DataHammer/scimrc huggingface dataset:
Question: List down some points on well the movie Oasis of the Zombies was perceived after launch
Evidence: Writing in The Zombie Movie Encyclopedia, academic Peter Dendle stated the film has had a negative reception, but it "presents a simple, unhurried, and unpretending appreciation of zombies and their habitat, a fresh and provocative desert landscape." Ian Jane of DVD Talk rated it 3/5 stars and wrote, "It's terrible, but somehow trance inducing in its own bizarre way." Gordon Sullivan of DVD Verdict wrote, "It's really a mess of tepid Eurosleaze masquerading as a zombie flick, and it doesn't even do that well."
Answer: 1. Despite receiving a poor review, the movie, according to academic Peter Dendle, "presents a simple, unhurried, and unpretentious appreciation of zombies and their habitat, a fresh and provocative desert landscape."
2. It received a 3/5 star rating from Ian Jane of DVD Talk, who also stated, "It's terrible, but somehow trance inducing in its own bizarre way."
3. "It's really a mess of tepid Eurosleaze masquerading as a zombie flick, and it doesn't even do that well," Gordon Sullivan of DVD Verdict commented.
Question: What languages are considered?
Evidence: A 6-layer 10-language TDNN was trained on the GlobalPhone corpus, also using 40-high resolution MFCC features as input, as described in BIBREF20 . For speaker adaptation, a 100-dimensional i-vector was appended to the the MFCC input features. The TDNN was trained with a block-softmax, with the hidden layers shared across all languages and a separate output layer for each language. Each of the six hidden layers had 625 dimensions, and was followed by a 39-dimensional bottleneck layer with ReLU activations and batch normalisation. Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone.
Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone.
Answer: The answers are shown as follows:
* Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese
Question: Give me 3 ideas for hobbies that I can recommend to somebody who is retiring. These should all be activities that require gentle movement and are not overly demanding on the body.
Answer: Retirement is an exciting opportunity for people to develop and cultivate their passions. Here are a few ideas you could recommend that are both gentle on the body but require some movement:
- Gardening: while this is a seasonal pursuit, deciding on which seeds to plant, nursing them through a growing season, and learning the ins and outs of the hobby can become a lifelong passion for those with a "green thumb"
- Walking and picking up trash: carry a bag and "grabber" pinchers to collect trash in your community while staying active on a walk. Rubber-tipped pinchers means you will not need to bend or stretch too far to reach, and you will be surprised how much of a difference this makes to your community members!
- Fishing: most fishing can be done from the banks of a river or lake, and is a great way to experience the calming effects of a body of water while still being physically active. Whether fly fishing or reel fishing, many people who have entered retirement find this an engaging way to engage with nature either solo or as part of a community of fisher-people.
Question: What are secondary colors?
Answer: Secondary colors are created by combining primary colors. Primary colors are blue, red, and yellow. Secondary colors include purple, orange, and green.
Question: Which embeddings do they detect biases in?
Evidence: For both word2vec BIBREF0 and gensim BIBREF7 we adapted the code so that the input terms of the analogy query are allowed to be returned. Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 .
Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 .
Answer: Word embeddings trained on GoogleNews and Word embeddings trained on Reddit dataset
Question: What kind of dataset aiming does the paper propose?
Evidence: Nowadays, dialog systems are usually designed for a single domain BIBREF0 . They store data in a well-defined format with a fixed number of attributes for entities that the system can provide. Because data in this format can be stored as a two-dimensional table within a relational database, we call the data flat. This data representation allows the system to query the database in a simple and efficient way. It also allows to keep the dialog state in the form of slots (which usually correspond to columns in the table) and track it through the dialog using probabilistic belief tracking BIBREF1 , BIBREF2 .
However, the well-defined structure of the database of a typical dialog system comes with a high cost of extending it as every piece of new information has to fit the format. This is especially a problem when we one is adapting the system for a new domain because its entities could have different attributes.
A dialog system based on knowledge bases offers many advantages. First, the knowledge base, which can be represented as knowledge graph containing entities connected by relations, is much more flexible than the relational database. Second, freely available knowledge bases, such as Freebase, Wikidata, etc. contain an enormous amount of structured information, and are still growing. A dialog system which is capable of working with this type of information would be therefore very useful.
In this paper we propose a dataset aiming to help develop and evaluate dialog systems based on knowledge bases by interactive learning motivated in Section "Motivation" Section "Dialog policies" describes policies that can be used for retrieving information from knowledge bases. In Section "Dialog Simulation" is introduced a dialog simulation from natural conversations which we use for evaluation of interactive learning. The dataset collection process allowing the dialog simulation is described in Section "Dataset Collection Process" and is followed by properties of the resulting dataset in Section "Dataset Properties" Evaluation guidelines with proposed metrics can be found in Section "Interactive Learning Evaluation" The planned future work is summarized in Section "Future Work" We conclude the paper with Section "Conclusion" 
In this paper we propose a dataset aiming to help develop and evaluate dialog systems based on knowledge bases by interactive learning motivated in Section "Motivation" Section "Dialog policies" describes policies that can be used for retrieving information from knowledge bases.
Answer: A dataset aiming to help develop and evaluate dialog systems based on knowledge bases by interactive learning motivated in Section "Motivation" Section "Dialog policies" describes policies that can be used for retrieving information from knowledge bases.
Question: Identify which instrument is string or percussion: Slit drum, Lute
Answer: Lute is string, Slit drum is percussion.
Question: How to detect that a number is odd or even?
Answer: You can detect that by checking if the number is divisible by 2 without a reminder, for example 4/2 = 2, so 4 is an even number, however 5/2 is 2 with 1 as a reminder, so 5 is an odd number
Question: Give me a list of the last five "best rap song" Grammy winners (just tell me the song name and the performing artist).
Answer: 1. The Heart Part 5 by Kendrick Lamar, 2023
2. Jail by Kanye West featuring Jay-Z, 2022
3. Savage by Megan Thee Stallion featuring Beyoncé, 2021
4. A lot, 21 Savage featuring J. Cole, 2020
5. God's plan, Drake, 2019
Question: Why should you visit Ireland?
Answer: Ireland is one of the most beautiful places that I've lived.  It does rain quite a bit but on a sunny day I've never seen a more beautiful place.  The grass is a vivid green and the nature comes to life on those sunny days.  If you like history it's also a great place to explore with various ruins scattered across the country as well as plenty of castles.  Ireland also has some great beaches and while the water is cold it's also refreshing and worth the beauty that you will find while you explore the beaches.  If it's nightlife you are looking for look no further than Temple Bar in Dublin.  The restaurants and pubs are very welcoming with a diverse cuisine that will satisfy anyone's appetite.
Question: I bought a new shirt online but I changed my mind. How can I return it?
Answer: If you are looking to return your shirt, you should first check the seller's website to see how many days after your order they will accept returns. You might be able to initiate a return on the website or you might need to contact customer support to start the process. The website should provide you instructions for making the return and some websites will provide a postage label you can print. Depending on the company, you might be able to return the shirt in a physical store, or mail it back to the company for a refund. If you try contacting customer support and they do not respond or they refuse to take back the shirt, you can try contacting your credit card provider and asking them to initiate a chargeback.
Question: Whos the lowest drafted NBA player to win the MVP (most valuable player) award?
Evidence: Each member of the voting panel casts a vote for first to fifth place selections. Each first-place vote is worth 10 points; each second-place vote is worth seven; each third-place vote is worth five, fourth-place is worth three and fifth-place is worth one. Starting from 2010, one ballot was cast by fans through online voting. The player with the highest point total wins the award. As of the 2021–22 season, the current holder of the award is Nikola Jokić of the Denver Nuggets. Jokić is the lowest draft pick to win the award, being selected 41st by Denver in the 2014 NBA draft.

Every player who has won this award and has been eligible for the Naismith Memorial Basketball Hall of Fame has been inducted. Kareem Abdul-Jabbar won the award a record six times. He is also the only player to win the award despite his team not making the playoffs back in the 1975–76 season. Both Bill Russell and Michael Jordan won the award five times, while Wilt Chamberlain and LeBron James won the award four times. Russell and James are the only players to have won the award four times in five seasons. Moses Malone, Larry Bird and Magic Johnson each won the award three times, while Bob Pettit, Karl Malone, Tim Duncan, Steve Nash, Stephen Curry, Giannis Antetokounmpo and Nikola Jokić have each won it twice. Russell, Chamberlain, and Bird are the only players to win the award in three consecutive years. Only two rookies have won the award: Chamberlain in the 1959–60 season and Wes Unseld in the 1968–69 season. Hakeem Olajuwon of Nigeria, Duncan of the U.S. Virgin Islands, Nash of Canada, Dirk Nowitzki of Germany, Antetokounmpo of Greece, and Jokić of Serbia are the only MVP winners considered "international players" by the NBA.
Answer: Nikola Jokic is the lowest draft pick to win the award.  Nikola Jokic was drafted 41st overall by the Denver Nuggets in 2014.
Question: Do they intend to provide any improvements to their method?
Evidence: In this paper, we propose a pre-training method for cross-lingual natural language generation (NLG) that can transfer monolingual NLG supervision signals to all pre-trained languages. With the pre-trained model, we achieve zero-shot cross-lingual NLG on several languages by only fine-tuning once. Experimental results show that our model outperforms the machine-translation-based pipeline model on several cross-lingual NLG tasks. For future work, we would like to improve our pre-training method towards the fully unsupervised setting.
In this paper, we propose a pre-training method for crosslingual natural language generation (NLG) that can transfer monolingual NLG supervision signals to all pre-trained languages. With the pre-trained model, we achieve zeroshot cross-lingual NLG on several languages by only finetuning once. Experimental results show that our model outperforms the machine-translation-based pipeline model on several cross-lingual NLG tasks. For future work, we would like to improve our pre-training method towards the fully unsupervised setting.
Answer: Yes, they may improve their pre-training method toward the fully unsupervised setting.
Question: What is the performance change of the textual semantic similarity task when no error and maximum errors (noise) are present?
Evidence: Figure FIGREF12stsa and FIGREF12stsb shows Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset after fine tuning for 10 and 50 epochs respectively.
FLOAT SELECTED: Figure 5: Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset
Figure FIGREF12stsa and FIGREF12stsb shows Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset after fine tuning for 10 and 50 epochs respectively.
FLOAT SELECTED: Figure 5: Pearson-Spearman correlation vs % of error for textual semantic similarity on STS-B dataset
Answer: 10 Epochs: pearson-Spearman correlation  drops  60 points when error increase by 20%
50 Epochs: pearson-Spearman correlation  drops  55 points when error increase by 20%
Question: How do they gather human judgements for similarity between relations?
Evidence: Human Judgments
Following BIBREF11 , BIBREF12 and the vast amount of previous work on semantic similarity, we ask nine undergraduate subjects to assess the similarity of 360 pairs of relations from a subset of Wikidata BIBREF8 that are chosen to cover from high to low levels of similarity. In our experiment, subjects were asked to rate an integer similarity score from 0 (no similarity) to 4 (perfectly the same) for each pair. The inter-subject correlation, estimated by leaving-one-out method BIBREF13 , is r = INLINEFORM0 , standard deviation = INLINEFORM1 . This important reference value (marked in fig:correlation) could be seen as the highest expected performance for machines BIBREF12 .
Human Judgments
Following BIBREF11 , BIBREF12 and the vast amount of previous work on semantic similarity, we ask nine undergraduate subjects to assess the similarity of 360 pairs of relations from a subset of Wikidata BIBREF8 that are chosen to cover from high to low levels of similarity. In our experiment, subjects were asked to rate an integer similarity score from 0 (no similarity) to 4 (perfectly the same) for each pair. 
Answer: By assessing similarity of 360 pairs of relations from a subset of Wikidata using an integer similarity score from 0 to 4
Question: What back-translations of the monolingual News corpus do the authors use in the paper?
Evidence: We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs: English INLINEFORM0 Czech, English INLINEFORM1 German, English INLINEFORM2 Romanian and English INLINEFORM3 Russian. Our systems are based on an attentional encoder-decoder BIBREF0 , using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary BIBREF1 . We experimented with using automatic back-translations of the monolingual News corpus as additional training data BIBREF2 , pervasive dropout BIBREF3 , and target-bidirectional models.
We experimented with using automatic back-translations of the monolingual News corpus as additional training data , pervasive dropout, and target-bidirectional models.
Answer: As additional training data , pervasive dropout, and target-bidirectional models.
Question: How many percentage points can be improved on the performance for all languages by encoding the full context?
Evidence: This paper describes our approach and results for Task 2 of the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection BIBREF0 . The task is to generate an inflected word form given its lemma and the context in which it occurs.
Morphological (re)inflection from context is of particular relevance to the field of computational linguistics: it is compelling to estimate how well a machine-learned system can capture the morphosyntactic properties of a word given its context, and map those properties to the correct surface form for a given lemma.
There are two tracks of Task 2 of CoNLL–SIGMORPHON 2018: in Track 1 the context is given in terms of word forms, lemmas and morphosyntactic descriptions (MSD); in Track 2 only word forms are available. See Table TABREF1 for an example. Task 2 is additionally split in three settings based on data size: high, medium and low, with high-resource datasets consisting of up to 70K instances per language, and low-resource datasets consisting of only about 1K instances.
The baseline provided by the shared task organisers is a seq2seq model with attention (similar to the winning system for reinflection in CoNLL–SIGMORPHON 2016, BIBREF1 ), which receives information about context through an embedding of the two words immediately adjacent to the target form. We use this baseline implementation as a starting point and achieve the best overall accuracy of 49.87 on Task 2 by introducing three augmentations to the provided baseline system: (1) We use an LSTM to encode the entire available context; (2) We employ a multi-task learning approach with the auxiliary objective of MSD prediction; and (3) We train the auxiliary component in a multilingual fashion, over sets of two to three languages.
In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results. Multi-task learning, paired with multilingual training and subsequent monolingual finetuning, scored highest for five out of seven languages, improving accuracy by another 9.86% on average.
In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results.
Answer: 11.15 percentage points on average.
Question: Which two sets of hidden and cell states does each non-bottom layer of CAS-LSTM accept?
Evidence: In this section, we give a detailed formulation of the architectures used in experiments.
Therefore, it can be said that each non-bottom layer of CAS-LSTM accepts two sets of hidden and cell states—one from the left context and the other from the below context. The left and the below context participate in computation with the equivalent procedure so that the information from lower layers can be efficiently propagated.
Answer: One from the left context and the other from the below context. The left and the below context participate in computation with the equivalent procedure so that the information from lotheyr layers can be efficiently propagated.
Question: What is the largest country in South America by land mass?
Answer: Brasil is the largest country in South America by land mass.
Question: What is the number one selling dog?
Answer: The American Kennel Club released its 2022 registration statistics and found that after being the most popular dog breed for three decades, the Labrador Retriever was outranked by the French Bulldog.
Question: Identify which animal species is alive or extinct: Indian Elephant, Irish Elk
Answer: Irish Elk is extinct, Indian Elephant is alive.
Question: I'm making a meal, please tell me which ingredients are fruits, vegetables, grains and meats. Lettuce, tomato, avocado, rice, beef, onion, garlic, cilantro.
Answer: Lettuce: vegetable, tomato: fruit, avocado: fruit, rice: grain, beef: meat, onion: vegetable, garlic: vegetable, cilantro: vegetable
Question: What data set does the author use to categorize the crowdfunded emotional Tweet data set?
Evidence: To asses the performance of these models on a totally unseen data, we tried to classify the CrowdFlower emotional tweets dataset. The CrowdFlower dataset consists of 40k tweets annotated via crowd-sourcing each with a single emotional label. This dataset is considered a hard dataset to classify with a lot of noise. The distribution of the dataset can be seen in Table TABREF18 . The labeling on this dataset is non-standard, so we used the following mapping for labels:
sadness INLINEFORM0 sadness
worry INLINEFORM0 fear
happiness INLINEFORM0 joy
love INLINEFORM0 love
surprise INLINEFORM0 surprise
anger INLINEFORM0 anger
We then classified emotions using the pre-trained models and emotionally fitted fastText embedding. The result can be seen in Table TABREF19 . The baseline results are from BIBREF33 done using BOW model and maximum entropy classifier. We saw a huge improvement from 26 point increase in F-measure for the emotion joy (happiness) up to 57 point increase for surprise with total average increase of 38.6 points. Bostan and Klinger did not report classification results for the emotion love so we did not include it in the average. These results show that our trained models perform exceptionally on a totally new dataset with a different method of annotation.
To asses the performance of these models on a totally unseen data, we tried to classify the CrowdFlower emotional tweets dataset. The CrowdFlower dataset consists of 40k tweets annotated via crowd-sourcing each with a single emotional label.
Answer: The CrowdFlower dataset
Question: In the given passage, List me some points about the TV show : Locke & Key
Evidence: After Rendell Locke is murdered at the hands of former student Sam Lesser, his wife Nina decides to move with her three children, Tyler, Kinsey, and Bode, from Seattle to Matheson, Massachusetts, and take residence in Rendell's family home, Keyhouse. The children soon discover a number of mysterious keys throughout the house that can be used to unlock various doors in magical ways. They soon become aware of a demonic entity that is also searching for the keys for its own malevolent purposes.
Answer: 1.When Nina’s husband Rendell was murdered by his own student Sam lesser, She moved with her family from Seattle to Matheson.
2.Nina with her three children, Tyler, Kinsey, and Bode moved to Rendell's family home, Keyhouse.
3.Throughout the house, children discovered a number of mysterious keys, that can be used to unlock various doors in magical ways.
4.Children soon become aware of a demons that were also searching the keys for malevolent purposes.
Question: What are the 5 largest ski and snowboard resorts in North America?
Answer: 1.) Whistler Blackcomb, 2.) Park City Resort, 3.) Big Sky, 4.) Vail, and 5.) Heavenly
Question: Tell me whether these are cities in India: Mumbai, Bengaluru, Chennai, Singapore, Kuala Lumpur, Sydney, New Delhi, Kolkota
Answer: Mumbai, Bengaluru, Chennai, New Delhi, Kolkota