DataHammer/scimrc huggingface dataset:
Question: Do the authors build simple yet strong models to show quantitatively?
Evidence: Large repositories of textual communications (e.g. forum and microblog posts) have gained recent popularity as proxies for dialog BIBREF0, BIBREF1, BIBREF2. However, conversations in these settings differ from natural dialog: turns may be sparsely scattered over a large temporal span, contain distinct syntax and vocabulary BIBREF3, and differ greatly in formality and focus BIBREF4. In this paper, we investigate how appropriate such data is for modeling natural dialog, and introduce Interview, a new high-quality large-scale open-domain conversational dataset grounded in interview settings with annotations for specific speaker roles.
We compare the performance of state-of-the-art language models fine-tuned on Interview and other popular conversational datasets, demonstrating that Interview contains more complex dialog and better models the characteristics of natural spoken conversations. Our dataset is an order of magnitude larger than existing high-quality natural dialog datasets and contains speaker role annotations for each turn, facilitating the development of conversational agents and assistive systems for settings involving specific speaker roles, such as doctor-patient interviews or hosted talk shows.
In particular, we explore the tasks of role modeling in media dialog and role change detection on Interview and find that leveraging role information can enable more nuanced, on-topic and natural dialog generation, as well as improve role change classification performance.
In summary, we present Interview, the first large-scale open-domain media dialog dataset. We explore two tasks for which it serves as a promising benchmark dataset: speaker role modeling and speaker change detection. We build simple yet strong models to show quantitatively that role labels from Interview improve performance on such tasks. Interview's scale, spoken origins, role diversity, and complex utterances make it a better source for grounded open-domain conversations.
We build simple yet strong models to show quantitatively that role labels from Interview improve performance on such tasks.
Answer: Yes, they do.
Question: What is the weather like in San Francisco?
Answer: The weather is typically mild in San Francisco. It is usually 50 to 65 degrees Fahrenheit in the both the winters and summers. San Francisco is known for having microclimates, which means that different neighborhoods might have different weather at the same time. The Mission District is known for being sunny whereas the Sunset is often foggy. San Francisco is often windy and typically gets cool at night. It's always smart to bring a jacket in San Francisco.
Question: Classify these as animals or vehicles: helicopter, car, sheep, dog, bike, bus, van, tiger, cat, llama, deer, tractor, airplane, ship, rabbit, snake
Answer: The animals are: helicopter, car, bike, bus, van, tractor, ship
The vehicles are: sheep, dog, tiger, cat, llama, deer, rabbit, snake
Question: Identify which instrument is string or percussion: Surdo, Kora
Answer: Kora is string, Surdo is percussion.
Question: Given this paragraph about Harold Brainerd Hersey, what did he do and when were his observations published?
Evidence: Harold Brainerd Hersey (April 11, 1893 – March 1956) was an American pulp editor and publisher, publishing several volumes of poetry. His pulp industry observations were published in hardback as Pulpwood Editor (1937).
Answer: Harold Brainerd Herse was an American pulp editor and publisher. His observations were published in 1937.
Question: What is the plot of One-Punch Man?
Evidence: On a supercontinent version of Earth that has four Moons, powerful monsters and supervillains wreak havoc. The millionaire Agoni creates the Hero Association, which employs superheroes to fight evil. Saitama, an unassociated hero, hails from City Z and performs heroic deeds as a hobby. For three years, he has trained enough to defeat any enemy with a single punch, his unmatched strength leaving him bored. He becomes a reluctant mentor to Genos, a cyborg seeking revenge against another cyborg who killed his family and destroyed his hometown, after Saitama defeats a monster that defeated Genos.
Answer: One-Punch Man is a manga and an anime about a protagonist, Saitama, who can defeat all of his enemies with one punch. It takes place on a version of Earth with one supercontinent and 4 moons, which has regular monster and supervillain attacks. To combat this, a Hero association was formed, but the protagonist is not part of this association and simply performs heroic deeds because he was bored. One of the highest ranked heros of the Hero association, Genos, sees Saitama's strength and becomes his disciple after Saitama beat a monster that beat Genos.
Question: Can artificial intelligence and large language models replace software engineers?
Answer: While it is unlikely that artificial intelligence (AI) and large language models (LLMs) while replace high performing software engineers in the near future, there is certainly an argument that these technologies could make engineers much more productive. This in turn could reduce the need and therefore replace lower level software engineering positions. These kinds of cultural and organizational changes will likely take time. Another important dynamic to consider is that generative AI technology could significantly lower the barrier to entry for these positions while also driving the need for newer kinds of roles based on the use of this technology platform.
Question: Identify which instrument is string or percussion: Stomp box, Gunjac
Answer: Gunjac is string, Stomp box is percussion.
Question: Write a letter to a school asking for an appointment to tour their facility
Answer: Dear Sir / Madam:

We live in Amsterdam and are parents of a bright 8 year old. We have heard wonderful things about your school and are interested in a tour. We would love to meet the admissions director and tour the facilities.

Can you please let us know your availability in the coming few weeks?

Thank you!
Question: Identify which animal species is alive or extinct: Tikataalik, Tree Kangaroo
Answer: Tree Kangaroo is alive, Tikataalik is extinct.
Question: What models are created for each variable?
Evidence: As discussed in the literature BIBREF5 , sarcasm detection may depend on sentiment and other cognitive aspects. For this reason, we incorporate both sentiment and emotion clues in our framework. Along with these, we also argue that personality of the opinion holder is an important factor for sarcasm detection. In order to address all of these variables, we create different models for each of them, namely: sentiment, emotion and personality. The idea is to train each model on its corresponding benchmark dataset and, hence, use such pre-trained models together to extract sarcasm-related features from the sarcasm datasets.
Now, the viable research question here is - Do these models help to improve sarcasm detection performance?' Literature shows that they improve the performance but not significantly. Thus, do we need to consider those factors in spotting sarcastic sentences? Aren't n-grams enough for sarcasm detection? Throughout the rest of this paper, we address these questions in detail. The training of each model is done using a CNN. Below, we explain the framework in detail. Then, we discuss the pre-trained models. Figure FIGREF6 presents a visualization of the proposed framework.
In order to address all of these variables, we create different models for each of them, namely: sentiment, emotion and personality.
Answer: Sentiment, emotion and personality.
Question: How is it demonstrated that the correct gender and number information is injected using this system?
Evidence: We compare the different conditions by comparing BLEU BIBREF5 with respect to the reference Hebrew translations. We use the multi-bleu.perl script from the Moses toolkit BIBREF6 . Table shows BLEU scores for the different prefixes. The numbers match our expectations: Generally, providing an incorrect speaker and/or audience information decreases the BLEU scores, while providing the correct information substantially improves it - we see an increase of up to 2.3 BLEU over the baseline. We note the BLEU score improves in all cases, even when given the wrong gender of either the speaker or the audience. We hypothesise this improvement stems from the addition of the word “said” which hints the model to generate a more “spoken” language which matches the tested scenario. Providing correct information for both speaker and audience usually helps more than providing correct information to either one of them individually. The one outlier is providing “She” for the speaker and “her” for the audience. While this is not the correct scenario, we hypothesise it gives an improvement in BLEU as it further reinforces the female gender in the sentence.
Results: Speaker. Figure FIGREF3 shows the result for controlling the morphological properties of the speaker ({he, she, I} said). It shows the proportion of gender-inflected verbs for the various conditions and the reference. We see that the baseline system severely under-predicts the feminine form of verbs as compared to the reference. The “He said” conditions further decreases the number of feminine verbs, while the “I said” conditions bring it back to the baseline level. Finally, the “She said” prefixes substantially increase the number of feminine-marked verbs, bringing the proportion much closer to that of the reference (though still under-predicting some of the feminine cases).
 Generally, providing an incorrect speaker and/or audience information decreases the BLEU scores, while providing the correct information substantially improves it - we see an increase of up to 2.3 BLEU over the baseline.
We see that the baseline system severely under-predicts the feminine form of verbs as compared to the reference. The “He said” conditions further decreases the number of feminine verbs, while the “I said” conditions bring it back to the baseline level. Finally, the “She said” prefixes substantially increase the number of feminine-marked verbs, bringing the proportion much closer to that of the reference (though still under-predicting some of the feminine cases).
Answer: The answers are shown as follows:
*  correct information substantially improves it - we see an increase of up to 2.3 BLEU over the baseline
* Finally, the “She said” prefixes substantially increase the number of feminine-marked verbs, bringing the proportion much closer to that of the reference
Question: Has there been previous work on SNMT?
Evidence: Recently, a number of researchers have endeavored to explore methods for simultaneous translation in the context of NMT BIBREF6, BIBREF7, BIBREF8, BIBREF9. Some of them propose sophisticated training frameworks explicitly designed for simultaneous translation BIBREF5, BIBREF10. These approaches are either memory inefficient during training BIBREF5 or hard to implement BIBREF10. Others utilize a full-sentence base model to perform simultaneous translation by modifications to the encoder and the decoding process. To match the incremental source context, they replace the bidirectional encoder with a left-to-right encoder BIBREF3, BIBREF11, BIBREF4, BIBREF12 or recompute the encoder hidden states BIBREF13. On top of that, heuristic algorithms BIBREF3, BIBREF14 or a READ/WRITE model trained with reinforcement learning BIBREF11, BIBREF4, BIBREF12 or supervised learning BIBREF13 are used to decide, at every step, whether to wait for the next source token or output a target token. However, these models either cannot directly use a pretrained vanilla CNMT model with bidirectional encoder as the base model or work in a sub-optimal way in the decoding stage.
Recently, a number of researchers have endeavored to explore methods for simultaneous translation in the context of NMT BIBREF6, BIBREF7, BIBREF8, BIBREF9. Some of them propose sophisticated training frameworks explicitly designed for simultaneous translation BIBREF5, BIBREF10. 
Answer: Yes.
Question: What does Section 2 mainly describe?
Evidence: This section describes the similarity and relatedness measures we integrate in our second–order co–occurrence vectors. We use two taxonomies in this study, SNOMED–CT and MeSH. SNOMED–CT (Systematized Nomenclature of Medicine Clinical Terms) is a comprehensive clinical terminology created for the electronic representation of clinical health information. MeSH (Medical Subject Headings) is a taxonomy of biomedical terms developed for indexing biomedical journal articles.
We obtain SNOMED–CT and MeSH via the Unified Medical Language System (UMLS) Metathesaurus (version 2016AA). The Metathesaurus contains approximately 2 million biomedical and clinical concepts from over 150 different terminologies that have been semi–automatically integrated into a single source. Concepts in the Metathesaurus are connected largely by two types of hierarchical relations: INLINEFORM0 / INLINEFORM1 (PAR/CHD) and INLINEFORM2 / INLINEFORM3 (RB/RN).
This section describes the similarity and relatedness measures we integrate in our second–order co–occurrence vectors.
Answer: This section describes the similarity and relatedness measures they integrate in their second–order co–occurrence vectors.
Question: Who is Mahatma Gandhi?
Evidence: Mohandas Karamchand Gandhi (/ˈɡɑːndi, ˈɡændi/ GA(H)N-dee, Gujarati: [ˈmoɦəndɑs ˈkəɾəmtʃənd ˈɡɑ̃dʱi]; 2 October 1869 – 30 January 1948) was an Indian lawyer, anti-colonial nationalist and political ethicist who employed nonviolent resistance to lead the successful campaign for India's independence from British rule. He inspired movements for civil rights and freedom across the world. The honorific Mahātmā (Sanskrit: "great-souled", "venerable"), first applied to him in 1914 in South Africa, is now used throughout the world.

Born and raised in a Hindu family in coastal Gujarat, Gandhi trained in the law at the Inner Temple, London, and was called to the bar at age 22 in June 1891. After two uncertain years in India, where he was unable to start a successful law practice, he moved to South Africa in 1893 to represent an Indian merchant in a lawsuit. He went on to live in South Africa for 21 years. It was here that Gandhi raised a family and first employed nonviolent resistance in a campaign for civil rights. In 1915, aged 45, he returned to India and soon set about organising peasants, farmers, and urban labourers to protest against excessive land-tax and discrimination.

Assuming leadership of the Indian National Congress in 1921, Gandhi led nationwide campaigns for easing poverty, expanding women's rights, building religious and ethnic amity, ending untouchability, and, above all, achieving swaraj or self-rule. Gandhi adopted the short dhoti woven with hand-spun yarn as a mark of identification with India's rural poor. He began to live in a self-sufficient residential community, to eat simple food, and undertake long fasts as a means of both introspection and political protest. Bringing anti-colonial nationalism to the common Indians, Gandhi led them in challenging the British-imposed salt tax with the 400 km (250 mi) Dandi Salt March in 1930 and in calling for the British to quit India in 1942. He was imprisoned many times and for many years in both South Africa and India.
Answer: Mahatma Gandhi was an Indian freedom fighter and peace loving leader. Mahatma Gandhi was born as Mohandas Karamchand Gandhi in Gujarat on 2nd October 1869. Born and raised in a Hindu family in coastal Gujarat, Gandhi trained in the law at the Inner Temple, London, and was called to the bar at age 22 in June 1891. Gandhi moved to South Africa in 1893 to represent an Indian merchant in a lawsuit after two uncertain years in India, where he was unable to start a successful law practice. He went on to live in South Africa for 21 years. Gandhi led nationwide campaigns for freedom movement, easing poverty, expanding women's rights, building religious and ethnic amity, ending untouchability, and, above all, achieving swaraj or self-rule. Gandhi started Dandi Salt March in 1930 calling quit India for the then rulers the British.
Question: What has the author further proved through the experiment?
Evidence: Question classification can enable targetting question answering models, but is challenging to implement with high performance without using rule-based methods. In this work we generate the most fine-grained challenge dataset for question classification, using complex and syntactically diverse questions, and show gains of up to 12% are possible with our question classification model across datasets in open, science, and medical domains. This model is the first demonstration of a question classification model achieving state-of-the-art results across benchmark datasets in open, science, and medical domains. We further demonstrate attending to question type can significantly improve question answering performance, with large gains possible as quesion classification performance improves. Our error analysis suggests that developing high-precision methods of question classification independent of their recall can offer the opportunity to incrementally make use of the benefits of question classification without suffering the consequences of classification errors on QA performance.
We further demonstrate attending to question type can significantly improve question answering performance, with large gains possible as quesion classifi- cation performance improves.
Answer: They further demonstrate attending to question type can significantly improve question answering performance, with large gains possible as quesion classifi- cation performance improves.
Question: Identify which instrument is string or percussion: Kendang, Gekkin
Answer: Gekkin is string, Kendang is percussion.
Question: who is Haile Sellassie?
Evidence: Haile Selassie I (Ge'ez: ቀዳማዊ ኀይለ ሥላሴ, romanized: Qädamawi Häylä Səllasé,[nb 2] Amharic pronunciation: [ˈhaɪlə sɨlˈlase] (listen);[nb 3] born Tafari Makonnen; 23 July 1892 – 27 August 1975) was Emperor of Ethiopia from 1930 to 1974. He rose to power as Regent Plenipotentiary of Ethiopia (Enderase) for Empress Zewditu from 1916. Haile Selassie is widely considered a defining figure in modern Ethiopian history, and the key figure of Rastafari, a religious movement in Jamaica that emerged shortly after he became emperor in the 1930s. He was a member of the Solomonic dynasty, which claims to trace lineage to Emperor Menelik I, believed to be the son of King Solomon and Makeda the Queen of Sheba.
Answer: Haile Selassie (Amharic: ቀዳማዊ ኀይለ ሥላሴ) was a former Emperor of Ethiopia. He ruled the country from 1930 to 1974.  His first name before he became emperor was Ras Teferi Mekonnen. The religion movement, Rastafari, after the name of Ras Teferi Mekonnen, emerged in Jamaica shortly after Haile Selassie became emperor.  His root is from an extended Solomon and Queen of Sheba(Makeda).
Question: What is the best language in the world
Answer: Bengali is the best language in the world Because it's very sweet to listen.
Question: What does Section 2 mainly describe?
Evidence: In this section, we first introduce the task definition and our protocol to extract structured answer-relevant relations. Then we formalize the task under the encoder-decoder framework with gated attention and dual copy mechanism.
In this section, we first introduce the task definition and our protocol to extract structured answer relevant relations. Then we formalize the task under the encoder-decoder framework with gated at tention and dual copy mechanism.
Answer: In this section, they first introduce the task definition and their protocol to extract structured answer relevant relations. Then they formalize the task under the encoder-decoder framework with gated at tention and dual copy mechanism.
Question: What is Cricket?
Evidence: Cricket is a bat-and-ball game played between two teams of eleven players on a field at the center of which is a 22-yard (20-metre) pitch with a wicket at each end, each comprising two bails balanced on three stumps. The batting side scores runs by striking the ball bowled at one of the wickets with the bat and then running between the wickets, while the bowling and fielding side tries to prevent this (by preventing the ball from leaving the field, and getting the ball to either wicket) and dismiss each batter (so they are "out"). Means of dismissal include being bowled, when the ball hits the stumps and dislodges the bails, and by the fielding side either catching the ball after it is hit by the bat, but before it hits the ground, or hitting a wicket with the ball before a batter can cross the crease in front of the wicket. When ten batters have been dismissed, the innings ends and the teams swap roles. The game is adjudicated by two umpires, aided by a third umpire and match referee in international matches. They communicate with two off-field scorers who record the match's statistical information.
Answer: Cricket is a bat and ball game, invented in England. The earliest reference to cricket is in South East England in the mid-16th century. It spread globally with the expansion of the British Empire, with the first international matches in the second half of the 19th century. The game's governing body is the International Cricket Council (ICC), which has over 100 members, twelve of which are full members who play Test matches. The game's rules, the Laws of Cricket, are maintained by Marylebone Cricket Club (MCC) in London. The sport is followed primarily in South Asia, Australasia, the United Kingdom, Southern Africa and the West Indies. There are 3 main forms/editions of Cricket: Test matches (usually lasting 5 days), ODI (one day international) usually lasting 6-8 hours, and more recent and popular T20 usually lasting 3-4 hours.
Question: What are some easy to buy birthday gifts for a coworker?
Answer: A book, a giftcard, a box of chocolates, a bag of candy, a desk plant or a desk decoration
Question: From the provided paragraph, write a bulleted list of how cheeses can be grouped.
Evidence: There are many different types of cheese. Cheeses can be grouped or classified according to criteria such as length of fermentation, texture, methods of production, fat content, animal milk, and country or region of origin. The method most commonly and traditionally used is based on moisture content, which is then further narrowed down by fat content and curing or ripening methods. The criteria may either be used singly or in combination, with no single method being universally used.
Answer: * Length of fermentation
* Texture
* Methods of production
* Fat content
* Animal milk
* Country or region of origin