DataHammer/scimrc huggingface dataset:
Question: Given a reference text about the Northern Colorado Bears, tell me the different conferences their teams compete in.
Evidence: The Northern Colorado Bears are the athletic teams representing the University of Northern Colorado in Greeley, Colorado in intercollegiate athletics. The university sponsors seventeen teams including men and women's basketball, cross country, golf, tennis, and track and field; women's-only soccer, softball, swimming and diving, and volleyball; and men's-only baseball, football, and wrestling. The Bears compete in NCAA Division I, with the football team competing at the FCS level, and most teams are members of the Big Sky Conference. Three Northern Colorado teams have separate affiliations in sports that the Big Sky does not sponsor. The baseball team competes in the Summit League, the women's swimming and diving team is an affiliate member of the Western Athletic Conference, and the wrestling team is a member of the Big 12 Conference.
Answer: The Northern Colorado Bears teams compete mostly in the Big Sky Conference. However, some teams compete in the Summit League, Western Athletic Conference, and the Big 12 Conference.
Question: What problem do the authors investigate in this paper?
Evidence: We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset. To tackle the RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. The methods for data collection, aspect annotation, and summary writing and scrutinizing by experts are described. Experimental results show that reader comments can improve the summarization performance, which demonstrate the usefulness of the proposed dataset.
We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset.
Answer: They investigate the problem of reader-aware multi-document summarization (RA-MDS) by jointly considering news documents.
Question: How long are the datasets?
Evidence: We compile three Chinese text corpora from online data for three domains, namely, “hotel", “mobile phone (mobile)", and “travel". All texts are about user reviews. Each text sample collected is first partitioned into clauses according to Chinese tokens. Three clause sets are subsequently obtained from the three text corpora.
In the second stage, five users are invited to label each text sample in the three raw data sets. The average score of the five users on each sample is calculated. Samples with average scores located in [0.6, 1] are labeled as “positive". Samples with average scores located in [0, 0.4] are labeled as “negative". Others are labeled as “neutral". The details of the labeling results are shown in Table 1.
FLOAT SELECTED: TABLE 1 Details of the three data corpora. Each corpus consists of raw samples (sentences or paragraphs) and partitioned clauses (sub-sentences).
We compile three Chinese text corpora from online data for three domains, namely, “hotel", “mobile phone (mobile)", and “travel". 
The details of the labeling results are shown in Table 1.
FLOAT SELECTED: TABLE 1 Details of the three data corpora. Each corpus consists of raw samples (sentences or paragraphs) and partitioned clauses (sub-sentences).
Answer: Travel dataset contains 4100 raw samples, 11291 clauses, Hotel dataset contains 3825 raw samples, 11264 clauses, and the Mobile dataset contains 3483 raw samples and 8118 clauses
Question: Whether the choice of parent language pair affects performance?
Evidence: In the neural encoder-decoder framework for MT BIBREF3 , BIBREF4 , BIBREF0 , BIBREF5 , BIBREF6 , we use a recurrent neural network (encoder) to convert a source sentence into a dense, fixed-length vector. We then use another recurrent network (decoder) to convert that vector to a target sentence. In this paper, we use a two-layer encoder-decoder system (Figure 1 ) with long short-term memory (LSTM) units BIBREF7 trained to optimize maximum likelihood (via a softmax layer) with back-propagation through time BIBREF8 . Additionally, we use an attention mechanism that allows the target decoder to look back at the source encoder, specifically the local attention plus feed-input model from luong2015effective.
We find that the choice of parent language pair affects performance, and provide an empirical upper bound on transfer performance using an artificial language.
Answer: Yes.
Question: Is there any research about Type-III?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet.
Answer: No, there isn't.
Question: Who is Phil Murphy?
Evidence: Philip Dunton Murphy (born August 16, 1957) is an American financier, diplomat, and politician serving as the 56th governor of New Jersey since January 2018. A member of the Democratic Party, he served as the United States ambassador to Germany from 2009 to 2013 under President Barack Obama.
Answer: Philip Dunton Murphy (born August 16, 1957) is an American financier, diplomat, and politician serving as the 56th governor of New Jersey since January 2018. A member of the Democratic Party, he served as the United States ambassador to Germany from 2009 to 2013 under President Barack Obama.

Murphy had a 23-year career at Goldman Sachs, where he held several high-level positions and accumulated considerable wealth before retiring in 2006. He is involved in many civic organizations and philanthropic pursuits. He served as finance chairman for the Democratic National Committee in the mid-late 2000s under Howard Dean. In the Obama administration, Murphy served as the United States ambassador to Germany from 2009 to 2013, during which time he dealt with the international fallout from the United States diplomatic cables leak.

While planning to run for governor, Murphy and his wife Tammy Murphy launched New Start New Jersey, a progressive organization active from November 2014 to December 2017, intended to increase his political visibility in the state. He defeated then-Lieutenant Governor Kim Guadagno in the 2017 gubernatorial election with 56% of the vote. In December 2019, Murphy became the chairperson of the Democratic Governors Association, a position he held for a year. He was reelected in 2021, defeating Republican nominee Jack Ciattarelli with 51.2% of the vote, becoming the first Democratic governor of New Jersey to win a second term since Brendan Byrne in 1977. In July 2022, Murphy became the first National Governors Association chair from New Jersey.
Question: Based on the following paragraph on the current use of obsidian, what's the difference between obsidian scalpels and steel scalpels?
Evidence: Obsidian can be used to make extremely sharp knives, and obsidian blades are a type of glass knife made using naturally occurring obsidian instead of manufactured glass. Obsidian is used by some surgeons[who?][example needed] for scalpel blades, although this is not approved by the US Food and Drug Administration (FDA) for use on humans. Well-crafted obsidian blades, like any glass knife, can have a cutting edge many times sharper than high-quality steel surgical scalpels: the cutting edge of the blade is only about three nanometers thick. All metal knives have a jagged, irregular blade when viewed under a strong enough microscope; however, obsidian blades are still smooth, even when examined under an electron microscope. One study found that obsidian incisions produced fewer inflammatory cells and less granulation tissue in a group of rats after seven days but the differences disappeared after twenty-one days. Don Crabtree has produced surgical obsidian blades and written articles on the subject. Obsidian scalpels may currently[when?] be purchased[by whom?] for surgical use on research animals.
Answer: Obsidian scalpels have a cutting edge that is many times sharper than a steel scalpel. In addition, obsidian scalpels have a smooth blade surface whereas all metal knifes have a jagged, irregular blade surface.
Question: Who was Pythagoras?
Answer: Pythagoras was a greek philosopher and mathematician. He is most famous for the theorem named after him - Pythagoras' theorem. It states that in a right triangle the area of the square of the hypontenuse is equal to the sum of the areas of the squares of the catheti. 
The hypotenuse is the side of a right triangle that sits opposite of the right angle. The catheti (or legs) are the sides thet form the right angle.
Question: Extract the names of people mentioned and their significance in the following format:

[Name]: [Significance]
Evidence: Angelo Moriondo, from Turin, is often erroneously credited for inventing the beverage, since he patented a steam-driven coffee beverage making device in 1884 (No. 33/256), probably the first Italian coffee machine similar to other French and English 1800s steam-driven coffee machines. The device is "almost certainly the first Italian bar machine that controlled the supply of steam and water separately through the coffee" and Moriondo is "certainly one of the earliest discoverers of the expresso  machine, if not the earliest". Seventeen years later, in 1901, Luigi Bezzera, from Milan, devised and patented several improved versions of the espresso machine, the first of which was applied for on 19 December 1901. Titled "Innovations in the machinery to prepare and immediately serve coffee beverage"; Patent No. 153/94, 61707, was granted on 5 June 1902, and was the first espresso machine. In 1903, the patent was bought by Desiderio Pavoni, who founded the La Pavoni company and began to produce the machine industrially, manufacturing one machine daily in a small workshop in Via Parini, Milan
Answer: Angelo Moriondo: Patented a steam-driven coffee beverage-making device and was often erroneously credited for inventing espresso
Luigi Bezzera: Patented the first espresso machine and later went on to create several improved versions
Desiderio Pavoni: Bought the patent titled "Innovations in the machinery to prepare and immediately serve coffee beverage" from Luigi Bezzera, founded the La Pavoni company, and began to produce the machine industrially
Question: Given this reference text, in which year was the youth novel by R.M. Ballantyne - which inspired Golding's first novel - written?
Evidence: Published in 1954, Lord of the Flies was Golding's first novel. The concept arose after Golding read what he deemed to be an unrealistic portrayal of stranded children in the youth novel The Coral Island: a Tale of the Pacific Ocean (1857) by R. M. Ballantyne, which includes themes of the civilising effect of Christianity and the importance of hierarchy and leadership. Golding asked his wife, Ann, if it would "be a good idea if I wrote a book about children on an island, children who behave in the way children really would behave?" As a result, the novel contains various references to The Coral Island, such as the rescuing naval officer's description of the boys' initial attempts at civilised cooperation as a "olly good show. Like the Coral Island." Golding's three central characters (Ralph, Piggy, and Jack) have also been interpreted as caricatures of Ballantyne's Coral Island protagonists.
Answer: 1857
Question: Is Belgrandia varica found in Europe or Australia?
Evidence: Belgrandia varica was previously considered to be extinct, however it has been classified as Critically Endangered (Possibly Extinct) by IUCN because it might still be present in a small spring that has been overlooked during previous surveys.

This species is endemic to France.
Answer: Belgrandia varica is endemic to France, so therefore it is found in Europe.
Question: List some of the minor houses in the series "A Song of Ice and Fire."
Answer: Florent, Caron, Graceford, Jordayne, Piper, Penrose, Waxley, Wydman, Wensington
Question: What is the purpose of a phone case?
Answer: Using a phone case protects your phone from damage. It prevents scratches and absorbs shock from impacts caused by things like dropping your phone.
Question: What does the author introduce and evaluate in this paper?
Evidence: Automated, or robotic, journalism aims at news generation from structured data sources, either as the final product or as a draft for subsequent post-editing. At present, automated journalism typically focuses on domains such as sports, finance and similar statistics-based reporting, where there is a commercial product potential due to the high volume of news, combined with the expectation of a relatively straightforward task.
News generation systems—especially those deployed in practice—tend to be based on intricate template filling, aiming to give the users the full control of the generated facts, while maintaining a reasonable variability of the resulting text. This comes at the price of having to develop the templates and specify their control logic, neither of which are tasks naturally fitting journalists' work. Further, this development needs to be repeated for every domain, as the templates are not easily transferred across domains. Examples of the template-based news generation systems for Finnish are Voitto by the Finnish Public Service Broadcasting Company (YLE) used for sports news generation, as well as Vaalibotti BIBREF0, a hybrid machine learning and template-based system used for election news.
BIBREF1 suggested a neural template generation, which jointly models latent templates and text generation. Such a system increases interpretability and controllability of the generation, however, recent sequence-to-sequence systems represent the state-of-the-art in data-to-text generation. BIBREF2
In this paper, we report on the development of a news generation system for the Finnish ice hockey news domain, based on sequence-to-sequence methods. In order to train such a system, we compile a corpus of news based on over 2000 game reports from the Finnish News Agency STT. While developing this corpus into a form suitable for training of end-to-end systems naturally requires manual effort, we argue that compiling and refining a set of text examples is a more natural way for journalists to interact with the system, in order for them to codify their knowledge and to adapt it for new domains.
Our aim is to generate reports that give an overview of a game based on information inferrable from the statistics. Such reports can be used either as a basis for further post-editing by a journalist imprinting own insights and background information, or even used directly as a news stream labelled as machine-generated.
In the following, we will introduce the news dataset and the process of its creation, introduce an end-to-end model for news generation, and evaluate its output respective to the abovementioned objectives.
In the following, we will introduce the news dataset and the process of its creation, introduce an end-to-end model for news generation, and evaluate its output respective to the abovementioned objectives.
Answer: The authors introduce a news dataset and the process of its creation, introduce an end-to-end model for news generation, and evaluate its output respective to the abovementioned objectives.
Question: What was the performance of both approaches on their dataset?
Evidence: FLOAT SELECTED: Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets.
FLOAT SELECTED: Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets.
Answer: ERR of 19.05 with i-vectors and 15.52 with x-vectors
Question: How they evaluate quality of generated output?
Evidence: Automatic evaluation of Natural Language Generation (NLG) systems is a challenging task BIBREF22. For QG, $n$-gram based similarity metrics are commonly used. These measures evaluate how similar the generated text is to the corresponding reference(s). While they are known to suffer from several shortcomings BIBREF29, BIBREF30, they allow to evaluate specific properties of the developed models. In this work, the metrics detailed below are proposed and we evaluate their quality through a human evaluation in subsection SECREF32.
In addition to the automatic metrics, we proceeded to a human evaluation. We chose to use the data from our SQuAD-based experiments in order to also to measure the effectiveness of the proposed approach to derive Curiosity-driven QG data from a standard, non-conversational, QA dataset. We randomly sampled 50 samples from the test set. Three professional English speakers were asked to evaluate the questions generated by: humans (i.e. the reference questions), and models trained using pre-training (PT) or (RL), and all combinations of those methods.
Before submitting the samples for human evaluation, the questions were shuffled. Ratings were collected on a 1-to-5 likert scale, to measure to what extent the generated questions were: answerable by looking at their context; grammatically correct; how much external knowledge is required to answer; relevant to their context; and, semantically sound. The results of the human evaluation are reported in Table TABREF33.
In this work, the metrics detailed below are proposed and we evaluate their quality through a human evaluation in subsection SECREF32.
In addition to the automatic metrics, we proceeded to a human evaluation. We chose to use the data from our SQuAD-based experiments in order to also to measure the effectiveness of the proposed approach to derive Curiosity-driven QG data from a standard, non-conversational, QA dataset. We randomly sampled 50 samples from the test set. Three professional English speakers were asked to evaluate the questions generated by: humans (i.e. the reference questions), and models trained using pre-training (PT) or (RL), and all combinations of those methods.

Before submitting the samples for human evaluation, the questions were shuffled. Ratings were collected on a 1-to-5 likert scale, to measure to what extent the generated questions were: answerable by looking at their context; grammatically correct; how much external knowledge is required to answer; relevant to their context; and, semantically sound. The results of the human evaluation are reported in Table TABREF33.
Answer: Through human evaluation where they are asked to evaluate the generated output on a likert scale.
Question: Given this paragraph describing the conclusion of the movie Groundhog Day, how did Phil manage to break the cycle and wake up on February 3 instead of back once again on February 2?
Evidence: During one iteration of the loop, Phil reports on the Groundhog Day festivities with such eloquence that other news crews stop working to listen to his speech, amazing Rita. Phil continues his day helping the people of Punxsutawney. That night, Rita witnesses Phil's expert piano-playing skills as the adoring townsfolk regale her with stories of his good deeds. Impressed by his apparent overnight transformation, Rita successfully bids for him at a charity bachelor auction. Phil carves an ice sculpture in Rita's image and tells her that no matter what happens, even if he is trapped in the loop forever, he is finally happy because he loves her. They share a kiss and retire to Phil's room. He wakes the next morning to "I Got You Babe" but finds Rita is still in bed with him and the radio banter has changed; it is now February 3. Phil tells Rita that he wants to live in Punxsutawney with her.
https://en.wikipedia.org/wiki/Groundhog_Day_(film)
Answer: This paragraph summarizing the conclusion of Groundhog Day explains what was happening when Phil wakes up on February 3 but does not explain why he is able to escape the loop.
Question: How does meditation help mental health
Answer: Meditation is beneficial for reducing stress. Studies show that it can also reduce depression and anxiety. Meditation can give you a sense of calm, peace and balance that benefits both your emotional well-being and your overall health. Research has found several physiological benefits such as better blood pressure, heart rate, metabolism, breathing pattern, oxygen utilization, and carbon dioxide elimination and relative blood flow to the brain.
Question: Who is the first one that introduces the word embeddings?
Evidence: Word embeddings were first introduced in BIBREF2 , BIBREF3 and there have been many variants BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . Common for all of them is the idea that words can be represented by latent feature vectors. These feature vectors are optimized to maximize the conditional probability of the dataset. Recently BIBREF1 extended the idea of word embeddings to other types of data. EqEmb expand the idea of word embeddings to a new type of data points – equations.
There have been different proposed approaches for representing mathematical equations. BIBREF8 introduced the symbol layout tree, a representation that encodes the spatial relationship of variables and operators for the purpose of indexing and retrieving mathematical equations. Our work also falls into the framework of mathematical language processing (MLP) BIBREF9 whose first step is converting mathematical solutions into a series of numerical features.
Word embeddings were first introduced in Rumelhart et al. (1986); Bengio et al. (2003) and there have been many variants (Mikolov et al., 2013a;b; Pennington et al., 2014; Levy & Goldberg, 2014).
Answer: Rumelhart et al. .
Question: Give me a title of one of the "Harry Potter" novels.
Answer: Harry Potter and the Prisoner of Azkaban