DataHammer/scimrc huggingface dataset:
Question: How does this approach differ from previous ones？
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:
In this paper, we propose a different solution that does not change the overall architecture, but only the attention transformation.
Answer: Their solution does not change the overall architecture, but only the attention transformation.
Question: per this description of the game of fooseball, how many full rotations are allowed in the game at the championship level?
Evidence: The game involves using figures mounted on rotating bars to kick a ball into the opposing goal. Table football tables can vary in size, but a typical table is about 120 centimetres (3.9 ft) long and 61 centimetres (2.00 ft) wide. The table usually contains eight rows of foos men, which are plastic, metal, wooden, or sometimes carbon-fibre figures mounted on horizontal metal bars. Each team of one or two human players controls four rows of foos men, one row each for the goalkeeper, defenders, midfield and strikers. Players manipulate the rods to control the figures, using them to hold up, pass or 'kick' the ball. Games begin when the ball is served through a hole at the side of the table, or simply placed by hand at the feet of a figure in the centre of the table. A coin toss is usually used to determine which player or team serves first. If it is not the first match or if the ball gets out of play or if the ball suddenly stops out, then the team scored last would get the serve after he scores one. The term for when the ball leaves the table or gets stopped somewhere out of the men-like figures’ reach is dead ball. Expert players have been known to move balls at speeds up to 56 km/h (35 mph) in competition.

The rules prohibit "over 360-degree shots", or "spinning": using the palm of the hand to swiftly spin the bar all around, instead of using wrist strokes to kick the ball with a bar-mounted figure. Since the establishment of the International Table Soccer Federation (ITSF), the rules have become standardised in most international competitions. However, since January 2012, the annual World Championships and the World Cup have permitted two full 360-degree rotations.

The winner is determined when one team scores a predetermined number of goals, typically five, ten or eleven in competition. When playing Bonzini competitions, the target number of goals is seven and players must win by at least two clear goals.
Answer: 2 360-degree rotations are allowed since January 2012.
Question: Van Halen (/væn ˈheɪlɛn/ van HAY-len) was an American rock band formed in Pasadena, California, in 1973. Credited with "restoring hard rock to the forefront of the music scene", Van Halen was known for its energetic live shows and for the virtuosity of its lead guitarist, Eddie Van Halen. The band was inducted into the Rock and Roll Hall of Fame in 2007.
Evidence: Where and when did Van Halen start as a band?
Answer: formed in Pasadena, California, in 1973
Question: From the passage identify the founders of Confinity. Display the results in comma separated format.
Evidence: PayPal was originally established by Max Levchin, Peter Thiel, and Luke Nosek in December 1998 as Confinity, a company that developed security software for hand-held devices. Having had no success with that business model, however, it switched its focus to a digital wallet. The first version of the PayPal electronic payments system was launched in 1999.
In March 2000, Confinity merged with x.com, an online financial services company founded in March 1999 by Elon Musk, Harris Fricker, Christopher Payne, and Ed Ho. Musk was optimistic about the future success of the money transfer business Confinity was developing. Musk and Bill Harris, then-president and CEO of X.com, disagreed about the potential future success of the money transfer business and Harris left the company in May 2000. In October of that year, Musk decided that X.com would terminate its other internet banking operations and focus on payments. That same month, Elon Musk was replaced by Peter Thiel as CEO of X.com, which was renamed PayPal in June 2001 and went public in 2002. PayPal's IPO listed under the ticker PYPL at $13 per share and generated over $61 million.
Answer: Max Levchin, Peter Thiel, Luke Nosek
Question: Is it safe to invest in stocks?
Answer: There are no guarantees of profits when you buy stock, which makes stock a risky investments. If a company doesn't do well or falls out of favor with investors, its stock can fall in price, and investors could lose money. With the other hand, Stocks offer investors the greatest potential for growth over the long haul. Investors willing to stick with stocks over long periods of time, say 15 years, generally have been rewarded with strong, positive returns.
Question: Notation changes completely in section 4.3. What do the statements mean? The notation starts with classic FL notation but then switches to the notation used in [MMRS20].
Evidence: We analyse the generalization error of CGPFL in this section. Before starting the analysis, we first introduce two important definitions as follows.
Definition 2 (Complexity) Let H be a hypothesis class (correspanding to ω ∈ R d in neural network), and |D| be the size of dataset D, the complexity of H can be expressed by the maximum disagreement between two hypotheses on a dataset D:
Definition 3 (Label-discrepancy) Consider a hypothesis class H, the label-discrepancy between two data distributions D 1 and D 2 is given by: disc
where
Theorem 2 (The generalization error of CGPFL) When Assumption 1 is satisfied, with probability at least 1 − δ, the following holds:
where B is a positive constant with
Remark 2 Theorem 2 gives the generalization error bound of CGPFL. When K = 1, it yields the error bound of PFL with single global model. As the number of clusters increases, the second terms become larger, while the last term get smaller. Hence, our CGPFL can alwalys reach better personalizationgeneralization trade-off by adjusting the number of clusters K, and further achieve higher accuracy than the existing PFL methods. The detailed proof of generalization error is given in the Appendix of this paper.
(Complexity) Let H be a hypothesis class (correspanding to ω ∈ Rd in neural network), and |D| be the size of dataset D, the complexity of H can be expressed by the maximum disagreement between two hypotheses on a dataset D
Answer: Actually, we explain that the hypothesis h has a direct relationship with the model parameter Ω in Definition 2 in Section 4.3.
Question: How is the proposed method comparing with state-of-the-art non-VAE-based GNN methods?
Evidence: To evaluate the effectiveness and interpretability of the proposed model, we conduct a series of experiments, including link prediction and community detection, on several real-world graphs.
Ablation study for the components of our method, including the community membership, node random factors and the HLSM decoder architecture are also conducted.
On all datasets, our model achieves better or comparable performances to the non-generative method SEAL. However, since it requires to materialize a subgraph for each link, SEAL is much more unscalable than our DLSM and takes about 20 multiples longer for running one epoch.
Answer: We consider your suggestion and have added SEAL [1] in the link prediction experiments. The results are presented in Table 1, 2 and 6 of our current draft. It seems that SEAL performs close to our proposed method on some datasets. However, compared to our method, SEAL is more unscalable with high time complexity as it requires to materialize a subgraph for each link. (Actually, it takes about 20 multiples longer than our method for running one epoch.)
More importantly, SEAL and other non-generative models for link prediction cannot generate representations as node embeddings for other downstream tasks such as community detection and node classification. There are some gaps between these methods and ours (as well as the baselines selected in our original draft). The main point and contribution of our work is to propose a novel graph representation learning method and generate interpretable latent variables to better represent the common properties of directed graphs, including link reciprocity, degree heterogeneity and community structure. These learned node representations are readily to be applied for multiple downstream tasks, including but not limited to link prediction and community detection. The experiments in our paper are conducted for the purpose of testing whether these learned representations are able to capture the interpreted graph properties. For example, the link prediction task verifies the ability to represent link reciprocity of directed graphs, and the community detection task verifies the ability for modeling clustering effects.
We sincerely hope the reviewer to recosider on this point since our work is not acturally aiming for link prediction. Besides, by comparing our method with other graph representation learning methods, we conclude that our method achieves the state-of-the-art performances on downstream tasks.
Question: What are monsoons in India?
Answer: Monsoons in India typically refer to the rainy season, which is generally between June and September. It follows the summer season. Monsoons also refer to the seasonal weather pattern that causes rains in India due to the changing direction of winds. The hot summers cause the air over the land to heat up and rise causing low pressure, which the moisture-filled air from the ocean rushes to fill. As this air rises over the mountains, it cools and brings rain to many parts of the Indian subcontinent.
Question: What is the algorithm evaluated against?
Evidence: Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines. If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required.
LID is further also an important step in harvesting scarce language resources. Harvested data can be used to bootstrap more accurate LID models and in doing so continually improve the quality of the harvested data. Availability of data is still one of the big roadblocks for applying data driven approaches like supervised machine learning in developing countries.
Having 11 official languages of South Africa has lead to initiatives (discussed in the next section) that have had positive effect on the availability of language resources for research. However, many of the South African languages are still under resourced from the point of view of building data driven models for machine comprehension and process automation.
Table TABREF2 shows the percentages of first language speakers for each of the official languages of South Africa. These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso). The Nguni languages are similar to each other and harder to distinguish. The same is true of the Sotho languages.
This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long. The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks.
Section SECREF2 reviews existing works on the topic and summarises the remaining research problems. Section SECREF3 of the paper discusses the proposed algorithm and Section SECREF4 presents comparative results.
The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks.
Answer: Recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks.
Question: What do they use the data API of GluonCV/NLP to do?
Evidence: GluonCV/NLP provide access to modular APIs to allow users to customize their model design, training, and inference by re-using efficient components across different models. Such common components include (but are not limited to) data processing utilities, models with individual components, initialization methods, and loss functions.
To elucidate how the modular API facilitates efficient implementation, let us take the data API of GluonCV/NLP as an example, which is used to build efficient data pipelines with popular benchmark data sets or those supplied by users. In computer vision and natural language processing tasks, inputs or labels often come in with different shapes, such as images with a varying number of objects and sentences of different lengths. Thus, the data API provides a collection of utilities to sample inputs or labels then transform them into mini-batches to be efficiently computed. Besides, users can access a wide range of popular data sets via the data API, including (but are not limited to) ImageNet of image classification, VOC of object detection, COCO of instance segmentation, SQuAD of question answering, and SST of sentiment analysis.
To elucidate how the modular API facilitates efficient implementation, let us take the data API of GluonCV/NLP as an example, which is used to build efficient data pipelines with popular benchmark data sets or those supplied by users.
Answer: To build efficient data pipelines with popular benchmark data sets or those supplied by users.
Question: Given a reference text about the Tampa Bay Buccaneers, how many Super Bowls have they won.
Evidence: The Tampa Bay Buccaneers are a professional American football team based in Tampa, Florida. The Buccaneers compete in the National Football League (NFL) as a member club of the league's National Football Conference (NFC) South division. The club joined the NFL in 1976 as an expansion team, along with the Seattle Seahawks, and played its first season in the American Football Conference (AFC) West division. Prior to the 1977 season, Tampa Bay switched conferences and divisions with Seattle, becoming a member of the NFC Central division. As a result of the league's realignment prior to the 2002 season, the Buccaneers joined three former NFC West teams to form the NFC South. The club is owned by the Glazer family and plays its home games at Raymond James Stadium in Tampa.

The Buccaneers have won two Super Bowl championships and, along with the Baltimore Ravens, are the only two NFL franchises who are undefeated in multiple Super Bowl appearances. They were regarded as a perennial losing franchise for most of their first two decades due to suffering 26 consecutive losses in their first two seasons (including a winless inaugural season) and 14 consecutive losing seasons from 1983 to 1996. Despite these early struggles, Tampa Bay is the first post-merger expansion team to clinch a division title, win a playoff game, and host a conference championship; feats they accomplished by their fourth season in 1979. The team's image improved by the time of their first championship in 2002, also the first for a franchise built after the merger, but they would not win another playoff game until their second Super Bowl championship season in 2020. Concurrent to the lack of success outside of their Super Bowl wins, the Buccaneers hold the NFL's lowest winning percentage at .404.
Answer: The Buccaneers have won two Super Bowl championships.
Question: What does the quantitative comparison demonstrate?
Evidence: Neural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, such as English-French BIBREF0, BIBREF1 and Chinese-English BIBREF2. However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
The understanding of NMT models has been approached primarily from two complementary perspectives. The first thread of work aims to understand the importance of representations by analyzing the linguistic information embedded in representation vectors BIBREF3, BIBREF4 or hidden units BIBREF5, BIBREF6. Another direction focuses on understanding the importance of input words by interpreting the input-output behavior of NMT models. Previous work BIBREF7 treats NMT models as black-boxes and provides explanations that closely resemble the attention scores in NMT models. However, recent studies reveal that attention does not provide meaningful explanations since the relationship between attention scores and model output is unclear BIBREF8.
In this paper, we focus on the second thread and try to open the black-box by exploiting the gradients in NMT generation, which aims to estimate the word importance better. Specifically, we employ the integrated gradients method BIBREF9 to attribute the output to the input words with the integration of first-order derivatives. We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
We analyze the linguistic behaviors of words with the importance and show its potential to improve NMT models. First, we leverage the word importance to identify input words that are under-translated by NMT models. Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods. Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language. For example, nouns are more important for Chinese$\Rightarrow $English translation, while prepositions are more important for English-French and -Japanese translation. This finding can inspire better design principles of NMT architectures for different language pairs. For instance, a better architecture for a given language pair should consider its own language characteristics.
We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
Answer: Demonstrate the gradient-based approach's superiority on estimating word importance.
Question: I need to plan a wedding and I have no idea where to start. Will you help me brainstorm a few things I should do?
Answer: Your wedding a celebration of you and partners love. Here are a couple ideas of where to start. 
1. Sit down with your partner and each share you respective vision, hopes and dreams for your wedding. Listen intently and take notes to what your partner says. 
2. Make a list of the people you would want at your wedding. Tier each potential invite as Tier 1,2 and 3 and who's side they represent (you, your partner, or both).
3. Identify a preliminary budget based on what you can afford to spend. 
4. Make a list of the most important things to you and your partner, in priority order (venue, band, flowers, dress, etc.)
5. Evaluate hiring a wedding planner to assist you in your planning efforts. 
Good luck as you begin this exciting process!
Question: What is Gettysburg College?
Answer: Gettysburg College is a private liberal arts college located in Gettysburg, Pennsylvania. It was founded in 1832 and is noted as being a key fixture in the Battle of Gettysburg fought during the Civil War.Gettysburg College has about 2,600 students and is 90 miles north of Washington D.C.
Question: Which 5 languages appear most frequently in AA paper titles?
Evidence: FLOAT SELECTED: Figure 11 A treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green.
FLOAT SELECTED: Figure 11 A treemap of the 122 languages arranged alphabetically and shaded such that languages that appear more often in AA paper titles have a darker shade of green.
Answer: English, Chinese, French, Japanese and Arabic
Question: From the given text, provide a couple of points on Supreme Court of Iran
Evidence: The Supreme Court of Iran is the highest juridical authority in Iran, established to supervise the correct implementation of laws by courts of justice and consisting of the most prominent judges of the country. The head of the judiciary assigns criteria to ensure uniformity of judicial procedure and to carry out all legal responsibilities.
Answer: 1. The Supreme Court of Iran, which is composed of the most eminent judges in the nation and was founded to oversee the proper application of laws by courts of justice, is the highest legal authority in Iran.
2. To ensure judicial procedure is uniform and to fulfil all legal obligations, the head of the court assigns standards.