DataHammer/scimrc huggingface dataset:
Question: What are the three strategies to replace words with a placeholder?
Evidence: In addition to using the Multi30K dataset as is (standard setup), we probe the ability of our models to address the three linguistic phenomena where additional context has been proved important (Section ): ambiguities, gender-neutral words and noisy input. In a controlled experiment where we aim to remove the influence of frequency biases, we degrade the source sentences by masking words through three strategies to replace words by a placeholder: random source words, ambiguous source words and gender unmarked source words. The procedure is applied to the train, validation and test sets. For the resulting dataset generated for each setting, we compare models having access to text-only context versus additional text and multimodal contexts. We seek to get insights into the contribution of each type of context to address each type of degradation.
In this setting (RND) we simulate erroneous source words by randomly dropping source content words. We first tag the entire source sentences using the spacy toolkit BIBREF26 and then drop nouns, verbs, adjectives and adverbs and replace these with a default BLANK token. By focusing on content words, we differ from previous work that suggests that neural machine translation is robust to non-content word noise in the source BIBREF27 .
In this setting (AMB), we rely on the MLT dataset BIBREF11 which provides a list of source words with multiple translations in the Multi30k training set. We replace ambiguous words with the BLANK token in the source language, which results in two language-specific datasets.
In this setting (PERS), we use the Flickr Entities dataset BIBREF28 to identify all the words that were annotated by humans as corresponding to the category person. We then replace such source words with the BLANK token.
The statistics of the resulting datasets for the three degradation strategies are shown in Table TABREF10 . We note that RND and PERS are the same for language pairs as the degradation only depends on the source side, while for AMB the words replaced depend on the target language.
In a controlled experiment where we aim to remove the influence of frequency biases, we degrade the source sentences by masking words through three strategies to replace words by a placeholder: random source words, ambiguous source words and gender unmarked source words.
Answer: Random source words, ambiguous source words and gender unmarked source words.
Question: What are some common vegetables and fruits to put on tacos?
Answer: Common produce to put on tacos includes lettuce, tomatoe, onions, peppers, radishes, mushrooms, avocados, cauliflower, corn, cucumbers, cabbage, pineapple, olives, sweet potatoes, and eggplant.
Question: What ingredients do i need to make sour dough bread
Answer: Main ingredient is sour dough starter. You also need flour, water and salt.
Question: What are some criticism of Betty Friedan's Feminine Mystique?
Answer: While groundbreaking, Betty Friedman took a narrow view on what the experience of a women is. She mainly focused on middle class white women. This narrow view meant she missed some of the challenges that women of other classes and races. As an example she focuses on the desire of women wanting to work without thinking about poorer women who are forced to work.
Question: Which is a species of fish? Blenny or Benny
Answer: Blenny
Question: In World Athletic Championship Eugene, Oregon, Neeraj Chopra won the Silver medal by throwing how many meters
Answer: 88.13m
Question: What is a cookie?
Answer: A cookie a sweet wafer that can have a variety of different flavors.  Sweet cookie flavors consist of chocolate, vanilla, blueberry and lemon to name a few.  Cookies can be enjoyed with milk or another complementing drink.  Cookies can be crunchy or soft, and they have ingredients like flour, milk and sugar.
Question: What do they use to encode the entire available 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.
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 multitask 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.
Answer: An LSTM.
Question: Provide some key points about popular game Super Buster Bros from the given passage
Evidence: Super Buster Bros., released as Super Pang (スーパーパン) outside of North America, is a cooperative two-player shooting puzzle arcade video game developed by Mitchell and released in the United States in 1990 by Capcom. It is the second game in the Pang series and was ported to the Super Nintendo Entertainment System in 1992. It is also featured in the compilations Buster Bros. Collection for the original PlayStation and Capcom Puzzle World for PlayStation Portable. It was the tenth game released for the CP System hardware.
Answer: 1. Mitchell created the cooperative two-player shooting puzzle arcade video game Super Buster Bros., which was published by Capcom in the United States in 1990 under the name Super Pang outside of North America.
2. It was transferred to the Super Nintendo Entertainment System in 1992 and is the second game in the Pang series.
3. It's included in compilations like Buster Bros.
4. Both Capcom Puzzle World for the PlayStation Portable and the Collection for the original PlayStation.
5. The CP System hardware had just received its tenth game release.
Question: Based on this paragraph, why was Jake released from prison?
Evidence: Jesus Shuttlesworth, the top high-school basketball player in the United States, is being pursued by the top college basketball programs in the nation. His father, Jake, is a convicted felon serving time at Attica Correctional Facility for accidentally killing his wife, Martha, Jesus' mother, six years earlier. Jake is granted a work release by the governor, an influential alumnus of "Big State," one of the colleges Jesus is considering, so that he might persuade his son to sign with Big State. If successful, he'll get an early release from prison.
https://en.wikipedia.org/wiki/He_Got_Game
Answer: Jake was released from prison to try to help convince his son to play basketball for the governor's alma mater.
Question: Does Delta Lake support multi-table transactions?
Answer: Delta Lake does not support multi-table transactions and foreign keys. Delta Lake supports transactions at the table level.
Question: When did the England national team last win the football World Cup
Answer: In 1966 the England football team won the FIFA World Cup, defeating West Germany 4-2
Question: What is cloud computing?
Answer: Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining physical data centers and servers, you can access technology services, such as computing power, storage, and databases, on an as-needed basis from a cloud provider like Amazon Web Services (AWS).
Question: Which is a species of fish? Goat or Goatfish
Answer: Goatfish
Question: Home many storage options does Azure Synapse provide?
Answer: Azure Synapse can store or persist data in five different technologies or locations:
1. Azure Data Lake Store, 
2. Dedicated SQL Pools (DW), and
3. Cosmos DB,
4. Azure Data Share, and
5. Power BI (with its Dataset Cache features)
Question: What is NER？
Evidence: Named Entity Recognition (NER) is a foremost NLP task to label each atomic elements of a sentence into specific categories like "PERSON", "LOCATION", "ORGANIZATION" and othersBIBREF0. There has been an extensive NER research on English, German, Dutch and Spanish language BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, and notable research on low resource South Asian languages like HindiBIBREF6, IndonesianBIBREF7 and other Indian languages (Kannada, Malayalam, Tamil and Telugu)BIBREF8. However, there has been no study on developing neural NER for Nepali language. In this paper, we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data pre-processing.
Recent neural architecture like BIBREF1 is used to relax the need to hand-craft the features and need to use part-of-speech tag to determine the category of the entity. However, this architecture have been studied for languages like English, and German and not been applied to languages like Nepali which is a low resource language i.e limited data set to train the model. Traditional methods like Hidden Markov Model (HMM) with rule based approachesBIBREF9,BIBREF10, and Support Vector Machine (SVM) with manual feature-engineeringBIBREF11 have been applied but they perform poor compared to neural. However, there has been no research in Nepali NER using neural network. Therefore, we created the named entity annotated dataset partly with the help of Dataturk to train a neural model. The texts used for this dataset are collected from various daily news sources from Nepal around the year 2015-2016.
Following are our contributions:
We present a novel Named Entity Recognizer (NER) for Nepali language. To best of our knowledge we are the first to propose neural based Nepali NER.
As there are not good quality dataset to train NER we release a dataset to support future research
We perform empirical evaluation of our model with state-of-the-art models with relative improvement of upto 10%
In this paper, we present works similar to ours in Section SECREF2. We describe our approach and dataset statistics in Section SECREF3 and SECREF4, followed by our experiments, evaluation and discussion in Section SECREF5, SECREF6, and SECREF7. We conclude with our observations in Section SECREF8.
To facilitate further research our code and dataset will be made available at github.com/link-yet-to-be-updated
We present a novel Named Entity Recognizer (NER) for Nepali language.
Answer: It's short for Named Entity Recognizer.
Question: Why are so many movies and tv shows merely recycled stories from previously successful stories ?
Answer: One theory has to do with the increase of streaming choices and that the volume and frequency of new content means that it’s difficult to produce entirely unique stories.
Question: What are some popular fantasy book series
Answer: Game of Thrones, The Witcher, Lord of the Rings, Red Rising, Harry Potter, The Hunger Games, Dune, Star Wars
Question: How many of Farington's buildings are designated Grade 3?
Evidence: Farington is a civil parish in the South Ribble district of Lancashire, England. It contains seven listed buildings that are recorded in the National Heritage List for England. All of the listed buildings are designated at Grade II, the lowest of the three grades, which is applied to "buildings of national importance and special interest". The parish is largely residential and contains the former villages of Farington and Farington Moss. The listed buildings comprise houses and associated structures, and a church.
Answer: None of Farington's buildings are designated Grade 3. All of Farington's buildings are designated Grade II (Grade 2).
Question: Classify each of the following as renewable or non-renewable forms of energy: Coal, solar, wind, natural gas, nuclear, hydroelectric, petroleum
Answer: Coal: non-renewable, Solar: renewable, Wind: renewable, Natural Gas: non-renewable, Nuclear: non-renewable, Hydroelectric: renewable, Petroleum: non-renewable
Question: Which of these foods are kosher? Lobster, cheeseburger, hamburger and fries, shrimp and grits, thai red curry with tofu, thai red curry with chicken, birthday cake, lentil soup with ham hock.
Answer: Modern kosher interpretations of the Biblical framework has a few general guidelines. Only vegetarian animals with hooves, only fish with fins and scales, and only non-predatory birds are allowed. In addition, meat and dairy are not consumed in a single meal. Therefore the following items from that list are kosher: hamburger and fries, thai red curry with tofu, thai red curry with chicken, and birthday cake.
Question: Who is the NBA's all time leading scorer?
Answer: In February 2023, LeBron James became the NBA's all time leading scorer surpassing Kareem Abdul-Jabbar. James scored 38 points that night while moving past Abdul-Jabbar's career total of 38,387 points.
Question: What are the specialties of the two types of sentence pair models？
Evidence: Various neural networks have been proposed for sentence pair modeling, all of which fall into two types of approaches. The sentence encoding approach encodes each sentence into a fixed-length vector and then computes sentence similarity directly. The model of this type has advantages in the simplicity of the network design and generalization to other NLP tasks. The sentence pair interaction approach takes word alignment and interactions between the sentence pair into account and often show better performance when trained on in-domain data. Here we outline the two types of neural networks under the same general framework:
Various neural networks have been proposed for sentence pair modeling, all of which fall into two types of approaches. The sentence encoding approach encodes each sentence into a fixed-length vector and then computes sentence similarity directly. The model of this type has advantages in the simplicity of the network design and generalization to other NLP tasks. The sentence pair interaction approach takes word alignment and interactions between the sentence pair into account and often show better performance when trained on in-domain data.
Answer: The one which encodes each sentence into a fixed-length vector and then computes sentence similarity directly has advantages in the simplicity of the network design and generalization to other NLP tasks. The other one which takes word alignment and interactions between the sentence pair into account often shows better performance when trained on in-domain data.
Question: What are some good US and Canada summer vacation ideas if I like mountains, lakes, and trees?
Answer: There are many great locations in the US and Canada. Here's a list restricted to places with mountains, lakes, and trees:
1. Maine and Vermont
2. Minnesota
3. Yosemite and Lake Tahoe
4. Northern California, Oregon, and Washington
5. British Columbia
6. Lake Louise
Question: Why are the New England Patriots the greatest NFL team ever?
Answer: The New England Patriots dominance over the NFL has never been matched.  They have had the longest duration of success lasting over 20 years.  Since 2001 they have made the playoffs 18 times, winning the Division 17 times, winning the AFC championship 9 times and winning the Super Bowl 6 times
Question: I have problems to connect to the remote service. How I could debug the networking issue?
Answer: There are few steps that could help to find the source of the problems. First we need to make sure that remote address is resolved correctly - we can use commands like nslookup & dig to resolve a host name to IP Address. If it's resolvable, we need to check that remote service is reachable - we can use tools like ping, traceroute, and related to check that network traffic is routed correctly. If it's not reachable, we need to check if routing is configured correctly and no firewall is blocking traffic between. If remote service is reachable, then we need to check if we can access the specific port on which service is listening - this is usually done with commands like telnet, nc and nmap. If port is not reachable, it's often happens when a firewall blocking traffic from external networks or traffic on non-standard ports.
Question: In the work of Duan et al., what is used as pseudo training data?
Evidence: Several previous methods have been proposed for cross-lingual abstractive summarization. BIBREF7 xnhg and BIBREF8 xsummacl use translated documents or summaries as pseudo training data. BIBREF9 ncls incorporate monolingual summarization and machine translation in the training procedure to improve cross-lingual summarization. However, the systems only conduct experiments that generate summaries with different languages from the input language, rather than transferring supervision signals across all language pairs. BIBREF10 kumar2019cross introduce a cross-lingual model for question generation, which uses training data annotated in multiple languages to jointly train a sequence-to-sequence model. In contrast, our method can also be applied to zero-shot settings across languages.
Various training objectives are designed to pretrain text encoders used for general-purpose language representations, such as language modeling BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, auto-encoding BIBREF16, and machine translation BIBREF17. Apart from pre-training encoders, several pre-trained models BIBREF18, BIBREF19 are proposed for generation tasks. In comparison, our goal is to investigate a pre-training method for cross-lingual NLG tasks.
Cross-lingual pre-training aims at building universal cross-lingual encoders that can encode multilingual sentences to a shared embedding space. BIBREF20 artetxe2018massively use the sequence encoder of the multilingual translation model BIBREF3 to produce cross-lingual sentence embeddings. However, as shown in the experiments (Section SECREF4), it is difficult to control the target language by directly fine-tuning the pre-trained translation model on downstream NLG tasks. BIBREF4 xnli propose an alignment loss function to encourage parallel sentences to have similar representations. By pre-training BERT BIBREF13 on corpora of multiple languages, it shows a surprising ability to produce cross-lingual representations BIBREF21. More recently, BIBREF5 xlm extend mask language modeling pre-training to cross-lingual settings, which shows significant improvements on cross-lingual text classification and unsupervised machine translation. By comparison, we pretrain both encoder and decoder for cross-lingual generation tasks, rather than only focusing on encoder.
Cross-Lingual NLG Several previous methods have been proposed for cross-lingual abstractive summarization. Shen et al. (2018) and Duan et al. (2019) use translated documents or summaries as pseudo training data.
Answer: Translated documents or summaries.
Question: What other multimodal knowledge base embedding methods are there?
Evidence: A number of methods utilize an extra type of information as the observed features for entities, by either merging, concatenating, or averaging the entity and its features to compute its embeddings, such as numerical values BIBREF26 (we use KBLN from this work to compare it with our approach using only numerical as extra attributes), images BIBREF27 , BIBREF28 (we use IKRL from the first work to compare it with our approach using only images as extra attributes), text BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , and a combination of text and image BIBREF35 . Further, BIBREF7 address the multilingual relation extraction task to attain a universal schema by considering raw text with no annotation as extra feature and using matrix factorization to jointly embed KB and textual relations BIBREF36 . In addition to treating the extra information as features, graph embedding approaches BIBREF37 , BIBREF38 consider observed attributes while encoding to achieve more accurate embeddings.
A number of methods utilize an extra type of information as the observed features for entities, by either merging, concatenating, or averaging the entity and its features to compute its embeddings, such as numerical values BIBREF26 (we use KBLN from this work to compare it with our approach using only numerical as extra attributes), images BIBREF27 , BIBREF28 (we use IKRL from the first work to compare it with our approach using only images as extra attributes), text BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , and a combination of text and image BIBREF35 . Further, BIBREF7 address the multilingual relation extraction task to attain a universal schema by considering raw text with no annotation as extra feature and using matrix factorization to jointly embed KB and textual relations BIBREF36 . In addition to treating the extra information as features, graph embedding approaches BIBREF37 , BIBREF38 consider observed attributes while encoding to achieve more accurate embeddings.
Answer: The answers are shown as follows:
* merging, concatenating, or averaging the entity and its features to compute its embeddings
* graph embedding approaches
* matrix factorization to jointly embed KB and textual relations
Question: How strong was the Kent earthquake
Evidence: The 2007 Kent earthquake registered 4.3 on the Richter scale and struck south east Kent, South East England on 28 April 2007 at 07:18:12 UTC (08:18:12 local time), at a shallow depth of 5.3 km.

The worst affected area was the town of Folkestone, although the towns of Deal, Dover and Ashford were also affected. The tremors could be felt across much of Kent and south east England, including as far as East Sussex, Essex and Suffolk, as well as on the other side of the English Channel at Calais and Brussels.
Answer: The 2007 Kent earthquake registered 4.3 on the Richter scale and struck south east Kent, South East England on 28 April 2007 at 07:18:12 UTC (08:18:12 local time), at a shallow depth of 5.3 km.
Question: Classify each of the following as a mammal, reptile, or neither: alligator, frog, cat, moose, lizard, snake, dog
Answer: Alligator: Reptile
Frog: Neither
Cat: Mammal 
Moose: Mammal
Lizard: Reptile
Snake: Reptile
Dog: Mammal