DataHammer/scimrc huggingface dataset:
Question: Where is the source code of  the Typology-manipulated Dataset from?
Evidence: There are various types of typology in languages. For example, in English the typology order is subject-verb-object (SVO) order, but in Japanese and Korean the order is subject-object-verb (SOV). We construct a typology-manipulated dataset to examine if the typology order of the training data influences the transfer learning results. If the model only learns the semantic mapping between different languages, changing English typology order from SVO to SOV should improve the transfer ability from English to Japanese. The method used to generate datasets is the same as BIBREF21.
The source code is from a GitHub repository named Shaul1321/rnn_typology, which labels given sentences to CoNLL format with StanfordCoreNLP and then re-arranges them greedily.
Table TABREF23 shows that when we change the English typology order to SOV or OSV order, the performance on Korean is improved and worsen on English and Chinese, but very slightly. The results show that the typology manipulation on the training set has little influence. It is possible that multi-BERT normalizes the typology order of different languages to some extent.
The source code is from a GitHub repository named Shaul1321/rnn typology, which labels given sentences to CoNLL format with StanfordCoreNLP and then re-arranges them greedily.
Answer: A GitHub repository named Shaul1321/rnn typology.
Question: What model do they use to train the dataset with extended POS Tags?
Evidence: In the research field of Natural Language Processing (NLP) there are several tasks that contribute to understanding natural text. These tasks can manipulate natural language, such as tokenization process, and consequently can be used in other implementations, in order to extract syntactic or semantic information. One such task for syntactic components is Part of Speech Tagging (POS Tagging). Part of Speech Tagging in corpus linguistics is a process where a word is assigned with a label of the grammatical term, given the context it appears in. In many languages, POS Tagging models achieve an accuracy of 96 to 97 percent BIBREF0.
Part of Speech Tagging for highly inflective languages, such as Greek is quite a difficult task. In the Greek Language, words can have different morphological forms, depending on the part of speech (verbs have up to ten different forms). For that purpose, there is a need for a tagset that can support morphological features for improvement of Greek POS Tagging BIBREF1.
Another main task for extracting semantic information is Named Entity Recognition (NER). Named Entity Recognition is a process where a word or a set of words reference to a world object. Most Natural Language Processing models classify named entities that describe people, locations, organizations, following the ENAMEX type or can be more complex by detecting numerical types, like percentages (NUMEX) or dates (TIMEX) BIBREF2.
The greek Part of Speech Tagging and Named Entity Recognition models presented in this paper were developed using the spaCy library BIBREF3. SpaCy is an open source, Natural Language Processing library that supports a variety of tasks, including POS Tagging, Named Entity Recognition, Dependency Parsing, etc. SpaCy uses sophisticated neural network-based models for the implementation of Natural Language Processing components that achieve state-of-the-art results in many of these tasks.
In the following chapters the process for implementing Part of Speech Tagging and Named Entity Recognition for the Greek Language is explained. A dataset with extended POS Tags was found and matched to a set of morphological rules, according to a treebank. The dataset was then processed, fed to the spaCy model and used for training. Similarly, for Named Entity Recognition, datasets from different sources were compared to a custom set of rules for named entities. Finally, different experiments were conducted for evaluating the accuracy of the models.
A dataset with extended POS Tags was found and matched to a set of morphological rules, according to a treebank. The dataset was then processed, fed to the spaCy model and used for training.
Answer: SpaCy model.
Question: What is Pinot Noir?
Evidence: Pinot noir (French: [pino nwaʁ]) is a red-wine grape variety of the species Vitis vinifera. The name may also refer to wines created predominantly from pinot noir grapes. The name is derived from the French words for pine and black. The word pine alludes to the grape variety having tightly clustered, pinecone–shaped bunches of fruit.

Pinot noir is grown around the world, mostly in cooler climates, and the variety is chiefly associated with the Burgundy region of France. Pinot noir is now used to make red wines around the world, as well as champagne, sparkling white wines such as the Italian Franciacorta, and English sparkling wines. Regions that have gained a reputation for red pinot noir wines include the Willamette Valley of Oregon; the Carneros, Central Coast, Sonoma Coast, and Russian River AVAs of California; the Elgin and Walker Bay wine regions of South Africa; the Mornington Peninsula, Adelaide Hills, Great Southern, Tasmania, and Yarra Valley in Australia; and the Central Otago, Martinborough, and Marlborough wine regions of New Zealand. Pinot noir is the most planted varietal (38%) used in sparkling wine production in Champagne and other wine regions.
Answer: Pinot noir (French: [pino nwaʁ]) is a red-wine grape variety of the species Vitis vinifera. Pinot noir is grown around the world, mostly in cooler climates, and the variety is chiefly associated with the Burgundy region of France. Pinot noir is now used to make red wines around the world, as well as champagne, sparkling white wines such as the Italian Franciacorta, and English sparkling wines.
Question: Write a description of your favorite place to visit in San Francisco and why you like it so much.
Answer: My favorite place in San Francisco is the JFK promenade in Golden Gate Park, which is a dedicated pedestrian walkway in Golden Gate park. The promenade is decorated with street paintings, art exhibits and public sitting places. It also goes near iconic parts of Golden Gate Park such as the DeYoung Museum and the Academy of Sciences. I love that it is always bustling with people enjoying the natural beauty of the park and getting to experience San Francisco like the locals.
Question: According to the following passage, what was the cause of the Great Chicago Fire?
Evidence: The fire is claimed to have started at about 8:30 p.m. on October 8, in or around a small barn belonging to the O'Leary family that bordered the alley behind 137 W. DeKoven Street. The shed next to the barn was the first building to be consumed by the fire. City officials never determined the cause of the blaze, but the rapid spread of the fire due to a long drought in that year's summer, strong winds from the southwest, and the rapid destruction of the water pumping system, explain the extensive damage of the mainly wooden city structures. There has been much speculation over the years on a single start to the fire. The most popular tale blames Mrs. O'Leary's cow, who allegedly knocked over a lantern; others state that a group of men were gambling inside the barn and knocked over a lantern. Still other speculation suggests that the blaze was related to other fires in the Midwest that day.
Answer: There has been much speculation to the cause of the Great Chicago fire, but city officials never determined the cause of the blaze. The most popular theory is that it was caused by Mrs. O'Leary's cow, who knocked over a lantern.
Question: What is the fastest train in the world?
Answer: Shanghai Maglev in Shanghai, China
Question: What toolkit do they use to tag the entire source sentences?
Evidence: In addition to using the Multi30K dataset as is (standard setup), we probe the ability of our models to address the three linguistic phenomena where additional context has been proved important (Section ): ambiguities, gender-neutral words and noisy input. In a controlled experiment where we aim to remove the influence of frequency biases, we degrade the source sentences by masking words through three strategies to replace words by a placeholder: random source words, ambiguous source words and gender unmarked source words. The procedure is applied to the train, validation and test sets. For the resulting dataset generated for each setting, we compare models having access to text-only context versus additional text and multimodal contexts. We seek to get insights into the contribution of each type of context to address each type of degradation.
In this setting (RND) we simulate erroneous source words by randomly dropping source content words. We first tag the entire source sentences using the spacy toolkit BIBREF26 and then drop nouns, verbs, adjectives and adverbs and replace these with a default BLANK token. By focusing on content words, we differ from previous work that suggests that neural machine translation is robust to non-content word noise in the source BIBREF27 .
In this setting (AMB), we rely on the MLT dataset BIBREF11 which provides a list of source words with multiple translations in the Multi30k training set. We replace ambiguous words with the BLANK token in the source language, which results in two language-specific datasets.
In this setting (PERS), we use the Flickr Entities dataset BIBREF28 to identify all the words that were annotated by humans as corresponding to the category person. We then replace such source words with the BLANK token.
The statistics of the resulting datasets for the three degradation strategies are shown in Table TABREF10 . We note that RND and PERS are the same for language pairs as the degradation only depends on the source side, while for AMB the words replaced depend on the target language.
We first tag the entire source sentences using the spacy toolkit (Honnibal and Montani, 2017) and then drop nouns, verbs, adjectives and adverbs and replace these with a default BLANK token.
Answer: The spacy toolkit.
Question: What is the least common birthday in the United States?
Evidence: In the United States between 1973 and 1999, September 16 is the most common birthday in the United States and December 25 the least common birthday (other than February 29, because of leap years). In 2011, October 5 and 6 were reported as the most frequently occurring birthdays.
Answer: The least common birthday in the United States between 1973 and 1999 was February 29 because of leap years.
Question: Which existing language ID systems are tested?
Evidence: For the benchmarks, we selected five systems. We picked first the langid.py library which is frequently used to compare systems in the literature. Since our work is in neural-network LID, we selected two neural network systems from the literature, specifically the encoder-decoder EquiLID system of BIBREF6 and the GRU neural network LanideNN system of BIBREF10. Finally, we included CLD2 and CLD3, two implementations of the Naïve Bayes LID software used by Google in their Chrome web browser BIBREF4, BIBREF0, BIBREF8 and sometimes used as a comparison system in the LID literature BIBREF7, BIBREF6, BIBREF8, BIBREF2, BIBREF10. We obtained publicly-available implementations of each of these algorithms, and test them all against our three datasets. In Table TABREF33, we report each algorithm's accuracy and F1 score, the two metrics usually reported in the LID literature. We also included precision and recall values, which are necessary for computing F1 score. And finally we included the speed in number of messages handled per second. This metric is not often discussed in the LID literature, but is of particular importance when dealing with a massive dataset such as ours or a massive streaming source such as Twitter.
For the benchmarks, we selected five systems. We picked first the langid.py library which is frequently used to compare systems in the literature. Since our work is in neural-network LID, we selected two neural network systems from the literature, specifically the encoder-decoder EquiLID system of BIBREF6 and the GRU neural network LanideNN system of BIBREF10. Finally, we included CLD2 and CLD3, two implementations of the Naïve Bayes LID software used by Google in their Chrome web browser BIBREF4, BIBREF0, BIBREF8 and sometimes used as a comparison system in the LID literature BIBREF7, BIBREF6, BIBREF8, BIBREF2, BIBREF10.
Answer: The answers are shown as follows:
* langid.py library
* encoder-decoder EquiLID system
* GRU neural network LanideNN system
* CLD2
* CLD3
Question: Write 5 distinct, unique and creative statements that help someone think about Friedrich Nietzsche's "Beyond Good & Evil", which critically analyzes the idea of binaries in complexity
Answer: 1. Black and white movies contain a high number of greyscale pixels.
2. The scientific community in the 21st century embraces that for some complex diagnoses, the evaluation should be placed on a spectrum.
3. The point-forward is an especially valuable basketball position that is never found on an official roster, which only contains guards and forwards. 
4. Those who write intelligently are often verbose and those who write simply are often grammatically incorrect; the best sentences are simple and intelligent. 
5.  The wealthiest people went to elite universities where in some cases 90 percent of students are denied but some say "C students run the world".
Question: What are the things I can do to pass time while waiting for my appointment at a DMV office? I don’t want to use my phone.
Answer: Waiting for your turn at a DMV office, or for that matter, any office can be a boring and exhausting affair. Here are a few things you could do to pass some time. 1. Check out the different kinds of shoes people are wearing. Can you guess the kind of profession they have? 2. Snoop in on people’s conversations. How does their life fee different from yours? 3. Practice deep breathing. It helps you relax. 4. Recall past times from a great vacation you had. What made you like it? 5. Feel your teeth with your tongue and count them.
Question: What's the first 3 things you do when you wake up in the morning ?
Answer: Acknowledge that I am about to wake up
Refuse to wake up 
Accept that time cannot be stopped and wake up
Question: What are the key points that can be extracted regarding antisemitism from the below text?
Evidence: In 1998, Ignatz Bubis, a leader of the German Jewish community, pointed to a "spreading intellectual nationalism" that made him fear a revival of German antisemitism. Others point to Germany's growing Muslim population, both the Turkish "guest workers" who began to arrive in the 1950s, and the large wave of migrants from the Muslim countries who arrive during the European migrant crisis that began in 2015. In 2002, the historian Julius Schoeps said that "resolutions by the German parliament to reject antisemitism are drivel of the worst kind" and "all those ineffective actions are presented to the world as a strong defense against the charge of antisemitism. The truth is: no one is really interested in these matters. No one really cares."
Answer: 1. A prominent member of the German Jewish community, Ignatz Bubis, expressed his concern about the resurgence of antisemitism in his country in 1998 by pointing to a "spreading intellectual nationalism."
2. Others highlight the rising Muslim population in Germany, which includes both the 1950s-era "guest workers" from Turkey and the massive influx of Muslim refugees during the 2015–2016 European refugee crisis.
3. Julius Schoeps, a historian, stated in 2002 "resolutions by the German parliament to reject antisemitism are drivel of the worst kind and all those ineffective actions are presented to the world as a strong defense against the charge of antisemitism.The truth is: no one is really interested in these matters. No one really cares".
Question: How are the models evaluated？
Evidence: We evaluated the produced ELMo models for all languages using two evaluation tasks: a word analogy task and named entity recognition (NER) task. Below, we first shortly describe each task, followed by the evaluation results.
We evaluated the produced ELMo models for all languages using two evaluation tasks: a word analogy task and named entity recognition (NER) task.
Answer: They evaluated the produced ELMo models for all languages using two evaluation tasks: a word analogy task and named entity recognition (NER) task.
Question: What is the best movie of all time
Answer: Shawshank Redemption
Question: What is the significance of Thanks Giving.
Answer: The first American Thanksgiving was celebrated 43 years later in 1621 at the site of Plymouth Plantation, in Massachusetts. The Wampanoag Native Americans helped the pilgrims who arrived in Massachusetts cultivate the land and fish, saving them from starvation. At harvest time in the winter of 1621, they were very thankful that they had a good crop of food to eat during the coming winter. They thanked God and the Wampanoags for teaching them how to grow crops. The Thanksgiving holiday became a national phenomenon during the Civil War and a true national holiday during FDR's presidency.
Question: What is a weed wacker?
Answer: A weed wacker is a stringed trimmer that is powered by a better or by gasoline.  The string spins at a very fast circular velocity for cutting.  It is used for cutting weeds, grass and making the edge of lawns look uniform.  Its important to where proper safety equipment when using a weed wacker to prevent injury to your legs from spinning plastic string.
Question: How much text corpora do they present preliminary experimental results on?
Evidence: A clinician can look at a patient's electronic health record (EHR) and not only decide whether the patient has diabetes but also produce a succinct summary of the clinical evidence. Replicating this feat with computational tools has been the focus of much research in clinical informatics. There are major initiatives underway to codify clinical knowledge into formal representations, most often as deterministic rules that can be applied in a semi-automated fashion BIBREF0 . However, representing the intuitive judgments of human experts can be challenging, particularly when the formal system does not match the expert's knowledge. For example, many deterministic disease classifiers used in clinical informatics rely heavily upon administrative codes not available at time of diagnosis. Further, developing and testing such systems is time- and labor-intensive.
We propose instead a lightweight information theoretic framework for codifying informal human knowledge and then use it to extract interpretable latent topics from text corpora. For example, to discover patients with diabetes in a set of clinical notes, a doctor can begin by specifying disease-specific anchor terms BIBREF1 , BIBREF2 , such as “diabetes” or “insulin.” Our framework then uses these to help discover both latent topics associated with diabetes and records in which diabetes-related topics occur. The user can then add (or remove) additional anchor terms (e.g., “metformin”) to improve the quality of the learned (diabetes) topics.
In this workshop paper, we introduce a simple approach to anchored information theoretic topic modeling using a novel combination of Correlation Explanation (CorEx) BIBREF3 and the information bottleneck BIBREF4 . This flexible framework enables the user to leverage domain knowledge to guide exploration of a collection of documents and to impose semantics onto latent factors learned by CorEx. We present preliminary experimental results on two text corpora (including a corpus of clinical notes), showing that anchors can be used to discover topics that are more specific and relevant. What is more, we demonstrate the potential for this framework to perform weakly supervised learning in settings where labeling documents is prohibitively expensive BIBREF5 , BIBREF6 .
With respect to interpretable machine learning, our contributions are twofold. First, our framework provides a way for human users to share domain knowledge with a statistical learning algorithm that is both convenient for the human user and easily digestible by the machine. Second, our experimental results confirm that the introduction of simple anchor words can improve the coherence and human interpretability of topics discovered from data. Both are essential to successful and interactive collaboration between machine learning and human users.
We present preliminary experimental results on two text corpora (including a corpus of clinical notes), showing that anchors can be used to discover topics that are more specific and relevant.
Answer: Two text corpora.
Question: Why is the uncertainty layer used indeed?
Evidence: In the process of forming the latent space in our proposed model, VAE formulates the reconstruction
loss through the latent vector sampled from the embedded input distribution. Since classification was
performed through the distance in the latent space, we tried to improve the classification accuracy by
lowering the uncertainty of the model. By adding the calculation process to logits without changing
the structure of the model, it was possible to predict uncertainty and increase the accuracy by making
the model robust against adversarial perturbations. Furthermore, since the model is not trained to
handle uncertain information, it not only increases accuracy but also makes similar features exist in
similar positions in the latent space. The embedded dataset visualized with t-SNE is illustrated in
Figure 10 showing that data of the same class forms more aggregated clusters in latent space when
the uncertainty layer is applied. Through this latent space property, prototypes form more obvious
observations that can express the whole dataset better, as Figure 11, because prototype distributions
are learned through distances at latent space.
Answer: Since VAE trains the model based on sampling, we hypothesized that the uncertainty in the classification process through the location in the latent space may increase by repeating the sampling process. Therefore, we tried to make the latent space as suitable for classification with VAE properties as possible. As a result, MNIST and FashionMNIST showed a 1% performance improvement. The motivation for using this uncertainty layer is briefly mentioned in Appendix A.5.
While applying the uncertainty layer, the configuration of the latent space is well clustered by class so that it is more suitable for classification, shown in Figure 10 of the Appendix. If you look at the part where the uncertainty layer is not used in Figure 10 (b), you can see that the pink cluster and the surrounding cluster are a little bit mixed. On the other hand, if you look at the part where the uncertainty layer is used in Figure 10 (b), you can see that each cluster is neatly disentangled. Since the locations of the prototypes are determined for accurate classification in this latent space, the prototypes are positioned at a certain point that is better formed when the uncertainty layer is used as shown in Figure 11. This part was inspired by [1] to create certain data by shifting uncertain data from the latent space of VAE in a direction to reduce uncertainty.
We aim to reduce the uncertainty of the classification process to make the configuration of the latent space and the location of the prototype more suitable for classification. Therefore, we think that the uncertainty layer can show advantages in other complex datasets. It seems that this was not well expressed in the paper, so we will mention the motivation in section 3.3 Using uncertainty.