DataHammer/scimrc huggingface dataset:
Question: Which of the following are not noble gases: helium, sodium, neon, oxygen, argon, hydrogen, calcium, magnesium, lithium, boron, krypton, nitrogen, xenon, aluminium, radon.
Answer: The following are not noble gases - sodium, oxygen, hydrogen, calcium, magnesium, lithium, boron, nitrogen, aluminium.
Question: Why is soccer the most popular sport in the world?
Answer: Soccer is the most popular sport in the world because of several reasons including: It is easy to understand the rules and thus it is easy to play.  The scoring system is simple - one goal equals 1 point.  It does not require a lot of equipment.  As a soccer player you have a lot of freedom in decision making compared to other sports like American football where players are expected to follow specific directions and plays from the coach.  Soccer can be played in many different formats - from official team sizes with 11 players vs. 11 players to smaller teams of 2 players vs 2 players.  Soccer is fun and exciting to play or watch.
Question: Can you give me a summary of the story of the Vidhivilasa movie?
Evidence: Vidhivilasa is a 1962 Kannada language swashbuckler film directed by S. V. Mahesh. The film stars Rajkumar, Leelavathi and K. S. Ashwath. The film is based on the story of a King who confronts destiny (fate), which appears to him in the form of a young lady, by challenging it on how he would die. The King tries in vain all means at his disposal to prevent events that destiny foretold would happen. Finally, destiny wins, employing a twisted turn of events. The film has musical score by T. Padman. The story, screenplay, dialogues and lyrics were written by H. L. Narayana Rao who happens to be the father of actor Vishnuvardhan.
Answer: A king is confronted by destiny in person of a young lady and tries by any means possible to avoid his fate.  However the king is not able to prevent his destiny.
Question: What is the neural architecture that the author proposes?
Evidence: Quality estimation (QE) refers to the task of measuring the quality of machine translation (MT) system outputs without reference to the gold translations BIBREF0 , BIBREF1 . QE research has grown increasingly popular due to the improved quality of MT systems, and potential for reductions in post-editing time and the corresponding savings in labor costs BIBREF2 , BIBREF3 . QE can be performed on multiple granularities, including at word level, sentence level, or document level. In this paper, we focus on quality estimation at word level, which is framed as the task of performing binary classification of translated tokens, assigning “OK” or “BAD” labels.
Early work on this problem mainly focused on hand-crafted features with simple regression/classification models BIBREF4 , BIBREF5 . Recent papers have demonstrated that utilizing recurrent neural networks (RNN) can result in large gains in QE performance BIBREF6 . However, these approaches encode the context of the target word by merely concatenating its left and right context words, giving them limited ability to control the interaction between the local context and the target word.
In this paper, we propose a neural architecture, Context Encoding Quality Estimation (CEQE), for better encoding of context in word-level QE. Specifically, we leverage the power of both (1) convolution modules that automatically learn local patterns of surrounding words, and (2) hand-crafted features that allow the model to make more robust predictions in the face of a paucity of labeled data. Moreover, we further utilize stacked recurrent neural networks to capture the long-term dependencies and global context information from the whole sentence.
We tested our model on the official benchmark of the WMT18 word-level QE task. On this task, it achieved highly competitive results, with the best performance over other competitors on English-Czech, English-Latvian (NMT) and English-Latvian (SMT) word-level QE task, and ranking second place on English-German (NMT) and German-English word-level QE task.
In this paper, we propose a neural architecture, Context Encoding Quality Estimation (CEQE), for better encoding of context in word-level QE.
Answer: Context Encoding Quality Estimation.
Question: How to capture contextual features?
Evidence: Figure FIGREF6 summarizes the proposed model architecture. The model takes character sequence as input and captures contextual features using BERT. A CRF layer is applied to extract entities from the sentence. To effectively transmit information between entity recognition and relation extraction, soft label embedding is built on the top of CRF logits. To solve the problem that one entity belongs to multiple triplets, a multi-sigmoid layer is applied. We find that adding an auxiliary global relation prediction task also improve the performance.
The model takes character sequence as input and captures contextual features using BERT.
Answer: The model captures contextual features using BERT.
Question: How much NDCG score does their best performing model on  the‘teststd’ split?
Evidence: This manuscript describes our approach for the Visual Dialog Challenge 2018. We use an ensemble of three discriminative models with different encoders and decoders for our final submission. Our best performing model on 'test-std' split achieves the NDCG score of 55.46 and the MRR value of 63.77, securing third position in the challenge.
This manuscript describes our approach for the Visual Dialog Challenge 2018. We use an ensemble of three discriminative models with different encoders and decoders for our final submission. Our best performing model on ‘teststd’ split achieves the NDCG score of 55.46 and the MRR value of 63.77, securing third position in the challenge.
Answer: 55.46.
Question: In which domains is sarcasm conveyed in different ways?
Evidence: We now discuss the relations among the results of the different experiments to gain some further insights into the sarcastic content of our corpora. From the in-corpus experiments, we obtain good results on SarcasmCorpus, which is the only corpus containing Amazon reviews. Unfortunately, when we train our models in a cross-corpora or all-corpora setting, our results drop dramatically, especially in the cross-corpora case. These results mean that the sarcasm in SarcasmCorpus is conveyed through features that are not present in the other corpora. This is especially true when considering that in the inter-corpora experiments, using SarcasmCorpus as a training set in all cases yields results that are only better than the ones obtained when using irony-context as a training set.
From the in-corpus experiments, we obtain good results on SarcasmCorpus, which is the only corpus containing Amazon reviews. Unfortunately, when we train our models in a cross-corpora or all-corpora setting, our results drop dramatically, especially in the cross-corpora case. These results mean that the sarcasm in SarcasmCorpus is conveyed through features that are not present in the other corpora.
Answer: The answers are shown as follows:
* Amazon reviews
Question: What is used to evaluate the methods?
Evidence: Our datasets can be used for various hate speech tasks. In this paper, we focus on generative hate speech intervention.
The goal of this task is to generate a response to hate speech that can mitigate its use during a conversation. The objective can be formulated as the following equation:
where $c$ is the conversation, $r$ is the corresponding intervention response, and $D$ is the dataset. This task is closely related to the response generation and dialog generation, though several differences exist including dialog length, language cadence, and word imbalances. As a baseline, we chose the most common methods of these two tasks, such as Seq2Seq and VAE, to determine the initial feasibility of automatically generate intervention responses. More recent Reinforcement Learning method for dialog generation BIBREF21 can also be applied to this task with slight modification. Future work will explore more complex, and unique models.
Similar to BIBREF21, a generative model is considered as an agent. However, different from dialog generation, generative intervention does not have multiple turns of utterance, so the action of the agent is to select a token in the response. The state of the agent is given by the input posts and the previously generated tokens. Another result due to this difference is that the rewards with regard to ease of answering or information flow do not apply to this case, but the reward for semantic coherence does. Therefore, the reward of the agent is:
where $rw(c,r)$ is the reward with regard to the conversation $c$ and its reference response $r$ in the dataset. $p(r|c)$ denotes the probability of generating response $r$ given the conversation $c$, and $p_{back}(c|r)$ denotes the backward probability of generating the conversation based on the response, which is parameterized by another generation network. The reward is a weighted combination of these two parts, which are observed after the agent finishing generating the response. We refer the readers to BIBREF21 for details.
The methods are evaluated using F-1 score, Precision-Recall (PR) AUC, and ReceiverOperating-Characteristic (ROC) AUC.
Answer: The methods are evaluated using F-1 score, Precision-Recall (PR) AUC, and ReceiverOperating-Characteristic (ROC) AUC.
Question: Is there a Romanian Royal Family?
Answer: The Romanian Royal Family consists of members of the former royal family of Michael I, the last king of Romania. This includes his two daughters, Margareta and Elena. Romania was a kingdom between 1881 and 1947. Carol I of Romania was the first king, proclaimed in 1881. Michael I of Romania was the last king, forced to abdicate in 1947.
Question: What was the Golden State Warriors win-loss record in the 1964-65 season ?
Evidence: The Warriors won the inaugural Basketball Association of America (BAA) championship in 1947, and won again in 1956, led by Hall of Fame trio Paul Arizin, Tom Gola, and Neil Johnston. After the trade of star Wilt Chamberlain in January 1965, the team finished the 1964–65 season with the NBA's worst record (17–63). Their rebuilding period was brief due in large part to the Warriors' drafting of Rick Barry four months after the trade. In 1975, star players Barry and Jamaal Wilkes powered the Warriors to their third championship, widely considered one of the biggest upsets in NBA history.
Answer: The Golden State Warriors record in the 1964-65 season was 17 wins and 63 losses.
Question: What is CloudFormation?
Answer: AWS CloudFormation is an infrastructure as code (IaC) service that allows you to easily model, provision, and manage AWS and third-party resources.
Question: What can help with puppy teething?
Answer: Puppy teething will typically last until they are 4 or 5 months old. To help with the discomfort, you can give them puppy safe chew toys, a frozen carrot or banana, bully sticks, or cooling teethers. You can also try giving them a frozen kong toy, or wet and twist a rag, then freeze it.
Question: What is the dataset used as input to the Word2Vec algorithm?
Evidence: The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.
The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of “numerical representation" linked to certain words, it was also decided to replace every number present in the text with the particular $\langle NUM \rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\,224$ words.
The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.
All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\,224$ words.
Answer: Italian Wikipedia and Google News extraction producing final vocabulary of 618224 words
Question: what makes the Sun bright ?
Answer: The sun is a huge massive sphere of gas and it is powered by the energy released by turning hydrogen to helium at its core. This process is called nuclear fusion reaction. Nuclear fusion involves two or more nuclei combining together to form one or more different atomic nuclei. Nuclear fusion releases termendous amount of energy in the form of light and heat.
Question: What's your favorite stars in the sky?
Answer: My favorite star in the sky is Sirius, which is the brightest star at night. The star is designated α Canis Majoris and is 8.6 light years from the Earth. I won't tell you that I actually came from Sirius B which is already a white dwarf that orbits the Sirius A.
Question: what evaluation metrics are discussed?
Evidence: When evaluating classifiers it is common to use accuracy, precision and recall as well as Hamming loss. Accuracy, precision and recall are defined by the the four terms true positive ( INLINEFORM0 ), true negative ( INLINEFORM1 ), false positive ( INLINEFORM2 ) and false negative ( INLINEFORM3 ) which can be seen in table TABREF16 .
It has been shown that when calculating precision and recall on multi-label classifiers, it can be advantageous to use micro averaged precision and recall BIBREF6 . The formulas for micro averaged precision are expressed as DISPLAYFORM0 DISPLAYFORM1
When evaluating classifiers it is common to use accuracy, precision and recall as well as Hamming loss. 
It has been shown that when calculating precision and recall on multi-label classifiers, it can be advantageous to use micro averaged precision and recall BIBREF6 . The formulas for micro averaged precision are expressed as DISPLAYFORM0 DISPLAYFORM1
Answer: The answers are shown as follows:
* precision 
* recall 
* Hamming loss
* micro averaged precision and recall
Question: Extract the teams that the footballer Sócrates played during his career, separate it by comma.
Evidence: Sócrates Brasileiro Sampaio de Souza Vieira de Oliveira (19 February 1954 – 4 December 2011), simply known as Sócrates [ˈsɔkɾat͡ʃis], was a Brazilian footballer who played as a midfielder. His medical degree and his political awareness, combined with style and quality of his play, earned him the nickname "Doctor Socrates".

Easily recognizable for his beard and headband, Sócrates became the "symbol of cool for a whole generation of football supporters". He is considered to be one of the greatest midfielders of his generation. In 1983, he was named South American Footballer of the Year. In 2004, he was named by Pelé in the FIFA 100 list of the world's greatest living players.

Socrates played for Brazil for seven years, scoring 22 goals and representing the nation in two World Cups. He captained the team in the 1982 FIFA World Cup; playing in midfield alongside Zico, Falcão, Toninho Cerezo and Éder, considered one of the greatest Brazilian national teams ever. He also appeared in the 1979 and 1983 Copa América. At club level, Sócrates played for Botafogo-SP before joining Corinthians in 1978. He moved to Italy to play for Fiorentina, returning to Brazil in 1985 to end his career.
Answer: Brazil, Botafogo-SP, Corinthians, Fiorentina
Question: Whether the proposed classifier can improve the final performance?
Evidence: There have been many advances in machine learning methods which help machines understand human behavior better than ever. One of the most important aspects of human behavior is emotion. If machines could detect human emotional expressions, it could be used to improve on verity of applications such as marketing BIBREF0 , human-computer interactions BIBREF1 , political science BIBREF2 etc.
Emotion in humans is complex and hard to distinguish. There have been many emotional models in psychology which tried to classify and point out basic human emotions such as Ekman's 6 basic emotions BIBREF3 , Plutchik's wheel of emotions BIBREF4 , or Parrott's three-level categorization of emotions BIBREF5 . These varieties show that emotions are hard to define, distinguish, and categorize even for human experts.
By adding the complexity of language and the fact that emotion expressions are very complex and context dependant BIBREF6 , BIBREF7 , BIBREF8 , we can see why detecting emotions in textual data is a challenging task. This difficulty can be seen when human annotators try to assign emotional labels to the text, but using various techniques the annotation task can be accomplished with desirable agreement among the annotators BIBREF9 .
We show this by using a recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole, and we show that this can improve the final performance significantly.
Answer: Yes, it can improve the final performance significantly.
Question: Classify the following hockey teams as either National Hockey League (NHL) or American Hockey League (AHL): Abbotsford Canucks, Bakersfield Condors, Belleville Senators, Bridgeport Islanders, Calgary Wranglers, Charlotte Checkers, Chicago Wolves, Cleveland Monsters, Coachella Valley Firebirds, Colorado Eagles, Grand Rapids Griffins, Hartford Wolf Pack, Henderson Silver Knights, Hershey Bears, Iowa Wild, Laval Rocket, Lehigh Valley Phantoms, Manitoba Moose, Milwaukee Admirals, Ontario Reign, Providence Bruins, Rochester Americans, Rockford IceHogs, San Diego Gulls, San Jose Barracuda, Springfield Thunderbirds, Syracuse Crunch, Texas Stars, Toronto Marlies, Tucson Roadrunners, Utica Comets, Wilkes-Barre/Scranton Penguins, Anaheim Ducks, Arizona Coyotes, Boston Bruins, Buffalo Sabres, Calgary Flames, Carolina Hurricanes, Chicago Blackhawks, Colorado Avalanche, Columbus Blue Jackets, Dallas Stars, Detroit Red Wings, Edmonton Oilers, Florida Panthers, Los Angeles Kings, Minnesota Wild, Montreal Canadiens, Nashville Predators, New Jersey Devils, New York Islanders, New York Rangers, Ottawa Senators, Philadelphia Flyers, Pittsburgh Penguins, San Jose Sharks, Seattle Kraken, St Louis Blues, Tampa Bay Lightning, Toronto Maple Leafs, Vancouver Canucks, Vegas Golden Knights, Washington Capitals, Winnipeg Jets
Answer: AHL: Abbotsford Canucks, Bakersfield Condors, Belleville Senators, Bridgeport Islanders, Calgary Wranglers, Charlotte Checkers, Chicago Wolves, Cleveland Monsters, Coachella Valley Firebirds, Colorado Eagles, Grand Rapids Griffins, Hartford Wolf Pack, Henderson Silver Knights, Hershey Bears, Iowa Wild, Laval Rocket, Lehigh Valley Phantoms, Manitoba Moose, Milwaukee Admirals, Ontario Reign, Providence Bruins, Rochester Americans, Rockford IceHogs, San Diego Gulls, San Jose Barracuda, Springfield Thunderbirds, Syracuse Crunch, Texas Stars, Toronto Marlies, Tucson Roadrunners, Utica Comets, Wilkes-Barre/Scranton Penguins

NHL: Anaheim Ducks, Arizona Coyotes, Boston Bruins, Buffalo Sabres, Calgary Flames, Carolina Hurricanes, Chicago Blackhawks, Colorado Avalanche, Columbus Blue Jackets, Dallas Stars, Detroit Red Wings, Edmonton Oilers, Florida Panthers, Los Angeles Kings, Minnesota Wild, Montreal Canadiens, Nashville Predators, New Jersey Devils, New York Islanders, New York Rangers, Ottawa Senators, Philadelphia Flyers, Pittsburgh Penguins, San Jose Sharks, Seattle Kraken, St Louis Blues, Tampa Bay Lightning, Toronto Maple Leafs, Vancouver Canucks, Vegas Golden Knights, Washington Capitals, Winnipeg Jets
Question: How many passages are there in the RACE dataset?
Evidence: We evaluate our model on RACE dataset BIBREF6 , which consists of two subsets: RACE-M and RACE-H. RACE-M comes from middle school examinations while RACE-H comes from high school examinations. RACE is the combination of the two.
We compare our model with the following baselines: MRU(Multi-range Reasoning) BIBREF12 , DFN(Dynamic Fusion Networks) BIBREF11 , HCM(Hierarchical Co-Matching) BIBREF8 , OFT(OpenAI Finetuned Transformer LM) BIBREF13 , RSM(Reading Strategies Model) BIBREF14 . We also compare our model with the BERT baseline and implement the method described in the original paper BIBREF7 , which uses the final hidden vector corresponding to the first input token ([CLS]) as the aggregate representation followed by a classification layer and finally a standard classification loss is computed.
Results are shown in Table 2 . We can see that the performance of BERT $_{base}$ is very close to the previous state-of-the-art and BERT $_{large}$ even outperforms it for 3.7%. But experimental result shows that our model is more powerful and we further improve the result for 2.2% computed to BERT $_{base}$ and 2.2% computed to BERT $_{large}$ .
RACE contains 27,933 passages and 97,687 questions in total, which is recognized as one of the largest and most difficult datasets in multi-choice reading comprehension.
Answer: 27,933 passages.
Question: Tell me which animals are bigger than the average human: Dog, Mouse, Elephant, Rhino, Hippo, Cat, Squirrel.
Answer: Sure. Here are the selections from above that are larger than the average human: Elephant, Rhino, and Hippo.
Question: How is the word segmentation task evaluated?
Evidence: We report segmentation performance using precision, recall, and F-measure on boundaries (BP, BR, BF), and tokens (WP, WR, WF). We also report the exact-match (X) metric which computes the proportion of correctly segmented utterances. Our main results are in Figure FIGREF47, where we report averaged scores over 10 runs. As a comparison with another bilingual method inspired by the “align to segment” approach, we also include the results obtained using the statistical models of BIBREF9, denoted Pisa, in Table TABREF46.
We report segmentation performance using precision, recall, and F-measure on boundaries (BP, BR, BF), and tokens (WP, WR, WF). We also report the exact-match (X) metric which computes the proportion of correctly segmented utterances.
Answer: The answers are shown as follows:
* precision, recall, and F-measure on boundaries (BP, BR, BF), and tokens (WP, WR, WF)
*  exact-match (X) metric
Question: Give me a list of some quotes from Bhagavad Gita.
Answer: 1. Whatever happened, happened for the good. Whatever is happening, is happening for the good. Whatever will happen, will also happen for the good.
2. Change is the law of The Universe.
3. You are only entitles to the action, never to its fruits.
4. You are what you believe in. You become which you believe you can become.
5. One can destroy the body. The spirit is beyond destruction. No one can being an end to spirit which is everlasting.
Question: What is pseudoscience?
Evidence: The demarcation between science and pseudoscience has scientific, philosophical, and political implications. Philosophers debate the nature of science and the general criteria for drawing the line between scientific theories and pseudoscientific beliefs, but there is widespread agreement "that creationism, astrology, homeopathy, Kirlian photography, dowsing, ufology, ancient astronaut theory, Holocaust denialism, Velikovskian catastrophism, and climate change denialism are pseudosciences." There are implications for health care, the use of expert testimony, and weighing environmental policies.Addressing pseudoscience is part of science education and developing scientific literacy
Answer: Which is not based on mainstream factual science.