DataHammer/scimrc huggingface dataset:
Question: What are the functions of BioS systems?
Evidence: The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases.
The main problems in BioIE are similar to those in Information Extraction:
This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain.
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .
BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field.
Answer: BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field.
Question: Why is the attention mechanism introduced？
Evidence: While the LSTM encoder has richer capacity than NBOW, it produces the same representation for the entire text description regardless of its contexts. However, the text description may present an entity from various aspects, and various relations only focus on fractional aspects of the description. This phenomenon also occurs in structure embedding for an entity BIBREF8 , BIBREF19 .
Given a relation for an entity, not all of words/phrases in its text description are useful to model a specific fact. Some of them may be important for the given relation, but may be useless for other relations. Therefore, we introduce an attention mechanism BIBREF20 to utilize an attention-based encoder that constructs contextual text encodings according to different relations.
For each position $i$ of the text description, the attention for a given relation $r$ is defined as $\alpha _i(r)$ , which is 
$$e_i(r) &= \mathbf {v}_a^T \tanh (\mathbf {W}_a {\mathbf {z}}_i + \mathbf {U}_a \mathbf {r}), \\
\alpha _i(r)&=\operatorname{\mathbf {softmax}}(e_i(r))\nonumber \\
&=\frac{\exp (e_i(r))}{\sum ^{n}_{j=1} \exp (e_j(r))},$$   (Eq. 12) 
 where $\mathbf {r}\in \mathbb {R}^d$ is the relation embedding; ${\mathbf {z}}_i \in \mathbb {R}^d$ is the output of BLSTM at position $i$ ; $\mathbf {W}_a,\mathbf {U}_a \in \mathbb {R}^{d\times d}$ are parameters matrices; $\mathbf {v}_a \in \mathbb {R}^{d}$ is a parameter vector.
The attention $\alpha _i(r)$ is interpreted as the degree to which the network attends to partial representation $\mathbf {z}_{i}$ for given relation $r$ .
The contextual encoding of text description can be formed by a weighted sum of the encoding $\mathbf {z}_{i}$ with attention. 
$$\mathbf {enc_3}(x_{1:n};r) &= \sum _{i=1}^{n} \alpha _i(r) * \mathbf {z}_i.$$   (Eq. 13) 
Given a relation for an entity, not all of words/phrases in its text description are useful to model a specific fact. Some of them may be important for the given relation, but may be useless for other relations. Therefore, we introduce an attention mechanism [Bahdanau et al., 2014] to utilize an attentionbased encoder that constructs contextual text encodings according to different relations.
Answer: As not all of words/phrases in its text description are useful to model a specific fact，attentionbased encoder can construct contextual text encodings according to different relations.
Question: Tell me which of these things are fruits or vegetables: Carrot, Apple, Orange, Potato, Peas, Broccoli, Pears
Answer: From the list you provided: Carrot, Potato, Peas, and Broccoli are Vegetables. 

The rest are fruits: Apples, Oranges and Pears.
Question: How to analyze the corpora in this paper?
Evidence: Question answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language. Three tasks have been proposed for selection-based QA. Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks. Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e.g., IBM Watson, MIT Start).
Several datasets have been released for selection-based QA. wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task. feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts. yang:15a introduced WikiQA for answer selection and triggering. jurczyk:16 created SelQA for large real-scale answer triggering. rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension. Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection.
These corpora make it possible to evaluate the robustness of statistical question answering learning. Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made. In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA. We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ).
We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section 2 ). We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section 3 ). Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section 4 ).
Answer: The authors make intrinsic analysis and extrinsic analysis to these corpora.
Question: What popular wine varietal is produced in Washington?
Answer: Washington is well known for its white wine, Riesling, and red wine, Cabernet Sauvignon.
Question: tell me how much Amazon Prime would cost
Evidence: Amazon Prime Video, also known simply as Prime Video, is an American subscription video on-demand over-the-top streaming and rental service of Amazon offered as a standalone service or as part of Amazon's Prime subscription. The service primarily distributes films and television series produced by Amazon Studios and MGM Holdings or licensed to Amazon, as Amazon Originals, with the service also hosting content from other providers, content add-ons, live sporting events, and video rental and purchasing services.

Operating worldwide, the service may require a full Prime subscription to be accessed. In countries such as the United States, United Kingdom, and Germany, the service can be accessed without a full Prime subscription, whereas in Australia, Canada, France, India, Turkey, and Italy, it can only be accessed through a dedicated website. Prime Video additionally offers a content add-on service in the form of channels, called Amazon Channels, or Prime Video Channels, which allow users to subscribe to additional video subscription services from other content providers within Prime Video.

Launched on September 7, 2006, as Amazon Unbox in the United States, the service grew with an expanding library, and added the Prime Video membership upon the development of the Prime subscription. It was then renamed as Amazon Instant Video on Demand. After acquiring the UK-based streaming and DVD-by-mail service LoveFilm in 2011, Prime Video was added to the Prime subscription in the United Kingdom, Germany and Austria in 2014, available on a monthly subscription of £/€8.99 per month, continuing the plan of LoveFilm Instant. The service was previously available in Norway, Denmark, and Sweden in 2012, but was discontinued in 2013. On April 18, 2016, Amazon split Prime Video from Amazon Prime in the US for $8.99 per month.
Answer: Depends on the region, Amazon Prime in US cost USD 8.99
Question: Give me a list of pizza restaurants in San Francisco
Answer: There are a lot of different types of pizza restaurants in San Francisco. For more casual pizza restaurants, try any Round Table, Dominos, Pizza Hut, or for a more local experience, Golden Boy Pizza. 

If you're interested in sit down dining pizza options, try Del Popolo, Tony's Slice House, or Il Casaro in North Beach.
Question: Did they experiment with the dataset on some tasks?
Evidence: In this work, we publish TWNERTC dataset in which named entities and categories of sentences have been automatically annotated. We use Turkish Wikipedia dumps as the text source and Freebase to construct a large-scale gazetteers to map fine-grained types to entities. To overcome noisy and ambiguous data, we leverage domain information which is given by Freebase and develop domain-independent and domain-dependent methodologies. All versions of datasets can be downloaded from our project web-page. Our main contributions are (1) the publication of Turkish corpus for coarse-grained and fine-grained NER, and TC research, (2) six different versions of corpus according to noise reduction methodology and entity types, (3) an analysis of the corpus and (4) benchmark comparisons for NER and TC tasks against human annotators. To the best of our knowledge, these datasets are the largest datasets available for Turkish NER ad TC tasks.
Our main contributions are (1) the publication of Turkish corpus for coarse-grained and fine-grained NER, and TC research, (2) six different versions of corpus according to noise reduction methodology and entity types, (3) an analysis of the corpus and (4) benchmark comparisons for NER and TC tasks against human annotators. 
Answer: Yes.
Question: What is a capital gain in finance?
Answer: You make a capital gain if you sell or dispose of a long−term asset (such as a building) for more than it cost you.
Question: How many parameters does the presented model have?
Evidence: The end-to-end system (prefix E2E) uses the DNN topology depicted in Figure FIGREF8 . We present results with 3 distinct size configurations (infixes 700K, 318K, and 40K) each representing the number of approximate parameters, and 2 types of training recipes (suffixes 1stage and 2stage) corresponding to end-to-end and encoder+decoder respectively, as described in UID7 . The input to all DNNs consist of a sequence with INLINEFORM0 frames of left and INLINEFORM1 frames of right context; each with a stride of INLINEFORM2 . More specifically, the E2E_700K model uses INLINEFORM3 nodes in the first 4 SVDF layers, each with a memory INLINEFORM4 , with intermediate bottleneck layers each of size 64; the following 3 SVDF layers have INLINEFORM5 nodes, each with a memory INLINEFORM6 . This model performs 350K multiply-accumulate operations per inference (every 20ms of streaming audio). The E2E_318K model uses INLINEFORM7 nodes in the first 4 SVDF layers, each with a memory INLINEFORM8 , with intermediate bottleneck layers each of size 64; the remainder layers are the same as E2E_700K. This model performs 159K multiply-accumulate operations per inference. Finally, the E2E_40K model uses INLINEFORM9 nodes in the first 4 SVDF layers, each with a memory INLINEFORM10 , with intermediate bottleneck layers each of size 32; the remainder layers are the same as the other two models. This model performs 20K multiply-accumulate operations per inference.
The end-to-end system (prefix E2E) uses the DNN topology depicted in Figure FIGREF8 . We present results with 3 distinct size configurations (infixes 700K, 318K, and 40K) each representing the number of approximate parameters, and 2 types of training recipes (suffixes 1stage and 2stage) corresponding to end-to-end and encoder+decoder respectively, as described in UID7 .
Answer: The answers are shown as follows:
* (infixes 700K, 318K, and 40K) each representing the number of approximate parameters
Question: How is the game "ultimate frisbee" different from "frisbee golf"?
Answer: These two games are both played with a frisbee (or "disc"), which is a plastic disc that people throw through the air like a ball. The difference is that ultimate frisbee is a team sport that combines several of the rules of football, basketball and soccer. Like football, a team takes possession of the frisbee and tries to move it down field and into an end zone. Like basketball, once you are in possession of the frisbee, you cannot move your pivot foot. However, there is no "dribbling" with a frisbee so if a player has possession of the frisbee outside of their end zone, they have to pass it before they can move their pivot foot. Ultimate frisbee is played on a soccer field and the player movement looks most like that sport. Frisbee golf, on the other hand, follows the rules of golf, except that the player throws a frisbee to the hole instead of hitting a ball with clubs. Some people play the sport on dedicated frisbee golf courses, using a set of specialized discs of various weights and carrying them in a bag from hole to hole like a golfer would do with their set of clubs. Others simply take a standard 175-gram frisbee and make up a course as they go. For example, two players might point to a tree on the other side of a park and compete by seeing who can hit the tree with a frisbee in the fewest consecutive attempts.
Question: What is a fake news?
Answer: There is no clear definition of fake news. However in general a fake news is a false or misleading information spread across media. Fake news in social media may be used to attract visualisations and likes so the person posting gets returns in higher visibility and popularity. Sometimes, fake news may also be used in a more architected manner in marketing campaigns with the aim of damaging reputations of brands or persons or interfere with mass opinion in democratic countries for example during elections
Question: Categorize the following Taylor Swift songs based on what album they are in: The Archer, tolerate it, Lover, Love Story (Taylor's Version), Bejeweled, Don't Blame Me, 22 (Taylor's version), Look What You Made Me Do, Lavender Haze, willow, Cruel Summer
Answer: The following songs are in Taylor Swift's album Lover: The Archer, Lover, Cruel Summer.
The following song is in the album Fearless (Taylor's Version): Love Story (Taylor's Version)
The following songs are in the album evermore: tolerate it, willow
The following songs are in the album Reputation: Don't Blame Me, Look What You Made Me Do
The following song is in the album Red (Taylor's Version): 22 (Taylor's version) 
The following songs are in the album Midnights: Bejeweled, Lavender Haze
Question: What are three ways to camp.
Answer: Tent camping, car camping, cabin camping
Question: What is the difference between tennis shoes and sandals?
Answer: Tennis shoes are a form of footware that fully covers and supports your feet. These shoes are designed to endure a lot of wear and tear, as they are often used for physical activity and general daily use. Tennis shoes typically have full heal support that limits the movement of your foot within the shoe as you wear them. Tennis shoes also have laces, which help the wearer to tighten the shoe fabric around your foot, providing further support. Sandals are a type of footware that does not fully cover or support your foot. Sandals typically have some sort of wrap or toe fixture that helps to keep the shoe on the wearer as they walk. This type of shoe is mostly used for short-term wear, at the beach, or at the pool, as they are extremely convenient to put on and take off.
Question: Increased money supply (i.e. money printing)
Answer: Demand-pull inflation.
Cost-push inflation.
Increased money supply (i.e. money printing)
Currency devaluation.
Government policies and regulations.
Question: Using examples taken from the text give me a summary of why the Boston Marathon is well known.
Evidence: The Boston Marathon is an annual marathon race hosted by several cities and towns in greater Boston in eastern Massachusetts, United States. It is traditionally held on Patriots' Day, the third Monday of April. Begun in 1897, the event was inspired by the success of the first marathon competition in the 1896 Summer Olympics. The Boston Marathon is the world's oldest annual marathon and ranks as one of the world's best-known road racing events. It is one of six World Marathon Majors. Its course runs from Hopkinton in southern Middlesex County to Copley Square in Boston.

The Boston Athletic Association (B.A.A.) has organized this event annually since 1897, except for 2020 when it was canceled due to the COVID-19 pandemic. In 2021, it was held later, in October. The race has been managed by DMSE Sports, Inc., since 1988. Amateur and professional runners from all over the world compete in the Boston Marathon each year, braving the hilly Massachusetts terrain and varying weather to take part in the race.

The event attracts 500,000 spectators each year, making it New England's most widely viewed sporting event. Starting with just 15 participants in 1897, the event now attracts an average of about 30,000 registered participants each year, with 30,251 people entering in 2015. The Centennial Boston Marathon in 1996 established a record as the world's largest marathon with 38,708 entrants, 36,748 starters, and 35,868 finishers.
Answer: The Boston Marathon, which began in 1897, is the world's oldest annual marathon inspired by the 1896 Summer Olympics. The Boston Marathon is one of the six World Marathon Majors. The race has been held annually since 1897 except for 2020, when the race was canceled due to the COVID-19 pandemic. The marathon is traditionally held on Patriot's Day, the third Monday of April. It attracts 500,000 spectators and 30,000 registered participants each year making it one of the most well known road racing events.
Question: What datasets are used for experiments on three other tasks?
Evidence: By combining both NSA and GSA, we obtain an enhanced SA module. We then construct our Normalized and Geometry-aware Self-Attention Network, namely NG-SAN, by replacing the vanilla SA modules in the encoder of the self-attention network with the proposed one. Extensive experiments on MS-COCO validates the effectiveness of our proposals. In particular, our NG-SAN establishes a new state-of-the-art on the MS-COCO evaluation sever, improving the best single-model result in terms of CIDEr from 125.5 to 128.6. To demonstrate the generality of NSA, we further present video captioning, machine translation, and visual question answering experiments on the VATEX, WMT 2014 English-to-German, and VQA-v2 datasets, respectively. On top of the strong Transformer-based baselines, our methods can consistently increase accuracies on all tasks at a negligible extra computational cost.
To demonstrate the generality of NSA, we further present video captioning, machine translation, and visual question answering experiments on the VATEX, WMT 2014 English-to-German, and VQA-v2 datasets, respectively. 
Answer: The answers are shown as follows:
* VATEX, WMT 2014 English-to-German, and VQA-v2 datasets
Question: In the United States of America, which state is the largest in size?
Answer: In the Unites States of America, Alaska is the largest state in physical size.  Although not the most populated state is is 663,300 in square miles.  The next largest state is Texas followed by California.
Question: What are the benefits of using the ReLU activation function over the classic sigmoid function in neural networks?
Answer: 1. Gradient vanishing problem: The max value of the derivative of sigmoid function is 0.25, which can result in the total gradients becoming very small during back propagation. In contrast ReLU has a constant gradient 1 for positive inputs. 
2. Compute efficiency: ReLU can be computed using max(0, x), more efficient than exponential operations in sigmoid function. 
3. Convergence speed: it's also reported that using ReLU as activation function in neural networks converges faster than using sigmoid function.
Question: I don't like how my coffee tastes. What are some reasons that the coffee I brew isn't good?
Answer: - Using too much or too little coffee can make the coffee too strong or weak. 
- Grinding coffee too finely can make it too strong and bitter, or too coarsely can make it weak. Use the right grind for your brewing method; finer for drip coffee, coarser for french press.
- The quality of the coffee beans affects the flavor of the brewed coffee. Fresh high-quality beans can improve the coffee's flavor.
- Fresh-ground coffee will typically make better brewed coffee than previously-ground coffee
- The temperature and quality of the water can affect the coffee's flavor. Hard water can negatively impact the coffee's flavor.
Question: Classify each of the following sports terminologies by sport: Earned Run Average, WHIP, Bicycle Kick, Touchdown, Safety, Quality Start, Runs, RBI, Stolen Base, Batting Average, Inning, Strikeout, Shortstop, Striker, Sweeper, Stopper, Relief Pitcher, Nutmeg, Slide Tackle, Extra Point, Barrel Rate, Midfielder, Yellow Card, Red Card, Corner Kick, Goal Kick, Set Piece, Direct Free Kick, FIFA, MLS, Concacaf, Half Volley, Full Volley, Base on Balls, Batter's Box, Mound, Breaking Ball, Slider, Designated Hitter, Knuckle Ball, No Hitter, Triple Play, Double Play, Wild Pitch, Linebacker, Running Back, Quarterback, Wide Receiver, Tight End, Chop Block
Answer: Soccer: Bicycle Kick, Striker, Sweeper, Stopper, Nutmeg, Slide Tackle, Midfielder, Yelow Card, Red Card, Corner Kick, Goal Kick, Set Piece, Direct Free Kick, FIFA, Concacaf, MLS, Half Volley, Full Volley
Baseball: Earned Run Average, WHIP, Quality Start, Runs, RBI, Stolen Base, Batting Average, Inning, Strikeout, Shortstop, Relief Pitcher, Barrel Rate, Base on Balls, Batter's Box, Mound, Breaking Ball, Slider, Designated Hitter, Knuckle Ball, No Hitter, Triple Play, Double Play, Wild Pitch
Football: Touchdown, Safety, Extra Point, Linebacker, Running Back, Quarterback, Wide Receiver, Tight End, Chop Block
Question: Replace the word "lawyer" with "judge" and the word "pacifist" with "peace lover"
Evidence: Hermann Fernau (born 1883 or 1884) was a German lawyer, writer, journalist and pacifist.
Answer: Hermann Fernau (born 1883 or 1884) was a German judge, writer, journalist and peace lover.