DataHammer/scimrc huggingface dataset:
Question: I'm about to leave San Francisco and I love my rickety old apartment. Write it a tribute and make it poetic!
Answer: To my San Francisco apartment,

I’ll miss how you rumble in the wind. How I can hear my partner on a call even with two doors closed in between us. Your front staircase, which makes every visitor feel like they’ve been transported back in time to a 1980s Palm Beach condo. The garbage trucks waking us up on Friday morning. The way your bathroom door never fully closes — much to the dismay of our guests — and the countless post-surfing wetsuits hanging from the never-used bath. The view from your back porch and the magical sunsets and sunrises we witnessed. 

When we were locked down to slow the spread of COVID-19, I banged on your screen door every evening at 8pm, pretending I was a percussionist, waving at my elderly neighbors and joining in the collective shouts to show our first responders gratitude. I sat outside on the small patch of grass we called your backyard and cried when the stress was too much. After all these years, my tears are likely soaked into your beige walls and carpet, which we haphazardly plastered with frames and discounted rugs from the Pottery Barn warehouse. 

And how could I forgot our downstairs neighbors, who slowly warmed to us and then loudly went through a divorce? Or our friendly landlord, who gifted us cookies and wine every holiday season, and never once increased our rent — even offering to lower it to tempt us to stay when we gave our notice?

You gave us a home and sheltered us through it all. You provided four walls within which we could blast music, fight off anxiety attacks, lazily binge tv shows, host game nights and late nights with friends, experience earthquakes and hide away from the world while still experiencing the beauty around us. 

For that, I will be forever grateful.
Question: Given that DeCov results are not from the original paper, how were its hyperparameters tuned?
Evidence: For the hyperparameters: The loss weight is chosen from {0.00001, 0.00005, 0.0001, 0.0005, 0.001, 0.005, 0.01} for both our approach and Decov and γ in the radial basis function is chosen from {0.01, 0.1.1, 10, 50, 100}.
Answer: As mentioned in Section 5.1: For the hyperparameters: The loss weight is chosen from 0.00001, 0.00005, 0.0001, 0.0005, 0.001, 0.005, 0.01 for both our approach and Decov based on the validation set.
Question: What was the twist in this short story?
Evidence: Matilda is woken up by the wind; she looks out the window; her neighbour, Marie, is fetching flowers from the garden and then Matilda’s mother is called for the telephone by Bogey, Matilda’s brother. Matilda is off to Mr Bullen's for her music lesson. Her mom does not want her to go due to the strong wind, but she goes anyway. After the lesson, she goes for a walk with her brother to the esplanade. Here, the story changes from present to past narrative as Mansfield shows that the music lesson, the walk etc. all occurred in Matilda's past, and she and her brother are actually sailing away on board a ship several years down the line, that all that went before were memories.
Answer: In the middle of the short story, the story changes from present to past narrative as Mansfield shows that events such as the music lesson, the walk, etc. all occurred in Matilda's past, and she and her brother are actually sailing away on board a ship several years down the line. The reader finds out that all that went before were memories.
Question: Why do some people select private schools over public schools?
Answer: Some of the typical reasons people select private schools over public schools are
1. They can afford private school and believe they can get marginal advantages
2. Private schools offer a smaller student to teacher ratio
3. Private schools have connections to some of the niche colleges 
4. The curriculum is usually ahead of the public school for the same grade.
Question: Do they train a different training method except from scheduled sampling?
Evidence: Since the official test set has not been released publicly, results reported on the official test set have been provided by the challenge organizers. For the prototype test set and for the ablation study presented in Table TABREF24 , we use the same code for evaluation metrics as used by BIBREF11 for fairness and comparability. We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below:
We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below:
Answer: Answer with content missing: (list missing) 
Scheduled sampling: In our experiments, we found that models trained with scheduled sampling performed better (about 0.004 BLEU-4 on validation set) than the ones trained using teacher-forcing for the AVSD dataset. Hence, we use scheduled sampling for all the results we report in this paper.

Yes.
Question: On which level does propaganda has been tackled mostly?
Evidence: Propaganda has been tackled mostly at the article level. BIBREF3 created a corpus of news articles labelled as propaganda, trusted, hoax, or satire. BIBREF4 experimented with a binarized version of that corpus: propaganda vs. the other three categories. BIBREF5 annotated a large binary corpus of propagandist vs. non-propagandist articles and proposed a feature-based system for discriminating between them. In all these cases, the labels were obtained using distant supervision, assuming that all articles from a given news outlet share the label of that outlet, which inevitably introduces noise BIBREF6.
A related field is that of computational argumentation which, among others, deals with some logical fallacies related to propaganda. BIBREF7 presented a corpus of Web forum discussions with instances of ad hominem fallacy. BIBREF8, BIBREF9 introduced Argotario, a game to educate people to recognize and create fallacies, a by-product of which is a corpus with $1.3k$ arguments annotated with five fallacies such as ad hominem, red herring and irrelevant authority, which directly relate to propaganda.
Unlike BIBREF8, BIBREF9, BIBREF7, our corpus uses 18 techniques annotated on the same set of news articles. Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 BIBREF10 and describes a corpus that is a subset of the one used for this shared task.
Propaganda has been tackled mostly at the article level.
Answer: Article level.
Question: For me it remains unclear why we need to both adapt large model to target data as well as train the compact model on source data + maintain a close distance to large model output over target. What would the performance be if I were to just train the compact model on a loss fn with source + KL-divergence (without having adapted the global model to target data first)? Or has this been done in previous work?
Evidence: As shown in Table 8 and Table 9, TO-KD outperforms SHOT in terms of the inference accuracy on the target data. The gain comes from the large model capacity of large model.****The gain of our schemes can be explained as follows. The large target model used in STU-KD can learn more fine-grained representations of the target data than the compact model. Thus, the compact model that inherits the knowledge from the large target model can achieve higher inference accuracy on the target data, compared with the compact model that are directly trained using the target data.****According to Table 6 and Table 7, TO-KD and STU-KD (our schemes) outperforms other schemes in terms of the inference accuracy on the target data, and these two schemes achieve comparable inference accuracy on the target data.
Answer: Significant performance gain can be obtained by first adapting a large source model to a large target model and then transferring the knowledge of the large target model to the compact model, as explained below. The large source model has much larger model capacity than that of the compact model. Thus, after domain adaptation, the large target model can learn more fine-grained representations of the target data than the compact model. By transferring the knowledge of the large target model to the compact model via knowledge distillation (KD), the compact model can inherit those fine-grained representations of the target data. As a result, the compact model trained via the above procedure achieves much higher inference accuracy on the target data, as compared to the case where the compact model is directly trained over the target data. Training the compact model on source data is to prevent the compact model from forgetting the knowledge of the source data during the knowledge transfer process, and  the reason is explained in **A1**. If the compact model is trained via the approach mentioned in **Q2**, the compact model can only inherit the knowledge of the target data from the large source model. However, the large source model does not learn the fine-grained representations of the target data. As a result, the compact model can suffer low inference accuracy on the target data. Note that the approach mentioned in **Q2** has been adopted by MobileDA [1], and the experimental results in our paper show that our scheme outperforms MobileDA in terms of inference accuracy on the target data.
Question: Which tweets are used to output the daily sentiment signal?
Evidence: There are two options of getting the Tweets. First, Twitter provides an API to download the Tweets. However, rate limit and history limit make it not an option for this paper. Second, scrapping Tweets directly from Twitter website. Using the second option, the daily Tweets for stocks of interest from 2015 January to 2017 June were downloaded.
For this reason, two companies from the same industry, Tesla and Ford are investigated on how Twitter sentiment could impact the stock price. Tesla is an electronic car company that shows consecutive negative operating cash flow and net income but carries very high expectation from the public. Ford, is a traditional auto maker whose stock prices has been stabilized to represent the company fundamentals.
To translate each tweet into a sentiment score, the Stanford coreNLP software was used. Stanford CoreNLP is designed to make linguistic analysis accessible to the general public. It provides named Entity Recognition, co-reference and basic dependencies and many other text understanding applications. An example that illustrate the basic functionality of Stanford coreNLP is shown in Figure. FIGREF5
Second, scrapping Tweets directly from Twitter website. Using the second option, the daily Tweets for stocks of interest from 2015 January to 2017 June were downloaded.
For this reason, two companies from the same industry, Tesla and Ford are investigated on how Twitter sentiment could impact the stock price.
To translate each tweet into a sentiment score, the Stanford coreNLP software was used.
Answer: The answers are shown as follows:
* Tesla and Ford are investigated on how Twitter sentiment could impact the stock price
Question: How should I pick a name for my baby?
Answer: There is no right answer to this question.  It really depends on your preferences.  If family names matter to you, that can make your choice easy; just pick the name of a close relative.  If you like hearing others' opinions, you can ask lots of family and friends for ideas.  If you want to explore new ideas, there are lots of websites and books of baby names.
Question: Identify which instrument is string or percussion: Sakara, Cavaquinho
Answer: Cavaquinho is string, Sakara is percussion.
Question: What are nice things to do for someone on their birthday?
Answer: Ideas for nice things that you could do for someone on their birthday include serving them breakfast in bed, sending them a text message, sending them a birthday card, baking them a birthday cake, giving them a gift, taking them out for a meal, or doing an activity with them that they enjoy.
Question: From the passage mention the services provided by Jio. Also consider the services to be provided in future. Display the results in comma separated format.
Evidence: Reliance Jio Infocomm Limited, doing business as Jio, is an Indian telecommunications company and a subsidiary of Jio Platforms, headquartered in Navi Mumbai, Maharashtra, India. It operates a national LTE network with coverage across all 22 telecom circles. Jio offers 4G and 4G+ services all over India and 5G service in many cities. Its 6G service is in the works.

Jio soft launched on 27 December 2015 with a beta for partners and employees, and became publicly available on 5 September 2016. It is the largest mobile network operator in India and the third largest mobile network operator in the world with over 42.62 crore (426.2 million) subscribers.

In September 2019, Jio launched a fiber to the home service, offering home broadband, television, and telephone services. As of September 2020, Reliance Industries has raised ₹1.65 lakh crore (US$21 billion) by selling nearly 33% equity stake in Jio Platforms.
Answer: LTE, 4G, 4G+, 5G, 6G
Question: Do the authors are the first to perform such an extensive evaluation of the extremely low-resource MT problem?
Evidence: Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3 . Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4 . Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios.
However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child). On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs. However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En). Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9 .
In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. Furthermore, the amount of indirect in-domain parallel corpora, i.e., Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small. As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT. To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9 .
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows:
To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
and our explorations have derived rational contributions (CTs) as follows: To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it.
Answer: To the best of their knowledge, they are the first.
Question: How can I ensure exactly-once delivery when reading data from Kafka with Spark?
Answer: To ensure exactly once delivery, you can use the idempotent producer setting in Kafka and the checkpointing mechanism in Spark Structured Streaming. This is possible to achieve if your sink is a delta table, but not if the sink is another Kafka topic.
Question: What's the function of the comparing mechanism?
Evidence: In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps. Then, the two-staged CNN architecture extracts features through word-level and sentence-level. At the same time, attention mechanism helps CNN focus more on the important part of the passage based on the query information. Finally, the prediction layer find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.
First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps.
Answer: To compares between the given passage, query, and multiple answer choices to build similarity maps.
Question: Are these oceans or lakes: Superior, Michigan, Keuka, Ontario, Tahoe. and Yellowstone?
Answer: They are lakes.
Question: What are some good rainy day activities?
Answer: Watching a movie, baking, knitting, drawing, coloring, organizing your closet, organizing your kitchen cabinets, getting rid of clutter around the house, painting a picture, cleaning ceiling fans and baseboards
Question: What is the effective interest rate (EIR)?
Answer: The effective interest rate (EIR), effective annual interest rate, annual equivalent rate (AER) or simply effective rate is the percentage of interest on a loan or financial product if compound interest accumulates over a year during which no payments are made. It is the compound interest payable annually in arrears, based on the nominal interest rate. It is used to compare the interest rates between loans with different compounding periods, such as weekly, monthly, half-yearly or yearly.
Question: What neural classifiers are used?
Evidence: As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4.
As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27.
Answer: The answers are shown as follows:
*  convolutional neural network (CNN) BIBREF29
Question: In what year was pickleball invented?
Evidence: The game was created in 1965 on Bainbridge Island, Washington, at the summer home of Joel Pritchard, who later served in the United States Congress and as Washington's lieutenant governor. Pritchard and two of his friends, Barney McCallum and Bill Bell, are credited with devising the game and establishing the rules.
Answer: Pickleball was created in 1965 on Bainbridge Island, Washington.
Question: give me a list of places I have to visit in Rome.
Answer: 1. Colosseum
2. Fontana di trevi
3. Vatican museums
4.St.Peter's Basilica
5. Roman Forum
6. Pantheon
7. Piazza Navona
8. Piazza Spagna
9. Villa Borghese
10.Castel Sant'Angelo
Question: How does the universal surrogate model compare with a model that learns to combine sources through knowledge distillation?
Evidence: In this experiment, we use Cifar-10, TieredT84, and TieredV 56 as source datasets, and use three ResNet-18 respectively trained on the source datasets as source models. First, we build another model G that has three heads corresponds to the three source datasets. The backbone of G is the same with that of the proposed ICE. The first head that has 10 output nodes is used to classify Cifar-10 images. The second head which has 351 output nodes is used to classify TieredT84 images. The third head which has 257 output nodes is used to classify TieredV 56 images. Second, we use the three source models and three datasets to train G with offline knowledge distillation. The model G obtain test accuracies of 97.5%, 61.7%, and 70.3% on Cifar-10, TieredT84, and TieredV 56, respectively.Finally, we use the model G to disturb the images from Cifar-100. The GTA success rates on the target models MobileNet-V3, VGG-16, ResNet-18, ResNet-34, SeResNet-26, and DenseNet-26 are reported in Table 11.
Table 11: GTA success rates when using a single trained model. Target dataset is Cifar-100.
Answer: We performed another experiment in rebuttal to answer your question. In this experiment, we use Cifar-10, Tiered T84 and Tiered V56 as source datasets, and use three ResNet-18 respectively trained on the source datasets as source models. First, we build another model that has three heads corresponding to the three source datasets. The backbone of G is the same as that of the proposed ICE (Shown in Figure 4). The first head that has 10 output nodes is used to classify Cifar-10 images. The second head which has 351 output nodes is used to classify Tiered T84  images. The third head which has 257 output nodes is used to classify Tiered T56 images. Second, we use the three source models and three datasets to train  with offline knowledge distillation. The model G obtains test accuracies of 97.5%, 61.7%, and 70.3% on Cifar-10, Tiered and Tiered respectively. Finally, we use the baseline methods and model G to disturb the images from Cifar-100. The GTA success rates on the target models MobileNet-V3, VGG-16, ResNet-18, ResNet-34, SeResNet-26, and DenseNet-26 are:
FGSM: 46.4%, 59.6%, 51.0%, 51.4%, 55.1%, and 70.6%.
PGD: 36.9%, 49.6%, 40.3%, 40.1%, 43.4%, and 61.3%.
MI: 43.9%, 56.6%, 47.6%, 47.3%, 52.4%, and 68.4%.
TI: 49.2%, 43.7%, 45.9%, 46.5%, 44.6%, and 54.4%.
Compared with the results in the first row of Table 2, we can conclude that using knowledge-distillation to obtain a single model G cannot improve the GTA performances of baselines. This experiment will be included in the revision (Section A.5.6).
Question: Identify which car manufacturer is French or American: Peugeot, Ford
Answer: Peugeot is French, Ford is American