DataHammer/scimrc huggingface dataset:
Question: Do the authors provide strong reference results for all transfer directions between the eight languages?
Evidence: There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions.
The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification.
We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English.
Answer: Yes, they do.
Question: What does the paper explore?
Evidence: Word embeddings are low-dimensional, dense vector representations that capture semantic properties of words. Recently, they have gained tremendous popularity in Natural Language Processing (NLP) and have been used in tasks as diverse as text similarity BIBREF0 , part-of-speech tagging BIBREF1 , sentiment analysis BIBREF2 , and machine translation BIBREF3 . Although word embeddings are widely used across NLP, their stability has not yet been fully evaluated and understood. In this paper, we explore the factors that play a role in the stability of word embeddings, including properties of the data, properties of the algorithm, and properties of the words. We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks.
Using the overlap between nearest neighbors in an embedding space as a measure of stability (see sec:definingStability below for more information), we observe that many common embedding spaces have large amounts of instability. For example, Figure FIGREF1 shows the instability of the embeddings obtained by training word2vec on the Penn Treebank (PTB) BIBREF4 . As expected, lower frequency words have lower stability and higher frequency words have higher stability. What is surprising however about this graph is the medium-frequency words, which show huge variance in stability. This cannot be explained by frequency, so there must be other factors contributing to their instability.
In the following experiments, we explore which factors affect stability, as well as how this stability affects downstream tasks that word embeddings are commonly used for. To our knowledge, this is the first study comprehensively examining the factors behind instability.
In this paper, we explore the factors that play a role in the stability of word embeddings, including properties of the data, properties of the algorithm, and properties of the words. We find that word embeddings exhibit substantial instabilities, which can have implications for downstream tasks.
Answer: The factors that affect the stability of word embeddings, as well as how this stability affects downstream tasks that word embeddings are commonly used for.
Question: Who wrote the poker mindset
Answer: The Poker Mindset was written by Ian Taylor and Matthew Hilger
Question: what models did they compare to?
Evidence: The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . The results indicate that the bag-of-words MVN outperforms most methods, but obtains lower accuracy than the state-of-the-art results achieved by the tree-LSTM BIBREF21 , BIBREF22 and the high-order CNN BIBREF16 . However, when augmented with 4 convolutional features as described in Section SECREF9 , the MVN strategy surpasses both of these, establishing a new state-of-the-art on this benchmark.
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 .
Answer: High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM
Question: What is the best way to get back in shape when over 50 years of age?
Answer: Experts recommend some sort of activity, at least 30 minutes a day. One can start walking, rowing, going to the gym, running or do high intensity interval training (HIIT), but not recommended if just starting again after a long time without exercising. It is recommended to set small goals and increase them over time.
Question: Given this paragraph about British Shorthairs, what are the typical characteristics of this cat breed's temperament?
Evidence: British Shorthairs are an easygoing and dignified breed, not as active and playful as many, but sweet-natured and devoted to their owners, making them a favorite of animal trainers. They tend to be safe around other pets and children since they will tolerate a fair amount of physical interaction, but as a rule do not like to be picked up or carried. They require only minimal grooming and take well to being kept as indoor-only cats; however, they can be prone to obesity unless care is taken with their diet. They are quiet and vigilant, but if they trust their owners, they will silently follow the owners’ activities and stay with them. British Shorthair cats are not lap cats. They are more accustomed to staying beside people than squatting on people’s laps. Similarly, they prefer the feeling of having their feet on the ground. They have low voice and a moderate activity level. When there is no one at home, they will stay at home quietly and wait for their owners to come back.
Answer: British Shorthairs are an easygoing, quiet and dignified breed. They are sweet-natured and devoted to their owners. When there is no one at home, they will stay at home quietly and wait for their owners to come back.
Question: In music, what key is the relative minor to the key of B major?
Answer: G# minor is B major's relative minor.
Question: Identify which instrument is string or percussion: Goema, Requinto
Answer: Goema is percussion, Requinto is string.
Question: Who is the NBA's all team leading scorer?
Answer: LeBron James as of 2023, before he broke the record it was Kareem Abdul-Jabbar. Both of them accomplished this feat as part of the Los Angeles Lakers.
Question: Which NMT architecture do they use?
Evidence: The neural machine translation was trained using Nematus. For the NMT system as well as for the PreMT system, we used the default configuration. In order to limit the vocabulary size, we use BPE as described in BIBREF0 with 40K operations. We run the NMT system for 420K iterations and stored a model every 30K iterations. We selected the model that performed best on the development data. For the ensemble system we took the last four models. We did not perform an additional fine-tuning.
The neural machine translation was trained using Nematus. For the NMT system as well as for the PreMT system, we used the default configuration.
Answer: The answers are shown as follows:
* trained using Nematus
* default configuration
Question: Extract the person who founded Microsoft
Evidence: Logging was Seattle's first major industry, but by the late 19th century, the city had become a commercial and shipbuilding center as a gateway to Alaska during the Klondike Gold Rush. Growth after World War II was partially due to the local Boeing company, which established Seattle as a center for aircraft manufacturing. The Seattle area developed into a technology center from the 1980s onwards with companies like Microsoft becoming established in the region; Microsoft founder Bill Gates is a Seattleite by birth. Internet retailer Amazon was founded in Seattle in 1994, and major airline Alaska Airlines is based in SeaTac, Washington, serving Seattle's international airport, Seattle–Tacoma International Airport. The stream of new software, biotechnology, and Internet companies led to an economic revival, which increased the city's population by almost 50,000 between 1990 and 2000.
Answer: Bill Gates
Question: People love cats but can be highly divided about what breed of cat is the best - What is your favorite, and why?
Answer: British Shorthair cats are the best because of their personalities and appearance. Not only are British Shorthair cats adorably a loose assembly of spheres, they are inquisitive, playful, and adopt the strangest and amusing positions. Sometimes they appear to be broken.
Question: What is the baseline method for the task?
Evidence: For the emotion recognition from text, we manually transcribe all utterances of our AMMER study. To exploit existing and available data sets which are larger than the AMMER data set, we develop a transfer learning approach. We use a neural network with an embedding layer (frozen weights, pre-trained on Common Crawl and Wikipedia BIBREF36), a bidirectional LSTM BIBREF37, and two dense layers followed by a soft max output layer. This setup is inspired by BIBREF38. We use a dropout rate of 0.3 in all layers and optimize with Adam BIBREF39 with a learning rate of $10^{-5}$ (These parameters are the same for all further experiments). We build on top of the Keras library with the TensorFlow backend. We consider this setup our baseline model.
For the emotion recognition from text, we manually transcribe all utterances of our AMMER study. To exploit existing and available data sets which are larger than the AMMER data set, we develop a transfer learning approach. We use a neural network with an embedding layer (frozen weights, pre-trained on Common Crawl and Wikipedia BIBREF36), a bidirectional LSTM BIBREF37, and two dense layers followed by a soft max output layer. This setup is inspired by BIBREF38. We use a dropout rate of 0.3 in all layers and optimize with Adam BIBREF39 with a learning rate of $10^{-5}$ (These parameters are the same for all further experiments). We build on top of the Keras library with the TensorFlow backend. We consider this setup our baseline model.
Answer: For the emotion recognition from text they use described neural network as baseline.
For audio and face there is no baseline.
Question: Identify which animal is domesticated or wild: Guinea pig, Common
Answer: Common is wild, Guinea pig is domesticated
Question: Name some of the bravolebrities from The Real Housewives of Atlanta
Answer: Sanya Richards-Ross, Marlo Hampton, Drew Sidora, Kenya Moore and Shereé Whitfield
Question: Do they differentiate insights where they are dealing with learned or engineered representations?
Evidence: We extract 5 Surface and Lexical features, namely sequence length in number of tokens, average word length, type-token ratio, and lexical to tokens ratio (ratio of adjectives, verbs, nouns, and adverbs to tokens). Bag of INLINEFORM0 -grams features are extracted on the word and POS level. We use frequency cut-offs of INLINEFORM1 for INLINEFORM2 -grams from 1 to 5 respectively for the smaller datasets and ten times higher for the Yahoo! and Amazon datasets. For POS INLINEFORM3 -grams we use cut-offs 10 for unigrams and 20 for bigrams and higher. For the Yahoo! and Amazon datasets we use cut-offs of INLINEFORM4 . The INLINEFORM5 -grams features are then also extracted using the hashing trick with the same cut-offs to reduce the final feature vector size when combined with other features. scikit-learn's BIBREF14 FeatureHasher is used with output vectors sizes of INLINEFORM6 INLINEFORM7 INLINEFORM8 for ngrams from INLINEFORM9 respectively and INLINEFORM10 INLINEFORM11 INLINEFORM12 are used for POS ngrams. We extract lexical and POS level Language model features based on external language models, namely sentence log probabilities, perplexities, and surprisal in units of bits. Building the language model and extracting the features is done by providing the path to the compiled binaries for kenlm BIBREF15 . Finally we extract N-gram Frequency Quantile Distribution features with the same cut-offs as in the bag of ngrams features, with 4 quantiles and an OOV quantile. NLTK BIBREF16 is used for tokenization and POS tagging.
We extracted two features that use a learned representation: Firstly, we get a sentence embedding feature that is built by averaging the word embeddings of an input sentence. Secondly, we extract a fastText representation using the fastText library with the same parameters as reported in Joulin et al. joulin2016bag.
We extract 5 Surface and Lexical features, namely sequence length in number of tokens, average word length, type-token ratio, and lexical to tokens ratio (ratio of adjectives, verbs, nouns, and adverbs to tokens).
We extracted two features that use a learned representation: Firstly, we get a sentence embedding feature that is built by averaging the word embeddings of an input sentence. Secondly, we extract a fastText representation using the fastText library with the same parameters as reported in Joulin et al. joulin2016bag.
Answer: Yes.
Question: Why are airlines tickets so expensive, right now?
Answer: Post pandemic, airline travel has gotten more expensive due to all the major airlines having to cancel a lot of daily flights on popular routes. This is mainly due to staff shortages being faced by the industry. This has led to misalignment on the demand-supply front
Question: The first human case of Monkeypox was recorded in
Answer: Democratic Republic of the Congo(DRC)
Question: Describe ways to deal with naughty kids as a parent
Answer: Well, there is no 1 exact rule that applies to all, there are many things which impact how a kid behaves. But one of the main things to remember is kids learn more by observation than when it's told to them. As a parent, an important thing to remember would be to practice what you preach. Usually, we put restrictions on screen time for kids, but remember even we are answerable to them when we spend too much time on gadgets than with people around us. We expect kids to respect us, so the same holds good for us, try to behave with them the way you to see them behave with you. Try to dedicate a few hours to spend time with them, remember it's the quality and not the quantity that matters here. Try not to correct them for everything they do, and try to set a boundary on things you can let go of. It's not a kid's job to deal with kids after all so you need to have some patience to understand them and then try to be understood.
Question: Which numbers between 1 to 10 are prime numbers and which are composite numbers?
Answer: 2,3,5,7 are prime numbers. 4,6,8,9,10 are composite numbers.
Question: Given a reference text about Ingwelala, tell me which game farms are on the reserve.
Evidence: Ingwelala is a private stakeholder nature reserve on the bank of the Nhlaralumi River in the heart of the lowveld, about 65 km from the town of Hoedspruit. The reserve of some 3,000 hectares forms part of the Bushbuckridge Municipality, and adjoins the Umbabat Nature Reserve and the unfenced Kruger National Park.

Ingwelala includes the following game farms:

Argyle – 1,499 ha registered as Ingwelala Shareblock Ltd.
Buffelsbed – 1,018 ha registered as Buffelsbed Shareblock Ltd.
Goedehoop – 372 ha registered as Ingwelala Holdings Ltd.
Si Bon – 257 ha registered as Si Bon Property Holdings Ltd
Answer: Argyle, Buffelsbed, Goedehoop, and Si Bon are the game farms on Ingwelala.
Question: What is the weather like in New Jersey?
Answer: New Jersey is a state in the northeastern part of the United Sates of America. New Jersey borders New York state to the north and east, Pennsylvania to the south and west, and Delaware to the south. So, New Jersey's climate is similar to these other states. 

Summers in New Jersey are relatively hot and humid, Springs and Falls are usually tempered, and Winters vary greatly in terms of snowfall and temperatures. 

New Jersey, being in the northeastern part of the United States, is also subject to a Nor'easter weather events. A Nor’easter is a storm along the East Coast of North America, so called because the winds over the coastal area are typically from the northeast. These storms may occur at any time of year but are most frequent and most violent between September and April. When Nor'easter occurs in the Winter, it can bring a lot of snow to New Jersey, especially the northern part of the state.
Question: What is the central limit theorem
Evidence: In probability theory, the central limit theorem (CLT) establishes that, in many situations, for identically distributed independent samples, the standardized sample mean tends towards the standard normal distribution even if the original variables themselves are not normally distributed.

The theorem is a key concept in probability theory because it implies that probabilistic and statistical methods that work for normal distributions can be applicable to many problems involving other types of distributions.

This theorem has seen many changes during the formal development of probability theory. Previous versions of the theorem date back to 1811, but in its modern general form, this fundamental result in probability theory was precisely stated as late as 1920, thereby serving as a bridge between classical and modern probability theory.
Answer: For example, suppose that a sample is obtained containing many observations, each observation being randomly generated in a way that does not depend on the values of the other observations, and that the arithmetic mean of the observed values is computed. If this procedure is performed many times, the central limit theorem says that the probability distribution of the average will closely approximate a normal distribution.

The central limit theorem has several variants. In its common form, the random variables must be independent and identically distributed (i.i.d.). In variants, convergence of the mean to the normal distribution also occurs for non-identical distributions or for non-independent observations, if they comply with certain conditions.

The earliest version of this theorem, that the normal distribution may be used as an approximation to the binomial distribution, is the de Moivre–Laplace theorem.
Question: How do they obtain the logical forms of their questions in their dataset?
Evidence: We crowdsourced multiple-choice questions in two parts, encouraging workers to be imaginative and varied in their use of language. First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., “If a surface has more friction, then an object will travel slower”), and asked to enter two objects, people, or situations to compare. They then created a question, guided by a large number of examples, and were encouraged to be imaginative and use their own words. The results are a remarkable variety of situations and phrasings (Figure FIGREF4 ).
Second, the LFs were elicited using a novel technique of reverse-engineering them from a set of follow-up questions, without exposing workers to the underlying formalism. This is possible because of the constrained space of LFs. Referring to LF templates (1) and (2) earlier (Section SECREF13 ), these questions are as follows:
From this information, we can deduce the target LF ( INLINEFORM0 is the complement of INLINEFORM1 , INLINEFORM2 , we arbitrarily set INLINEFORM3 =world1, hence all other variables can be inferred). Three independent workers answer these follow-up questions to ensure reliable results.
First, workers were given a seed qualitative relation q+/-( INLINEFORM0 ) in the domain, expressed in English (e.g., “If a surface has more friction, then an object will travel slower”), and asked to enter two objects, people, or situations to compare. They then created a question, guided by a large number of examples, and were encouraged to be imaginative and use their own words.
Second, the LFs were elicited using a novel technique of reverse-engineering them from a set of follow-up questions, without exposing workers to the underlying formalism. This is possible because of the constrained space of LFs. Referring to LF templates (1) and (2) earlier (Section SECREF13 ), these questions are as follows:

From this information, we can deduce the target LF ( INLINEFORM0 is the complement of INLINEFORM1 , INLINEFORM2 , we arbitrarily set INLINEFORM3 =world1, hence all other variables can be inferred).
Answer: The answers are shown as follows:
*  workers were given a seed qualitative relation
* asked to enter two objects, people, or situations to compare
* created a question, guided by a large number of examples
* LFs were elicited using a novel technique of reverse-engineering them from a set of follow-up questions
Question: What are some different types of used clothing stores?
Answer: Used clothing can be purchased in a variety of settings. There are second hand or thrift stores. These often rely on donations of used clothing from community members. Sometimes the organizations running these stores uses some of the proceeds to run community building or religious programming. There are also second hand stores that buy used clothing, to sell at a premium. Some antique stores also have sections for vintage clothing. There are also online retailers of vintage or other used clothing.
Question: Tell me whether these Pokemon are ice or rock types: Vulpix, Dewgong, Cloyster, Geodude, Golem, Onix
Answer: Vulpix: Ice
Dewgong: Ice
Cloyster: Ice
Geodude: Rock
Golem: Rock
Onix: Rock
Question: What is the National Payments Corporation of India?
Answer: The National Payments Corporation of India is an umbrella organization for operating retail payments and settlement systems in India, is an initiative of the Reserve Bank of India (RBI) and Indian Banks’ Association (IBA) under the provisions of the Payment and Settlement Systems Act, 2007, for creating a robust Payment & Settlement Infrastructure in India. It was created by RBI for operating retail payments and settlement systems in India.

Founded in December 2008, the NPCI is a not-for-profit organisation registered under Section 8 of the Companies Act 2013, established by the Reserve Bank of India and Indian Banks' Association. The organisation is owned by a consortium of major banks, and has been promoted by the country's central bank, the Reserve Bank of India. The NPCI was incorporated in December 2008 and the Certificate of Commencement of Business was issued in April 2009. The authorised capital has been pegged at ₹3 billion (US$38 million) and paid-up capital is ₹1 billion (US$13 million).