DataHammer/scimrc huggingface dataset:
Question: In the modified IMP, when using clean labels for training, where is the randomness coming from? How is the expectation defined?
Evidence: In setting i, we use an untrained CNN as the dense model, and solve the DIP optimization (Ulyanov et al., 2018) θ ∗= arg minθ E(fθ(z); ˜x), x∗= fθ ∗(z), (1) where E(; ) denotes the Mean Square Error (MSE), x˜ is the corrupted version of the image x ∈ R3×H×W , fθ represents the dense model f with initial parameter θ and z is the random tensor that z ∈ RC×H×W . We choose the same hourglass architecture with skip connections as in (Ulyanov et al., 2018), to be the dense model by default.
Answer: The randomness only comes from the input random tensor z (defined in Equation 1). In the single-image based IMP, there are no expectations. While in the multi-image based IMP, the expectation is the average loss value of multiple images.
Question: What matrics are used for evaluation?
Evidence: We use the following three evaluation metrics:
Phoneme Error Rate (PER) is the Levenshtein distance between the predicted phoneme sequences and the gold standard phoneme sequences, divided by the length of the gold standard phoneme sequences.
Word Error Rate (WER) is the percentage of words in which the predicted phoneme sequence does not exactly match the gold standard phoneme sequence.
Word Error Rate 100 (WER 100) is the percentage of words in the test set for which the correct guess is not in the first 100 guesses of the system.
In system evaluations, WER, WER 100, and PER numbers presented for multiple languages are averaged, weighting each language equally BIBREF13 .
It would be interesting to compute error metrics that incorporate phoneme similarity, such as those proposed by hixon2011phonemic. PER weights all phoneme errors the same, even though some errors are more harmful than others: and are usually contrastive, whereas and almost never are. Such statistics would be especially interesting for evaluating a multilingual system, because different languages often map the same grapheme to phonemes that are only subtly different from each other. However, these statistics have not been widely reported for other g2p systems, so we omit them here.
We use the following three evaluation metrics: Phoneme Error Rate (PER) is the Lev- enshtein distance between the predicted phoneme sequences and the gold standard phoneme sequences, divided by the length of the gold standard phoneme sequences. Word Error Rate (WER) is the percentage of words in which the predicted phoneme sequence does not exactly match the gold standard phoneme sequence. Word Error Rate 100 (WER 100) is the percentage of words in the test set for which the correct guess is not in the first 100 guesses of the system.
Answer: Phoneme Error Rate (PER), Word Error Rate (WER) , and Word Error Rate 100 (WER 100).
Question: How often are the newspaper websites crawled daily?
Evidence: The Logoscope retrieves newspaper articles from several RSS feeds in French on a daily basis. The newspaper articles are preprocessed such that only the journalistic content is kept. The articles are then segmented into paragraphs and word forms. The resulting forms are filtered based on an exclusion list (French words found in several lexicons and corpora). They are then reordered in such a way that those words which are the most likely new word candidates appear on top, using a supervised classification method which will be described more in detail in Section SECREF71 .
The Logoscope retrieves newspaper articles from several RSS feeds in French on a daily basis.
Answer: The answers are shown as follows:
* RSS feeds in French on a daily basis
Question: What are three new proposed architectures?
Evidence: Let us propose two further extensions of this baseline architecture. To improve reproducibility of the research the code of the studied models is open. Both extensions aim to improve the quality of information decomposition within the latent representation. In the first one, shown in Figure FIGREF12, a special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information. The loss of this discriminator is defined as
The second extension of the baseline architecture does not use an adversarial component $D_z$ that is trying to eradicate information on $c$ from component $z$. Instead, the system, shown in Figure FIGREF16 feeds the "soft" generated sentence $\tilde{G}$ into encoder $E$ and checks how close is the representation $E(\tilde{G} )$ to the original representation $z = E(x)$ in terms of the cosine distance. We further refer to it as shifted autoencoder or SAE. Ideally, both $E(\tilde{G} (E(x), c))$ and $E(\tilde{G} (E(x), \bar{c}))$, where $\bar{c}$ denotes an inverse style code, should be both equal to $E(x)$. The loss of the shifted autoencoder is
We also study a combination of both approaches described above, shown on Figure FIGREF17.
In the first one, shown in Figure FIGREF12, a special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information.
The second extension of the baseline architecture does not use an adversarial component $D_z$ that is trying to eradicate information on $c$ from component $z$. Instead, the system, shown in Figure FIGREF16 feeds the "soft" generated sentence $\tilde{G}$ into encoder $E$ and checks how close is the representation $E(\tilde{G} )$ to the original representation $z = E(x)$ in terms of the cosine distance. We further refer to it as shifted autoencoder or SAE.
We also study a combination of both approaches described above, shown on Figure FIGREF17.
Answer: The answers are shown as follows:
* special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information
* shifted autoencoder or SAE
* combination of both approaches
Question: Name five Shakespeare plays or dramas.
Answer: Shakespeare has written approximately 39 dramatic works and collaborated on many more. His most renowned five dramas and plays include:
A Midsummer Night's Dream
Hamlet
Romeo and Juliet
Macbeth
Julius Caesar
Question: What style of music is the Grateful Dead?
Answer: The Grateful Dead span many genres and have bene influenced by many styles of music, which is evident in the variety one experiences across albums, live shows and eras throughout the band's history. They have also had many band members come and go and each have brought a different influence to the bands repertoire. Many evident styles are folk, jazz, ragtime, rock and roll, and country. The broad spectrum of styles is likely the reason they have such a large following.
Question: What languages are explored in this paper?
Evidence: In our experiment, we use a conversational Mandarin-English code-switching speech corpus called SEAME Phase II (South East Asia Mandarin-English). The data are collected from spontaneously spoken interviews and conversations in Singapore and Malaysia by bilinguals BIBREF21 . As the data preprocessing, words are tokenized using Stanford NLP toolkit BIBREF22 and all hesitations and punctuations were removed except apostrophe. The split of the dataset is identical to BIBREF9 and it is showed in Table TABREF6 .
In our experiment, we use a conversational Mandarin-English code-switching speech corpus called SEAME Phase II (South East Asia Mandarin-English). 
Answer: The answers are shown as follows:
* Mandarin
* English
Question: What should I do if I disagree with a friend, co-worker or family member on something I feel passionate about, such as politics?
Answer: Seeing things differently from someone you interact with regularly or even care about can be a hard thing to experience. It can also a wonderful thing. Each person on this planet is different and their beliefs are based on their culture, upbringing, personal experiences, education, surroundings, etc. Recognizing this and truly listening to the other persons perspective is important. In many cases, when a difference of opinion escalates, it is because one or both people involved did not listen or respect the other. If you are being close-minded, then you could be missing out on learning something new.
If both parties still disagree and it is not something completely against your beliefs, culture, or ethics, that is OK too. Agree to disagree and move on. Life is too short to be angry at people you care about just because you do not see eye to eye.
Question: List five titles by Kevin Kwan.
Answer: Sex and Vanity, Crazy Rich Asians, China Rich Girlfriend, Rich People Problems, Luck: The Essential Guide
Question: What are the key components of an MLOps pipeline?
Answer: MLOps unlike traditional DevOps in software development is not only concerned with the management of code but must additionally account for data and models. A functional MLOps pipeline must be able to link together, and track changes, to the code used to develop a model and prepare the data, the data used for training and validation of the model and finally the model object itself. Therefore, an MLOps pipeline must consist of, at minimum, of the following components: A source code repository, a data versioning tool, a model repository, a CI/CD pipeline, a compute environment for training and development, model serving and finally, monitoring capabilities both for the data inputs and model outputs.
Question: Based on this paragraph, which two objects account for the majority of the mass of the Solar System?
Evidence: The Solar System is the gravitationally bound system of the Sun and the objects that orbit it. It formed 4.6 billion years ago from the gravitational collapse of a giant interstellar molecular cloud. The vast majority (99.86%) of the system's mass is in the Sun, with most of the remaining mass contained in the planet Jupiter. The planetary system around the Sun contains eight planets. The four inner system planets—Mercury, Venus, Earth and Mars—are terrestrial planets, being composed primarily of rock and metal. The four giant planets of the outer system are substantially larger and more massive than the terrestrials. The two largest, Jupiter and Saturn, are gas giants, being composed mainly of hydrogen and helium; the next two, Uranus and Neptune, are ice giants, being composed mostly of volatile substances with relatively high melting points compared with hydrogen and helium, such as water, ammonia, and methane. All eight planets have nearly circular orbits that lie near the plane of Earth's orbit, called the ecliptic.
https://en.wikipedia.org/wiki/Solar_System
Answer: Based on this paragraph, the Sun and Jupiter are the two objects that account for the majority of the mass of the Solar System, with the Sun responsible for 99.86% of the total.
Question: What are the first six things I have to do in order to build a house?
Answer: 1. Buy the Land
2. Determine where your utilities will come from, water, electricity, and sewage. Call the electric company to find out how far into the land they will pull electricity and where it will be pulled from
3. Have a perk test done for sewage if you have to install a septic tank
4. Have the house plans drawn up by an architect on the specific place you want to build on your land 
5. Submit your drawings to a lender for a construction loan, unless you are paying cash
6. Apply for permits with the county, your builder will do this
Question: Who can improve the performance of the rnn-based method better? User embeddings or user type embeddings?
Evidence: News portals often allow their readers to comment on articles, in order to get feedback, engage their readers, and build customer loyalty. User comments, however, can also be abusive (e.g., bullying, profanity, hate speech), damaging the reputation of news portals, making them liable to fines (e.g., when hosting comments encouraging illegal actions), and putting off readers. Large news portals often employ moderators, who are frequently overwhelmed by the volume and abusiveness of comments. Readers are disappointed when non-abusive comments do not appear quickly online because of moderation delays. Smaller news portals may be unable to employ moderators, and some are forced to shut down their comments.
In previous work BIBREF0 , we introduced a new dataset of approx. 1.6M manually moderated user comments from a Greek sports news portal, called Gazzetta, which we made publicly available. Experimenting on that dataset and the datasets of Wulczyn et al. Wulczyn2017, which contain moderated English Wikipedia comments, we showed that a method based on a Recurrent Neural Network (rnn) outperforms detox BIBREF1 , the previous state of the art in automatic user content moderation. Our previous work, however, considered only the texts of the comments, ignoring user-specific information (e.g., number of previously accepted or rejected comments of each user). Here we add user embeddings or user type embeddings to our rnn-based method, i.e., dense vectors that represent individual users or user types, similarly to word embeddings that represent words BIBREF2 , BIBREF3 . Experiments on Gazzetta comments show that both user embeddings and user type embeddings improve the performance of our rnn-based method, with user embeddings helping more. User-specific or user-type-specific scalar biases also help to a lesser extent.
Experiments on Gazzetta comments show that both user embeddings and user type embeddings improve the performance of our rnn-based method, with user embeddings helping more.
Answer: User embeddings.
Question: What is an oligopoly?
Evidence: An oligopoly (from Greek ὀλίγος, oligos "few" and πωλεῖν, polein "to sell") is a market structure in which a market or industry is dominated by a small number of large sellers or producers. Oligopolies often result from the desire to maximize profits, which can lead to collusion between companies. This reduces competition, increases prices for consumers, and lowers wages for employees.

Many industries have been cited as oligopolistic, including civil aviation, electricity providers, the telecommunications sector, Rail freight markets, food processing, funeral services, sugar refining, beer making, pulp and paper making, and automobile manufacturing.

Most countries have laws outlawing anti-competitive behavior. EU competition law prohibits anti-competitive practices such as price-fixing and manipulating market supply and trade among competitors. In the US, the United States Department of Justice Antitrust Division and the Federal Trade Commission are tasked with stopping collusion. However, corporations can evade legal consequences through tacit collusion, as collusion can only be proven through actual and direct communication between companies.

It is possible for oligopolies to develop without collusion and in the presence of fierce competition among market participants. This is a situation similar to perfect competition, where oligopolists have their own market structure. In this situation, each company in the oligopoly has a large share in the industry and plays a pivotal, unique role. With post-socialist economies, oligopolies may be particularly pronounced. For example in Armenia, where business elites enjoy oligopoly, 19% of the whole economy is monopolized (BEEPS 2009 database), making it the most monopolized country in the region. 
Answer: Oligopoly is a market structure that is along the spectrum of competition ranging from monopoly to perfect competition. Oligopoly is a market structure that has few sellers or producers. Oligopoly is similar to monopoly except there are multiple sellers instead of just one single dominate producer.
Question: What are the four forums the data comes from?
Evidence: FLOAT SELECTED: Table 3: Test set results at the NP level in within-forum and cross-forum settings for a variety of different systems. Using either Brown clusters or gazetteers gives mixed results on cross-forum performance: only one of the improvements (†) is statistically significant with p < 0.05 according to a bootstrap resampling test. Gazetteers are unavailable for Blackhat and Nulled since we have no training data for those forums.
FLOAT SELECTED: Table 3: Test set results at the NP level in within-forum and cross-forum settings for a variety of different systems. Using either Brown clusters or gazetteers gives mixed results on cross-forum performance: only one of the improvements (†) is statistically significant with p < 0.05 according to a bootstrap resampling test. Gazetteers are unavailable for Blackhat and Nulled since we have no training data for those forums.
Answer: Darkode,  Hack Forums, Blackhat and Nulled.
Question: What year did the trek in Finding Farley end?
Evidence: Finding Farley is a 2009 documentary directed by Leanne Allison as she and her husband Karsten Heuer travel across Canada in the literary footsteps of the Canadian writer Farley Mowat.

Heuer, a biologist and author, had written a book on his experiences making the documentary Being Caribou, in which he and Allison traveled 1,500 kilometres (930 mi) by foot across Arctic tundra following a herd of 120,000 Porcupine caribou. After reading a draft of Heuer's account, Mowat invited them to visit him at his summer farm in Cape Breton Island.

Accompanied by their two-year-old son Zev and dog Willow, the couple left their home in Canmore in May 2007 for a 5,000 kilometres (3,100 mi), six-month trek east across Canada. From Canmore, 100 kilometres west of Calgary, they canoed to Hudson Bay, visiting many of the settings that Mowat wrote about in Never Cry Wolf, Lost in the Barrens and People of the Deer. From Hudson Bay, their plan was to travel by sea to northern Labrador, the setting of Mowat's stories such as The Serpent's Coil, Grey Seas Under, Sea of Slaughter and A Whale for the Killing. From Newfoundland and Labrador they planned a final journey by water, arriving at Cape Breton near the end of October. Finding Farley was the top film at the 2010 Banff Mountain Film Festival, receiving both the Grand Prize and People's Choice awards.
Answer: 2007.  Started in May 2007 and lasted 6 months.  Which would have been November 2007.
Question: What is the architecture of their model?
Evidence: Our model is based on a standard stacked character-based LSTM BIBREF4 with two layers, followed by a hidden layer and a final output layer with softmax activations. The only modification made to accommodate the fact that we train the model with text in nearly a thousand languages, rather than one, is that language embedding vectors are concatenated to the inputs of the LSTMs at each time step and the hidden layer before the softmax. We used three separate embeddings for these levels, in an attempt to capture different types of information about languages. The model structure is summarized in fig:model.
In our experiments we use 1024-dimensional LSTMs, 128-dimensional character embeddings, and 64-dimensional language embeddings. Layer normalization BIBREF5 is used, but no dropout or other regularization since the amount of data is very large (about 3 billion characters) and training examples are seen at most twice. For smaller models early stopping is used. We use Adam BIBREF6 for optimization. Training takes between an hour and a few days on a K40 GPU, depending on the data size.
Our model is based on a standard stacked character-based LSTM (Hochreiter and Schmidhuber, 1997) with two layers, followed by a hidden layer and a final output layer with softmax activations.
Answer: It is based on a standard stacked character-based LSTM (Hochreiter and Schmidhuber, 1997) with two layers, followed by a hidden layer and a final output layer with softmax activations.
Question: What are the models used to perform constituency and dependency parsing?
Evidence: Using an encoder-decoder backbone, our model may be regarded as an extension of the constituent parsing model of BIBREF18 as shown in Figure FIGREF4. The difference is that in our model both constituent and dependency parsing share the same token representation and shared self-attention layers and each has its own individual Self-Attention Layers and subsequent processing layers. Our model includes four modules: token representation, self-attention encoder, constituent and dependency parsing decoder.
Our Model ::: Constituent Parsing Decoder
Our Model ::: Dependency Parsing Decoder
Using an encoder-decoder backbone, our model may be regarded as an extension of the constituent parsing model of BIBREF18 as shown in Figure FIGREF4. The difference is that in our model both constituent and dependency parsing share the same token representation and shared self-attention layers and each has its own individual Self-Attention Layers and subsequent processing layers. Our model includes four modules: token representation, self-attention encoder, constituent and dependency parsing decoder.
Constituent Parsing Decoder
Dependency Parsing Decoder
Answer: The answers are shown as follows:
* token representation
* self-attention encoder,
* Constituent Parsing Decoder
*  Dependency Parsing Decoder
Question: What is the difference between this paper and that brought by a paper by Wu et al., in 2016？
Evidence: Recently, it has gained lots of interests to jointly learn the embeddings of knowledge graph and text information. There are several methods using textual information to help KG representation learning.
 BIBREF3 represent an entity as the average of its word embeddings in entity name, allowing the sharing of textual information located in similar entity names.
 BIBREF5 jointly embed knowledge and text into the same space by aligning the entity name and its Wikipedia anchor, which brings promising improvements to the accuracy of predicting facts. BIBREF6 extend the joint model and aligns knowledge and words in the entity descriptions. However, these two works align the two kinds of embeddings on word level, which can lose some semantic information on phrase or sentence level.
 BIBREF25 also represent entities with entity names or the average of word embeddings in descriptions. However, their use of descriptions neglects word orders, and the use of entity names struggles with ambiguity. BIBREF7 jointly learn knowledge graph embeddings with entity descriptions. They use continuous bag-of-words and convolutional neural network to encode semantics of entity descriptions. However, they separate the objective functions into two energy functions of structure-based and description-based representations. BIBREF26 embeds both entity and relation embeddings by taking KG and text into consideration using CNN. To utilize both representations, they need further estimate an optimum weight coefficients to combine them together in the specific tasks.
Besides entity representation, there are also a lot of works BIBREF27 , BIBREF28 , BIBREF29 to map textual relations and knowledge base relations to the same vector space and obtained substantial improvements.
While releasing the current paper we discovered a paper by BIBREF30 proposing a similar model with attention mechanism which is evaluated on link prediction and triplet classification. However, our work encodes text description as a whole without explicit segmentation of sentences, which breaks the order and coherence among sentences.
While releasing the current paper we discovered a paper by [Wu et al., 2016] proposing a similar model with attention mechanism which is evaluated on link prediction and triplet classification. However, our work encodes text description as a whole without explicit segmentation of sentences, which breaks the order and coherence among sentences.
Answer: Their work encodes text description as a whole without explicit segmentation of sentences, which breaks the order and coherence among sentences.
Question: Identify which animal species is alive or extinct: Shastasaurus, Arctic Fox
Answer: Arctic Fox is alive, Shastasaurus is extinct.