DataHammer/scimrc huggingface dataset:
Question: What is the baseline for experiments?
Evidence: We set up the baselines and proposed models as follows:
LSTM with text embedding: CNNs and LSTMs are widely used to encode textual contents for sentiment analysis in BIBREF45 , BIBREF46 and many online tutorials. Here we select the standard LSTM with pre-trained word embedding as input, and add one fully-connected layer with sigmoid activation top of the LSTM encoder (same as all other models), denoted as T-LSTM.
LSTM with emoji embedding: We consider the emoji as one special word and input both pre-trained text and emoji embeddings into the same LSTM network, namely E-LSTM. Similarly, we concatenate the pre-trained bi-sense emoji embedding as one special word to feed into the LSTM network. This model is called BiE-LSTM.
Attention-based LSTM with emojis:We also use the word-emoji embedding to calculate the emoji-word attention following Equation EQREF20 and EQREF21 , and the only difference is that we replace the attention-derived senti-emoji embedding with the pre-trained word-emoji embedding by fasttext, denoted as ATT-E-LSTM.
We set up the baselines and proposed models as follows:
LSTM with text embedding: CNNs and LSTMs are widely used to encode textual contents for sentiment analysis in BIBREF45 , BIBREF46 and many online tutorials. Here we select the standard LSTM with pre-trained word embedding as input, and add one fully-connected layer with sigmoid activation top of the LSTM encoder (same as all other models), denoted as T-LSTM.
LSTM with emoji embedding: We consider the emoji as one special word and input both pre-trained text and emoji embeddings into the same LSTM network, namely E-LSTM. Similarly, we concatenate the pre-trained bi-sense emoji embedding as one special word to feed into the LSTM network. This model is called BiE-LSTM.
Attention-based LSTM with emojis:We also use the word-emoji embedding to calculate the emoji-word attention following Equation EQREF20 and EQREF21 , and the only difference is that we replace the attention-derived senti-emoji embedding with the pre-trained word-emoji embedding by fasttext, denoted as ATT-E-LSTM.
Answer: The answers are shown as follows:
* LSTM with text embedding
* LSTM with emoji embedding
* Attention-based LSTM with emojis
Question: What is the current reigning dynasty of Thailand?
Answer: The current reigning dynasty of Thailand is Chakri dynasty and the current King of the dynasty is Maha Vajiralongkorn.
Question: What are your recommendations to achieve a healthy lifestyle?
Answer: A healthy lifestyle consists of a healthy diet, regular exercise, quality sleep and a limited intake of alcohol and coffee.

It’s recommend to eat a balanced and healthy diet. This generally consists of fruits, vegetables, whole grains, seeds, nuts and foods that contain proteins, such as beans, peas, meat or fish. Healthy foods usually contain relatively low amounts of sugar, salt and trans fats. 

Moreover, it’s important to exercise. This could be achieved by doing sports, but basically every form of exercise helps, for instance by walking or gardening. 

Furthermore, quality sleep is recommended for seven or eight hours per day. In addition, it’s best to don’t drink alcohol and limit your coffee intake to maximal three cups a day.
Question: What are the applications of building emotion classifiers?
Evidence: Emotion detection has long been a topic of interest to scholars in natural language processing (NLP) domain. Researchers aim to recognize the emotion behind the text and distribute similar ones into the same group. Establishing an emotion classifier can not only understand each user's feeling but also be extended to various application, for example, the motivation behind a user's interests BIBREF0. Based on releasing of large text corpus on social media and the emotion categories proposed by BIBREF1, BIBREF2, numerous models have provided and achieved fabulous precision so far. For example, DeepMoji BIBREF3 which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER BIBREF4 learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.
As methods become mature, text-based emotion detecting applications can be extended from a single utterance to a dialogue contributed by a series of utterances. Table TABREF2 illustrates the difference between single utterance and dialogue emotion recognition. The same utterances in Table TABREF2, even the same person said the same sentence, the emotion it convey may be various, which may depend on different background of the conversation, tone of speaking or personality. Therefore, for emotion detection, the information from preceding utterances in a conversation is relatively critical.
In SocialNLP 2019 EmotionX, the challenge is to recognize emotions for all utterances in EmotionLines dataset, a dataset consists of dialogues. According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT) BIBREF5, FriendsBERT and ChatBERT. In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning.
Establishing an emotion classifier can not only understand each user’s feeling but also be extended to various application, for example, the motivation behind a user’s interests.
Answer: The motivation behind a user’s interests.
Question: When was scala created?
Answer: Scala was created in 2001 by Martin Odersky.
Question: What is a fun and cheap activity you can do in London when it's summer?
Answer: In the summer it is nice to go to St James park, where you can feed squirrels and birds such as geese. If you first go to the Tesco's, you can buy some peanuts for under £2. At the park there will be several squirrels, little green birds, geese and swans. They can eat the peanuts out of your hand. For the little green birds you may have to peel the peanuts first, but it's worth it when a small green birds sits on your hand.
Question: Name the three best professional sports teams based in California.
Answer: The best professional sports teams based in California are the Los Angeles Dodgers, the Golden State Warriors, and the San Francisco 49ers.
Question: Given a reference text about George Koukou, tell me when he was arrested and why.
Evidence: George Koukou (born 25 May 1945) was acting Speaker of the National Transitional Legislative Assembly of Liberia from 17 March 2005 to January 2006. Originally from Nimba County, he now resides in the capital city of Monrovia. He is a member of the Mano ethnic group.

Prior to becoming a member of the National Transitional Legislative Assembly, Koukou was a senator from Nimba County. Koukou replaced George Dweh as Speaker of the transitional legislature on 17 March 2005 after Dweh was suspended indefinitely on 14 March for spending money without proper authorization.

On 17 July 2007, Koukou was arrested along with several others, including former army chief of staff Charles Julue, for alleged participation in a coup plot against President Ellen Johnson Sirleaf. He and Julu were charged with treason. Koukou claimed that he did not have the money to hire a lawyer. On 27 August, Monrovia Magistrate Court Judge Milton Taylor ruled that his court could not try Koukou because treason was above its jurisdiction, and he transferred the trial to Criminal Court "A".
Answer: George Koukou was arrested on July 17, 2007 for allegedly participating in a coup against President Ellen Johnson Sirleaf.
Question: What's the research field?
Evidence: Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an"apparently positive"sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network's baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.
In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection.
Answer: Sarcasm detection.
Question: Why boys are best over girls
Answer: Brcause Boys always love to have peace so
Question: Classify each of the following as healthy or unhealthy: cake, apple, broccoli, donut, candy, soda, kombucha, and ice cream
Answer: Healthy: apple, broccoli, and kombucha
Unhealthy: cake, donut, candy, sode, and ice cream
Question: What is NLG?
Evidence: Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets.
Natural language generation (NLG) is a critical component in a spoken dialogue system.
Answer: Natural language generation, a critical component in a spoken dialogue system.
Question: As per the passage what are the four names of the Vedas?
Evidence: In the most generic context, a Samhita may refer to any methodical collection of text or verses: Any shastra, sutra, or Sanskrit Epic, along with Vedic texts, might be referred to as a Samhita.Samhita, however, in contemporary literature typically implies the earliest, archaic part of the Vedas. These contain mantras – sacred sounds with or without literal meaning, as well as panegyrics, prayers, litanies and benedictions petitioning nature or Vedic deities. Vedic Samhita refer to mathematically precise metrical archaic text of each of the Vedas (Rigveda, Yajurveda, Samaveda and Atharvaveda).The Vedas have been divided into four styles of texts – the Samhitas (mantras and benedictions), the Brahmanas (text on rituals, ceremonies, sacrifices and symbolic-sacrifices), the Aranyakas (commentaries on rituals, ceremonies and sacrifices), and the Upanishads (text discussing meditation, philosophy and spiritual knowledge). The Samhitas are sometimes identified as karma-khanda (कर्म खण्ड, action / ritual-related section), while the Upanishads are identified as jnana-khanda (ज्ञान खण्ड, knowledge / spirituality-related section). The Aranyakas and Brahmanas are variously classified, sometimes as the ceremonial karma-khanda, other times (or parts of them) as the jnana-khanda.The Vedic Samhitas were chanted during ceremonies and rituals, and parts of it remain the oldest living part of Hindu tradition.A collective study of Vedas and later text suggests that the compendium of Samhitas and associated Vedic texts were far larger than currently available. However, most have been lost at some point or over a period of Indian history.
Answer: Rigveda, Yajurveda, Samaveda and Atharvaveda
Question: What component do you need to have to build a PC?
Answer: You will need at least the following components to build a PC:

* Motherboard
* Processor (CPU)
* Storage (hard drive or SSD)
* Memory (RAM)
* Case
* CPU Fans/Cooling Unit
* Power supply
* GPU (Optionally)
Question: Identify which instrument is string or percussion: Bubon, Kobyz
Answer: Kobyz is string, Bubon is percussion.
Question: Was Tyler Thompson a diplomat
Answer: Tyler Thompson (1907–2002) was an American Ambassador to Finland and Iceland. He also served as the Director General of the Foreign Service.

Tyler entered the Foreign Service in 1931 after graduating from Princeton University.
Question: By how much, the proposed method improves BiDAF and DCN on SQuAD dataset?
Evidence: FLOAT SELECTED: Table 1: Effect of Character Embedding
FLOAT SELECTED: Table 1: Effect of Character Embedding
Answer: In terms of F1 score, the Hybrid approach improved by 23.47% and 1.39% on BiDAF and DCN respectively. The DCA approach improved by 23.2% and 1.12% on BiDAF and DCN respectively.
Question: Is euthanasia ethical?
Answer: Euthanasia or mercy killing, is an act of doctor assisted suicide and withdrawing of medical care. 
Some proponents ascribe to the right of dignity in life and death. 
However, valid arguments are to be made that misuse of the law can cause unintended deaths and will then be homicides. 
This topic is controversial
Question: Who is better : Superman or Batman?
Answer: When it comes down to it, in every aspect, Superman is better than Batman. Superman is stronger, he's been around longer, he's a hard-working man, and he's just a better person.
Question: Which ingredients does a margarita pizza consist of?
Answer: A pizza margarita is made with pizza dough, tomato sauce, mozzarella, and basil. Some recipes drizzle the pizza with oil or add spices such as salt and pepper after baking. The pizza dough typically consists of yeast, flour, salt, olive oil, and water. Margarita pizza is vegetarian, but substituting the mozzarella cheese with nuts could make it a vegan dish.
Question: What are the architectural differences between BERT and BioBERT?
Evidence: ‘BERT’ and BioBERT are very similar in terms of architecture. Difference is that ‘BERT’ is pretrained on Wikipedia articles, whereas BioBERT version used in our experiments is pretrained on Wikipedia, PMC and PubMed articles. Therefore BioBERT model is expected to perform well with biomedical text, in terms of generating contextual word embeddings.
BioBERT model used in our experiments is based on BERT-Base Architecture; BERT-Base has 12 transformer Layers where as BERT-Large has 24 transformer layers. Moreover contextual word embedding vector size is 768 for BERT-Base and more for BERT-large. According to BIBREF1 Bert-Large, fine-tuned on SQuAD 1.1 question answering data BIBREF7 can achieve F1 Score of 90.9 for Question Answering task where as BERT-Base Fine-tuned on the same SQuAD question answering BIBREF7 data could achieve F1 score of 88.5. One downside of the current version BioBERT is that word-piece vocabulary is the same as that of original BERT Model, as a result word-piece vocabulary does not include biomedical jargon. Lee et al. BIBREF0 created BioBERT, using the same pre-trained BERT released by Google, and hence in the word-piece vocabulary (vocab.txt), as a result biomedical jargon is not included in word-piece vocabulary. Modifying word-piece vocabulary (vocab.txt) at this stage would loose original compatibility with ‘BERT’, hence it is left unmodified.
In our future work we would like to build pre-trained ‘BERT’ model from scratch. We would pretrain the model with biomedical corpus (PubMed, ‘PMC’) and Wikipedia. Doing so would give us scope to create word piece vocabulary to include biomedical jargon and there are chances of model performing better with biomedical jargon being included in the word piece vocabulary. We will consider this scenario in the future, or wait for the next version of BioBERT.
Difference is that ‘BERT’ is pretrained on Wikipedia articles, whereas BioBERT version used in our experiments is pretrained on Wikipedia, PMC and PubMed articles.
Answer: Difference is that ‘BERT’ is pretrained on Wikipedia articles, whereas BioBERT version used in our experiments is pretrained on Wikipedia, PMC and PubMed articles.
Question: Given the reference text about Tottenham Hotspur, what was the name of the stadium that the new stadium replaced?
Evidence: Tottenham Hotspur Football Club, commonly referred to as Tottenham (/ˈtɒtənəm/) or Spurs, is a professional football club based in Tottenham, London, England. It competes in the Premier League, the top flight of English football. The team has played its home matches in the 62,850-capacity Tottenham Hotspur Stadium since April 2019, replacing their former home of White Hart Lane, which had been demolished to make way for the new stadium on the same site.

Founded in 1882, Tottenham's emblem is a cockerel standing upon a football, with the Latin motto Audere est Facere ("to dare is to do"). The club has traditionally worn white shirts and navy blue shorts as their home kit since the 1898–99 season. Their training ground is on Hotspur Way in Bulls Cross, Enfield. After its inception, Tottenham won the FA Cup for the first time in 1901, the only non-League club to do so since the formation of the Football League in 1888. Tottenham were the first club in the 20th century to achieve the League and FA Cup Double, winning both competitions in the 1960–61 season. After successfully defending the FA Cup in 1962, in 1963 they became the first British club to win a UEFA club competition – the European Cup Winners' Cup. They were also the inaugural winners of the UEFA Cup in 1972, becoming the first British club to win two different major European trophies. They collected at least one major trophy in each of the six decades from the 1950s to 2000s, an achievement only matched by Manchester United.
Answer: The name of the stadium that was replaced by the new stadium is White Hart Lane.
Question: What baselines have been used in this work?
Evidence: We compare FSDM with four baseline methods and two ablations.
NDM BIBREF7 proposes a modular end-to-end trainable network. It applies de-lexicalization on user utterances and responses.
LIDM BIBREF9 improves over NDM by employing a discrete latent variable to learn underlying dialogue acts. This allows the system to be refined by reinforcement learning.
KVRN BIBREF13 adopts a copy-augmented Seq2Seq model for agent response generation and uses an attention mechanism on the KB. It does not perform belief state tracking.
TSCP/RL BIBREF10 is a two-stage CopyNet which consists of one encoder and two copy-mechanism-augmented decoders for belief state and response generation. TSCP includes further parameter tuning with reinforcement learning to increase the appearance of response slots in the generated response. We were unable to replicate the reported results using the provided code, hyperparameters, and random seed, so we report both the results from the paper and the average of 5 runs on the code with different random seeds (marked with $^\dagger $ ).
We compare FSDM with four baseline methods and two ablations.

NDM BIBREF7 proposes a modular end-to-end trainable network. It applies de-lexicalization on user utterances and responses.

LIDM BIBREF9 improves over NDM by employing a discrete latent variable to learn underlying dialogue acts. This allows the system to be refined by reinforcement learning.

KVRN BIBREF13 adopts a copy-augmented Seq2Seq model for agent response generation and uses an attention mechanism on the KB. It does not perform belief state tracking.

TSCP/RL BIBREF10 is a two-stage CopyNet which consists of one encoder and two copy-mechanism-augmented decoders for belief state and response generation.
Answer: NDM, LIDM, KVRN, and TSCP/RL
Question: Give me examples of birds that do not have the ability to fly.
Answer: Kiwi, Ostrich, Penguin, Weka, Steamer duck, Kakapo, Takahe.
Question: What does fasting mean?
Evidence: Fasting is the abstention from eating and sometimes drinking. From a purely physiological context, "fasting" may refer to the metabolic status of a person who has not eaten overnight (see "Breakfast"), or to the metabolic state achieved after complete digestion and absorption of a meal. Metabolic changes in the fasting state begin after absorption of a meal (typically 3–5 hours after eating).
Answer: Fasting is the abstention from eating and sometimes drinking.
Question: What is an anemone?
Answer: Anemone can be a small plant on land with pink, blue or white flowers 
or a soft sea creature living on the rocks under the water and resembling a flower.
Question: Classify each of the following as an electric or gas vehicle: Tesla Model Y, Kia Telluride, Lexus IS250, Mustang Mach-e, Chevy Bolt, Toyota Rav4
Answer: Tesla Model Y: Electric Vehicle
Kia Telluride: Gas Vehicle
Lexus IS250: Gas Vehicle
Mustang Mach-e: Electric Vehicle
Chevy Bolt: Electric Vehicle
Toyota Rav4: Gas Vehicle
Question: What modalities does Quah Jing Wen specialize on?
Evidence: Quah Jing Wen (Chinese: 柯敬文; pinyin: Kē Jìngwén; born 20 December 2000) is a Singaporean professional swimmer who specialises in individual medley, butterfly and freestyle events.

Education
Quah was educated at Methodist Girls' School and Anglo-Chinese School (Independent), before graduating from Texas A&M University in 2021 with a bachelor's degree in biomedical science.

Swimming career
Quah Jing Wen clocked 2min 12.95sec and set an Under-17 200m butterfly Singaporean national record at the Neo Garden 13th Singapore National Swimming Championships, breaking Tao Li's record set in 2005.

Quah won the bronze in the 2015 SEA Games when making her debut in the 400m IM.

In 2017, Quah won 5 gold medals and a silver medal at the Commonwealth Youth Games, held in Nassau. In the same year, she also won 5 gold medals in the 2017 SEA Games.[1
Answer: Quah Jing Wen is specialized on individual medley, butterfly and freestyle events.
Question: Where was the game of Pickle Ball invented.
Answer: The game was created in 1965 on Bainbridge Island, Washington, at the summer home of Joel Pritchard, who later served in the United States Congress and as Washington's lieutenant governor.  Pritchard and two of his friends, Barney McCallum and Bill Bell, are credited with devising the game and establishing the rules.

According to Joan Pritchard, Joel Pritchard's wife, "The name of the game became Pickle Ball after I said it reminded me of the pickle boat in crew where oarsmen were chosen from the leftovers of other boats." Other sources state that the name "pickleball" was derived from the name of the Pritchard's family dog, Pickles.
Question: How do you throw a cutter in Wiffle Ball?
Answer: Wiffle Ball is a game meant to simulate baseball with a ball and bat, pitching and batting, but with plastic bat and ball to make the game more accessible and convenient for small groups often as little as 2 players to enjoy.  A cutter, or "cut fastball" can be thrown in Wiffle Ball using a number of different methods, however, some popular approach include hold the middle of the ball and using the index finger to cut across the middle holes on the Wiffle Ball.  Other grips with two fingers together in the middle of the ball can be used for variations of movement on the cutter and experimenting with different angles across the holes of the Wiffle Ball will generate a cutter type movement.  The pitcher can throw with the same arm action as a fastball and allow the grip to create the late movement typical of a cutter.
Question: Give me few tips on  how not to drive a car?
Answer: First and foremost- you need to have a valid drivers license . Never distract during driving . Have clear judgement and always drive when you are in stable mental condition.