DataHammer/scimrc huggingface dataset:
Question: Which two network structures are designed for BRLM?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
In particular, we design two network structures for BRLM, which are divided into Hard Alignment (BRLM-HA) and Soft Alignment (BRLM SA) according to the way of generating the alignment information.
Answer: Hard Alignment (BRLM-HA) and Soft Alignment (BRLM SA).
Question: Does the system trained only using XR loss outperform the fully supervised neural system?
Evidence: FLOAT SELECTED: Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p < 0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from (He et al., 2018a). Numbers for Semisupervised are from (He et al., 2018b).
FLOAT SELECTED: Table 1: Average accuracies and Macro-F1 scores over five runs with random initialization along with their standard deviations. Bold: best results or within std of them. ∗ indicates that the method’s result is significantly better than all baseline methods, † indicates that the method’s result is significantly better than all baselines methods that use the aspect-based data only, with p < 0.05 according to a one-tailed unpaired t-test. The data annotations S, N and A indicate training with Sentence-level, Noisy sentence-level and Aspect-level data respectively. Numbers for TDLSTM+Att,ATAE-LSTM,MM,RAM and LSTM+SynATT+TarRep are from (He et al., 2018a). Numbers for Semisupervised are from (He et al., 2018b).
Answer: Yes.
Question: What is a PIN?
Answer: A personal identification number (PIN), or sometimes redundantly a PIN number or PIN code, is a numeric (sometimes alpha-numeric) passcode used in the process of authenticating a user accessing a system.

The PIN has been the key to facilitating the private data exchange between different data-processing centers in computer networks for financial institutions, governments, and enterprises. PINs may be used to authenticate banking systems with cardholders, governments with citizens, enterprises with employees, and computers with users, among other uses.

In common usage, PINs are used in ATM or POS transactions, secure access control (e.g. computer access, door access, car access), internet transactions, or to log into a restricted website.
Question: What are some good ways school kids can get started with chess in the San Francisco Bay Area?
Answer: The San Francisco Bay Area offers a variety of learning opportunities for chess enthusiasts. You could attend classes in-person or online, attend drop-in clubs in-school or after-school, and play unrated or rated chess tournaments.

Here are some suggestions:
1. Watch introductory chess videos from chesskid.com. It offers both free and paid memberships.
2. Buy introductory chess books and work through graded chess puzzles. 
3. Attend some chess clubs and play games. 
4. Play in an unrated tournament and then in USCF rated scholastic chess tournaments. 

One such organization is Bay Area Chess, which offers chess events all over the Bay Area.
Question: Can you give me a short summary of Mindfulness and how to practice it?
Answer: Mindfulness is a technique that can be used to bring your mind and focus into the present moment, and calm your nervous system.

The most common way to begin to practice mindfulness is to narrow and focus your attention on your breath. Some techniques prefer breathing in through the nose and out through the mouth, although this is not required. One way to narrow your attention is to closely observe the sensation of cool air as it passes over the tip of your nose, and the sensation of your chest falling as you exhale. Allow any rising thoughts to pass through your mind without paying attention to them, or engaging with them.

Other techniques involve focusing your attention to sensations in your body like hot and cold, or pain. You can also bring your focus to feelings like fear, anxiety, or love - focus your attention on where exactly in the body you feel those feelings. You can also move your attention to external stimulus like sounds, and your visual field.

Mindfulness is an extremely powerful technique that anyone can do from anywhere. Good luck practicing!
Question: In the series A Song of Ice and Fire, who is the founder of House Tarly?
Answer: House Tarly's ancestry can be linked to Harlon the Hunter, who built the Horn Hill castle with his twin brother Herndon of the Horn.
Question: What labels are in the dataset?
Evidence: FLOAT SELECTED: Figure 1: An example of stress being expressed in social media from our dataset, from a post in r/anxiety (reproduced exactly as found). Some possible expressions of stress are highlighted.
FLOAT SELECTED: Figure 1: An example of stress being expressed in social media from our dataset, from a post in r/anxiety (reproduced exactly as found). Some possible expressions of stress are highlighted.
Answer: binary label of stress or not stress
Question: Does the use of out-of-domain data improve the performance of the method?
Evidence: Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants.
This paper presented the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. We presented the results obtained by our system in translating from Czech to Polish. Our system achieved competitive performance ranking second among ten teams in the competition in terms of BLEU score. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.
Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants.
The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.
Answer: No.
Question: Write a polite rejection response to a LinkedIn recruiter
Answer: Hi there, thank you for your interest. At this time, I am not interested to explore. I will save your name and reconnect when I am open to a new opportunity.
Question: What is the class with highest error rate in SNLI-VE?
Evidence: Xie also propose the SNLI-VE dataset as the first dataset for VTE. SNLI-VE is built from the textual entailment SNLI dataset BIBREF0 by replacing textual premises with the Flickr30k images that they originally described BIBREF2. However, images contain more information than their descriptions, which may entail or contradict the textual hypotheses (see Figure FIGREF3). As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\sim }31\%$ errors in this class, and ${\sim }1\%$ for the contradiction and entailment classes.
As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\sim }31\%$ errors in this class, and ${\sim }1\%$ for the contradiction and entailment classes.
Answer: The answers are shown as follows:
* neutral class
Question: Write a guide for how to catch a smallmouth bass.
Answer: The smallmouth bass is a common freshwater fish, found in the rivers and lakes of North America. Its habitat stretches between northeastern Canada and a small range of waterways in Mexico. Fishermen of all ages enjoy catching the smallmouth bass, because of its propensity for jumping out of the water and trying to evade capture with vigor. You will find smallmouth enjoying shallower waters in the cool parts of the day, attracted to surface baits like imitation frogs, minnows, and spinnerbaits. As the day warms up, smallmouth retreat to cooler waters, and you might find them schooling near a drop-off or around a deep-water mound. Smallmouth are typically caught between June and October.

Note: Please adhere to local fishing regulations to know when smallmouth bass are in season. Most jurisdictions require a license to fish and have limits on how many fish can be kept.
Question: Tomb of Sand  was translated from Hindi to English by
Answer: Daisy Rockwell
Question: What year did the Titanic sink?
Answer: The Titanic sank in 1912.
Question: I want to buy a new car. This is a difficult financial decision, but our car is 12 years old. Can you write some reasons I can give to my partner so she's on board with this decision.
Answer: Buying a new car is usually one of the main financial decisions a person has to take. A new car depreciates quickly, and it is definitely not an investment, but there are some reasons to buy a new car if you have an old one, for example:

- New cars are more ecological, and usually include electric propulsion, which is better for the environment. 
- New cars are safer. A lot of the R&D budget from manufacturers goes into making the car safer both for the people in it and for the pedestrians.
- A new car usually has more features which can make driving a better experience: GPS connectivity, better seats or better lightning are some of them. 
-  A new car is also an experience - if you like driving, it can make you happier to drive a new models and enjoy its features.
Question: Summarize Clemson University to me
Evidence: Clemson University (/ˈklɛmp.sən, ˈklɛm.zən/[note a]) is a public land-grant research university in Clemson, South Carolina. Founded in 1889, Clemson is the second-largest university by enrollment in South Carolina. For the fall 2019 semester, the university enrolled a total of 20,195 undergraduate students and 5,627 graduate students, and the student/faculty ratio was 18:1. Clemson's 1,400-acre campus  is in the foothills of the Blue Ridge Mountains. The campus now borders Lake Hartwell, which was formed by the dam completed in 1962. The university manages the nearby 17,500-acre Clemson Experimental Forest that is used for research, education, and recreation.

Clemson University consists of seven colleges: Agriculture, Forestry and Life Sciences; Architecture, Arts and Humanities; The Wilbur O. and Ann Powers College of Business; Behavioral, Social and Health Sciences; Education; Engineering, Computing and Applied Sciences; and Science. U.S. News & World Report ranks Clemson University 77th for 2022 among national U.S. universities. Clemson University is classified among "R1: Doctoral Universities – Very high research activity."[12
Answer: Clemson University is based in the upstate of South Carolina in a town called Clemson. It was founded in 1889 and is the 2nd largest university in South Carolina. Clemson University consists of seven colleges: Agriculture, Forestry and Life Sciences; Architecture, Arts and Humanities; The Wilbur O. and Ann Powers College of Business; Behavioral, Social and Health Sciences; Education; Engineering, Computing and Applied Sciences; and Science. Clemson ranks 77th for 2022 among national U.S. universities.
Question: What is Audemars Piguet Royal Oak ?
Answer: Audemars Piguet Royal Oak is a luxury watch. This watch was invented in 1971 by Gérald Genta and presented at the Bâle exhibition. It is the first steel watch at the price of a gold watch. It is recognizable to its specific octagonal shape and its handmade dial representing a « petite tapisserie ». It is also one of the first watches with a bracelet integrated into the case. This first Royal Oak had 2 hands (hours, minutes) and the date. It is also called Jumbo because of its imposing size for the time: 39mm in diameter. This watch is an icon for all collectors. It is now available in several versions (size, complications, thickness).
Question: How did the introduction of broadband in India in the early 2000s impact Indian movie culture?
Answer: The introduction of broadband in India in the early 2000s gave them access to a wide range of popular Hollywood classics that were previously unavailable to them. This was due in part to the rise of P2P file sharing, which allowed users to download large files from other users over the internet. As a result, many Indians were able to watch films by directors such as Quentin Tarantino, Martin Scorsese, and Sidney Lumet for the first time. This had a significant impact on Indian movie culture, as it exposed viewers to new and different styles of filmmaking.
Question: What is a baseball hat?
Answer: A baseball hat is a fabric hat with a bill on the front that shades the eyes from the sun. It is referred to as a baseball hat because all baseball players wear this style of hat while in the field playing defense, and they have their team’s logo on the front of the hat. Many people, however, refer to a hat as a baseball hat if it is a fabric material with a front bill, even if it does not have the logo of a baseball team on the front.
Question: Give a short bulleted list of key points in the history of coffee
Evidence: The history of coffee dates back to centuries of old oral tradition in modern day Ethiopia. However, neither where coffee was first cultivated nor direct evidence of its consumption prior to the 15th century have been found. Sufi monasteries in Yemen employed coffee as an aid to concentration during prayers. Coffee later spread to the Levant and Persia in the early 16th century; it caused some controversy on whether it was halal in Ottoman and Mamluk society. Coffee arrived in Italy the second half of the 16th century through commercial Mediterranean trade routes, while Central and Eastern Europeans learnt of coffee from the Ottomans. By the mid 17th century, it had reached India and the East Indies.

Coffee houses would establish themselves in Western Europe by the late 17th century, especially in England and Germany. One of the earliest cultivations of coffee in the New World was when Gabriel de Clieu brought coffee seedlings to Martinique in 1720. These beans later sprouted 18,680 coffee trees which enabled its spread to other Caribbean islands like Saint-Domingue and also to Mexico. By 1788, Saint-Domingue supplied half the world's coffee.

By 1852, globally, Brazil became the largest producer of coffee and has held that status ever since. The period since 1950 saw the widening of the playing field due to the emergence of several other major producers, notably Colombia, Ivory Coast, Ethiopia, and Vietnam; the latter overtook Colombia and became the second-largest producer in 1999. Modern production techniques along with the mass productization of coffee has made it a household item today.
Answer: * Oral tradition has coffee drinking originating in modern day Ethiopia
* Sufi monasteries in Yemen used coffee to aid prayers in the 15th century
* Spread to the Levant and Persia in the 16th century
* Arrived in Italy in the second half of the 16th century via Ottoman empire
* By mid-17th century had arrived in India and the East Indies
* Established in Western Europe by the late 17th century, especially in England and Germany
* First cultivated in Martinique in the New World in 1720 and spread to Sanit-Domingue and Mexico
* By 1852 Brazil had become the world's largest coffee producer
* Since 1950 the major growers have been Colombia, Ivory Coast, Ethiopia, and Vietnam
Question: From the below text, list the top 3 chapters of the Bhagvad Geeta, with the most verses.
Evidence: There are total 18 chapters and 700 verses in Bhagavad Gita. These are:

Chapter	Name	Total Verses
1	Arjunvishadyog	47
2	Sankhya Yoga	72
3	Karma Yoga	43
4	gnana-Karma-Sanyasa Yoga	42
5	Karma-Sanyasa Yoga	29
6	Atma-Samyama Yoga	47
7	gnana-Vignana Yoga	30
8	Aksara-ParaBrahma Yoga	28
9	Raja-Vidya-Raja-Guhya Yoga	34
10	Vibhuti Yoga	42
11	Vishwarupa-Darsana Yoga	55
12	Bhakti Yoga the ones in XML	20
13	Ksetra-Ksetrajna-Vibhaga Yoga	34
14	Gunatraya-Vibhaga Yoga	27
15	Purushottama Yoga	20
16	Daivasura-Sampad-Vibhaga Yoga	24
17	Shraddhatraya-Vibhaga Yoga	28
18	Moksha-Sanyasa Yoga	78
Total	700
Answer: Moksha-Sanyasa Yoga, the 18th chapter of the Bhagavad Gita, has 78 verses and is the longest chapter, followed by the 2nd chapter Sankhya Yoga with 72 verses and 11th chapter Vishwarupa-Darsana Yoga with 55 verses.
Question: You come across a bear which is brown in color, has flat shoulders, and has large ears. What kind of bear is it?
Answer: It is probably a black bear. Black bears can have many different colors, including brown. But compared to brown bears, they have flat shoulders, larger ears, smaller overall size, and a face that is straight from the eyes to the nose. In contrast, brown bears have a prominent shoulder hump, have small ears, have an overall larger size, and have a face that is bowl-shaped from the eyes to the nose.
Question: What are five different ground Pokemon?
Answer: Five different ground Pokeon are Diglett, Cubone, Sandshrew, Dugtrio, and Marowak.
Question: Tell me whether each food item is high or low in protein: steak, chicken, egg, strawberry, watermelon, carrot
Answer: Steak: High in Protein
Chicken: High in Protein
Egg: High in Protein
Strawberry: Low in Protein
Watermelon: Low in Protein
Carrot: Low in Protein
Question: Who directed the premiere episode of season four of Game of Thrones?
Answer: D.B. Weiss, who also co-authored the season four premiere episode, directed "Two Swords."
Question: What is the most obvious necessarity of preprocessing for the SemEval-2010 benchmark dataset？
Evidence:  This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified.
While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availabil-ity of the SemEval-2010 benchmark dataset (Kim et al., 2010). This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems.
Answer: As SemEval-2010 benchmark dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance.
Question: Extract the nationalities of the population of the Azores islands in Portugal. Separate them with a comma.
Evidence: The Azores were uninhabited when Portuguese navigators arrived in the early 15th century; settlement began in 1439 with migrants mostly from mainland Portugal and, to a lesser extent, Flemish, Spanish, French, Italians, Germans, Scottish, Jews, Moors and blacks from Guinea, Cape Verde and São Tomé.
Answer: Portuguese, Flemish, Spanish, French, Italians, Germans, Scottish, Jews, Moors, Blacks
Question: What is the difference between the Netherlands and Holland?
Answer: The two are often used interchangeably to refer to the country of the Netherlands. They are not the same however. The Netherlands is a country in Western Europe. Holland is a region in the Netherlands with the majority of the population, roughly consisting of the two provinces North Holland and South Holland.
Question: Why do people like Blue Light Therapy?
Answer: Blue Light Therapy, similar to Red Light Therapy, helps with acne and clearing up skin. It helps reduce the bacteria on your face and can also decrease blemishes. Some devices are only Blue Light Therapy, but if you are interested in both Blue and Red Light Therapy, you can find some that offer both settings.
Question: Who was the 27th president of the United States of America?
Answer: William Howard Taft was the 27th president of the United States from 1909 to 1913.