DataHammer/scimrc huggingface dataset:
Question: Which continent can Callixena versicolora be found?
Evidence: Callixena versicolora is a moth of the family Noctuidae first described by Max Saalmüller in 1891. The species can be found in Africa, where it is known from Ghana, Nigeria, the Democratic Republic of the Congo, Malawi, Kenya, South Africa, Comoros, Madagascar and Réunion.

This species has a wingspan from 29 to 40 mm.

References
Saalmüller & von Heyden (1891). Lepidopteren von Madagascar. Zweite Abtheilung. Heterocera: Noctuae, Geometrae, Microlepidoptera. :247–531, pls. 7–14 (in German)
 De Prins, J. & De Prins, W. (2019). "Magusa versicolora (Saalmüller, 1891)". Afromoths. Retrieved December 22, 2020.
Answer: Africa
Question: Does their model start with any prior knowledge of words?
Evidence: The objectives of this study were to build a robot that learns words related to places and efficiently utilizes this learned vocabulary in self-localization. Lexical acquisition related to places is expected to enable a robot to improve its spatial cognition. A schematic representation depicting the target task of this study is shown in Fig. FIGREF3 . This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes. The robot then performs self-localization while moving around in the environment, as shown in Fig. FIGREF3 (a). An utterer speaks a sentence including the name of the place to the robot, as shown in Fig. FIGREF3 (b). For the purposes of this study, we need to consider the problems of self-localization and lexical acquisition simultaneously.
This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes.
Answer: No.
Question: What dataset is used?
Evidence: The ACL-Embeddings (300 and 100 dimensions) from ACL collection were trained . ACL Anthology Reference Corpus contains the canonical 10,921 computational linguistics papers, from which I have generated 622,144 sentences after filtering out sentences with lower quality.
ACL Anthology Reference Corpus contains the canonical 10,921 computational linguistics papers, from which I have generated 622,144 sentences after filtering out sentences with lower quality.
Answer: The answers are shown as follows:
* ACL Anthology Reference Corpus
Question: Why the certain animals called the big five ?
Answer: These refer specifically to 5 most dangerous types of animals in Africa, that kill humans. They also sometimes referred to the 5 most dangerous types of animals to hunt in Africa.
Question: How many matrics are used for evaluation？
Evidence: We evaluated our attention transformations on three language pairs. We focused on small datasets, as they are the most affected by coverage mistakes. We use the IWSLT 2014 corpus for De-En, the KFTT corpus for Ja-En BIBREF19 , and the WMT 2016 dataset for Ro-En. The training sets have 153,326, 329,882, and 560,767 parallel sentences, respectively. Our reason to prefer smaller datasets is that this regime is what brings more adequacy issues and demands more structural biases, hence it is a good test bed for our methods. We tokenized the data using the Moses scripts and preprocessed it with subword units BIBREF20 with a joint vocabulary and 32k merge operations. Our implementation was done on a fork of the OpenNMT-py toolkit BIBREF21 with the default parameters . We used a validation set to tune hyperparameters introduced by our model. Even though our attention implementations are CPU-based using NumPy (unlike the rest of the computation which is done on the GPU), we did not observe any noticeable slowdown using multiple devices.
As baselines, we use softmax attention, as well as two recently proposed coverage models:
We also experimented combining the strategies above with the sparsemax transformation.
As evaluation metrics, we report tokenized BLEU, METEOR ( BIBREF22 , as well as two new metrics that we describe next to account for over and under-translation.
As evaluation metrics, we report tokenized BLEU, METEOR (Denkowski and Lavie (2014), as well as two new metrics that we describe next to account for over and under-translation.6
Answer: Four.
Question: Which fruits are most often candied fruit?
Evidence: Dried fruit is fruit from which the majority of the original water content has been removed either naturally, through sun drying, or through the use of specialized dryers or dehydrators. Dried fruit has a long tradition of use dating back to the fourth millennium BC in Mesopotamia, and is prized because of its sweet taste, nutritive value and long shelf life.
Today, dried fruit consumption is widespread. Nearly half of the dried fruits sold are raisins, followed by dates, prunes, figs, apricots, peaches, apples, and pears. These are referred to as "conventional" or "traditional" dried fruits: fruits that have been dried in the sun or in heated wind tunnel dryers. Many fruits such as cranberries, blueberries, cherries, strawberries, and mango are infused with a sweetener (e.g. sucrose syrup) prior to drying. Some products sold as dried fruit, like papaya, kiwifruit and pineapple, are most often candied fruit.
Dried fruits retain most of the nutritional value of fresh fruits. The specific nutrient content of the different dried fruits reflects their fresh counterpart and the processing method.
Answer: Papaya, kiwifruit and pineapple, are most often candied fruit.
Question: What are the three main nutrient groups?
Answer: The three main nutrient groups, also called macronutrients and macros for short, are protein, carbohydrates, and fat.
Question: What are the two social networks in which they focused on matching accounts?
Evidence: For the purposes of this paper, we focused on matching accounts between two of the largest social networks: Twitter and Facebook. In order to proceed with our study, we needed a sizeable (few thousand) number of English speaking users with accounts on both Twitter and Facebook. We also needed to know the precise matching between the Twitter and Facebook accounts for our ground truth.
To that end, we crawled publicly available, English-language, Google Plus accounts using the Google Plus API and scraped links to the users' other social media profiles. (Note that one of the reasons why we used Twitter and Facebook is that they were two of the most common sites linked to on Google Plus). We used a third party social media site (i.e., Google Plus), one that was not used in our analysis to compile our ground truth in order to limit selection bias in our data collection.
We discarded all users who did not link to an account for both Twitter and Facebook and those whose accounts on either of these sites were not public. We then used the APIs of Twitter and Facebook to collect posts made by the users on these sites. We only collected the linguistic content and the date and time at the which the posts were made. For technical and privacy reasons, we did not collect any information from the profile of the users, such as the location, screen name, or birthday.
Our analysis focused on activity of users for one whole year, from February 1st, 2014 to February 1st, 2015. Since we can not reliably model the behaviour patterns of users with scarce data, users with less than 20 posts in that time period on either site were discarded. Overall, we collected a dataset of $5,612$ users with each having a Facebook and Twitter account for a total of $11,224$ accounts.
Figure 1 shows the distribution of the number of posts per user for Twitter and Facebook for our collected dataset. In the figure, the data for the number of posts has been divided into 500 bins. For the Twitter data, each bin corresponds to 80 tweets, while for the Facebook data, it corresponds to 10 posts. Table 1 shows some statistics about the data collected, including the average number of posts per user for each of the sites.
For the purposes of this paper, we focused on matching accounts between two of the largest social networks: Twitter and Facebook.
Answer: Twitter and Facebook.
Question: What are the advatages and disadvatages of commuting with bikes comparing to cars?
Answer: There are several advantages that matter a lot to me personally. Bikes are much cheaper than cars, and don't require subsequent payment for gas and insurance. Finding a parking space and paying for parking can also be a concern for cars. Commuting by bicycle for half an hour to an hour every day can relax my mind. As for the disadvantages, I can bike with caltrain/bart to commute between different cities, but it is inevitably slower than driving. Also, rainy days are very unfriendly to biking, which happens a lot these days.
Question: Extract and list the names of characters a player can control in The Last of Us mentioned in the paragraph. Also describe relationships between those characters.
Evidence: The Last of Us is an action-adventure game played from a third-person perspective. The player traverses post-apocalyptic environments such as towns, buildings, forests, and sewers to advance the story. The player can use firearms, improvised weapons, hand-to-hand combat, and stealth to defend against hostile humans and cannibalistic creatures infected by a mutated strain of the Cordyceps fungus. For most of the game, the player takes control of Joel, a man tasked with escorting a young girl, Ellie, across the United States. The player also controls Ellie throughout the game's winter segment and briefly controls Joel's daughter, Sarah, in the opening sequence.
Answer: Joel: The man who is controlled by the player most of the game.
Ellie: A young girl who is being escorted by Joel across a post-apocalyptic United States.
Sarah: Joel's daughter.
Question: What datasets are used to evaluate the model?
Evidence: Datasets: We experimented on four standard datasets: WN18 and FB15k are extracted by BIBREF5 from Wordnet BIBREF32 Freebase BIBREF33 . We used the same train/valid/test sets as in BIBREF5 . WN18 contains 40,943 entities, 18 relations and 141,442 train triples. FB15k contains 14,951 entities, 1,345 relations and 483,142 train triples. In order to test the expressiveness ability rather than relational pattern learning power of models, FB15k-237 BIBREF34 and WN18RR BIBREF35 exclude the triples with inverse relations from FB15k and WN18 which reduced the size of their training data to 56% and 61% respectively. Baselines: We compare MDE with several state-of-the-art relational learning approaches. Our baselines include, TransE, TransH, TransD, TransR, STransE, DistMult, NTN, RESCAL, ER-MLP, and ComplEx and SimplE. We report the results of TransE, DistMult, and ComplEx from BIBREF13 and the results of TransR and NTN from BIBREF36 , and ER-MLP from BIBREF15 . The results on the inverse relation excluded datasets are from BIBREF11 , Table 13 for TransE and RotatE and the rest are from BIBREF35 .
Datasets: We experimented on four standard datasets: WN18 and FB15k are extracted by BIBREF5 from Wordnet BIBREF32 Freebase BIBREF33 .
Answer: The answers are shown as follows:
* WN18 and FB15k
Question: Identify which instrument is string or percussion: Semantron, Saw sam sai
Answer: Saw sam sai is string, Semantron is percussion.
Question: Do they report results only on English data?
Evidence: We can see at least three reasons for these observed correlations. First, some correlations can be attributed to overlapping feature definitions. For instance, expletive arguments (e.g. There are birds singing) are, by definition, non-canonical arguments, and thus are a subset of add arg. However, some added arguments, such as benefactives (Bo baked Mo a cake), are not expletives. Second, some correlations can be attributed to grammatical properties of the relevant constructions. For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). Third, some correlations may be a consequence of the sources sampled in CoLA and the phenomena they focus on. For instance, the unusually high correlation of Emb-Q and ellipsis/anaphor can be attributed to BIBREF18 , which is an article about the sluicing construction involving ellipsis of an embedded interrogative (e.g. I saw someone, but I don't know who).
Expletives, or “dummy” arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. Arguments are usually selected for by the head, and they are generally not optional. In this case, the expletive occupies a syntactic argument slot, but it is not semantically selected by the verb, and there is often a syntactic variation without the expletive. See [p.170-172]adger2003core and [p.82-83]kim2008syntax.
For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?). 
Expletives, or “dummy” arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives.
Answer: Yes.
Question: What is the difference between pets and cattle?
Answer: Although both are animals at the end of the day, however, the main difference comes partially in the form of perception and, historically, the relationship of humans with certain animals. Pets, e.g. dogs, cats, etc., are used more for companionship and not for food. On the contrary, cattle, e.g. cows, pigs, chickens, etc., are primarily raised for food, i.e. meat, milk, eggs, etc.
Question: Who has the most #1 hits in music?
Answer: The Beatles, unsurprisingly, lead the way with a record 20 No. 1s, all earned between 1964 and 1970. The Fab Four also scored 34 top 10s (second only to Madonna’s 38), hitting No. 1 in over half their visits to the top 10.
Question: What are the sizes of both datasets?
Evidence: The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains. Examples of text types are newsletters, newspaper articles, legal texts, subtitles and blog posts. All texts except for texts from social media have been automatically tokenized, POS tagged and lemmatized. It contains significantly more data and more varied data than the Europarl corpus. Due to the high amount of data in the corpus, only three subparts are used: Wikipedia texts, reports and newspaper articles. These subparts are chosen because the number of wrongly used die and dat is expected to be low.
The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains.
Answer: The answers are shown as follows:
* The Dutch section consists of 2,333,816 sentences and 53,487,257 words.
* The SONAR500 corpus consists of more than 500 million words obtained from different domains.
Question: How big is slot filing dataset?
Evidence: In our experiments, we use Onsei Intent Slot dataset. Table TABREF21 shows the statics of this dataset. We use the following hyper parameters in our model: We set the word embedding and POS embedding to 768 and 30 respectively; The pre-trained BERT BIBREF17 embedding are used to initialize word embeddings; The hidden dimension of the Bi-LSTM, GCN and feed forward networks are 200; the hyper parameters $\alpha $, $\beta $ and $\gamma $ are all set to 0.1; We use Adam optimizer with learning rate 0.003 to train the model. We use micro-averaged F1 score on all labels as the evaluation metric.
FLOAT SELECTED: Table 1: Label Statistics
In our experiments, we use Onsei Intent Slot dataset. Table TABREF21 shows the statics of this dataset.
FLOAT SELECTED: Table 1: Label Statistics
Answer: Dataset has 1737 train, 497 dev and 559 test sentences.
Question: What is better a mac or a PC?
Answer: It depends on user preferences. Generally, a Mac is considered easier than a PC, but a Mac is typically more expensive.
Question: What is the difference between iOS and MacOS?
Answer: MacOS and iOS are both operating systems created by Apple. The MacOS focuses on desktop computing using a mouse and keyboard as primary input devices. iOS is a mobile operating system used on iPhones and iPads primarily using a touchscreen interface.
Question: From the passage provided, which runways are used in the West Plan?
Evidence: During normal operations (approximately 81% of the time), domestic departures use Runways 1L and 1R for departure while overseas international departures and all arrivals use Runways 28L and 28R, taking advantage of the prevailing west-northwesterly wind coming through the San Bruno Gap. During periods of heavy winds or if operations at Oakland International Airport conflict with SFO departures (approximately 15% of the time), Runways 1L and 1R cannot be used, and so all departures and all arrivals use Runways 28L and 28R. These configurations are known collectively as the West Plan, and accommodate arrivals at a rate of up to 60 aircraft per hour. Under visual flight rules, aircraft may safely land side-by-side essentially simultaneously on 28L and 28R while maintaining visual separation.
Answer: Runways 28L and 28R
Question: How do bystanders acknowledge the importance of a bullying episode
Evidence: Similarly to traditional bullying, cyberbullying involves a number of participants that adopt well-defined roles. Researchers have identified several roles in (cyber)bullying interactions. Although traditional studies on bullying have mainly concentrated on bullies and victims BIBREF46 , the importance of bystanders in a bullying episode has been acknowledged BIBREF47 , BIBREF48 . Bystanders can support the victim and mitigate the negative effects caused by the bullying BIBREF48 , especially on social networking sites, where they hold higher intentions to help the victim than in real life conversations BIBREF49 . While BIBREF46 distinguish four different bystanders, BIBREF50 distinguish three main types: i) bystanders who participate in the bullying, ii) who help or support the victim and iii) those who ignore the bullying. Given that passive bystanders are hard to recognise in online text, only the former two are included in our annotation scheme.
Bystanders can support the victim and mitigate the negative effects caused by the bullying (Salmivalli, 2010), especially on social networking sites.
Answer: Bystanders can support the victim and mitigate the negative effects.
Question: How exactly the search was performed in section 3.3?
Evidence: To better demonstrate the iterative greedy search method introduced in Section 3.3, we summarize
the important details in Algorithm 1. In a nutshell, we 1) start from a small model defined in Table 1;
2) in each scaling step, we increase each scaling factor alone to meet the pre-set hardware-cost target;
3) we then select the one with the best accuracy vs. efficiency trade-off out of those architectures
resulting from increasing each scaling factor standalone in the previous step; and 4) the selected
architecture from the previous step will be used as the starting point in the next step.
Answer: Sorry for not making it sufficiently clear. To better demonstrate how the search is performed and how the target constraints are reached, we have added the details of our iterative greedy search into the algorithm illustrated in Appendix G. In a nutshell, we 1) start from a small model defined in Table 1; 2) in each scaling step, we increase each scaling factor alone to meet the pre-set hardware-cost target; 3) we then select the one with the best accuracy vs. efficiency trade-off out of those architectures resulting from increasing each scaling factor standalone in the previous step; and 4) the selected architecture from the previous step will be used as the starting point in the next step. Regarding how different target constraints are reached, we gradually increase the scaling factors until the corresponding architecture’s hardware-cost is larger than the target one (i.e., line 9 - 12 in Algorithm 1 of Appendix G).
Question: What does your vacation rental have to offer?
Answer: Our cabin sits nestled in the beautiful Rocky Mountains near Gatlinburg, Tennessee. It offers breathtaking views across the mountains, and you can access several trails to get out and view the beauty yourself. Hiking not for you? Enjoy the view from the hot tub on the back deck for nature sightings.
Question: Why does it feel so hot in Houston?
Answer: Being near the Gulf Coast, Houston usually has a very high humidity. This makes it feel warmer and stickier than many of the surrounding Texas cities and other cities across the United States on a similar latitude.
Question: What is found when they manually analyze a sample of the dataset?
Evidence: Paragraphs and other longer texts typically make multiple references to the same entities. Tracking these references and resolving coreference is essential for full machine comprehension of these texts. Significant progress has recently been made in reading comprehension research, due to large crowdsourced datasets BIBREF0, BIBREF1, BIBREF2, BIBREF3. However, these datasets focus largely on understanding local predicate-argument structure, with very few questions requiring long-distance entity tracking. Obtaining such questions is hard for two reasons: (1) teaching crowdworkers about coreference is challenging, with even experts disagreeing on its nuances BIBREF4, BIBREF5, BIBREF6, BIBREF7, and (2) even if we can get crowdworkers to target coreference phenomena in their questions, these questions may contain giveaways that let models arrive at the correct answer without performing the desired reasoning (see §SECREF3 for examples).
We introduce a new dataset, Quoref , that contains questions requiring coreferential reasoning (see examples in Figure FIGREF1). The questions are derived from paragraphs taken from a diverse set of English Wikipedia articles and are collected using an annotation process (§SECREF2) that deals with the aforementioned issues in the following ways: First, we devise a set of instructions that gets workers to find anaphoric expressions and their referents, asking questions that connect two mentions in a paragraph. These questions mostly revolve around traditional notions of coreference (Figure FIGREF1 Q1), but they can also involve referential phenomena that are more nebulous (Figure FIGREF1 Q3). Second, inspired by BIBREF8, we disallow questions that can be answered by an adversary model (uncased base BERT, BIBREF9, trained on SQuAD 1.1, BIBREF0) running in the background as the workers write questions. This adversary is not particularly skilled at answering questions requiring coreference, but can follow obvious lexical cues—it thus helps workers avoid writing questions that shortcut coreferential reasoning.
Quoref contains more than 15K questions whose answers are spans or sets of spans in 3.5K paragraphs from English Wikipedia that can be arrived at by resolving coreference in those paragraphs. We manually analyze a sample of the dataset (§SECREF3) and find that 78% of the questions cannot be answered without resolving coreference. We also show (§SECREF4) that the best system performance is 49.1% $F_1$, while the estimated human performance is 87.2%. These findings indicate that this dataset is an appropriate benchmark for coreference-aware reading comprehension.
We manually analyze a sample of the dataset (§SECREF3) and find that 78% of the questions cannot be answered without resolving coreference.
Answer: 78% of the questions cannot be answered without resolving coreference.