DataHammer/scimrc huggingface dataset:
Question: What model do they use to model equations?
Evidence: Building on our previous method, we define a new model which we call equation unit embeddings (EqEmb-U). EqEmb-U model equations by treating them as sentences where the words are the equation variables, symbols and operators which we refer to as units. The first step in representing equations using equation units is to tokenize them. We use the approach outlined in BIBREF8 which represents equations into a syntax layout tree (SLT), a sequence of SLT tuples each of which contains the spatial relationship information between two equation symbols found within a particular window of equation symbols. Figure FIGREF11 shows example SLT representations of three equations.
Each equation INLINEFORM0 is a sequence of equation units INLINEFORM1 , INLINEFORM2 similar to a sentence where the words are the equation units. For each equation unit INLINEFORM3 we assign interaction INLINEFORM4 and feature INLINEFORM5 vectors.
We assume that the context of the word INLINEFORM0 will be the original context ( INLINEFORM1 ) and the equation units ( INLINEFORM2 ) of any equations that are in the word-equation context window. In addition for each equation unit we define its unit context INLINEFORM3 to be the set of surrounding equation units in a small window INLINEFORM4 around it: DISPLAYFORM0 
The objective is over two conditionals, one for each context type: DISPLAYFORM0 
The two parameters are: DISPLAYFORM0 
We define equation-level representations by averaging the representations of their constituent units: DISPLAYFORM0 
Building on our previous method, we define a new model which we call equation unit embeddings (EqEmb-U). EqEmb-U model equations by treating them as sentences where the words are the equation variables, symbols and operators which we refer to as units.
Answer: Equation unit embeddings.
Question: What are five fun ideas for a first date?
Answer: 1. Take a cooking class together
2. Tour a local landmark and eat lunch or dinner at local legendary restaurant
3. Go to a local dance club where the opening hour provides dance instruction (line dancing, samba, etc..)
4. Do something youthful: mini golf, go carts, with burgers and ice cream after
5. Spend an hour walking dogs at the local animal shelter followed by a nice lunch
Question: What are all the ways I can get around in San Francisco?
Answer: In San Francisco, there are four main ways to get around the city. Those ways are by car, by public transit, by foot or by bikes and scooters. Because San Francisco has many public transportation and ride share options, many residents choose not to own a car. For public transportation, there are buses, trolleys, trains, subways and hybrid buses and trains called Munis. The subway in San Francisco is called Bart (Bay Area Rapid Transit), and it even goes under the San Francisco Bay to cities like Oakland. For getting around by foot, walking is a good option for many areas of San Francisco. Bikes and scooters will help cover longer distances. Shared ride services like taxis, Ubers and Lyft are available throughout the city. Many of those services offer rentals of bikes, scooters, mopeds and ebikes.
Question: What are few different ways to describe the changing shape of the moon?
Answer: There are a number of ways in which to describe the different moon phases. As the moon appears to grow bigger it is said to be 'waxing', as it decreases in size it is 'waning'. There are a number of different full moons, depending on how many there are in a month and the size of the moon in the sky and when there is no moon due to the earth's shadow covering its surface it is said to be a 'new moon'.
Question: Why do we love green
Answer: Because Green is good for our eyes. and that makes our life pretty
Question: Describe how social media can help companies make better business decisions. What social media data can be collected to aid in business decisions? List four pieces of social media data (e.g., Twitter mentions and the number of retweets would be counted as two pieces).
Answer: With 60% of the world’s population on social media, it presents an excellent opportunity for brands to reach and influence their consumers. The brands are already exploiting and interacting with consumers by leveraging social media as a channel. 
  
Social media is a treasure trove of information for brands. Brands can leverage social media to collect the data below to help them improve their business and overall operations. 

Customer feedback and complaints 

Brands can monitor social media to connect with customers and watch customer feedback and complaints. See the below example of how Target engages with its customer via Twitter. 

Find influential users to promote the brand 

 Brands can find influential users in a specific segment, understand the industry pulse, and partner with significant users to promote their brand—an example of influential Instagram users per industry segment. 

Consumer trends 

The information on social media is diverse and includes most of the markets and audience segments. This presents an excellent opportunity for the brands to mine the data and detect and predict consumer trends for product development. 

Gauge sentiment around the brand 

Brands can leverage the product/brand mentions in social media via the social media API and sentiment analysis services like Google NLP to detect sentiment for PR activities.
Question: Identify which instrument is string or percussion: Tombak, Cizhonghlu
Answer: Cizhonghlu is string, Tombak is percussion.
Question: How is the training data collected?
Evidence: The following data sources were used to train the RNN-T and associated RNN-LMs in this study.
Source-domain baseline RNN-T: approximately 120M segmented utterances (190,000 hours of audio) from YouTube videos, with associated transcripts obtained from semi-supervised caption filtering BIBREF28.
Source-domain normalizing RNN-LM: transcripts from the same 120M utterance YouTube training set. This corresponds to about 3B tokens of the sub-word units used (see below, Section SECREF30).
Target-domain RNN-LM: 21M text-only utterance-level transcripts from anonymized, manually transcribed audio data, representative of data from a Voice Search service. This corresponds to about 275M sub-word tokens.
Target-domain RNN-T fine-tuning data: 10K, 100K, 1M and 21M utterance-level {audio, transcript} pairs taken from anonymized, transcribed Voice Search data. These fine-tuning sets roughly correspond to 10 hours, 100 hours, 1000 hours and 21,000 hours of audio, respectively.
The following data sources were used to train the RNN-T and associated RNN-LMs in this study.

Source-domain baseline RNN-T: approximately 120M segmented utterances (190,000 hours of audio) from YouTube videos, with associated transcripts obtained from semi-supervised caption filtering BIBREF28.

Source-domain normalizing RNN-LM: transcripts from the same 120M utterance YouTube training set. This corresponds to about 3B tokens of the sub-word units used (see below, Section SECREF30).

Target-domain RNN-LM: 21M text-only utterance-level transcripts from anonymized, manually transcribed audio data, representative of data from a Voice Search service. This corresponds to about 275M sub-word tokens.

Target-domain RNN-T fine-tuning data: 10K, 100K, 1M and 21M utterance-level {audio, transcript} pairs taken from anonymized, transcribed Voice Search data. These fine-tuning sets roughly correspond to 10 hours, 100 hours, 1000 hours and 21,000 hours of audio, respectively.
Answer: The answers are shown as follows:
* from YouTube videos, with associated transcripts obtained from semi-supervised caption filtering
* from a Voice Search service
Question: What is AWS EKS?
Answer: Amazon Elastic Kubernetes Service (Amazon EKS) is a managed Kubernetes service that makes it easy for you to run Kubernetes
Question: How do they predict the final task-specific label?
Evidence: In this section, we give a detailed formulation of the architectures used in experiments.
To predict the final task-specific label, we apply a task-specific feature extraction function φ to the sentence representation(s) and feed the extracted features to a classifier network.
Answer: They apply a task-specific feature extraction function φ to the sentence representation(s) and feed the extracted features to a classifier network.
Question: What is Delphi?
Answer: Delphi is a general-purpose programming language and a software product that uses the Delphi dialect of the Object Pascal programming language and provides an integrated development environment (IDE) for rapid application development of desktop, mobile, web, and console software, currently developed and maintained by Embarcadero Technologies.
Question: Do they release MED?
Evidence: To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section "Dataset" ). Compared with manual or automatic construction, we can collect naturally-occurring examples by crowdsourcing and well-designed ones from linguistics publications. To enable the evaluation of skills required for monotonicity reasoning, we annotate each example in our dataset with linguistic tags associated with monotonicity reasoning.
To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section "Dataset" ).
Answer: Yes.
Question: what were their experimental results in the low-resource dataset?
Evidence: Table TABREF21 shows results for Korean INLINEFORM0 English, using the same configurations (1, 2 and 8) as for German–English. Our results confirm that the techniques we apply are successful across datasets, and result in stronger systems than previously reported on this dataset, achieving 10.37 BLEU as compared to 5.97 BLEU reported by gu-EtAl:2018:EMNLP1.
Table TABREF21 shows results for Korean INLINEFORM0 English, using the same configurations (1, 2 and 8) as for German–English. Our results confirm that the techniques we apply are successful across datasets, and result in stronger systems than previously reported on this dataset, achieving 10.37 BLEU as compared to 5.97 BLEU reported by gu-EtAl:2018:EMNLP1.
Answer: The answers are shown as follows:
* 10.37 BLEU
Question: In preparing to raise a family what is the ideal number of children to raise if you planned to encourage them to go to college?
Answer: The ideal number of children is two children as it allows the children to develop a sibling relationship which fosters important life skills in sharing, communication, supporting others and developing long term relationships
Question: What is the contribution of this work?
Evidence: Out of nearly 7000 languages spoken worldwide, current speech (ASR, TTS, voice search, etc.) technologies barely address 200 of them. Broadening ASR technologies to ideally all possible languages is a challenge with very high stakes in many areas and is at the heart of several fundamental research problems ranging from psycholinguistic (how humans learn to recognize speech) to pure machine learning (how to extract knowledge from unlabeled data). The present work focuses on the narrow but important problem of unsupervised Acoustic Unit Discovery (AUD). It takes place as the continuation of an ongoing effort to develop a Bayesian model suitable for this task, which stems from the seminal work of BIBREF0 later refined and made scalable in BIBREF1 . This model, while rather crude, has shown that it can provide a clustering accurate enough to be used in topic identification of spoken document in unknown languages BIBREF2 . It was also shown that this model can be further improved by incorporating a Bayesian "phonotactic" language model learned jointly with the acoustic units BIBREF3 . Finally, following the work in BIBREF4 it has been combined successfully with variational auto-encoders leading to a model combining the potential of both deep neural networks and Bayesian models BIBREF5 . The contribution of this work is threefold:
The contribution of this work is threefold: • we compare two Bayesian models ([2] and [6]) for acoustic unit discovery (AUD) on a very low resource language speech corpus, • we investigate the use of ”informative prior” to improve the performance of Bayesian models by using information from resourceful languages, • as an extrinsic evaluation of AUD quality, we cascade AUD with sequence/lattice based word discovery [7].
Answer: • The authors compare two Bayesian models for acoustic unit discovery (AUD) on a very low resource language speech corpus, • The authors investigate the use of ”informative prior” to improve the performance of Bayesian models by using information from resourceful languages, • as an extrinsic evaluation of AUD quality, the authors cascade AUD with sequence/lattice based word discovery.
Question: Write me a recipe for pizza dough
Answer: Mix

250ml of warm water
Yeast
1 tea spoon of salt
2 tea spoons of olive oil
500g of wheat flour

until you have a firm dough and let it rest for at least 2 hours
Question: Is the model evaluated against other Aspect-Based models?
Evidence: Experiments ::: Baselines
To comprehensively evaluate our AGDT, we compare the AGDT with several competitive models.
Experiments ::: Baselines
To comprehensively evaluate our AGDT, we compare the AGDT with several competitive models.
Answer: Yes.
Question: What is TextTiling?
Evidence: An early and classic work on text segmentation is TextTiling, proposed in BIBREF12 . The idea is to measure the similarity between two successive sentences with smoothing techniques; then segmentation is accomplished by thresholding of the depth of a “valley.” In the original form of TextTiling, the cosine of term frequency features is used as the similarity measure. Joty et al. BIBREF22 apply divisive clustering instead of thresholding for segmentation. Malioutov et al. BIBREF23 formalize segmentation as a graph-partitioning problem and propose a minimum cut model based on tf INLINEFORM0 idf features to segment lectures. Ye et al. BIBREF24 minimize between-segment similarity while maximizing within-segment similarity. However, the above complicated approaches are known as global methods: when we perform segmentation between two successive sentences, future context information is needed. Therefore, they are inapplicable to real-time chat-bots, where conversation utterances can be viewed as streaming data.
In our study, we prefer the simple yet effective TextTiling approach for open-domain dialogue session segmentation, but enhance it with modern advances of word embeddings, which are robust in capturing semantics of words. We propose a tailored algorithm for word embedding learning by combining a query and context as a “virtual document”; we also propose several heuristics for similarity measuring.
An early and classic work on text segmentation is TextTiling, proposed in [13]. The idea is to measure the similarity between two successive sentences with smoothing techniques; then segmentation is accomplished by thresholding of the depth of a “valley.”
Answer: To measure the similarity between two successive sentences with smoothing techniques; then segmentation is accomplished by thresholding of the depth of a “valley.”
Question: Do Africans share the same origin and culture?
Evidence: The Culture of Africa is varied and manifold, consisting of a mixture of countries with various tribes that each have their unique characteristic from the continent of Africa. It is a product of the diverse populations that inhabit the continent of Africa and the African diaspora. Generally, Culture can be defined as a collective mass of distinctive qualities belonging to a certain group of people. These qualities include laws, morals, beliefs, knowledge, art, customs, and any other attributes belonging to a member of that society. Africa has numerous ethnic nationalities all with varying qualities such as language, dishes, greetings, and dances. However, all African peoples share a series of dominant cultural traits which distinguish African Culture from the rest of the world. For example, social values, religion, morals, political values, economics, and aesthetic values all contribute to African Culture. Expressions of culture are abundant within Africa, with large amounts of cultural diversity being found not only across different countries but also within single countries. Even though African cultures are widely diverse, they are also, when closely studied, seen to have many similarities; for example, the morals they uphold, their love and respect for their culture, as well as the strong respect they hold for the aged and the important, i.e. kings and chiefs.
Answer: No, Africa is a multicultural continent, with many diverse ethnic groups. While these ethnic groups don't share the same origin, many of these groups share similar cultural traits such as social values and religious values.  They also generally have high regard for their elderly and tribal leaders.
Question: Can you give me the biography of Lia Cirio
Evidence: Lia Cirio is an American ballet dancer. She is a principal dancer at Boston Ballet.

Biography
Cirio is half Filipino. She trained at Swarthmore Ballet Theatre and Central Pennsylvania Youth Ballet. At age 16, she joined Boston Ballet II. She joined the main company's corps de ballet in 2004, at age 16, and was the company's Princess Grace Award nominee the same year. Cirio was named second soloist in 2006 and soloist the following year. In 2008 and 2009, Cirio joined the Trey McIntyre Project, and toured with the company in the United States and the world. She returned to Boston Ballet in the 2009-10 season and was promoted to principal in 2010. She has danced roles such as Odette/Odile in Swan Lake and the title role in Cinderella.

Cirio has also choreographed work for Boston Ballet's ChoreograpHER initiative.

Cirio is the older sister of Jeffrey Cirio, a Lead Principal Dancer at English National Ballet. They have established an artistic collective called Cirio Collective in 2015.
Answer: Lia Cirio is an American ballet dancer. She is a principal dancer at Boston Ballet. 

Cirio is half Filipino. She trained at Swarthmore Ballet Theatre and Central Pennsylvania Youth Ballet. At age 16, she joined Boston Ballet II. She joined the main company's corps de ballet in 2004, at age 16, and was the company's Princess Grace Award nominee the same year. Cirio was named second soloist in 2006 and soloist the following year. In 2008 and 2009, Cirio joined the Trey McIntyre Project, and toured with the company in the United States and the world. She returned to Boston Ballet in the 2009-10 season and was promoted to principal in 2010. She has danced roles such as Odette/Odile in Swan Lake and the title role in Cinderella.

Cirio has also choreographed work for Boston Ballet's ChoreograpHER initiative.

Cirio is the older sister of Jeffrey Cirio, a Lead Principal Dancer at English National Ballet. They have established an artistic collective called Cirio Collective in 2015.
Question: Identify which of the following are members of House Targaryen or House Baratheon: Visenya, Aenar, Aegon, Aerys, Daenerys, Viserys, Orys, Argella, Robert, Steffon, Renly
Answer: House Targaryen: Visenya, Aenar, Aegon, Aerys, Daenerys, Viserys
House Baratheon: Orys, Argella, Robert, Steffon, Renly
Question: What are the encoder blocks used in QANet composed of?
Evidence: The used architecture of the QA model is briefly summarized below. Here we choose QANet BIBREF2 as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF BIBREF17 when reaching the same performance on SQuAD.
The network architecture is illustrated in Figure FIGREF2 . The left blocks and the right blocks form two QANets, each of which takes a document and a question as the input and outputs an answer. In QANet, firstly, an embedding encoder obtains word and character embeddings for each word in INLINEFORM0 or INLINEFORM1 and then models the temporal interactions between words and refines word vectors to contextualized word representations. All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention. The intuition here is that convolution components can model local interactions and self-attention components focus on modeling global interactions. The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words. After that, a model encoder layer containing seven encoder blocks captures the interactions among the context words conditioned on the question. Finally, the output layer predicts a start position and an end position in the document to extract the answer span from the document.
All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention.
Answer: All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention.
Question: What U.S. President has left the White House to be re-elected again?
Answer: Grover Cleveland. Mr. Cleveland served his first term from 1885 - 1889 and his second term was from 1893 - 1897.
Question: Give me a numbered list of five great fictional books.
Answer: 1. Little Women
2. Pachinko
3. East of Eden
4. To Kill a Mockingbird
5. Harry Potter and the Sorcerer's Stone
Question: Which is a bird or fish: Bluethroat, Blue catfish
Answer: Bluethroat is a bird, Blue catfish is a fish
Question: Is Seattle a good place to live at?
Answer: Seattle is one of the biggest cities in the pacific northwest, it is very well known for the beautiful nature, outdoor activities, coffee, technology and of course the rain. 
The Seattle area is relatively very safe compared to other large metropolitan areas across the US. Public schools in the area are generally considered to be of great quality, some neighborhoods in the greater Seattle area are home to some of the best public schools across the country. 
The Seattle area is also home to major company headquarters such as Microsoft, Amazon, Starbucks and more. It's also home to a large number of startups including a few unicorns such as Smartsheet, Remitly and Outreach. 
If you are considering moving to Seattle the above factors might help you decide but also note that the cost of living in the Seattle area is relatively high compared to cost of living across the country, this is mainly driven by the tech industry being the largest industry in the area.
Question: Extract the number of men and women living in the municipality of Paradela and list them using bullets in the format {men} - {number} and {woman} - {number) in a comma separated format.
Evidence: Paradela is a municipality in Lugo province in Galicia in north-west Spain. It is situated in the region of Sarria. There were 2,193 people living in Paradela as of 2008 (1,101 men and 1,092 women). It is known for the connection it has with the Way of St. James. The French route of such a way, ending in Santiago de Compostela, passes through five parishes of Paradela: Ferreiros, Francos, Laxe, Cortes and Loio.
Answer: {men} - {1,101}, {woman} - {1,092}
Question: What is the best way to travel across Japan as a foreign tourist staying on a tourist visa?
Answer: JR rail pass is probably the most efficient and economic option to get around the country. You need to purchase this pass in advance before arriving in the country. Once you are in Japan, you go to a train station and activate your pass. If you want to travel around and visit multiple cities in a short time, this pass is definitely worth it.