DataHammer/scimrc huggingface dataset:
Question: What is the baseline for the experiments?
Evidence: FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.
BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.
BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently.
BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline.
BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset.
Answer: The answers are shown as follows:
* FastText
* BiLSTM
* BERT
Question: How to set the value of K in this work?
Evidence: MLP contains three layers - an input layer, an output layer and some hidden layers. Input layer receives the signal, the output layer gives a decision or prediction about the input and the computation of the MLP is conducted in the hidden layers. In our system, we use 100 layers. For weight optimization, we use Limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization algorithm.
SVM gives an optimal hyper-plane and it maximizes the margin between classes. We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped. For decision function shape, we use the original one-vs-one (ovo) decision function.
NBC is based on Bayes' Theorem which gives probability of an event occurrence based on some conditions related to that event. We use Multinomial Naive Bayes Classifier with smoothing parameter equals to 0.1. A zero probability cancels the effects of all the other probabilities.
Stochastic gradient descent optimizes an objective function with suitable smoothness properties BIBREF27. It selects few examples randomly instead of whole data for each iteration. We use 'L2' regularization for reduction of overfitting.
Gradient Boosting Classifier produces a prediction model consisting of weak prediction models. Gradient boosting uses decision trees. We use 100 boosting stages in this work.
K-NN is a supervised classification and regression algorithm. It uses the neighbours of the given sample to identify its class. K determines the number of neighbours needed to be considered. We set the value of K equals to 13 in this work.
RF is an ensemble learning technique. It constructs large number of decision trees during training and then predicts the majority class. We use 500 decision trees in the forest and "entropy" function to measure the quality of a split.
We set the value of K equals to 13 in this work.
Answer: The authors set the value of K equals to 13 in this work.
Question: Classify each of these as either motorcycle brands or car brands: Maruti Suzuki, Ducati, Triumph, Royal Enfield, Harley Davidson,  Mahindra, Tata, Skoda, Honda, Toyota, Hero, TVS, Yamaha, Suzuki, Kawasaki, Volkswagen, Ferrari, Lamborgini, Volvo, Rolls royce, Bajaj, Kia, Hyundai, Ford, Chevrolet, Crystler, Fiat, Hummer, Citreon, KTM
Answer: Motorcycle brands : Ducati, Triumph, Royal Enfield, Harley Davidson, TVS, Yamaha, Suzuki, Kawasaki,Bajaj,KTM, Hero, Honda
Car Brands: Maruti Suzuki, Mahindra, Tata, Skoda, Honda, Toyota,Volkswagen, Ferrari, Lamborgini, Volvo, Rolls royce,Kia, Hyundai, Ford, Chevrolet, Crystler, Fiat, Hummer, Citreon
Question: What are the deficiencies of recent research on clusters?
Evidence: Early research efforts, such as the TDT program BIBREF5 , have studied news clustering for some time. The problem of online monolingual clustering algorithms (for English) has also received a fair amount of attention in the literature. One of the earlier papers by aggarwal2006framework introduced a two-step clustering system with both offline and online components, where the online model is based on a streaming implementation of INLINEFORM0 -means and a bag-of-words document representation. Other authors have experimented with distributed representations, such as ahmed2011unified, who cluster news into storylines using Markov chain Monte Carlo methods, rehureklrec who used incremental Singular Value Decomposition (SVD) to find relevant topics from streaming data, and sato2017distributed who used the paragraph vector model BIBREF6 in an offline clustering setting.
More recently, crosslingual linking of clusters has been discussed by rupnik2016news in the context of linking existing clusters from the Event Registry BIBREF7 in a batch fashion, and by steinberger2016mediagist who also present a batch clustering linking system. However, these are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, rupnik2016news compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting. As detailed before, we adapted the cluster-linking dataset from rupnik2016news to evaluate our online crosslingual clustering approach. Preliminary work makes use of deep learning techniques BIBREF8 , BIBREF9 to cluster documents while learning their representations, but not in an online or multilingual fashion, and with a very small number of cluster labels (4, in the case of the text benchmark).
In our work, we studied the problem of monolingual and crosslingual clustering, having experimented several directions and methods and the impact they have on the final clustering quality. We described the first system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream.
However, these are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, Rupnik et al. (2016) compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting.
Answer: These are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, Rupnik et al. compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting.
Question: Given the following paragraph, where was Omar ibn Said born?
Evidence: Omar ibn Said was a real person, born and raised in Futa Toro, an Islamic state located in the part of West Africa that is now Senegal. His family was wealthy and he was highly educated as a Muslim scholar. He was captured by slavers in 1807 at the age of 37 and was taken to America to be sold in the Charleston slave market. Initially purchased by a harsh master, he escaped after two years and traveled to Fayetteville, North Carolina. There he was captured and jailed, but ultimately sold to planter James Owen. Owen was impressed by ibn Said's education, since very few slaves even knew how to read and write. Owen attempted to convert him to Christianity and provided him with a Bible and other books. He also urged him to write his memoirs, which he did in 1831. He wrote at least thirteen other Arabic documents, mostly on history and theology. Ibn Said lived until his mid-90s and died in 1864, still enslaved.
Answer: Omar ibn Said was born and raised in Futa Toro, an Islamic state located in the part of West Africa that is now Senegal.
Question: Extract the names of persons and countries in two different lists separated by commas: "Sunday’s blast has echoes of the car bombing that killed Darya Dugina, the daughter of influential ultra-nationalist philosopher Alexander Dugan in August 2022. Alexander Dugan is credited with being the architect, or “spiritual guide,” to Russia’s invasion of Ukraine. Dugina and Tatarsky moved in the same circles, and they had been photographed multiple times together"
Answer: Persons: Darya Dugina, Alexander Dugan, Tatarsky
Countries: Russia, Ukraine
Question: Will AI replace all human beings?
Answer: When I was writing down those words used to train an AI model, I feel funny. Some of my friends expressed concern that AI could soon replace humans and take away all of our jobs. However, I see things differently. I believe that AI has the potential to develop rapidly, and I'm excited to see how it will transform the world in my lifetime. If AI can create more resources, we may be able to live more comfortably and work fewer hours. This could allow us to pursue our passions and interests without worrying so much about survival. Additionally, in the future, humans may be able to live alongside AI, which mean live more longer and stronger.
Question: Classify these as winter or summer sports: beach volleyball, cross country skiing, biathlon, triathlon, kayaking
Answer: beach volleyball: summer, cross country skiing: winter, biathlon: winter, triathlon: summer, kayaking: summer
Question: What is their approach motivated by?
Evidence: In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.
In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live).
Answer: Little research has evaluated on the quality evaluation on voice knowledge sharing.
Question: You should consider buying an electric bicycle, as it will help you driving long distance
Answer: How do we analyse the pros and cons while buying a bicycle?
Question: What model do they use a baseline to estimate satisfaction?
Evidence: To evaluate the proposed BiLSTM model with attention (BiLSTM+att), it is compared with three of its own variants: a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM). Additional baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24.
To evaluate the proposed BiLSTM model with attention (BiLSTM+att), it is compared with three of its own variants: a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM). Additional baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24.
Answer: The answers are shown as follows:
* a BiLSTM without attention (BiLSTM) as well as a single forward-LSTM layer with attention (LSTM+att) and without attention (LSTM)
* baselines are defined by BIBREF32 who already proposed an LSTM-based architecture that only uses non-temporal features, and the SVM-based estimation model as originally used for reward estimation by BIBREF24
Question: What kind of dog breed do you get when you mate a poodle with an old english sheep dog?
Answer: sheepadoodle
Question: Who was the Italian dictator responsible for introducing fascism to Italy and joining the Axis in World War 2?
Answer: Benito Mussolini
Question: Given the following note about training in a specific style of karate, tell me why its practitioners consider its fist alignment to offer advantages for their striking.
Evidence: Shōrin-ryū is generally characterized by natural breathing, natural (narrow, high) stances, and circular, rather than direct movements. Shōrin-ryū practitioners assert that correct motion, moving quickly to evade violence with fluid movements and flexible positions are important, and that a solid structure is vital for powerful blocks and strikes. Stances that are too deep generally make body movement difficult. Another feature in this system is how the student is taught to punch. Generally, there is neither a horizontal nor vertical punch in Shōrin-ryū. Punches are slightly canted to the inside (Isshin-ryū), with the largest knuckle of the forefinger (third from the tip) in vertical alignment with the second knuckle of the pinky finger. It is believed that this position is key in lining up the bones of the arm and creates a faster, more stable and powerful strike.
Answer: Practitioners of Shōrin-ryū believe lining up the second and third knuckle, along with the bones of the arm, enables them to deliver faster and more powerful punches.
Question: Given this paragraph about video games in the Netherlands, how many people work in the games industry?
Evidence: The Netherlands' mainstream video games market, not taking into consideration the serious and casual games, is the sixth largest in Europe. In 2008, the Dutch market took up 3.95% of the entire European market in total sales and 4.19% in software sales.

A significant part of the Netherlands' gaming industry is in serious games, in which Dutch companies make a significant part of the worldwide industry.

In the Netherlands, an estimated of 4,000 people are working in the games industry, at more than 600 companies. Over 45 of the companies are located in the Dutch Game Garden, a government subsidized organization with the aim of promoting and improving the video games industry in the Netherlands.
Answer: 4,000
Question: Should investors time the market?
Answer: Timing the market is based on predictions of the future, which is a challenging endeavor. Benjamin Graham, the father of value investing and mentor of Warren Buffett, famously said: "In the short run, the market is a voting machine but in the long run, it is a weighing machine." This means that in the short term, market prices can be driven by unpredictable events, market sentiment, and speculation. However, in the long term, a company's fundamentals tend to drive the market. By avoiding attempts to time the market, investors can leverage the long-term growth potential of the market and avoid the risks associated with trying to predict short-term price movements. Therefore, in most cases, investors should not try to time the market.
Question: What are some popular books about baseball?
Answer: "Moneyball" by Michael Lewis, "The Boys of Summer" by Roger Kahn, and "The Rookie" by Jim Morris are all examples of popular baseball books.
Question: Classify these animals as either a reptile or an amphibian
Answer: crocodile, snake, alligator, frog, salamander
Question: What is the contrastive evaluation?
Evidence: Contrastive evaluation is different from conventional evaluation of machine translation in that it does not require any translation. Rather than testing a model's ability to translate, it is a method to test a model's ability to discriminate between given good and bad translations.
We exploit the fact that NMT systems are in fact language models of the target language, conditioned on source text. Like language models, NMT systems can be used to compute a model score (the negative log probability) for an existing translation. Contrastive evaluation, then, means to compare the model score of two pairs of inputs: INLINEFORM0 and INLINEFORM1 . If the model score of the actual reference translation is higher, we assume that this model can detect wrong pronoun translations.
However, this does not mean that systems actually produce the reference translation when given the source sentence for translation. An entirely different target sequence might rank higher in the system's beam during decoding. The only conclusion permitted by contrastive evaluation is whether or not the reference translation is more probable than a contrastive variant.
If the model score of the reference is indeed higher, we refer to this outcome as a “correct decision” by the model. The model's decision is only correct if the reference translation has a higher score than any contrastive translation. In our evaluation, we aggregate model decisions on the whole test set and report the overall percentage of correct decisions as accuracy.
During scoring, the model is provided with reference translations as target context, while during translation, the model needs to predict the full sequence. It is an open question to what extent performance deteriorates when context is itself predicted, and thus noisy. We highlight that the same problem arises for sentence-level NMT, and has been addressed with alternative training strategies BIBREF27 .
Rather than testing a model’s ability to translate, it is a method to test a model’s ability to discriminate between given good and bad translations.
Answer: It is a method to test a model’s ability to discriminate between given good and bad translations.
Question: When sailing on a boat between 26ft and 40ft in San Francisco Bay, what items are 'required', 'useful', or 'neither'. A compass, laser printer, floatation devices for all passengers, fog horn, water, sun lotion, hat, fire extinguisher (one type B-2 or two type B-1), soft toys, tv,  visual distress signals, sunglasses, snacks, ford mustang, American Airlines
Answer: Required: floatation devices for all passengers, Sound Signaling Device (fog horn), fire extinguisher (one type B-2 or two type B-1), and visual distress signals (flares)

Useful: compass, water, sun lotion, hat, sunglasses, snacks

Neither: laser printer, soft toys, tv, ford mustang, American Airlines
Question: Who are the most commonly mentioned best rappers of all time?
Answer: In no particular order, the most common top 5 rappers in "top 5 dead or alive" lists are as follows: Jay-Z, Nas, Tupac, The Notorious BIG, Eminem. Obviously everyone has their own preferences and styles they prefer, but more often than not, these are the 5 rappers mentioned in most lists. These rappers all had great commercial success and critical acclaim from the industry and their peers.
Question: Who is the founder of Vanguard?
Answer: John C. Bogle
Question: According to the text, given the initial cost of building the auditorium and the equivalent cost in 2021, what is the value of 1892 dollars in 2021?
Evidence: The auditorium opened as the Union Gospel Tabernacle in 1892. Its construction was spearheaded by Thomas Ryman (1843–1904), a Nashville businessman who owned several saloons and a fleet of riverboats. Ryman conceived the idea of the auditorium as a tabernacle for the influential revivalist Samuel Porter Jones. He had attended one of Jones' 1885 tent revivals with the intent to heckle, but was instead converted into a devout Christian who pledged to build the tabernacle so the people of Nashville could attend large-scale revivals indoors. It took seven years to complete and cost US$100,000 (equivalent to $3,015,926 in 2021). Jones held his first revival at the site on May 25, 1890, when only the building's foundation and six-foot (1.8 m) walls had been completed.
Answer: 30.15926, since 3,015,926/100,000 = 30.15926
Question: What two kinds of speech acts do stereotypes lead to?
Evidence: Stereotypes are ideas about how other (groups of) people commonly behave and what they are likely to do. These ideas guide the way we talk about the world. I distinguish two kinds of verbal behavior that result from stereotypes: (i) linguistic bias, and (ii) unwarranted inferences. The former is discussed in more detail by beukeboom2014mechanisms, who defines linguistic bias as “a systematic asymmetry in word choice as a function of the social category to which the target belongs.” So this bias becomes visible through the distribution of terms used to describe entities in a particular category. Unwarranted inferences are the result of speculation about the image; here, the annotator goes beyond what can be glanced from the image and makes use of their knowledge and expectations about the world to provide an overly specific description. Such descriptions are directly identifiable as such, and in fact we have already seen four of them (descriptions 2–5) discussed earlier.
I distinguish two kinds of verbal behavior that result from stereotypes: (i) linguistic bias, and (ii) unwarranted inferences.
Answer: (i) linguistic bias, and (ii) unwarranted inferences.
Question: What optimizer is used when training models?
Evidence: The decoder is the one used in BIBREF12, BIBREF13, BIBREF10 with the same hyper-parameters. For the encoder module, both the low-level and high-level encoders use a two-layers multi-head self-attention with two heads. To fit with the small number of record keys in our dataset (39), their embedding size is fixed to 20. The size of the record value embeddings and hidden layers of the Transformer encoders are both set to 300. We use dropout at rate 0.5. The models are trained with a batch size of 64. We follow the training procedure in BIBREF21 and train the model for a fixed number of 25K updates, and average the weights of the last 5 checkpoints (at every 1K updates) to ensure more stability across runs. All models were trained with the Adam optimizer BIBREF37; the initial learning rate is 0.001, and is reduced by half every 10K steps. We used beam search with beam size of 5 during inference. All the models are implemented in OpenNMT-py BIBREF38. All code is available at https://github.com/KaijuML/data-to-text-hierarchical
All models were trained with the Adam optimizer [13]; the initial learning rate is 0.001, and is reduced by half every 10K steps.
Answer: The Adam optimizer.
Question: What classes do keyword list records consist of after pre-trained model on the Greek entities?
Evidence: In order to gain more information about the context of the Greek entities, a percentage of Greek Wikipedia was used. After applying sentence and token segmentation on Wikipedia text and using a pretrained model from polyglot, the keyword list increased. The keyword list had at this point about 350,000 records and consisted of 4 classes: location (LOC), organization (ORG), person (PERSON) and facility (FAC). A percentage of Greek Wikipedia was parsed and used for training in spaCy. The results from the training are presented in SECREF13.
In order to gain more information about the context of the Greek entities, a percentage of Greek Wikipedia was used. After applying sentence and token segmentation on Wikipedia text and using a pretrained model from polyglot, the keyword list increased. The keyword list had at this point about 350,000 records and consisted of 4 classes: location (LOC), organization (ORG), person (PERSON) and facility (FAC).
Answer: Location (LOC), organization (ORG), person (PERSON) and facility (FAC).
Question: In which Harry Potter's book did we learn about Voldemort's childhood?
Answer: Voldemort's childhood was depicted in Book 6 of Harry Potter and The Half-Bood's Price. Through the private lessons Harry had with Dumbledore, Harry learned about Voldemort's family and upbringing.