DataHammer/scimrc huggingface dataset:
Question: What is NLI?
Evidence:  This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/.
Sentence pair modeling is a fundamental technique underlying many NLP tasks, including the following:
Traditionally, researchers had to develop different methods specific for each task. Now neural networks can perform all the above tasks with the same architecture by training end to end. Various neural models BIBREF1 , BIBREF0 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 have declared state-of-the-art results for sentence pair modeling tasks; however, they were carefully designed and evaluated on selected (often one or two) datasets that can demonstrate the superiority of the model. The research questions are as follows: Do they perform well on other tasks and datasets? How much performance gain is due to certain system design choices and hyperparameter optimizations?
To answer these questions and better understand different network designs, we systematically analyze and compare the state-of-the-art neural models across multiple tasks and multiple domains. Namely, we implement five models and their variations on the same PyTorch platform: InferSent model BIBREF16 , Shortcut-stacked Sentence Encoder Model BIBREF17 , Pairwise Word Interaction Model BIBREF1 , Decomposable Attention Model BIBREF10 , and Enhanced Sequential Inference Model BIBREF0 . They are representative of the two most common approaches: sentence encoding models that learn vector representations of individual sentences and then calculate the semantic relationship between sentences based on vector distance and sentence pair interaction models that use some sorts of word alignment mechanisms (e.g., attention) then aggregate inter-sentence interactions. We focus on identifying important network designs and present a series of findings with quantitative measurements and in-depth analyses, including (i) incorporating inter-sentence interactions is critical; (ii) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter data; (iii) Enhanced Sequential Inference Model has the most consistent high performance for larger datasets, while Pairwise Word Interaction Model performs better on smaller datasets and Shortcut-Stacked Sentence Encoder Model is the best performaning model on the Quora corpus. We release our implementations as a toolkit to the research community.
•Natural Language Inference (NLI), also known as recognizing textual entailment (RTE), which concerns whether a hypothesis can be inferred from a premise, requiring understanding of the semantic similarity between the hypothesis and the premise (Dagan et al., 2006; Bowman et al., 2015).
Answer: It is short for Natural Language Inference, also known as recognizing textual entailment (RTE), which concerns whether a hypothesis can be inferred from a premise, requiring understanding of the semantic similarity between the hypothesis and the premise (Dagan et al., 2006; Bowman et al., 2015).
Question: What is a yellow or red card in soccer?
Evidence: A foul occurs when a player commits an offence listed in the Laws of the Game while the ball is in play. The offences that constitute a foul are listed in Law 12. Handling the ball deliberately, tripping an opponent, or pushing an opponent, are examples of "penal fouls", punishable by a direct free kick or penalty kick depending on where the offence occurred. Other fouls are punishable by an indirect free kick.

The referee may punish a player's or substitute's misconduct by a caution (yellow card) or dismissal (red card). A second yellow card in the same game leads to a red card, which results in a dismissal. A player given a yellow card is said to have been "booked", the referee writing the player's name in their official notebook. If a player has been dismissed, no substitute can be brought on in their place and the player may not participate in further play. Misconduct may occur at any time, and while the offences that constitute misconduct are listed, the definitions are broad. In particular, the offence of "unsporting behaviour" may be used to deal with most events that violate the spirit of the game, even if they are not listed as specific offences. A referee can show a yellow or red card to a player, substitute, substituted player, and to non-players such as managers and support staff.

Rather than stopping play, the referee may allow play to continue if doing so will benefit the team against which an offence has been committed. This is known as "playing an advantage". The referee may "call back" play and penalise the original offence if the anticipated advantage does not ensue within "a few seconds". Even if an offence is not penalised due to advantage being played, the offender may still be sanctioned for misconduct at the next stoppage of play.

The referee's decision in all on-pitch matters is considered final. The score of a match cannot be altered after the game, even if later evidence shows that decisions (including awards/non-awards of goals) were incorrect.
Answer: In the game of soccer, whenever a player commits a foul, the referee can give a player a yellow card or a red card. A yellow card is a caution/warning. A red card is a dismissal and the player has to leave the field.
Question: How is each block input connected directly into the last subblock?
Evidence: Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words. Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system.
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6 . We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers. Liptchinsky et al. BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout.
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks. Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10 . We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR. As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs.
It is possible to increase the capacity of the Jasper model by stacking these operations. Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters). We use residual connections to enable this level of depth. We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR).
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2.95% WER and SOTA results among end-to-end models on LibriSpeech test-other. We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S). Using only greedy decoding without a language model we achieve 3.86% WER on LibriSpeech test-clean.
This paper makes the following contributions:
Each block input is connected directly into the last subblock via a residual connection.
Answer: Each block input is connected directly into the last subblock via a residual connection.
Question: What advice would you give to FBLA competitors for future years?
Answer: Please do not procrastinate and really focus on the prejudged part of the portion of your report, because that is a prerequisite if you want to compete at states. Also, make sure to thoroughly read the rubric so you hit every single requirement on the rubric, especially the part about providing a recommendation and plan. As for your recommendation, try to come up with a creative acronym to make it memorable. For us, we thought of CHIP, as the topic was about microchips. Although notes are technically allowed, every single team memorizes their presentation, so make sure to have your presentation very well memorized by the conference. Also it is important to get plenty of sleep before the night of the competition, eat breakfast, and act confident when you are presenting. Finally, if you do make a mistake it is okay (part of the learning), just don't act surprised and keep going!
Question: What makes trees and plants green?
Answer: Trees, plants and algae are green because of chlorophyl and are actually perceived as green because chlorophyll absorbs red and blue light but reflects green light.
Question: Are these methods evaluated together on the same corpus?
Evidence: This research gathers the results of Vietnamese word segmentation of several methods into one table as show in Table II. It is noted that they are not evaluated on a same corpus. The purpose of the result illustration is to provide an overview of the results of current Vietnamese word segmentation systems based on their individual features. All studies mentioned in the table have accuracy around 94-97% based on their provided corpus.
This study also evaluates the Vietnamese word segmentation based on existing toolkits using the same annotated Vietnamese word segmentation corpus. There are two available toolkits to evaluate and to segment. To be neutral to both toolkits, we use the EVBNews Vietnamese corpus, a part of EVBCorpus, to evaluate Vietnamese word segmentation. The EVBNews corpus contains over 45,000 segmented Vietnamese sentences extracted from 1,000 general news articles (as shown in Table III) BIBREF16 . We used the same training set which has 1000 files and 45,531 sentences. vnTokenizer outputs 831,455 Vietnamese words and 1,206,475 tokens. JVnSegmenter outputs 840,387 words and 1,201,683. We correct tags (BIO), and compare to previous outputs, we have rate from vnTokenizer is 95.6% and from JVnsegmenter is 93.4%. The result of both vnTokenizer and JVnSegmenter testing on the EVBNews Vietnamese Corpus are provided in Table IV.
This research gathers the results of Vietnamese word segmentation of several methods into one table as show in Table II. It is noted that they are not evaluated on a same corpus.
Answer: No.
Question: Given this paragraph, how much control of the world's cocaine market did the Cali Cartel have at its peak?
Evidence: The Cali Cartel (Spanish: Cartel de Cali) was a drug cartel based in southern Colombia, around the city of Cali and the Valle del Cauca. Its founders were the brothers Gilberto Rodríguez Orejuela, Miguel Rodríguez Orejuela and José Santacruz Londoño. They broke away from Pablo Escobar and his Medellín associates in 1987, when Hélmer "Pacho" Herrera joined what became a four-man executive board that ran the cartel.

At the height of the Cali Cartel's reign from 1993 to 1995, they were cited as having control of over 80% of the world's cocaine market and were said to be directly responsible for the growth of the cocaine market in Europe, controlling 80% of the market there as well. By the mid-1990s, the leaders of the Cali Cartel's multibillion-dollar international drug trafficking empire were operating a US$20 billion a year global criminal empire.
Answer: At the Cali Cartel's peak from 1993 to 1995, they controlled over 80% of the world's cocaine market.
Question: what bottlenecks were identified?
Evidence: In this paper we proposed a methodology to identify words that could lead to confusion at any given node of a speech recognition based system. We used edit distance as the metric to identifying the possible confusion between the active words. We showed that this metric can be used effectively to enhance the performance of a speech solution without actually putting it to people test. There is a significant saving in terms of being able to identify recognition bottlenecks in a menu based speech solution through this analysis because it does not require actual people testing the system. This methodology was adopted to restructuring the set of active words at each node for better speech recognition in an actual menu based speech recognition system that caters to masses.
We hypothesize that we can identify the performance of a menu based speech system by identifying the possible confusion among all the words that are active at a given node. If active words at a given node are phonetically similar it becomes difficult for the speech recognition system to distinguish them which in turn leads to recognition errors. We used Levenshtein distance BIBREF4 , BIBREF5 a well known measure to analyze and identify the confusion among the active words at a given node. This analysis gives a list of all set of words that have a high degree of confusability among them; this understanding can be then used to (a) restructure the set of active words at that node and/or (b) train the words that can be confused by using a larger corpus of speech data. This allows the speech recognition engine to be equipped to be able to distinguish the confusing words better. Actual use of this analysis was carried out for a speech solution developed for Indian Railway Inquiry System to identify bottlenecks in the system before its actual launch.
In this paper we proposed a methodology to identify words that could lead to confusion at any given node of a speech recognition based system. We used edit distance as the metric to identifying the possible confusion between the active words. 
There is a significant saving in terms of being able to identify recognition bottlenecks in a menu based speech solution through this analysis because it does not require actual people testing the system. 
Actual use of this analysis was carried out for a speech solution developed for Indian Railway Inquiry System to identify bottlenecks in the system before its actual launch.
Answer: Confusion in recognizing the words that are active at a given node by a speech recognition solution developed for Indian Railway Inquiry System.
Question: What does the Horse Statue in Ground Zero Represent?
Evidence: America's Response Monument, subtitled De Oppresso Liber, is a life-and-a-half scale bronze statue in Liberty Park overlooking the National September 11 Memorial & Museum in New York City. Unofficially known as the Horse Soldier Statue, it is the first publicly accessible monument dedicated to the United States Special Forces. It was also the first monument near Ground Zero to recognize heroes of the September 11 terrorist attacks.

The statue was conceived by sculptor Douwe Blumberg and first sold in 2003 as a small-scale, 18 inches (460 mm) version. In April 2011, an anonymous group of Wall Street bankers who lost friends in the 9/11 attacks commissioned a large, 16 feet (4.9 m) tall version. It was dedicated on Veterans Day, November 11, 2011, in a ceremony led by Vice President Joe Biden and Lt. Gen. John Mulholland, commander of Task Force Dagger and U.S. Army Special Operations Command during Operation Enduring Freedom.

The statue commemorates the service members of America's Special Operations forces and their response to 9/11, including those who fought during the first stages of the Afghanistan war. This operation led to the initial defeat of the Taliban in Afghanistan, although the Taliban would ultimately be victorious following their 20-year-long war with the United States.
Answer: America's Response Monument, De Oppresso Liber, commemorates the service members of America's Special Operations forces and their response to 9/11, including those who fought during the first stages of the Afghanistan war.
Question: The authors state that: larger N indicates more coarse-grained decomposition. Then, how to determine proper N for different down-stream tasks?
Evidence: A larger N in the graph piece extraction process leads to an increase in the number of atoms in graph pieces and a decrease in their frequency of occurrence, as illustrated in Figure. These two factors affect model performance in opposite ways. On the one hand, the entropy of the dataset decreases with more coarse-grained decomposition, which benefits model learning. On the other hand, the sparsity problem worsens as the frequency of graph pieces decreases, which hurts model learning. We propose a quantified method to balance entropy and sparsity. The entropy of the dataset given a set of graph pieces S is defined by the sum of the entropy of each graph piece normalized by the average number of atoms:
where P (P) is the relative frequency of graph piece P in the dataset and n S is the average number of atoms of graph pieces in S. The sparsity of S is defined as the reciprocal of the average frequency of graph pieces normalized by the size of the dataset:
where M is the number of molecules in the dataset and f S is the average frequency of occurrence of graph pieces in the dataset. Then the entropy -sparsity trade-off (T ) can be expressed as:
where γ balances the impacts of entropy and sparsity since the impacts vary across different tasks.
We assume that T S negatively correlates with downstream tasks. Given a task, we first sample several values of N to calculate their values of T and then compute the γ that minimize the Pearson correlation coefficient between T and the corresponding performance on the task. With the proper γ, Pearson correlation coefficients for the three downstream tasks in this paper are -0.987, -0.999, and -0.707, indicating strong negative correlations. For example, Figure shows the curve of entropysparsity trade-off with a maximum of 3,000 iteration steps for the property optimization task. From the curve, we choose N = 300 for the property optimization task.
Figure: Entropy -Sparsity trade-off, average number of atoms in graph pieces and average frequency of occurrence of graph pieces with a maximum of 3,000 iteration steps.
Then the entropy - sparsity trade-off (T) can be expressed as: Eq(12) where γ balances the impacts of entropy and sparsity since the impacts vary across different tasks.
Answer: In Section 5.2, we propose a quantified way to analyze the trade-off between the average size of graph pieces and their frequencies when N becomes larger. We can select proper N for different downstream tasks from the low points in the curve of this metric.
Question: What is the color of tennis balls?
Evidence: Tennis balls were originally made of cloth strips stitched together with thread and stuffed with feathers. Modern tennis balls are made of hollow vulcanized rubber with a felt coating. Traditionally white, the predominant colour was gradually changed to optic yellow in the latter part of the 20th century to allow for improved visibility. Tennis balls must conform to certain criteria for size, weight, deformation, and bounce to be approved for regulation play. The International Tennis Federation (ITF) defines the official diameter as 65.41–68.58 mm (2.575–2.700 in). Balls must weigh between 56.0 and 59.4 g (1.98 and 2.10 oz).
Answer: Modern tennis balls are yellow.
Question: What are the names of four large moons of Jupiter discovered by Galileo Galilei in 1610?
Evidence: Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass more than two and a half times that of all the other planets in the Solar System combined, and slightly less than one one-thousandth the mass of the Sun. Jupiter is the third brightest natural object in the Earth's night sky after the Moon and Venus, and it has been observed since prehistoric times. It was named after Jupiter, the chief deity of ancient Roman religion.

Jupiter is primarily composed of hydrogen, followed by helium, which constitutes a quarter of its mass and a tenth of its volume. The ongoing contraction of Jupiter's interior generates more heat than the planet receives from the Sun. Because of its rapid rotation rate of 1 rotation per 10 hours, the planet's shape is an oblate spheroid: it has a slight but noticeable bulge around the equator. The outer atmosphere is divided into a series of latitudinal bands, with turbulence and storms along their interacting boundaries. A prominent result of this is the Great Red Spot, a giant storm which has been observed since at least 1831.

Jupiter is surrounded by a faint planetary ring system and has a powerful magnetosphere. The planet's magnetic tail is nearly 800 million kilometres (5.3 astronomical units; 500 million miles) long. Jupiter has 95 known moons and probably many more, including the four large moons discovered by Galileo Galilei in 1610: Io, Europa, Ganymede, and Callisto. Ganymede, the largest of the four, is larger than the planet Mercury. Callisto is the second largest; Io and Europa are approximately the size of Earth's moon.

Pioneer 10 was the first spacecraft to visit Jupiter, making its closest approach to the planet in December 1973. Jupiter has since been explored by multiple robotic spacecraft, beginning with the Pioneer and Voyager flyby missions from 1973 to 1979. The Galileo orbiter arrived in orbit around Jupiter in 1995. In 2007, New Horizons visited Jupiter for a gravity assist to increase its speed and bend its trajectory on the way to Pluto. The latest probe to visit Jupiter, Juno, entered its orbit in July 2016. Future targets for exploration in the Jupiter system include its moon Europa, which probably has an ice-covered liquid ocean which scientists think could sustain life.
Answer: Io, Europa, Ganymede, and Callisto.