DataHammer/scimrc huggingface dataset:
Question: What causes rain?
Answer: Rain is the byproduct of the natural cycle of evaporation and condensation that happens all over the world. Moisture evaporates into the atmosphere from bodies of water and soil. Once this moisture is aloft in the atmosphere, certain conditions cause it to precipitate in the form of rain. These conditions include changes in atmospheric pressure, orographic lift cause by hilly terrain, and changes in temperature. Once the moisture starts to condense into larger and larger droplets, they eventually become heavy enough to fall from the cloud in the form of rain drops. This cycle then continues on with the newly fallen rain beginning to evaporate into the atmosphere.
Question: Do they experiment with combining both methods?
Evidence: Methods ::: Combining the two methods
We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length.
Methods ::: Combining the two methods
We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length.
Answer: Yes.
Question: What method do the authors propose?
Evidence: Pre-trained models BIBREF0, BIBREF1 have received much of attention recently thanks to their impressive results in many down stream NLP tasks. Additionally, multilingual pre-trained models enable many NLP applications for other languages via zero-short cross-lingual transfer. Zero-shot cross-lingual transfer has shown promising results for rapidly building applications for low resource languages. BIBREF2 show the potential of multilingual-BERT BIBREF0 in zero-shot transfer for a large number of languages from different language families on five NLP tasks, namely, natural language inference, document classification, named entity recognition, part-of-speech tagging, and dependency parsing.
Although multilingual models are an important ingredient for enhancing language technology in many languages, recent research on improving pre-trained models puts much emphasis on English BIBREF3, BIBREF4, BIBREF5. The current state of affairs makes it difficult to translate advancements in pre-training from English to non-English languages. To our best knowledge, there are only three available multilingual pre-trained models to date: (1) the multilingual-BERT (mBERT) that supports 104 languages, (2) cross-lingual language model BIBREF6 that supports 100 languages, and (3) Language Agnostic SEntence Representations BIBREF7 that supports 93 languages. Among the three models, LASER is based on neural machine translation approach and strictly requires parallel data to train.
Do multilingual models always need to be trained from scratch? Can we transfer linguistic knowledge learned by English pre-trained models to other languages? In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8. As the first step, we focus on building a bilingual language model (LM) of English and a target language. Starting from a pre-trained English LM, we learn the target language specific parameters (i.e., word embeddings), while keeping the encoder layers of the pre-trained English LM fixed. We then fine-tune both English and target model to obtain the bilingual LM. We apply our approach to autoencoding language models with masked language model objective and show the advantage of the proposed approach in zero-shot transfer. Our main contributions in this work are:
We propose a fast adaptation method for obtaining a bilingual BERT$_{\textsc {base}}$ of English and a target language within a day using one Tesla V100 16GB GPU.
We evaluate our bilingual LMs for six languages on two zero-shot cross-lingual transfer tasks, namely natural language inference BIBREF9 and universal dependency parsing. We show that our models offer competitive performance or even better that mBERT.
We illustrate that our bilingual LMs can serve as an excellent feature extractor in supervised dependency parsing task.
Our main contributions in this work are: We propose a fast adaptation method for obtaining a bilingual BERT$_{\textsc {base}}$ of English and a target language within a day using one Tesla V100 16GB GPU.
Answer: A fast adaptation method.
Question: Classify the following as smooth or sharp: broken glass, thorn, leather, baby skin, cactus, mirror
Answer: Broken Glass: Sharp
Thorn: Sharp
Leather: Smooth
Baby Skin: Smooth
Cactus: Sharp
Mirror: Smooth
Question: What is the training-testing ratio of the system？
Evidence: We apply our system to three datasets from different domains. SQuAD BIBREF3 consists of questions whose answers are free form spans of text from passages in Wikipedia articles. We follow the same setting as in BIBREF0 , and split $10\%$ of training questions as the test set, and report performance when training on subsets of the remaining data ranging from $1\%$ to $90\%$ of the full set. We also report the performance on the dev set when trained on the full training set ( $1^\ast $ in Table 2 ). We use the same hyperparameter settings as in prior work. We compare and study four different settings: 1) the Supervised Learning (SL) setting, which is only trained on the supervised data, 2) the best performing GDAN model from BIBREF0 , 3) pretraining on a Language Modeling (LM) objective and fine-tuning on the supervised data, and 4) pretraining on the Cloze dataset and fine-tuning on the supervised data. The LM and Cloze methods use exactly the same data for pretraining, but differ in the loss functions used. We report F1 and EM scores on our test set using the official evaluation scripts provided by the authors of the dataset.
TriviaQA BIBREF4 comprises of over 95K web question-answer-evidence triples. Like SQuAD, the answers are spans of text. Similar to the setting in SQuAD, we create multiple smaller subsets of the entire set. For our semi-supervised QA system, we use the BiDAF+SA model BIBREF1 – the highest performing publicly available system for TrivaQA. Here again, we compare the supervised learning (SL) settings against the pretraining on Cloze set and fine tuning on the supervised set. We report F1 and EM scores on the dev set.
We also test on the BioASQ 5b dataset, which consists of question-answer pairs from PubMed abstracts. We use the publicly available system from BIBREF14 , and follow the exact same setup as theirs, focusing only on factoid and list questions. For this setting, there are only 899 questions for training. Since this is already a low-resource problem we only report results using 5-fold cross-validation on all the available data. We report Mean Reciprocal Rank (MRR) on the factoid questions, and F1 score for the list questions.
We follow the same setting as in (Yang et al., 2017), and split 10% of training questions as the test set, and report performance when training on subsets of the remaining data ranging from 1% to 90% of the full set.
Answer: It ranged from 1:10 to 9:1.
Question: What components is the QnAMaker composed of?
Evidence: System description ::: Architecture
As shown in Figure FIGREF4, humans can have two different kinds of roles in the system: Bot-Developers who want to create a bot using the data they have, and End-Users who will chat with the bot(s) created by bot-developers. The components involved in the process are:
QnAMaker Portal: This is the Graphical User Interface (GUI) for using QnAMaker. This website is designed to ease the use of management APIs. It also provides a test pane.
QnaMaker Management APIs: This is used for the extraction of Question-Answer (QA) pairs from semi-structured content. It then passes these QA pairs to the web app to create the Knowledge Base Index.
Azure Search Index: Stores the KB with questions and answers as indexable columns, thus acting as a retrieval layer.
QnaMaker WebApp: Acts as a layer between the Bot, Management APIs, and Azure Search Index. WebApp does ranking on top of retrieved results. WebApp also handles feedback management for active learning.
Bot: Calls the WebApp with the User's query to get results.
System description ::: Architecture
The components involved in the process are:
QnAMaker Portal: This is the Graphical User Interface (GUI) for using QnAMaker. 
QnaMaker Management APIs: This is used for the extraction of Question-Answer (QA) pairs from semi-structured content. 
Azure Search Index: Stores the KB with questions and answers as indexable columns, thus acting as a retrieval layer.
QnaMaker WebApp: Acts as a layer between the Bot, Management APIs, and Azure Search Index. 
Bot: Calls the WebApp with the User's query to get results.
Answer: The answers are shown as follows:
* QnAMaker Portal
* QnaMaker Management APIs
* Azure Search Index
* QnaMaker WebApp
* Bot
Question: What are the benefits of using genetically modified crops in agriculture?
Answer: There are many benefits to using genetically modified crops in agriculture.  The benefits include making the crops hardier with greater resistance to diseases and droughts.  The resulting crops can also make food more nutritious and taste better.  An additional environmental benefit of genetically modified crops is that they have the potential to reduce resources needed to grow, including water and fertilizer.
Question: What is William Shakespeare's greatest play?
Answer: There is no common agreement on which play written by William Shakespeare is the greatest. In total, William Shakespeare wrote approximately 39 plays, each of which can be broadly categorized into three main groups: histories, comedies and tragedies. Henry V and Richard III are regarded amongst his finest histories, both featuring an English king as their eponymous character. The Tempest and A Midsummer Night's Dream are considered to be amongst his best comedies; whilst tragedies were arguably Shakespeare's greatest genre - Hamlet, Romeo and Juliet, Macbeth, Julius Caesar and King Lear are all amongst his finest contributions to this field. Of these, Romeo and Juliet is often considered to be the greatest love story ever written, and Hamlet is widely regarded as one of the most important analyses of the human condition. William Shakespeare's plays are widely believed to be amongst the greatest ever written, and to this day are regularly performed around the world.
Question: What are the 4 Data-driven approaches prompts?
Evidence: We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
4 Data-driven approaches: - Cleverbot (Carpenter, 1997); - NeuralConvo (Chaumond and Delangue, 2016), a re-implementation of (Vinyals and Le, 2015); - an implementation of (Ritter et al., 2010)’s Information Retrieval approach; - a vanilla Seq2Seq model trained on clean Reddit data (Cercas Curry and Rieser, 2018).
Answer: Cleverbot; NeuralConvo; an implementation of Information Retrieval approach; a vanilla Seq2Seq model trained on clean Reddit data.
Question: What color is the Rainbow?
Answer: The rainbow is the VIBGYOR that stands for Violet, Indigo, Blue, green, yellow, Orange and Red.
Question: What model was used by the top team?
Evidence: BIBREF9 Two different BERT models were developed. For Friends, pre-training was done using a sliding window of two utterances to provide dialogue context. Both Next Sentence Prediction (NSP) phase on the complete unlabeled scripts from all 10 seasons of Friends, which are available for download. In addition, the model learned the emotional disposition of each of six main six main characters in Friends (Rachel, Monica, Phoebe, Joey, Chandler and Ross) by adding a special token to represent the speaker. For EmotionPush, pre-training was performed on Twitter data, as it is similar in nature to chat based dialogues. In both cases, special attention was given to the class imbalance issue by applying “weighted balanced warming” on the loss function.
IDEA
BIBREF9 Two different BERT models were developed. For Friends, pre-training was done using a sliding window of two utterances to provide dialogue context. Both Next Sentence Prediction (NSP) phase on the complete unlabeled scripts from all 10 seasons of Friends, which are available for download. In addition, the model learned the emotional disposition of each of six main six main characters in Friends (Rachel, Monica, Phoebe, Joey, Chandler and Ross) by adding a special token to represent the speaker. For EmotionPush, pre-training was performed on Twitter data, as it is similar in nature to chat based dialogues. In both cases, special attention was given to the class imbalance issue by applying “weighted balanced warming” on the loss function.
Answer: The answers are shown as follows:
* Two different BERT models were developed
Question: Give me a bulleted list of countries that start with the letter B.
Answer: - Botswana
- Bulgaria
- Brazil
- Bolivia
- Belgium
- Bahrain
- Bahamas
Question: Extract the different ways of dealing with underfitting mentioned in the text
Evidence: Resolving underfitting
There are multiple ways to deal with underfitting:

1) Increase the complexity of the model: If the model is too simple, it may be necessary to increase its complexity by adding more features, increasing the number of parameters, or using a more flexible model. However, this should be done carefully to avoid overfitting.
2) Use a different algorithm: If the current algorithm is not able to capture the patterns in the data, it may be necessary to try a different one. For example, a neural network may be more effective than a linear regression model for some types of data.
3) Increase the amount of training data: If the model is underfitting due to lack of data, increasing the amount of training data may help. This will allow the model to better capture the underlying patterns in the data.
4) Regularization: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function that discourages large parameter values. It can also be used to prevent underfitting by controlling the complexity of the model.
5) Ensemble Methods: Ensemble methods combine multiple models to create a more accurate prediction. This can help to reduce underfitting by allowing multiple models to work together to capture the underlying patterns in the data.
6) Feature engineering: Feature engineering involves creating new model features from the existing ones that may be more relevant to the problem at hand. This can help to improve the accuracy of the model and prevent underfitting.
Answer: The different ways of deal with underfitting mentioned in the text are:
- Increase the complexity of the model
- Use a different algorithm
- Increase the amount of data
- Regularization
- Ensemble Methods
- Feature engineering
Question: What is a SCOBY in kombucha making?
Answer: A SCOBY is a symbiotic culture of bacteria and yeast. This is what allows the fermentation process to occur to make kombucha and is often referred to as the kombucha starter. The bacteria and yeast in the SCOBY convert the sugar present in the sweet tea used to make alcohol, and also creates the fizziness typical in kombucha. Often the pellicle that forms on the top of the kombucha liquid is referred to as the SCOBY but this is a heated topic amongst many kombucha makers if that is necessary to include as a starter when brewing your next batch of kombucha.
Question: Why did the authors add information about the position of the target sentence in its segment?
Evidence: Position (3 C features): A sentence on the boundaries of a speaker's segment could contain a reaction to another statement or could provoke a reaction, which in turn could signal a check-worthy claim. Thus, we added information about the position of the target sentence in its segment: whether it is first/last, as well as its reciprocal rank in the list of sentences in that segment.
Segment sizes (3 C features): The size of the segment belonging to one speaker might indicate whether the target sentence is part of a long speech, makes a short comment or is in the middle of a discussion with lots of interruptions. The size of the previous and of the next segments is also important in modeling the dialogue flow. Thus, we include three features with the sizes of the previous, the current and the next segments.
Metadata (8 C features): Check-worthy claims often contain mutual accusations between the opponents, as the following example shows (from the 2nd presidential debate):

Thus, we use a feature that indicates whether the target sentence mentions the name of the opponent, whether the speaker is the moderator, and also who is speaking (3 features). We further use three binary features, indicating whether the target sentence is followed by a system message: applause, laugh, or cross-talk.
A sentence on the boundaries of a speaker’s segment could contain a reaction to another statement or could provoke a reaction, which in turn could signal a check-worthy claim. Thus, we added information about the position of the target sentence in its segment: whether it is first/last, as well as its reciprocal rank in the list of sentences in that segment.
Answer: A sentence on the boundaries of a speaker’s segment could contain a reaction to another statement or could provoke a reaction, which in turn could signal a check-worthy claim.
Question: Identify which instrument is string or percussion: Dabakan, Kwitra
Answer: Kwitra is string, Dabakan is percussion.
Question: Does DCA or GMM-based attention perform better in experiments?
Evidence: FLOAT SELECTED: Table 3. MOS naturalness results along with 95% confidence intervals for the Lessac and LJ datasets.
FLOAT SELECTED: Table 3. MOS naturalness results along with 95% confidence intervals for the Lessac and LJ datasets.
Answer: About the same performance
Question: Which city is the best to relocate in Texas
Answer: Texas offers some great places to live, if you like city life. The prominent choices can be Houston, Dallas, San Antonio, Austin, all of which are part of the Texas triangle, and are connected by 2-3 hours drive. Houston and Dallas are bigger and dense metropolitans. San Antonio and Austin have smaller footprints but the proximity to hill country makes them more charming in terms of geography. Some other places to consider also can be Corpus Christi, Lubbock and El Paso, but are a bit distant.
Question: What are some common types of high heel shoes?
Answer: Common types of high heel shoes include the stiletto heel, block heel, flare heel, kitten heel, and wedge heel.
Question: What is the minimum feature a cell phone should have to avoid addiction
Answer: 1. Call facility
2. SMS
3.GPS
Question: Identify which instrument is string or percussion: Cowbell, Crwth
Answer: Crwth is string, Cowbell is percussion.
Question: Identify which animal species is alive or extinct: Ceratopsian, Sunda Tiger
Answer: Sunda Tiger is alive, Ceratopsian is extinct.
Question: Please sort the following numbers in ascending order: 6, 88, 4, 2074, 34
Answer: 4, 6, 34, 88, 2074
Question: How useful is toothpaste
Evidence: Toothpaste is a paste or gel dentifrice used with a toothbrush to clean and maintain the aesthetics and health of teeth. Toothpaste is used to promote oral hygiene: it is an abrasive that aids in removing dental plaque and food from the teeth, assists in suppressing halitosis, and delivers active ingredients (most commonly fluoride) to help prevent tooth decay (dental caries) and gum disease (gingivitis). Owing to differences in composition and fluoride content, not all toothpastes are equally effective in maintaining oral health. The decline of tooth decay during the 20th century has been attributed to the introduction and regular use of fluoride-containing toothpastes worldwide. Large amounts of swallowed toothpaste can be toxic. Common colors for toothpaste include white (sometimes with colored stripes or green tint) and blue.

Usefulness
Toothpastes are generally useful to maintain dental health. Toothpastes containing fluoride are effective at preventing tooth decay. Toothpastes may also help to control and remove plaque build-up, promoting healthy gums. A 2016 systematic review indicated that using toothpaste when brushing the teeth does not necessarily impact the level of plaque removal. However, the active ingredients in toothpastes are able to prevent dental diseases with regular use.
Answer: Toothpaste is a paste or gel dentifrice used with a toothbrush to clean and maintain the aesthetics and health of teeth. Toothpaste is used to promote oral hygiene: it is an abrasive that aids in removing dental plaque and food from the teeth, assists in suppressing halitosis, and delivers active ingredients (most commonly fluoride) to help prevent tooth decay (dental caries) and gum disease (gingivitis). Owing to differences in composition and fluoride content, not all toothpastes are equally effective in maintaining oral health. The decline of tooth decay during the 20th century has been attributed to the introduction and regular use of fluoride-containing toothpastes worldwide. Large amounts of swallowed toothpaste can be toxic. Common colors for toothpaste include white (sometimes with colored stripes or green tint) and blue.

Toothpastes are generally useful to maintain dental health. Toothpastes containing fluoride are effective at preventing tooth decay. Toothpastes may also help to control and remove plaque build-up, promoting healthy gums. A 2016 systematic review indicated that using toothpaste when brushing the teeth does not necessarily impact the level of plaque removal. However, the active ingredients in toothpastes are able to prevent dental diseases with regular use.