DataHammer/scimrc huggingface dataset:
Question: In the following initial summary of the plot of the series My Hero Academia, what is the secret that Izuku must keep and from whom?
Evidence: Izuku Midoriya is a young man who dreams of becoming a Hero despite being bullied by his violent childhood friend Katsuki Bakugo for lacking a Quirk. After having a few encounters with his idol, All Might, who is Japan's greatest hero, Izuku is chosen by All Might to inherit his Quirk "One For All" (which gives him the ability to endlessly stockpile his raw power) and become his successor, as All Might was severely injured by his arch-nemesis All For One (whose Quirk allows him to steal other Quirks for his use and pass them over to others at his will) and does not have much time left to live. Izuku begins attending the U.A. High School for heroes in training alongside Bakugo and his friends from Class 1-A while keeping his connection with All Might a secret. During the course of the school year, Izuku and his friends work hard to improve themselves and have a few encounters with the League of Villains led by All For One's apprentice Tomura Shigaraki, who desires to kill All Might as part of their plan to take over the world. During one of these encounters, All Might and All For One have one last fight, which ends with All For One defeated and imprisoned, and All Might, having exhausted the last of One For All's power in himself, forced to retire.
Answer: Izuku keeps his secret that All Might had chosen Izuku to inherit All Might's Quirk "One For All" from Bakugo and Izuku's friends from Class 1-A.
Question: What is the architecture of the model?
Evidence: We use the method of BIBREF5 to train neural sequence-to-sequence Spanish-English ST models. As in that study, before training ST, we pre-train the models using English ASR data from the Switchboard Telephone speech corpus BIBREF7, which consists of around 300 hours of English speech and transcripts. This was reported to substantially improve translation quality when the training set for ST was only tens of hours.
Obtaining gold topic labels for our data would require substantial manual annotation, so we instead use the human translations from the 1K (train20h) training set utterances to train the NMF topic model with scikit-learn BIBREF14, and then use this model to infer topics on the evaluation set. These silver topics act as an oracle: they tell us what a topic model would infer if it had perfect translations. NMF and model hyperparameters are described in Appendix SECREF7.
We use the method of BIBREF5 to train neural sequence-to-sequence Spanish-English ST models.
Obtaining gold topic labels for our data would require substantial manual annotation, so we instead use the human translations from the 1K (train20h) training set utterances to train the NMF topic model with scikit-learn BIBREF14
Answer: The answers are shown as follows:
* BIBREF5 to train neural sequence-to-sequence
* NMF topic model with scikit-learn BIBREF14
Question: Who is Marlon Brando and when he was born?
Answer: Marlon Brando was born April 3 1924 in Omaha Nebraska, U.S. He is an iconic American movie actor. One of the most popular movies of Brando is the Godfather. His performance in this movie earned him Oscar for best-actor the second time.
Question: Does ARAML outperform the baselines in terms of Self-BLEU?
Evidence: We adopted forward/reverse perplexity BIBREF33 and Self-BLEU BIBREF34 to evaluate the quality of generated texts. Forward perplexity (PPL-F) indicates the perplexity on the generated data provided by a language model trained on real data to measure the fluency of generated samples. Reverse perplexity (PPL-R) switches the roles of generated data and real data to reflect the discrepancy between the generated distribution and the data distribution. Self-BLEU (S-BLEU) regards each sentence in the generated collection as hypothesis and the others as reference to obtain BLEU scores, which evaluates the diversity of generated results.
Results are shown in Table TABREF33 . LeakGAN performs best on forward perplexity because it can generate more fluent samples. As for reverse perplexity, our model ARAML beats other baselines, showing that our model can fit the data distribution better. Other GANs, particularly LeakGAN, obtain high reverse perplexity due to mode collapse BIBREF12 , thus they only capture limited fluent expressions, resulting in large discrepancy between the generated distribution and data distribution. ARAML also outperforms the baselines in terms of Self-BLEU, indicating that our model doesn't fall into mode collapse with the help of the MLE training objective and has the ability to generate more diverse sentences.
We also provide standard deviation of each metric in Table TABREF33 , reflecting the stability of each model's performance. Our model ARAML nearly achieves the smallest standard deviation in all the metrics, indicating that our framework outperforms policy gradient in the stability of adversarial training.
ARAML also outperforms the baselines in terms of Self-BLEU, indicating that our model doesn’t fall into mode collapse with the help of the MLE training objective and has the ability to generate more diverse sentences.
Answer: Yes, it does.
Question: Let's say you have one million dollars, how are you going to invest that?
Answer: With the current financial climate, I would split it with 30% bond, 30% stock, 20% cash and 20% commodity. That's how I will invest until I see something break due to the persistent interest rate.
Question: What is the difference between a piano and a guitar?
Answer: A piano is a, usually large, percussive instrument where the player sits down facing the instrument and uses their fingers to press down on keys that cause a hammer to strike strings of varying tone. There are pedals on a piano that allow the strings to sustain their vibrations or dampen the strings to create a quieter playing effect. A guitar is a hand held instrument where the player uses one hand to pick or strum the strings and the other hand to press down on the strings in various positions in order to change the tone of the string.
Question: What are three meals that a small child is likely to eat for lunch?
Answer: Most small children will enjoy chicken nuggets, macaroni and cheese, or a peanut butter and jelly sandwich as the main course. You can include fruits, such as grapes, berries, apples, or oranges, as well as vegetables, such as carrots, celery, or broccoli, to make it a meal.
Question: How are rare words defined?
Evidence: In order to verify the reliability of our technique in coverage expansion for infrequent words we did a set of experiments on the Rare Word similarity dataset BIBREF6 . The dataset comprises 2034 pairs of rare words, such as ulcerate-change and nurturance-care, judged by 10 raters on a [0,10] scale. Table TABREF15 shows the results on the dataset for three pre-trained word embeddings (cf. § SECREF2 ), in their initial form as well as when enriched with additional words from WordNet.
In order to verify the reliability of our technique in coverage expansion for infrequent words we did a set of experiments on the Rare Word similarity dataset BIBREF6 . The dataset comprises 2034 pairs of rare words, such as ulcerate-change and nurturance-care, judged by 10 raters on a [0,10] scale. Table TABREF15 shows the results on the dataset for three pre-trained word embeddings (cf. § SECREF2 ), in their initial form as well as when enriched with additional words from WordNet.
Answer: The answers are shown as follows:
* judged by 10 raters on a [0,10] scale
Question: Extract a list of all of the communties and counties mentioned in this article about SH 115.
Evidence: SH 115 begins at an intersection with Interstate 20 and Farm to Market Road 1927 in Pyote. The route travels north along the eastern edge of town and through Far West Texas oil fields before reaching Wink. The route then turns northeast, next reaching Kermit. The route travels through downtown Kermit, passing the Winkler County Courthouse. The route continues to the northeast, crossing oil fields of Winkler and Andrews Counties, before reaching Andrews. The route travels through Andrews, intersecting U.S. Route 385. The route continues northeast, crossing through the intersection of Andrews, Gaines, Martin, and Dawson Counties before reaching its terminus at SH 349 at the unincorporated community of Patricia, Texas.
Answer: Pyote, Wink, Kermit, Winkler, Andrews, Gaines, Martin, Dawson, Patricia
Question: What is the best way to evaluate the performance of my marketing spend?
Answer: The primary purpose for evaluating the performance of marketing spend tends to be toward informing decisions around how to reallocate that marketing spend in future periods, in order to improve business performance overall. In this regard, we can look at this problem as a decision support problem space. Historically, marketers have used a range of approaches ranging from marketing mix modeling, to marketing attribution, to controlled experiments, and more. None of these approaches are perfect, but a combination of these approaches combined with a rigorous decision feedback loop (e.g. in the spirit of the Observe, Oriented, Decide, Act loop) can help marketers improve their marketing performance management, and hence their marketing performance, over time.
Question: What types of word representations are they evaluating?
Evidence: We trained word embeddings using either GloVe BIBREF11 or SGNS BIBREF12 on a small or a large corpus. The small corpus consists of the traditional Chinese part of Chinese Gigaword BIBREF13 and ASBC 4.0 BIBREF9 . The large corpus additionally includes the Chinese part of Wikipedia.
We trained word embeddings using either GloVe BIBREF11 or SGNS BIBREF12 on a small or a large corpus.
Answer: GloVE; SGNS
Question: What metrics do they use to automatically compare generated concept maps with reference maps?
Evidence: In this section, we briefly describe a baseline and evaluation scripts that we release, with a detailed documentation, along with the corpus.
In order to automatically compare generated concept maps with reference maps, we propose three metrics.9 As a concept map is fully defined by the set of its propositions, we can compute precision, recall and F1-scores between the two proposition set of generated and reference map.
Answer: Precision, recall and F1-scores.
Question: How does muli-agent dual learning work?
Evidence: The core idea of dual learning is to leverage the duality between the primal task (mapping from domain $\mathcal {X}$ to domain $\mathcal {Y}$) and dual task (mapping from domain $\mathcal {Y}$ to $\mathcal {X}$ ) to boost the performances of both tasks. MADL BIBREF0 extends the dual learning BIBREF1, BIBREF2 framework by introducing multiple primal and dual models. It was integrated into our submitted systems for German$\leftrightarrow $English and German$\leftrightarrow $French translations.
The core idea of dual learning is to leverage the duality between the primal task (mapping from domain $\mathcal {X}$ to domain $\mathcal {Y}$) and dual task (mapping from domain $\mathcal {Y}$ to $\mathcal {X}$ ) to boost the performances of both tasks. MADL BIBREF0 extends the dual learning BIBREF1, BIBREF2 framework by introducing multiple primal and dual models.
Answer: The answers are shown as follows:
* MADL BIBREF0 extends the dual learning BIBREF1, BIBREF2 framework by introducing multiple primal and dual models.
Question: What is the difference between interface and abstract class in Java?
Answer: Abstract classes can have methods with implementation whereas interface provides absolute abstraction and can't have any method implementations. Note that from Java 8 onwards, it's possible to create default and static methods in interface that contain the method implementations.
Question: What approach did previous models use for multi-span questions?
Evidence: MTMSN BIBREF4 is the first, and only model so far, that specifically tried to tackle the multi-span questions of DROP. Their approach consisted of two parts. The first was to train a dedicated categorical variable to predict the number of spans to extract. The second was to generalize the single-span head method of extracting a span, by utilizing the non-maximum suppression (NMS) algorithm BIBREF7 to find the most probable set of non-overlapping spans. The number of spans to extract was determined by the aforementioned categorical variable.
MTMSN BIBREF4 is the first, and only model so far, that specifically tried to tackle the multi-span questions of DROP. Their approach consisted of two parts. The first was to train a dedicated categorical variable to predict the number of spans to extract. The second was to generalize the single-span head method of extracting a span, by utilizing the non-maximum suppression (NMS) algorithm BIBREF7 to find the most probable set of non-overlapping spans. The number of spans to extract was determined by the aforementioned categorical variable
Answer: Only MTMSM specifically tried to tackle the multi-span questions. Their approach consisted of two parts: first train a dedicated categorical variable to predict the number of spans to extract and the second was to generalize the single-span head method of extracting a span
Question: What is it like being the last person on earth?
Answer: You would think the world would be yours to explore and enjoy. In actuality, it's incredibly lonely. The quiet is almost deafening. It's also much harder to get to and from as gas stations no longer work, as there is no electricity. I never thought I would miss so desperately to wait in line and go to a crowded restaurant.
Question: Give me a list of the best comedy podcast to listen to
Answer: Smartless, Conan O'brien Needs A Friend, Tim Dillon Show, The Toast, Girls Gotta Eat
Question: Identify which animal species is alive or extinct: Great White Shark, Patagotitan
Answer: Patagotitan is extinct, Great White Shark is alive.
Question: When protecting your company from a security breach, categorize each of the following as "Enhancing Security" or "Diminishing Security": Multi-factor Authentication, VPN, Unlocked Computers, Shared Passwords, Anti-Malware Solution
Answer: Multi-factor Authentication: Enhancing Security
VPN: Enhancing Security
Unlocked Computers: Diminishing Security
Shared Passwords: Diminishing Security
Anti-Malware Solution: Enhancing Security
Question: Extract the pupils of Abraham van Strij from the text. Separate them with a comma.
Evidence: Van Strij was born and died in Dordrecht. According to the RKD he was the son of Leendert and the brother of Jacob. Both he and his brother were pupils of his father, and after taking lessons from Joris Ponse, Abraham joined his father's workshop. He was a member of the Dordrecht Guild of St. Luke and later its director. His son Abraham II also became a painter, and besides his son, he and his brother took on pupils after taking over their father's workshop. Their pupils were Pieter Rudolph Kleijn, Johannes van Lexmond, Jacob de Meijer, Johannes Rutten, Johannes Schoenmakers, Johannes Christiaan Schotel, and Gillis Smak Gregoor.
Answer: Pieter Rudolph Kleijn, Johannes van Lexmond, Jacob de Meijer, Johannes Rutten, Johannes Schoenmakers, Johannes Christiaan Schotel, Gillis Smak Gregoor.
Question: What is Redis?
Answer: Redis is an in-memory data structure store, used as a distributed, in-memory key-value database, cache and message broker, with optional durability.
Question: What is a good approach to grocery shopping?
Answer: Grocery shopping can be a difficult task because grocery stores are often crowded.  A good approach to grocery shopping is to go during hours when not a lot of people are grocery shopping.  An example would be during a lunch break instead of going at night when most people are off of work or going during the work week instead of going on the weekend.  Avoid Sundays at all costs!  Sundays are the busiest day for grocery shopping.  Once you've selected your day and before you've gone to the store, make a list.  A list will make you more efficient once you get to the grocery store.  Organize your list by aisles.  An example of this is organizing your list by produce, meats, household supplies, snacks, and drinks.  This way you can go aisle-to-aisle skipping aisles that don't have what you are shopping for.
Question: Are there any work plans to improve the approach for the future？
Evidence: We have demonstrated that using word vectors that capture only semantic and syntactic characteristics may be improved by taking into account their sentimental aspects as well. Our approaches are cross-lingual and cross-domain. They can be applied to other domains and other languages than Turkish and English with minor changes.
Our study is one of the few ones that perform sentiment analysis in Turkish and leverages sentimental characteristics of words in generating word vectors and outperforms all the others. Any of the approaches we propose can be used independently of the others. Our approaches without using sentiment labels can be applied to other classification tasks, such as topic classification and concept mining.
The experiments show that even unsupervised approaches, as in the corpus-based approach, can outperform supervised approaches in classification tasks. Combining some approaches, which can compensate for what others lack, can help us build better vectors. Our word vectors are created by conventional machine learning algorithms; however, they, as in the corpus-based model, produce state-of-the-art results. Although we preferred to use a classical machine learning algorithm, which is SVM, over a neural network classifier to predict the labels of reviews, we achieved accuracies of over 90 per cent for the Turkish movie corpus and about 88 per cent for the English Twitter dataset.
We performed only binary sentiment classification in this study as most of the studies in the literature do. We will extend our system in future by using neutral reviews as well. We also plan to employ Turkish WordNet to enhance the generalisability of our embeddings as another future work.
We will extend our system in future by using neutral reviews as well. We also plan to employ Turkish WordNet to enhance the generalisability of our embeddings as another future work.
Answer: They will extend their system in future by using neutral reviews as well. They also plan to employ Turkish WordNet to enhance the generalisability of their embeddings as another future work.
Question: From the passage provided, extract the founders of Bloomberg L.P. Separate them with a comma.
Evidence: Bloomberg L.P. is a privately held financial, software, data, and media company headquartered in Midtown Manhattan, New York City. It was co-founded by Michael Bloomberg in 1981, with Thomas Secunda, Duncan MacMillan, Charles Zegar, and a 12% ownership investment by Bank of America through their brokerage subsidiary Merrill Lynch.
Answer: Michael Bloomberg, Thomas Secunda, Duncan MacMillan, Charles Zega
Question: How can a neural model be used for a retrieval if the input is the entire Wikipedia?
Evidence: The learning model for retrieval is trained by an oracle constructed using distant supervision. Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question. For example, for x_to_movie question type, the answer movie articles are the correct articles to be retrieved. On the other hand, for questions in movie_to_x type, the movie in the question should be retrieved. Having collected the labels, we train a retrieval model for classifying a question and article pair as relevant or not relevant.
Figure 5 gives an overview of the model, which uses a Word Level Attention (WLA) mechanism. First, the question and article are embedded into vector sequences, using the same method as the comprehension model. We do not use anonymization here, to retain simplicity. Otherwise, the anonymization procedure would have to be repeated several times for a potentially large collection of documents. These vector sequences are next fed to a Bi-GRU, to produce the outputs $v$ (for the question) and $H_c$ (for the document) similar to the previous section.
The learning model for retrieval is trained by an oracle constructed using distant supervision. Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question.
First, the question and article are embedded into vector sequences, using the same method as the comprehension model. We do not use anonymization here, to retain simplicity. Otherwise, the anonymization procedure would have to be repeated several times for a potentially large collection of documents. These vector sequences are next fed to a Bi-GRU, to produce the outputs $v$ (for the question) and $H_c$ (for the document) similar to the previous section.
Answer: The answers are shown as follows:
* Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question.
Question: How does Choi et al. detect controversy in news items?
Evidence: Analysis of controversy in Wikipedia, online news and social media has attracted considerable attention in recent years. Exploiting the collaborative structure of Wikipedia, estimators of the level of controversy in a Wikipedia article were developed based on the edit-history of the article BIBREF0, BIBREF3. Along these lines, BIBREF4 detect controversy based on mutual reverts, bi-polarity in the collaboration network, and mutually-reinforced scores for editors and articles. Similarly, BIBREF1 classify whether a Wikipedia page is controversial through the combined evaluation of the topically neighboring set of pages.
Content analysis of controversial Wikipedia articles has been used to evaluate the level of controversy of other documents (e.g., web pages) by mapping them to related Wikipedia articles BIBREF5. BIBREF6 further build a language model, which enhances predictions made by existing classifiers, by inferring word probabilities from Wikipedia articles prominent in Wikipedia controversy features (mainly signals in edit history as discussed above) and from articles retrieved by manually selected query terms, believed to indicate controversy.
BIBREF7 detect controversy in news items by inspecting terms with excessive frequency in contexts containing sentiment words, and BIBREF8 study controversy in user comments of news articles using lexicons. Finally, BIBREF9 suggest that controversy is not a universal but rather a community-related concept, and, therefore, should be studied in context.
Here we measure a concept's controversiality from the explicit sentence-level context in which it is mentioned. In this, our approach is reminiscent of BIBREF10, who suggest a similar approach to detect abstract concepts.
Choi et al. (2010) detect controversy in news items by inspecting terms with excessive frequency in contexts containing sentiment words, and Sree et al. (2015) study controversy in user comments of news articles using lexicons.
Answer: By inspecting terms with excessive frequency in contexts containing sentiment words.
Question: Identify which instrument is string or percussion: Jawbone, Grand Stick
Answer: Grand Stick is string, Jawbone is percussion.