DataHammer/scimrc huggingface dataset:
Question: What are the datasets used for training?
Evidence: We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. The SSEC corpus is an annotation of the SemEval 2016 Task 6 corpus with emotion labels. The re-annotation of the SemEval 2016 Task 6 corpus helps to bridge the gap between the unavailability of a corpus with sentiment and emotion labels. The SemEval 2016 corpus contains tweets which are classified into positive, negative or other. It contains 2,914 training and 1,956 test instances. The SSEC corpus is annotated with anger, anticipation, disgust, fear, joy, sadness, surprise and trust labels. Each tweet could belong to one or more emotion classes and one sentiment class. Table TABREF15 shows the data statistics of SemEval 2016 task 6 and SSEC which are used for sentiment and emotion analysis, respectively.
We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15.
Answer: The answers are shown as follows:
* SemEval 2016 Task 6 BIBREF7
* Stance Sentiment Emotion Corpus (SSEC) BIBREF15
Question: Using the given text, summarize the main features of the Fender Stratocaster
Evidence: The guitar introduced into the popular market several features that were innovative for electric guitars in the mid-1950s. The distinctive body shape, which has become commonplace among electric guitars, was revolutionary for the time period, and for the first time a mass-market electric guitar did not significantly resemble earlier acoustic models. The double cutaway, elongated horns, and heavily contoured back were all designed for better balance and comfort to play while standing up and slung off the shoulder with a strap. The three-pickup design offered players increased versatility and choice in tone quality over earlier one- and two-pickup electric guitars, and a responsive and simplified vibrato arm integrated into the bridge plate, which marked a significant design improvement over other vibrato systems, such as those manufactured by Bigsby. All of these design elements were popularized and later became an industry standard due to the success of the Stratocaster. The Fender Stratocaster is one of the most iconic electric guitar models of all time.
Answer: The Fender Stratocaster is an electric guiar model introduced in the 1950s. Some of its main design features were part of its revolutionary body shape. For the first time, an electric guitar didn't look like an acoustic counterpart. The double cutaway, long horns, three-pickup and a simplified vibrato arm integrated into the bridge plate are some of the features of the Fender Stratocaster. All of these features, with some variations, became standard in the design of electric guitars.
Question: How do these acoustic models manage to predict letters directly?
Evidence: The acoustic model is a convolutional neural network with gated linear units BIBREF0 , which is fed with the output of the learnable front-end. Following BIBREF11 , the networks uses a growing number of channels, and dropout BIBREF19 for regularization. These acoustic models are trained to predict letters directly with the Auto Segmentation Criterion (ASG) BIBREF20 . The only differences between the WSJ and Librispeech models are their depth, the number of feature maps per layer, the receptive field and the amount of dropout.
These acoustic models are trained to predict letters directly with the Auto Segmentation Criterion (ASG) [22].
Answer: These acoustic models are trained to predict letters directly with the Auto Segmentation Criterion (ASG) .
Question: Identify which car manufacturer is German or American: Audi, Buick
Answer: Audi is German, Buick is American
Question: What are the words of House Ambrose?
Answer: "Never Resting"
Question: What did the authors do with using the proposed model?
Evidence: Recently, with the emergence of neural seq2seq models, abstractive summarization methods have seen great performance strides BIBREF0, BIBREF1, BIBREF2. However, complex neural summarization models with thousands of parameters usually require a large amount of training data. In fact, much of the neural summarization work has been trained and tested in news domains where numerous large datasets exist. For example, the CNN/DailyMail (CNN/DM) BIBREF3, BIBREF4 and New York Times (NYT) datasets are in the magnitude of 300k and 700k documents, respectively. In contrast, in other domains such as student reflections, summarization datasets are only in the magnitude of tens or hundreds of documents (e.g., BIBREF5). We hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models, and we will indeed later show that this is the case for student reflections.
To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, we combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline.
Answer: The authors explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline.
Question: Give me some tips for cooking the best bolognese sauce.
Answer: In order to cook bolognese sauce, it is important to use quality ingredients. You're going to need the following: Italian tomato sauce, onion, celery, carrot, minced meat, extra virgin olive oil, salt, white or red wine and milk. Here are some useful tips:

- Take your time. Cooking a great bolognese sauce takes at least 4 hours. Set the stove level at low.
- Start by cooking the "sofritto". This is a mix of chopped onions, celery and carrots. -
- Add the meat to the "sofritto". Be sure to seal it so it doesn't leak water into your sauce. 
- Add some wine and boil it completely.
- When there is no liquid left, add the tomato sauce and some water, and cook it for at 3 to 5 hours. You can add more water during the process if needed.
Question: Does the data used for training only contain formal language?
Evidence: We trained ELMo models for seven languages: Slovenian, Croatian, Finnish, Estonian, Latvian, Lithuanian and Swedish. To obtain high-quality embeddings, we used large monolingual corpora from various sources for each language. Some corpora are available online under permissive licences, others are available only for research purposes or have limited availability. The corpora used in training datasets are a mix of news articles and general web crawl, which we preprocessed and deduplicated. Below we shortly describe the used corpora in alphabetical order of the involved languages. Their names and sizes are summarized in Table TABREF3.
Croatian dataset include hrWaC 2.1 corpus BIBREF9, Riznica BIBREF10, and articles of Croatian branch of Styria media house, made available to us through partnership in a joint project. hrWaC was built by crawling the .hr internet domain in 2011 and 2014. Riznica is composed of Croatian fiction and non-fiction prose, poetry, drama, textbooks, manuals, etc. The Styria dataset consists of 570,219 news articles published on the Croatian 24sata news portal and niche portals related to 24sata.
Estonian dataset contains texts from two sources, CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings BIBREF8, and news articles made available to us by Ekspress Meedia due to partnership in the project. Ekspress Meedia dataset is composed of Estonian news articles between years 2009 and 2019. The CoNLL 2017 corpus is composed of Estonian Wikipedia and webcrawl.
Finnish dataset contains articles by Finnish news agency STT, Finnish part of the CoNLL 2017 dataset, and Ylilauta downloadable version BIBREF11. STT news articles were published between years 1992 and 2018. Ylilauta is a Finnish online discussion board; the corpus contains parts of the discussions from 2012 to 2014.
Latvian dataset consists only of the Latvian portion of the ConLL 2017 corpus.
Lithuanian dataset is composed of Lithuanian Wikipedia articles from 2018, DGT-UD corpus, and LtTenTen. DGT-UD is a parallel corpus of 23 official languages of the EU, composed of JRC DGT translation memory of European law, automatically annotated with UD-Pipe 1.2. LtTenTen is Lithuanian web corpus made up of texts collected from the internet in April 2014 BIBREF12.
Slovene dataset is formed from the Gigafida 2.0 corpus BIBREF13. It is a general language corpus composed of various sources, mostly newspapers, internet pages, and magazines, but also fiction and non-fiction prose, textbooks, etc.
Swedish dataset is composed of STT Swedish articles and Swedish part of CoNLL 2017. The Finnish news agency STT publishes some of its articles in Swedish language. They were made available to us through partnership in a joint project. The corpus contains those articles from 1992 to 2017.
LtTenTen is Lithuanian web corpus made up of texts collected from the internet in April 2014 (Jakubicek et al., 2013).
Answer: No.
Question: Given this paragraph about marathon, what is the distance covered in kilometres and what are the most common ways to complete one?
Evidence: The marathon is a long-distance foot race with a distance of 42.195 km (26 mi 385 yd), usually run as a road race, but the distance can be covered on trail routes. The marathon can be completed by running or with a run/walk strategy. There are also wheelchair divisions. More than 800 marathons are held throughout the world each year, with the vast majority of competitors being recreational athletes, as larger marathons can have tens of thousands of participants.
Answer: The distance covered in a marathon is 42.195 kilometers and it can be completed by running and walking combined, though there are also possibilities for wheelchair participants.
Question: Which of the following are characters from Harry Potter: Hermione Granger, Harry Potter, Ron Weasley, Dobby, Draco Malfoy, Severus Snape, Professor Dumbledore, Luna Lovegood, Indiana Jones, Darth Vader, Jack Sparrow, Terminator, Batman, Superman, Joker, Gollum.
Answer: Hermione Granger, Harry Potter, Ron Weasley, Dobby, Draco Malfoy, Severus Snape, Professor Dumbledore, Luna Lovegood are from Harry Potter.
Question: Who are the best five living male comedians in the US?
Answer: 1. Dave Chapelle
2. Adam Sandler
3. Chris Rock
4. Kevin Hart
5. Aziz Ansari
Question: Are you going for a walk today?
Answer: Today I'm definitely going for a walk, the weather is great! These last rainy days haven't been the best, but now I'm feeling refreshed, so staying home would be a waste.
Question: Where is Hawaii?
Evidence: Hawaii (/həˈwaɪi/ (listen) hə-WY-ee; Hawaiian: Hawaiʻi [həˈvɐjʔi] or [həˈwɐjʔi]) is a state in the Western United States, about 2,000 miles (3,200 km) from the U.S. mainland in the Pacific Ocean. It is the only U.S. state outside North America, the only state that is an archipelago, and the only state in the tropics.
Answer: Hawaii is located in the Pacific Ocean, about two thousand miles from the west coast of North America
Question: What baseline function is used in REINFORCE algorithm?
Evidence: Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\hat{c}}_1 \ldots {\hat{c}}_K$, ${\hat{c}}_k \sim p_{\theta }(c|I)$,
The baseline for each sampled caption is defined as the average reward of the rest samples. That is, for caption $\hat{c}_k$, its baseline is
Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\hat{c}}_1 \ldots {\hat{c}}_K$, ${\hat{c}}_k \sim p_{\theta }(c|I)$,

The baseline for each sampled caption is defined as the average reward of the rest samples.
Answer: The answers are shown as follows:
* baseline for each sampled caption is defined as the average reward of the rest samples
Question: Given the following paragraph on siliceous ooze, how are oozes defined?
Evidence: Siliceous ooze is a type of biogenic pelagic sediment located on the deep ocean floor. Siliceous oozes are the least common of the deep sea sediments, and make up approximately 15% of the ocean floor. Oozes are defined as sediments which contain at least 30% skeletal remains of pelagic microorganisms. Siliceous oozes are largely composed of the silica based skeletons of microscopic marine organisms such as diatoms and radiolarians. Other components of siliceous oozes near continental margins may include terrestrially derived silica particles and sponge spicules. Siliceous oozes are composed of skeletons made from opal silica Si(O2), as opposed to calcareous oozes, which are made from skeletons of calcium carbonate organisms (i.e. coccolithophores). Silica (Si) is a bioessential element and is efficiently recycled in the marine environment through the silica cycle. Distance from land masses, water depth and ocean fertility are all factors that affect the opal silica content in seawater and the presence of siliceous oozes.
Answer: Oozes are defined as sediments which contain at least 30 percent skeletal remains of pelagic microorganisms.
Question: Provide a list of signs that you have won at life
Answer: 1. Your house is paid off
2. Your student loans are paid off
3. You are emotionally and physically healthy
4. You have meaningful relationships
5. You respect and love yourself
6. Your kids and spouse are content 
7. You are comfortably retired
Question: Give me a list of the 10 best European cities to visit
Answer: 1. London
2. Paris
3. Madrid
4. Vienna
5. Berlin
6. Zurich
7. Prague
8. Zagreb
9. Budapest
10. Rome
Question: Does the model proposed beat the baseline models for all the values of the masking parameter tested?
Evidence: FLOAT SELECTED: Figure 4: Graph of average results by MRT/RRT
FLOAT SELECTED: Table 9: Average results by MRT/RRT
FLOAT SELECTED: Figure 4: Graph of average results by MRT/RRT
FLOAT SELECTED: Table 9: Average results by MRT/RRT
Answer: No.
Question: Tell if these are grown in trees or bushes: apples, strawberries, bananas, pineapples, coconut, raspberries, blueberries.
Answer: Apples: trees, strawberries: bushes, bananas: trees, pineapples: bushes, coconuts: trees, raspberries: bushes, blueberries: bushes.
Question: Where is Malard
Evidence: Malard (Persian: ملارد, also Romanized as Malārd and Melārd; also known as Malār) is a city in the Central District of Malard County, Tehran province, Iran, and serves as capital of the county.

At the 2006 census, its population was 228,673 in 61,302 households. The following census in 2011 counted 290,817 people in 85,445 households. The latest census in 2016 showed a population of 281,027 people in 86,830 households.
Answer: Malard is a city in the Central District of Malard County, Tehran province, Iran, and serves as capital of the county.

At the 2006 census, its population was 228,673 in 61,302 households. The following census in 2011 counted 290,817 people in 85,445 households. The latest census in 2016 showed a population of 281,027 people in 86,830 households.
Question: classify the following as outfits vs appliances: dress, shoes, toaster, jackets, fridge, air fryer, instant pot, vest, gloves, pants, jeans
Answer: outfits: dress, shoes, jackets, vest, gloves, pants, jeans
appliances: toaster, fridge, air fryer, instant pot
Question: Did they only experiment with captioning task?
Evidence: In the latter part of this paper, we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics. In the datasets, the source and target have different word distributions, and thus adaptation of output parameters is important. We augment the output parameters to facilitate adaptation. Although we use captioning models in the experiments, our method can be applied to any neural networks trained with a cross-entropy loss.
we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics. 
Answer: Yes.
Question: What other kinds of generation models are used in experiments?
Evidence: In our work, we focus on comparing various methods that model $p(\mathbf {y}|\mathbf {x})$ differently. We compare our proposed discrete CVAE (DCVAE) with the two-stage sampling approach to three categories of response generation models:
Baselines: Seq2seq, the basic encoder-decoder model with soft attention mechanism BIBREF30 used in decoding and beam search used in testing; MMI-bidi BIBREF5, which uses the MMI to re-rank results from beam search.
CVAE BIBREF14: We adjust the original work which is for multi-round conversation for our single-round setting. For a fair comparison, we utilize the same keywords used in our network pre-training as the knowledge-guided features in this model.
Other enhanced encoder-decoder models: Hierarchical Gated Fusion Unit (HGFU) BIBREF12, which incorporates a cue word extracted using pointwise mutual information (PMI) into the decoder to generate meaningful responses; Mechanism-Aware Neural Machine (MANM) BIBREF13, which introduces latent embeddings to allow for multiple diverse response generation.
We compare our proposed discrete CVAE (DCVAE) with the two-stage sampling approach to three categories of response generation models:

Baselines: Seq2seq, the basic encoder-decoder model with soft attention mechanism BIBREF30 used in decoding and beam search used in testing; MMI-bidi BIBREF5, which uses the MMI to re-rank results from beam search.

CVAE BIBREF14: We adjust the original work which is for multi-round conversation for our single-round setting. For a fair comparison, we utilize the same keywords used in our network pre-training as the knowledge-guided features in this model.

Other enhanced encoder-decoder models: Hierarchical Gated Fusion Unit (HGFU) BIBREF12, which incorporates a cue word extracted using pointwise mutual information (PMI) into the decoder to generate meaningful responses; Mechanism-Aware Neural Machine (MANM) BIBREF13, which introduces latent embeddings to allow for multiple diverse response generation.
Answer: The answers are shown as follows:
*  Seq2seq
* CVAE
* Hierarchical Gated Fusion Unit (HGFU)
* Mechanism-Aware Neural Machine (MANM)
Question: What is the best way to improve your tennis skills?
Answer: There are many ways that you can improve your tennis skills. The most important point is to practice as tennis requires eyes and muscle coordination. Like many other activities, it requires dedications and practice to improve your skills.

One way to practice is to use a tennis wall. In your tennis club or in some parks, find a wall and use that wall as opponent. This way you can focus on hitting the ball consistently and improve your style.

While practicing with a wall is convenient, you will need to play with different people to improve your skills and respond different balls. There are many clubs that organize tournaments which can be a great opportunity to play with other tennis players.

The most important thing is enjoy your time while playing tennis and keeping to practice consistently.
Question: Give me a list of top five budget Android phone
Answer: 1. Samsung Galaxy A54 5G
2. Google Pixel 6a
3. OnePlus Nord N20 5G
4. Moto G Stylus 5G
5. Xiaomi 12 5G
Question: What are some good ice cream toppings that are gross when mixed together?
Answer: Some ice cream topping pairings that would probably not go well together are cinnamon candy and hot fudge, gummy bears and peanut butter cups, and cookie crumbs and pineapple.
Question: Why is working out good for you?
Answer: Working out provides many benefits. Studies have shown that working out is good for you long term health. It helps build muscle strength, which is shown to decrease in volume as you age. It also has benefits for your cardiovascular system. Working out also provides mental benefits, such as reduced anxiety and stress.
Question: Why do they only solve the problem within two hops?
Evidence: We introduce an open domain question answering system based on Chinese knowledge graph in this paper. We analyze the questions and find that most of the answers to the questions are within two hops. Therefore, we only solve the problem within two hops to reduce the complexity of the system. The system consists of a topic entity selection module, a relationship recognition module and an answer selection module. Firstly, we construct a scoring mechanism to select the core entity of the question; Then we score the relationship in the two-hop subgraph of the topic entity; Finally, we build a classier to judge whether a question is simple or complicated, so that we can choose the final relationship and generate sparql query.
We analyze the questions and find that most of the answers to the questions are within two hops. Therefore, we only solve the problem within two hops to reduce the complexity of the system.
Answer: To reduce the complexity of the system.