DataHammer/scimrc huggingface dataset:
Question: What pretrained LM is used?
Evidence: In contrast to such modularized dialogue system, end-to-end systems learn all components as a single model in a fully data-driven manner, and mitigate the lack of labeled data by sharing representations among different modules. In this paper, we build an end-to-end empathetic chatbot by fine-tuning BIBREF1 the Generative Pre-trained Transformer (GPT) BIBREF2 on the PersonaChat dataset BIBREF3 and the Empathetic-Dialogue dataset BIBREF4 . We establish a web-based user interface which allows multiple users to asynchronously chat with CAiRE online. CAiRE can also collect user feedback and continuously improve its response quality and discard undesirable generation behaviors (e.g. unethical responses) via active learning and negative training.
In this paper, we build an end-to-end empathetic chatbot by fine-tuning BIBREF1 the Generative Pre-trained Transformer (GPT) BIBREF2 on the PersonaChat dataset BIBREF3 and the Empathetic-Dialogue dataset BIBREF4 .
Answer: The answers are shown as follows:
* Generative Pre-trained Transformer (GPT)
Question: What dataset does the author collect?
Evidence: We assembled a dataset of 1,949 cleaned pathology reports. Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes. The reports are collected from four different body parts or primary sites from multiple patients. The distribution of reports across different primary diagnoses and primary sites is reported in tab:report-distribution. The dataset was developed in three steps as follows.
Collecting pathology reports: The total of 11,112 pathology reports were downloaded from NCI's Genomic Data Commons (GDC) dataset in PDF format BIBREF9 . Out of all PDF files, 1,949 reports were selected across multiple patients from four specific primary sites—thymus, testis, lung, and kidney. The selection was primarily made based on the quality of PDF files.
Cleaning reports: The next step was to extract the text content from these reports. Due to the significant time expense of manually re-typing all the pathology reports, we developed a new strategy to prepare our dataset. We applied an Optical Character Recognition (OCR) software to convert the PDF reports to text files. Then, we manually inspected all generated text files to fix any grammar/spelling issues and irrelevant characters as an artefact produced by the OCR system.
Splitting into training-testing data: We split the cleaned reports into 70% and 30% for training and testing, respectively. This split resulted in 1,364 training, and 585 testing reports.
We assembled a dataset of 1,949 cleaned pathology reports. Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes.
Answer: They assembled a dataset of 1,949 cleaned pathology reports. Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes.
Question: Given a reference text about the blood hammer phenomenon, tell me the background of the term "blood-hammer".
Evidence: The blood hammer phenomenon is a sudden increase of the upstream blood pressure in a blood vessel (especially artery or arteriole) when the bloodstream is abruptly blocked by vessel obstruction. The term "blood-hammer" was introduced in cerebral hemodynamics by analogy with the hydraulic expression "water hammer", already used in vascular physiology to designate an arterial pulse variety, the "water-hammer pulse". Complete understanding of the relationship between mechanical parameters in vascular occlusions is a critical issue, which can play an important role in the future diagnosis, understanding and treatment of vascular diseases.
Answer: The term "blood-hammer" was introduced by analogy with the hydraulic expression "water hammer" that was already used in vascular physiology.
Question: What is the difference between a fruit and vegetable?
Answer: Fruits contain seeds and are usually developed from the flowering part of a plant while Vegetables are an edible plant or part of a plant.
Question: What is the wealthiest zip code in the United States?
Answer: Atherton, California
Question: How do they obtain region descriptions and object annotations?
Evidence: In this work, we introduce a methodology that provides VQA algorithms with the ability to generate human interpretable attention maps which effectively ground the answer to the relevant image regions. We accomplish this by leveraging region descriptions and object annotations available in the Visual Genome dataset, and using these to automatically construct attention maps that can be used for attention supervision, instead of requiring human annotators to manually provide grounding labels. Our framework achieves competitive state-of-the-art VQA performance, while generating visual groundings that outperform other algorithms that use human annotated attention during training.
In this work, we introduce a methodology that provides VQA algorithms with the ability to generate human interpretable attention maps which effectively ground the answer to the relevant image regions. We accomplish this by leveraging region descriptions and object annotations available in the Visual Genome dataset, and using these to automatically construct attention maps that can be used for attention supervision, instead of requiring human annotators to manually provide grounding labels.
Answer: they are available in the Visual Genome dataset
Question: Extract the list of players mentioned in the text. Separate them with a comma.
Evidence: Meanwhile, in the cabin, goalkeeper Harry Gregg was regaining consciousness, thinking that he was dead. He felt blood on his face and "didn't dare put  hand up. [He] thought the top of  head had been taken off, like a hard boiled egg." Just above him, light shone into the cabin, so Gregg kicked the hole wide enough for him to escape. He also managed to save some passengers, among them teammates Bobby Charlton and Dennis Viollet, who were strapped into their seats away from the wreckage.
Answer: Harry Gregg, Bobby Charlton, Dennis Viollet
Question: Where are oysters found?
Evidence: Oyster is the common name for a number of different families of salt-water bivalve molluscs that live in marine or brackish habitats. In some species, the valves are highly calcified, and many are somewhat irregular in shape. Many, but not all oysters are in the superfamily Ostreoidea.
Answer: Oyster is the common name for a number of different families of salt-water bivalve molluscs that live in marine or brackish habitats.
Question: Are the generators of all the baselines the same as the authors'?
Evidence: We compared our model with MLE, RL and GAN baselines. Since COCO and EMNLP2017 WMT don't have input while WeiboDial regards posts as input, we chose the following baselines respectively:
MLE: a RNN model trained with MLE objective BIBREF4 . Its extension, Seq2Seq, can work on the dialogue dataset BIBREF2 .
SeqGAN: The first text GAN model that updates the generator with policy gradient based on the rewards from the discriminator BIBREF7 .
LeakGAN: A variant of SeqGAN that provides rewards based on the leaked information of the discriminator for the generator BIBREF11 .
MaliGAN: A variant of SeqGAN that optimizes the generator with a normalized maximum likelihood objective BIBREF8 .
IRL: This inverse reinforcement learning method replaces the discriminator with a reward approximator to provide dense rewards BIBREF12 .
RAML: A RL approach to incorporate MLE objective into RL training framework, which regards BLEU as rewards BIBREF17 .
DialogGAN: An extension of SeqGAN tuned to dialogue generation task with MLE objective added to the adversarial objective BIBREF16 .
DPGAN: A variant of DialogGAN which uses a language model based discriminator and regards cross-entropy as rewards BIBREF13 .
Note that MLE, SeqGAN, LeakGAN, MaliGAN and IRL are the baselines on COCO and EMNLP2017 WMT, while MLE, RAML, DialogGAN, and DPGAN on WeiboDial. The original codes are used to test the baselines.
As for the details of the baselines, the generators of all the baselines except LeakGAN are the same as ours.
Answer: No, they aren't.
Question: Why is India Pale Ale (IPA) the best type of beer?
Answer: India Pale Ales (IPAs) are an ideal type of beer for both producers and drinkers for a variety of reasons.

Producers like IPAs because they are cheap and fast to produce compared to other types of beers.  In contrast to a lager, an IPA can be brewed and ready for market in less than half the time.

Drinkers like IPAs for their complex flavors which can vary depending on style from East Coast or West Cost, dry hopped or hazy.  Additionally, IPA often have a higher alcohol content than many other similar beers - which lends to a higher "band for buck" value.
Question: did they outperform previous methods?
Evidence: Table TABREF32 shows the results for De-En, Tr-En, Tr-En-morph datasets, where the best accuracy for models with the same depth and of similar sizes are marked in boldface. In almost all genres, DenseNMT models are significantly better than the baselines. With embedding size 256, where all models achieve their best scores, DenseNMT outperforms baselines by 0.7-1.0 BLEU on De-En, 0.5-1.3 BLEU on Tr-En, 0.8-1.5 BLEU on Tr-En-morph. We observe significant gain using other embedding sizes as well.
 In almost all genres, DenseNMT models are significantly better than the baselines.
Answer: Yes.
Question: What were the model's results on flood detection?
Evidence: We have used the following hardware for the experimentation: Windows 10 Education desktop consisting of intel core i-7 processor and 16GB RAM. We have used python 3.6 and Google colab notebook to execute our model and obtained the results discussed below: The train and test data have divided into 70-30 ratio and we got these results as shown in Table TABREF17 for the individual dataset and the combination of both. The pre-trained network was already trained and we used the target data Queensland flood which provided 96% accuracy with 0.118 Test loss in only 11 seconds provided we used only 70% of training labeled data. The second target data is Alberta flood with the same configuration of train-test split which provided 95% accuracy with 0.118 Test loss in just 19 seconds. As we can see it takes very less time to work with 20,000 of tweets (combined) and at times of emergency it can handle a huge amount of unlabeled data to classify into meaningful categories in minutes.
The train and test data have divided into 70-30 ratio and we got these results as shown in Table TABREF17 for the individual dataset and the combination of both. The pre-trained network was already trained and we used the target data Queensland flood which provided 96% accuracy with 0.118 Test loss in only 11 seconds provided we used only 70% of training labeled data. The second target data is Alberta flood with the same configuration of train-test split which provided 95% accuracy with 0.118 Test loss in just 19 seconds.
Answer: The answers are shown as follows:
* Queensland flood which provided 96% accuracy
* Alberta flood with the same configuration of train-test split which provided 95% accuracy
Question: How do you gain muscle?
Answer: Gaining muscle mass requires proper exercise and a good diet. A good diet is important to give the body the nutrient it needs in order to sustain muscle growth. On the flip side, properly stressing the muscles will stimulate them to grow. This requires a variance of different exercises to work the muscle group. Otherwise, the muscle will build resistance to an often repeated exercise, and growth for that muscle will plateau.
Question: Without quoting directly from the text, give me a summary of how rainbows are formed.
Evidence: A rainbow is an optical phenomenon that can occur under certain meteorological conditions. It is caused by reflection, refraction and dispersion of light in water droplets resulting in an effect similar to a spectrum of light appearing in the sky. It takes the form of a multicoloured circular arc. Rainbows caused by sunlight always appear in the section of sky directly opposite the Sun.
Rainbows can be full circles. However, the observer normally sees only an arc formed by illuminated droplets above the ground, and centered on a line from the Sun to the observer's eye.
In a primary rainbow, the arc shows red on the outer part and violet on the inner side. This rainbow is caused by light being refracted when entering a droplet of water, then reflected inside on the back of the droplet and refracted again when leaving it.
In a double rainbow, a second arc is seen outside the primary arc, and has the order of its colours reversed, with red on the inner side of the arc. This is caused by the light being reflected twice on the inside of the droplet before leaving it.
Answer: Rainbows are formed when light shines through water droplets, creating an optical phenomenon. The light is bent and reflected, creating a multicolored arc. Rainbows created by sunlight can be found in the sky, opposite the sun. Rainbows are usually observed as an arc, but can appear as full circles. A double rainbow appears when the light shining through each water droplet is reflected twice, creating a second arc, with the colours appearing in reverse order. The colors of the rainbow include; red, orange, yellow, green, blue, and violet. The colors appear in order of their wavelength, from longest to shortest.
Question: Which months were added to the calendar and why?
Answer: In order for the Roman calendar year which was ten months long, to be in sync with the lunar year, the months of January and February were added.
Question: How many times did Gordie Howe retire?
Evidence: Howe retired for the first time in 1971 and was immediately inducted into the Saskatchewan Sports Hall of Fame that same year. He was then inducted into the Hockey Hall of Fame the next year, but came back two years later to join his sons Mark and Marty on the Houston Aeros of the WHA. Although in his mid-40s, he scored over 100 points twice in six years, won two straight Avco World Trophies (1974 and 1975) and was named most valuable player in 1974. He made a brief return to the NHL in 1979–80, playing one season with the Hartford Whalers, then retired at age 52. His involvement with the WHA was central to their brief pre-NHL merger success, forcing the NHL to recruit European talent and expand to new markets.
Answer: Based on the provided information, Howe retired two times.

Howe retired for the first time in 1971, but then later joined his sons Mark and Marty on the Houston Aeros of the WHA. Howe would then return to the NHL in 1979 where he played one season before retiring for a second time.
Question: The Khongjom Day is celebrated by which Indian state
Answer: Khongjom Day is celebrated by Manipur to honor the brave heroes of 1891 Anglo-Manipur war.
Question: What is specific to gCAS cell?
Evidence: In this paper, we introduce a novel policy model to output multiple actions per turn (called multi-act), generating a sequence of tuples and expanding agents' expressive power. Each tuple is defined as $(\textit {continue}, \textit {act}, \textit {slots})$, where continue indicates whether to continue or stop producing new acts, act is an act type (e.g., inform or request), and slots is a set of slots (names) associated with the current act type. Correspondingly, a novel decoder (Figure FIGREF5) is proposed to produce such sequences. Each tuple is generated by a cell called gated Continue Act Slots (gCAS, as in Figure FIGREF7), which is composed of three sequentially connected gated units handling the three components of the tuple. This decoder can generate multi-acts in a double recurrent manner BIBREF18. We compare this model with baseline classifiers and sequence generation models and show that it consistently outperforms them.
In this paper, we introduce a novel policy model to output multiple actions per turn (called multi-act), generating a sequence of tuples and expanding agents' expressive power. Each tuple is defined as $(\textit {continue}, \textit {act}, \textit {slots})$, where continue indicates whether to continue or stop producing new acts, act is an act type (e.g., inform or request), and slots is a set of slots (names) associated with the current act type. Correspondingly, a novel decoder (Figure FIGREF5) is proposed to produce such sequences. Each tuple is generated by a cell called gated Continue Act Slots (gCAS, as in Figure FIGREF7), which is composed of three sequentially connected gated units handling the three components of the tuple. This decoder can generate multi-acts in a double recurrent manner BIBREF18. 
Answer: It has three sequentially connected units to output continue, act and slots generating multi-acts in a doble recurrent manner.
Question: How large is the Wikipedia revision dump dataset?
Evidence: The Wikipedia revision dumps that were previously introduced by Leskovec et al. leskovec2010governance contain eight GB (compressed size) revision edits with meta data.
The Wikipedia revision dumps that were previously introduced by Leskovec et al. leskovec2010governance contain eight GB (compressed size) revision edits with meta data.
Answer: The answers are shown as follows:
* eight GB
Question: Name every month that has 30 days
Answer: January, March, April, May, June, July, August, September, October, November, December
Question: Who did Donald's grandfather body guard for?
Evidence: Ross was born in Nairn, in Middlesex County, Ontario, the son of Donald Ross and Margaret Halbert. His grandfather was part of Duke Wellington's body guard at Waterloo, and was an escort to Napoleon Bonaparte on his exile to St. Helena. Ross was educated at Nairn Public School, and moved to Manitoba in 1874, where he worked on the first telegraph line. In 1877, he established a real estate business. Ross served on the Winnipeg City Council for eight years, the Winnipeg School Board for twenty-three years, and the Winnipeg Public Parks Board for eleven years. In 1880, he married Margaret McIvor.
Answer: Donald's grandfather was a body guard for Duke Wellington.
Question: Is Maison Ikkoku a manga series
Evidence: Maison Ikkoku is a manga series written and illustrated by Rumiko Takahashi. It was serialized in Shogakukan's Big Comic Spirits from November 15, 1980 to April 20, 1987. It has been adapted into an anime series and a live action film and two episode TV drama. The plot follows Yusaku Godai, a rōnin who resides in an old apartment complex named Maison Ikkoku as he studies for the university entrance exam. Yusaku's life progression and interactions progress episodically and focuses on his growing relationship with the apartment manager, Kyoko Otonashi.
Answer: Maison Ikkoku is a manga series written and illustrated by Rumiko Takahashi. It was serialized in Shogakukan's Big Comic Spirits from November 15, 1980 to April 20, 1987. It has been adapted into an anime series and a live action film and two episode TV drama.
Question: Classify the cities based on whether they are located in Malawi or Norfolk Island.
Mzuzu, Blantyre, Kingston, Lilongwe
Answer: Malawi - Blantyre, Lilongwe, Mzuzu
Norfolk Island - Kingston
Question: What are the pros and cons of high interest rates?
Answer: Rising interest rates are an advantage for savers who deposit money. Banks pay savers a higher return on their savings accounts. High interest rates also dampen high inflation as they make borrowing more expensive. This reduces the demand and can lead to falling prices. 
On the other hand, high interest rates make things like financing a house more difficult. Furthermore, the decreased demand can reduce economic growth.
Question: What was the Rolling Stones first no 1 hit
Answer: Its all over now
Question: My trophy does not fit my suitcase because it is to small.  I need to travel.  What could I do?
Answer: - Get a bigger suite case
- Don't bring the trophy
- Send the trophy in a box
Question: What data did the authors use to define new cross-lingual document classification tasks?
Evidence: There are many tasks in natural language processing which require the classification of sentences or longer paragraphs into a set of predefined categories. Typical applications are for instance topic identification (e.g. sports, news, $\ldots $ ) or product reviews (positive or negative). There is a large body of research on approaches for document classification. An important aspect to compare these different approaches is the availability of high quality corpora to train and evaluate them. Unfortunately, most of these evaluation tasks focus on the English language only, while there is an ever increasing need to perform document classification in many other languages. One could of course collect and label training data for other languages, but this would be costly and time consuming. An interesting alternative is “cross-lingual document classification”. The underlying idea is to use a representation of the words or whole documents which is independent of the language. By these means, a classifier trained on one language can be transferred to a different one, without the need of resources in that transfer language. Ideally, the performance obtained by cross-lingual transfer should be as close as possible to training the entire system on language specific resources. Such a task was first proposed by BIBREF0 using the Reuters Corpus Volume 2. The aim was to first train a classifier on English and then to transfer it to German, and vice versa. An extension to the transfer between English and French and Spanish respectively was proposed by BIBREF1 . However, only few comparative results are available for these transfer directions.
The contributions of this work are as follows. We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese. For each language, we define a train, development and test corpus. We also provide strong reference results for all transfer directions between the eight languages, e.g. not limited to the transfer between a foreign language and English. We compare two approaches, based either on multilingual word or sentence embeddings respectively. By these means, we hope to define a clear evaluation environment for highly multilingual document classification.
We extend previous works and use the data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks for eight very different languages, namely English, French, Spanish, Italian, German, Russian, Chinese and Japanese.
Answer: The data in the Reuters Corpus Volume 2 to define new cross-lingual document classification tasks.
Question: Why can't the whole world be 2 countries?
Answer: The whole world cannot be split into two countries because it would be impossible to maintain the balance of power between the two countries.
Question: How many planets are there?
Answer: There are eight planets in our native solar system. We used to consider there to be nine, but then scientists decided that Pluto doesn't fit the requirements to be a planet. I don't really know what that is, but that's what they decided. Mercury is the closest planet to the sun, and doesn't really have an atmosphere. Venus is subject to a runaway greenhouse effect. Earth is where we live. Mars is red, and slightly smaller than Earth. We've recently found evidence of water there. The asteroid belt comes next, which isn't really a planet. Following the asteroid belt are the gas giants Jupiter, Saturn, and Uranus. They have rings. Neptune comes last. Other solar systems seem to have planets too - in fact this appears to be far more common than we would have thought.