DataHammer/scimrc huggingface dataset:
Question: Is a simple combination of data as effective as more complex kinds of polyglot training?
Evidence: The standard approach to multilingual NLP is to design a single architecture, but tune and train a separate model for each language. While this method allows for customizing the model to the particulars of each language and the available data, it also presents a problem when little data is available: extensive language-specific annotation is required. The reality is that most languages have very little annotated data for most NLP tasks.
ammar2016malopa found that using training data from multiple languages annotated with Universal Dependencies BIBREF1 , and represented using multilingual word vectors, outperformed monolingual training. Inspired by this, we apply the idea of training one model on multiple languages—which we call polyglot training—to PropBank-style semantic role labeling (SRL). We train several parsers for each language in the CoNLL 2009 dataset BIBREF0 : a traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset. To our knowledge, this is the first multilingual SRL approach to combine supervision from several languages.
The CoNLL 2009 dataset includes seven different languages, allowing study of trends across the same. Unlike the Universal Dependencies dataset, however, the semantic label spaces are entirely language-specific, making our task more challenging. Nonetheless, the success of polyglot training in this setting demonstrates that sharing of statistical strength across languages does not depend on explicit alignment in annotation conventions, and can be done simply through parameter sharing. We show that polyglot training can result in better labeling accuracy than a monolingual parser, especially for low-resource languages. We find that even a simple combination of data is as effective as more complex kinds of polyglot training. We include a breakdown into label categories of the differences between the monolingual and polyglot models. Our findings indicate that polyglot training consistently improves label accuracy for common labels.
We find that even a simple combination of data is as effective as more complex kinds of polyglot training.
Answer: Yes.
Question: Classify the cities based on whether they are located in Eritrea or Gabon.
Keren, Franceville, Asmara, Port-Gentil, Assab, Libreville
Answer: Eritrea - Asmara, Keren, Assab
Gabon - Libreville, Port-Gentil, Franceville
Question: What is a trumpet?
Answer: A trumpet is horned instrument that is made out of brass.  It is used in classical music and it is also very prevalent in jazz.  You have trumpets like the piccolo that have a very high sound and a bass trumpet which has a very low sound.  Trumpets can be played in a solo or in a group with other instruments.
Question: What are the Classifier Models in this paper?
Evidence: SVM: A linear support vector machine with bag-of-words sentence representations.
MLP: A multi-layer perceptron with USE embeddings BIBREF4 as input.
FastText: A shallow neural network that averages embeddings of n-grams BIBREF5.
CNN: A convolutional neural network with non-static word embeddings initialized with GloVe BIBREF6.
BERT: A neural network that is trained to predict elided words in text and then fine-tuned on our data BIBREF1.
Platforms: Several platforms exist for the development of task-oriented agents. We consider Google's DialogFlow and Rasa NLU with spacy-sklearn.
SVM: A linear support vector machine with bagof-words sentence representations. MLP: A multi-layer perceptron with USE embeddings (Cer et al., 2018) as input. FastText: A shallow neural network that averages embeddings of n-grams (Joulin et al., 2017). CNN: A convolutional neural network with nonstatic word embeddings initialized with GloVe (Pennington et al., 2014). BERT: A neural network that is trained to predict elided words in text and then fine-tuned on our data (Devlin et al., 2019). Platforms: Several platforms exist for the development of task-oriented agents. We consider Google’s DialogFlow4 and Rasa NLU5 with spacy-sklearn.
Answer: They are SVM, MLP, FastText, CNN, BERT and Platforms.
Question: What approach does the author take to the problem that it is often difficult to access the system with real users?
Evidence: A perfect data collection scenario for our dataset would use real running dialog system providing general information from the knowledge base to real users. This system could then ask for explanations and answers for questions which it is not able to answer.
However, getting access to systems with real users is usually hard. Therefore, we used the crowdsourcing platform CrowdFlower (CF) for our data collection.
A CF worker gets a task instructing them to use our chat-like interface to help the system with a question which is randomly selected from training examples of Simple questions BIBREF7 dataset. To complete the task user has to communicate with the system through the three phase dialog discussing question paraphrase (see Section "Interactive Learning Evaluation" ), explanation (see Section "Future Work" ) and answer of the question (see Section "Conclusion" ). To avoid poor English level of dialogs we involved CF workers from English speaking countries only. The collected dialogs has been annotated (see Section "Acknowledgments" ) by expert annotators afterwards.
The described procedure leads to dialogs like the one shown in the Figure 2 .
However, getting access to systems with real users is usually hard. Therefore, we used the crowdsourcing platform CrowdFlower (CF) for our data collection.
Answer: They used the crowdsourcing platform CrowdFlower (CF) for their data collection.
Question: What exactly is a modern Hopfield network?
Evidence: We briefly review continuous modern Hopfield networks that are used for deep learning architectures.
They are continuous and differentiable, therefore they a work with gradient descent in deep architec_x005f_x0002_tures. They retrieve with one update only, therefore they can be activated like other deep learning layers. They have exponential storage capacity, therefore they can tackle large problems. Hopfield networks are energy-based, binary associative memories, which popularized artificial neural networks in the 1980s (Hopfield, 1982; 1984). Associative memory networks have been designed to store and
retrieve samples. Their storage capacity can be considerably increased by polynomial terms in the energy function (Chen et al., 1986; Psaltis & Cheol, 1986; Baldi & Venkatesh, 1987; Gardner, 1987; Abbott & Arian, 1987; Horn & Usher, 1988; Caputo & Niemann, 2002; Krotov & Hopfield, 2016). In contrast to these binary memory networks, we use continuous associative memory networks with very high storage capacity. These modern Hopfield networks for deep learning architectures have an energy function with continuous states and can retrieve samples with only one update (Ramsauer et al., 2021; 2020). Modern Hopfield Networks have been successfully applied to immune repertoire classification (Widrich et al., 2020) and chemical reaction prediction (Seidl et al., 2021).
Answer: The reviewer is absolutely right. The explanation of modern Hopfield networks was very superficial to non-existing. We now write in the paper: “Hopfield networks are energy-based, binary associative memories, which popularized artificial neural networks in the 1980s (Hopfield,1982; 1984). Associative memory networks have been designed to store and retrieve samples. Their storage capacity can be considerably increased by polynomial terms in the energy function (Chen et al., 1986; Psaltis & Cheol, 1986; Baldi & Venkatesh, 1987; Gardner, 1987; Abbott & Arian, 1987; Horn & Usher, 1988; Caputo & Niemann, 2002; Krotov & Hopfield, 2016). In contrast to these binary memory networks, we use continuous associative memory networks with very high storage capacity. These modern Hopfield networks for deep learning architectures have an energy function with continuous states and can retrieve samples with only one update (Ramsauer et al., 2021; 2020). Modern Hopfield Networks have already been successfully applied to immune repertoire classification (Widrich et al., 2020) and chemical reaction prediction (Seidl et al., 2021)”
We also added to the paper: “Modern Hopfield networks is a new concept for contrastive learning. In bioinformatics the covariance structure in a sequence is reinforced by first retrieving similar sequences from a database and then aligning them. Conserved regions are characterized by high local covariance in the alignment (Dickson & Gloor, 2012; Kreth & Fodor, 2014). Modern Hopfield networks detect high covariances of embedded features, which is conveyed by the retrieved sample that corresponds to an alignment.”
Furthermore, we give a review of modern Hopfield networks in the Appendix A.4.
Question: Are the following books fantasy or science fiction: Red Mars, The City We Became, The Three-Body Problem, the Harry Potter series, A Game of Thrones, Dune, The Fifth Season.
Answer: These books are science fiction: Red Mars, The Three-Body Problem, and Dune, because they involve space travel and life on other planets. In addition, Red Mars and The Three-Body Problem include discussions of hard science. 

These books are fantasy: The City We Became, the Harry Potter series, and A Game of Thrones, because they involve magic and beings with supernatural powers. In addition, A Game of Thrones takes place in a different world that is not related to space travel.

The Fifth Season is considered "science fantasy", because it includes elements of both science fiction and fantasy. It takes place on a planet that is not Earth, but is not reached by space travel. Some of its characters have special powers. However, the plot depends on the hard science of plate tectonics.
Question: How do you do meal prep efficiently and make cooking easy?
Answer: Doing meal prep efficiently is mainly about getting things prepared before your cooking time and use the right kitchen appliances to free you up. You will want to prepare the ingratiations for multiple meals together. Bulk cutting and seasoning your vegetables and meats first. Put them in containers with the same dimensions like silicon bags in your freezer or refrigerator. Then, when you need to cook, you can start cooking right away. Also, use kitchen appliances like Instant Pot and oven can help you free up your hands to do other things. There are lots of Instant Pot or one pan recipes online to help you get inspired.
Question: How to introduce a new cat to the household if you already have cats?
Answer: Confine the new cat to a single room. Make sure the confined cat have access to everything he or she needs, including water, food, bedding, litterbox and toys. Feed your new and existing cats across the closed door, so that the cats can smell each other and associate each other's presence with food and happiness. Use synthetic feline facial pheromone in both the new cat’s room and in the area where the resident cat spends the most time will help to create feelings of familiarity and security within the physical environment. After a while, exchange pieces of bedding between the resident cats and the new cat so that the cats start to recognize each others scents. Slowly allow cats to interact with each other under supervision to ensure the cats build early positive experiences together.
Question: Identify which instrument is string or percussion: Bougarabou, Haegeum
Answer: Haegeum is string, Bougarabou is percussion.
Question: Give me 3 ideas for an instant serotonin boost.
Answer: If you want an instant boost of serotonin, try the following things: 
1. Work in a few minutes of exercise, even a short walk can help boost your mood. 
2. Take yourself out for your favorite treat, whether it's a meal at your favorite restaurant, going to your favorite coffee shop, or picking up your favorite ice cream.
3. Think of a happy memory and remember the events of that day.
Question: How is the corpus normalized?
Evidence: KTC is composed of 31 educational textbooks published from 2011 to 2018 in various topics by the MoE. We received the material from the MoE partly in different versions of Microsoft Word and partly in Adobe InDesign formats. In the first step, we categorized each textbook based on the topics and chapters. As the original texts were not in Unicode, we converted the content to Unicode. This step was followed by a pre-processing stage where the texts were normalized by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography based on the reference orthography of the Kurdistan Region of Iraq. In the normalization process, we did not remove punctuation and special characters so that the corpus can be easily adapted our current task and also to future tasks where the integrity of the text may be required.
This step was followed by a pre-processing stage where the texts were normalized by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography based on the reference orthography of the Kurdistan Region of Iraq.
Answer: The answers are shown as follows:
* by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography
Question: classify each of the following strings as positive or negative: ':)', ':(', 'i love you', 'i do not like you', 'happy', 'sad'
Answer: positive: ':)', 'i love you', 'happy'
negative: ':(', 'i do not like you', 'sad'
Question: Given this reference text, what sport other than baseball did David Ortiz play growing up, how many children did his parents have, and what age is he today?
Evidence: Ortiz was born on November 18, 1975, in Santo Domingo, Dominican Republic, as the oldest of four children of Enrique (Leo) Ortiz and Ángela Rosa Arias. As a boy, he followed the careers of standout pitcher Ramón Martinez and his younger brother Pedro, attending games whenever he could and building a friendship with Pedro that would only grow over the years. Ortiz graduated from Estudia Espaillat High School in the Dominican Republic, and was a standout baseball and basketball player there.
https://en.wikipedia.org/wiki/David_Ortiz
Answer: David Ortiz played basketball in addition to baseball. His parents had five total children, and today (on April 5, 2023) he is 47 years old.
Question: How is PIEWi annotated?
Evidence: PlEWi BIBREF20 is an early version of WikEd BIBREF21 error corpus, containing error type annotations allowing us to select only non-word errors for evaluation. Specifically, PlEWi supplied 550,755 [error, correction] pairs, from which 298,715 were unique. The corpus contains data extracted from histories of page versions of Polish Wikipedia. An algorithm designed by the corpus author determined where the changes were correcting spelling errors, as opposed to expanding content and disagreements among Wikipedia editors.
Specifically, PlEWi supplied 550,755 [error, correction] pairs, from which 298,715 were unique.
Answer: The answers are shown as follows:
* [error, correction] pairs
Question: What are some films produced by Takiko Mizunoe?
Evidence: Takiko Mizunoe (水の江 瀧子, Mizunoe Takiko), born Umeko Miura (三浦 ウメ子, Miura Umeko, 20 February 1915 – 16 November 2009), was a Japanese actress, film producer, and radio and TV presenter. She was born in Otaru, Hokkaido, and began her career by acting in Shochiku's musical theatre troupe. Later she became one of Japan’s first female film producers, working with the actors Yujiro Ishihara and Masumi Okada and the director Koreyoshi Kurahara at Nikkatsu during the studio’s golden age. Films she produced include Season of the Sun and Crazed Fruit. She also twice hosted the New Year’s Eve music show Kōhaku Uta Gassen.
Answer: Season of the Sun and Crazed Fruit
Question: Is the dynamics model learned well?
Evidence: With the implementation details described in Appendix C, we found that the MnM dynamics model trained stably, despite resembling a GAN. In Fig. 10, we plot the validation MSE of the MnM model throughout training, observing that it decreases monotonically.****Figure 10: MnM trains stably. Despite resembling a GAN, the MnM dynamics model trains stably, with the validation MSE decreasing steadily throughout training. Different colors correspond to different random seeds of MnM. The dashed line corresponds to the minimum validation MSE of a maximum likelihood dynamics model. With the implementation details described in Appendix C, we found that the MnM dynamics model trained stably, despite resembling a GAN. In Fig. 10, we plot the validation MSE of the MnM model throughout training, observing that it decreases monotonically. Different lines correspond to different random seeds, and the dashed line corresponds to the minimum MSE of a maximum likelihood model (a MBPO model). Note that the MnM model is not trained with this MSE objective, but with the GAN-like objective in Eq. 10. It is therefore not surprising that MnM does not perform as well on this objective as the maximum likelihood model.
Answer: The MnM dynamics model does train stably and its MSE decreases throughout training. As expected, the MSE of the MnM dynamics model is larger than the MSE of a maximum likelihood model. So, while the MnM dynamics model may not be as accurate as the maximum likelihood model, it may be more useful, as it sometimes results in higher return policies. We have revised the paper to add a figure (Figure 10) showing the MSE of the MnM dynamics model throughout training.
Question: Identify the bird from the list: Shrimp, Barnacle goose, Polar Bear
Answer: Barnacle goose
Question: What is the difference between blanc de noirs and blanc de blancs champagne?
Answer: In champagne, three grape varieties are commonly used. Two of these varieties are red grapes, pinot noir and pinot meunier, and the other is a white grape, chardonnay. Blanc de blancs, meaning white of whites, represents a version of champagne that only uses white grapes, while blanc de noirs, meaning white of blacks, represents a version that uses only red grapes.
Question: What is the fourth album from The Tragically Hip?
Evidence: Day for Night is the fourth studio album by the Canadian rock band The Tragically Hip. It is named for the film of the same name.
Answer: Day for Night is the Tragically Hip's 4th studio album released in 1994
Question: What statistics on the VIST dataset are reported?
Evidence: We used the Visual storytelling (VIST) dataset comprising of image sequences obtained from Flickr albums and respective annotated descriptions collected through Amazon Mechanical Turk BIBREF1. Each sequence has 5 images with corresponding descriptions that together make up for a story. Furthermore, for each Flickr album there are 5 permutations of a selected set of its images. In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
We used the Visual storytelling (VIST) dataset comprising of image sequences obtained from Flickr albums and respective annotated descriptions collected through Amazon Mechanical Turk BIBREF1. Each sequence has 5 images with corresponding descriptions that together make up for a story. Furthermore, for each Flickr album there are 5 permutations of a selected set of its images. In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
Answer: The answers are shown as follows:
* In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
Question: How many hidden layers are there in their acoustic model?
Evidence: We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion. In a conventional Gaussian Mixture Model - Hidden Markov Model (GMM-HMM) acoustic model, the state emission log-likelihood of the observation feature vector $o_t$ for certain tied state $s_j$ of HMMs at time $t$ is computed as
where M is the number of Gaussian mixtures in the GMM for state $j$ and $\pi _{jm}$ is the mixing weight. As the outputs from DNNs represent the state posteriors $p(s_j|\mathbf {o}_t)$, a DNN-HMM hybrid system uses pseudo log-likelihood as the state emissions that is computed as
where the state priors log $p(s_j )$ can be estimated using the state alignments on the training speech data.
We adopt a DNN-based acoustic model [1] with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion.
Answer: 11 hidden layers.
Question: What's the most intuitive way to evaluate the text answer?
Evidence: In SQA, each sample is a triple, INLINEFORM0 , where INLINEFORM1 is a question in either spoken or text form, INLINEFORM2 is a multi-sentence spoken-form document, and INLINEFORM3 is the answer in text from. The task of this work is extractive SQA; that means INLINEFORM4 is a word span from the reference transcription of INLINEFORM5 . An overview framework of SQA is shown in Figure FIGREF1 . In this paper, we frame the source domain as reference transcriptions and the target domain as ASR hypotheses. Hence, we can collect source domain data more easily, and adapt the model to the target domain.
In this task, when the machine is given a spoken document, it needs to find the answer of a question from the spoken document. SQA can be solved by the concatenation of an ASR module and a question answering module. Given the ASR hypotheses of a spoken document and a question, the question answering module can output a text answer.
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer. We used the standard evaluation script from SQuAD BIBREF1 to evaluate the performance.
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer.
Answer: To directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer.
Question: What decade is the Great Gatsby set in, and what is the nickname for that time period?
Answer: The Great Gatsby is set in the 1920s, known as the Roaring Twenties
Question: Extract 3 areas based on the following passage where electronics is widely used.
Evidence: Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, sensors and integrated circuits, and associated passive interconnection technologies. The nonlinear behaviour of active components and their ability to control electron flows makes digital switching possible, and electronics is widely used in information processing, telecommunications, and signal processing. Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
Today, most electronic devices use semiconductor components to perform electron control. The underlying principles that explain how semiconductors work are studied in solid state physics, whereas the design and construction of electronic circuits to solve practical problems are part of electronics engineering.
Answer: Electronics is widely used in 
1. information processing, 
2. telecommunications, and 
3. signal processing.