocktail bars in the world. For a unique experience, find a "public bathroom bar" or a speakeasy like Keystone Crescent Members Club. Shoreditch has plenty of options for the imbibers, like Ace Hotel Rooftop and Old St Records. Then there's Duke's Bar in Mayfair, which is a famous spot for a martini since Ian Fleming used to hang out there when he was writing James Bond novels.

Visit a museum. The Tate Modern offers free entry and an excellent view of the city. It's on the south side of the Thames, and you can wander from there to the Shakespeare Globe. The National Portrait Gallery will keep you entertained, even though it really is home to all portraits, but you can learn a lot about British history by reading through the biographies of public figures featured. I also recommend seeing which artist's work the Hayward Gallery at the Southbank Centre is exhibiting, since it's an excellent space. The Tower of London is a classic for visitors: the castle is a World Heritage Site and where you need to go if you want to admire the Crown Jewels.

See a show. If you live or often see Broadway shows in New York, this might be less exciting to you, but London shows are relatively easy to secure tickets for, and a great activity if you're interested in experiencing even more British culture.

Shop. Don't miss the famous Harrod's, the iconic department store with a iconic food floor. Liberty is another well-known London department store. Less expensive, and known for their stationary. And if department stores aren't your thing, pop into local boutiques!
Question: Which US city is known to be the bachelorette party center of the country, besides Las Vegas?
Answer: Nashville, TN
Question: What makes Barcelona a top tourist destination?
Answer: Barcelona is a city that is filled with great food, great music and is rich in culture.  Barcelona is located in the northeastern part of Spain in a region called Catalonia, along the Mediterranean Sea.  Lined with beautiful beaches and a vibrant nightlife, Barcelona is often one of the top tourist destinations because there is always something to do or see while visiting.  Some of the most popular tourist sights are those created by Antoni Gaudi, a modern art architect who modeled his structures after shapes found in nature.  The most well-known work of Gaudi's being La Sagrada Familia - a cathedral that has been in the works since 1882 and anticipated completion date of 2026.   Barcelona also has one of the best football teams in the world, FC Barcelona, the former team of World Cup champion Lionel Messi.  Barcelona is great for families, couples and backpackers and is known for welcoming tourists with open arms and a glass of sweet vermouth.
Question: What dataset do they use?
Evidence: Our experiments are modelled on the GermEval 2019 shared task and deal with the classification of books. The dataset contains 20,784 German books. Each record has:
Our experiments are modelled on the GermEval 2019 shared task and deal with the classification of books. The dataset contains 20,784 German books.
Answer: The answers are shown as follows:
* GermEval 2019 shared task
Question: why is american football the most poplar sport in the US?
Answer: There are many sports that are closely followed by Americans.  However, the most popular sport is Football.  Football is popular because it is fast-paced.  You have 11 players on each team who are in full-motion at the snap of the football.  Football also has a defined start and end time.  Unlike baseball, golf or cricket, football is timed with a clock and a winner is realized when the clock runs down to zero.  Football involves contact between players on opposing teams, and the contact can sometimes be violent as players collide, sometimes at full speed.  This contact creates an excitement and uncertainty among the fans, as you want to see what will happen on each play.  Finally, football is enjoyable to watch whether you are in person or watching on TV.
Question: Extract the name of the comedy films that Cage performed in. Separate them with a comma.
Evidence: Cage made his acting debut in the 1981 television pilot The Best of Times, which was never picked up by ABC. His film debut followed in 1982, with a minor role as an unnamed co-worker of Judge Reinhold's character in the coming-of-age film Fast Times at Ridgemont High, having originally auditioned for Reinhold's part. His experience on the film was marred by cast members endlessly quoting his uncle's films, which inspired him to change his name. Cage's first starring role came opposite Deborah Foreman in the romantic comedy Valley Girl (1983), in which he played a punk who falls in love with the titular valley girl, a plot loosely inspired by Romeo and Juliet. The film was a modest box office success and has been branded a cult classic. He auditioned for the role of Dallas Winston in his uncle's film The Outsiders, based on S.E. Hinton's novel, but lost to Matt Dillon. Cage, however, would co-star in Coppola's adaptation of another Hinton novel, Rumble Fish, in that year.

In 1984, Cage appeared in three period films, none of which fared well at the box office. In the drama, Racing with the Moon (1984), Cage featured opposite Sean Penn as friends who are awaiting deployment to the U.S. Marine Corps. Coppola's crime drama The Cotton Club saw him play a fictionalized version of mob hitman Vincent "Mad Dog" Coll, earning praise from critic Paul Attanasio for "artully  his few moments to sketch a brawny, violent thug". His final release of the year was Alan Parker's drama Birdy, in which he starred with Matthew Modine as two close friends and their trauma inflicted by serving in the Vietnam War. Cage lost weight for the role and had two of his front teeth pulled out to appear disfigured. Despite massively underperforming at the box office, the film, and Cage and Modine's performances, received positive reviews, with The New York Times critic Janet Maslin writing, "Mr. Cage very sympathetically captures Al's urgency and frustration. Together, these actors work miracles with what might have been unplayable."

In 1986, Cage starred in the little-seen Canadian sports drama The Boy in Blue and his uncle's fantasy comedy Peggy Sue Got Married (1987) as the husband to Kathleen Turner's character, who has travelled back in time to their high school days. He then starred in the Coen brothers' crime comedy Raising Arizona (1987) as a dim-witted ex-con. Cage's biggest breakthrough came in 1987 with the romantic comedy Moonstruck, in which he starred alongside Cher as a hot-tempered baker who falls in love with his estranged brother's widowed fiancé. The film was a hit with critics and audiences alike, earning Cage a Golden Globe nomination for Best Actor – Motion Picture Musical or Comedy  In his retrospective review, Roger Ebert wrote that he felt Cage's performance was worthy of an Oscar.
Answer: Valley Girl, Peggy Sue Got Married, Raising Arizona, Moonstruck
Question: What is Indian national anthem?
Answer: The Indian national anthem is Jana Gana Mana, composed by Rabindranath Tagore on 11 December 1911. The parent song, 'Bharoto Bhagyo Bidhata' is a Brahmo hymn which has five verses and only the first verse has been adopted as National Anthem.
Question: What are some examples of movies that won best picture in the 1990s?
Answer: Some of the movies that won an Oscar for best picture in the 1990s include: Titanic, The English Patient, Forrest Gump, and Dances with Wolves.
Question: What is the best NFL football team of all time?
Answer: It depends on how you measure 'best'.  If best refers to most Super Bowls won, then the Pittsburgh Steelers, New England Patriots, and San Francisco 49ers are the best teams.  If best refers to the team that scored the most points, then I would look up which team in which year scored the most points in a single season on NFL.com.  If best refers to my personal favorite team, then I would choose the Seattle Seahawks because that is where I grew up and first started loving football.
Question: how is yogurt made?
Answer: Yogurt is made from heating milk, and before milk can be made into yogurt the whey has to be removed from it.  You add a certain bacteria to it, and then it starts to coagulate into yogurt.  The bacteria that are added to the milk to turn it to yogurt at Lactobacillus bulgaricus and Streptococcus thermophilus.  After the yogurt is made sugar may be added to make it a specific flavor like chocolate or vanilla.
Question: Are there some results better than state of the art on these tasks?
Evidence: To verify the effectiveness of our CRU model, we utilize it into two different NLP tasks: sentiment classification and reading comprehension, where the former is sentence-level modeling, and the latter is document-level modeling. In the sentiment classification task, we build a standard neural network and replace the recurrent unit by our CRU model. To further demonstrate the effectiveness of our model, we also tested our CRU in reading comprehension tasks with a strengthened baseline system originated from Attention-over-Attention Reader (AoA Reader) BIBREF10. Experimental results on public datasets show that our CRU model could substantially outperform various systems by a large margin, and set up new state-of-the-art performances on related datasets. The main contributions of our work are listed as follows.
Experimental results on public datasets show that our CRU model could substantially outperform various systems by a large margin, and set up new state-of-the-art performances on related datasets.
Answer: Yes.
Question: Identify which instrument is string or percussion: Madal, Craviola
Answer: Craviola is string, Madal is percussion.
Question: Name some famous songs by George Gershwin
Answer: In his career, George Gershwin wrote influential works such as the orchestral compositions "Rhapsody in Blue" and "An American in Paris". Furthermore, he is known for the songs "Fascinating Rhythm", "Embraceable You", "I Got Rhythm", and "Isn't it a pity". The hit "Summertime" was also composed by Gershwin, as part of the opera "Porgy and Bess", which premiered in Boston in 1935.
Question: Do they use pretrained models as part of their parser?
Evidence: As a classifier, we use a feed-forward neural network with two hidden layers of 200 tanh units and learning rate set to 0.1, with linear decaying. The input to the network consists of the concatenation of embeddings for words, POS tags and Stanford parser dependencies, one-hot vectors for named entities and additional sparse features, extracted from the current configuration of the transition system; this is reported in more details in Table TABREF27 . The embeddings for words and POS tags were pre-trained on a large unannotated corpus consisting of the first 1 billion characters from Wikipedia. For lexical information, we also extract the leftmost (in the order of the aligned words) child (c), leftmost parent (p) and leftmost grandchild (cc). Leftmost and rightmost items are common features for transition-based parsers BIBREF17 , BIBREF18 but we found only leftmost to be helpful in our case. All POS tags, dependencies and named entities are generated using Stanford CoreNLP BIBREF19 . The accuracy of this classifier on the development set is 84%.
The embeddings for words and POS tags were pre-trained on a large unannotated corpus consisting of the first 1 billion characters from Wikipedia.
Answer: Yes.
Question: How many octaves does a harpsichord have?
Answer: 5 octaves