DataHammer/scimrc huggingface dataset:
Question: What baselines are the proposed method compared against?
Evidence: Adhering to the experimental settings of BIBREF1 , we concatenate sentence representations produced from our model with those obtained from the state-of-the-art unsupervised learning model (Layer Normalized Skip-Thoughts, ST-LN) BIBREF31 . We evaluate the quality of sentence representations produced from different variants of our encoders on well-known transfer tasks: movie review sentiment (MR) BIBREF32 , customer reviews (CR) BIBREF33 , subjectivity (SUBJ) BIBREF34 , opinion polarity (MPQA) BIBREF35 , paraphrase identification (MSRP) BIBREF36 , binary sentiment classification (SST) BIBREF37 , SICK entailment and SICK relatedness BIBREF38 .
Following the experimental design of BIBREF1 , we conduct experiments on three different learning objectives: Cap2All, Cap2Cap, Cap2Img. Under Cap2All, the model is trained to predict both the target caption and the associated image: INLINEFORM0 . Under Cap2Cap, the model is trained to predict only the target caption ( INLINEFORM1 ) and, under Cap2Img, only the associated image ( INLINEFORM2 ).
Adhering to the experimental settings of BIBREF1 , we concatenate sentence representations produced from our model with those obtained from the state-of-the-art unsupervised learning model (Layer Normalized Skip-Thoughts, ST-LN) BIBREF31 .
Following the experimental design of BIBREF1 , we conduct experiments on three different learning objectives: Cap2All, Cap2Cap, Cap2Img.
Answer: The answers are shown as follows:
* (Layer Normalized Skip-Thoughts, ST-LN) BIBREF31
* Cap2All, Cap2Cap, Cap2Img
Question: What is Mount Rainier?
Answer: Mount Rainier is a mountain in Washington State. It is part of a Cascade Range that spans across the Pacific Northwest of the America. Having an elevation of 14,411 feet. its highest point is always covered with snow. It is also a popular destination for many people for hiking and other outdoor activities.
Question: What is Mario Puzo's most famous book?
Answer: The Godfather.  Originally published in 1969, is a novel about a fictional crime family in New York.
Question: What are the baselines?
Evidence: FLOAT SELECTED: Table 2: Results of baselines and scores of top 3 participants on valid and test datasets.
FLOAT SELECTED: Table 2: Results of baselines and scores of top 3 participants on valid and test datasets.
Answer: CNN, LSTM, BERT
Question: How many professions did John Dassow have
Answer: John Dassow, Jr. (August 10, 1845 – October 8, 1917) was an American farmer, manufacturer, and politician.

Born in what is now Mecklenburg, Germany, Dassow emigrated with his parents, in 1861 to the United States and settled on a farm in the town of Sheboygan Falls, Sheboygan County, Wisconsin. In 1864 and 1865, Dassow served in the 30th Wisconsin Volunteer Infantry Regiment during the American Civil War. Dassow was a farmer and owned the Sheboygan County Mills. He was also involved with cheese factories and the real estate business. Dassow served on the Sheboygan County Board of Supervisors and was chairman of the county board. He was also treasurer of the local school board. In 1893, Dassow served in the Wisconsin State Assembly and was a Democrat. In 1917, Dassow died at his house in the town of Sheboygan Falls from ill health.
Question: What year did the Star Wars Trilogy premier in theaters?
Answer: The three films that consist of the original Star Wars Trilogy were released between 1977 and 1983. The films are below, listed in chronological order with their respective release dates:

- Star Wars (1977)
- The Empire Strikes Back (1980)
- Return of the Jedi (1983)

Following the original trilogy, another three films referred to as the prequel trilogy was released from 1999-2005:

- The Phantom Menace (1999)
- Attack of the Clones (2002)
- Revenge of the Sith (2005)

The most recent, sequel trilogy was released between 2015 and 2019:

- The Force Awakens (2015)
- The Last Jedi (2017)
- The Rise of Skywalker (2019)
Question: Classify each of the following as either titles by Ann M. Martin or Gertrude Chandler Warner: Bummer Summer, Belle Teal, A Corner of the Universe, Ten Kids, No Pets, Everything for a Dog, Surprise Island, Blue Bay Mystery, Mystery Ranch, Benny Uncovers a Mystery, The Boxcar Children
Answer: Ann M. Martin: Bummer Summer, Belle Teal, A Corner of the Universe, Ten Kids, No Pets, Everything for a Dog
Gertrude Chandler Warner: Surprise Island, Blue Bay Mystery, Mystery Ranch, Benny Uncovers a Mystery, The Boxcar Children
Question: Do vegetarians eat meat?
Answer: No, vegetarians do not eat meat. People who eat meat are called carnivores or omnivores. People who do not eat meat are called vegetarians. People who eat only fish are called pescatarians. People who do not consume any animals products are called vegans.
Question: where is Shelburne?
Answer: Shelburne lies at the southwest corner of Nova Scotia, at roughly the same latitude as Portland, Maine in the United States. The Mi'kmaq call the large and well-sheltered harbour Logumkeegan or Sogumkeagum.
Question: Give me 3 totally distinct ways to eat a banana
Answer: Peel and take bites of the whole banana; cut up the banana into slices; mash the banana and mix it with milk in a blender
Question: What are the four main linguistic and cultural regions of Switzerland?
Evidence: Switzerland originates from the Old Swiss Confederacy established in the Late Middle Ages, following a series of military successes against Austria and Burgundy; the Federal Charter of 1291 is considered the country's founding document. Swiss independence from the Holy Roman Empire was formally recognised in the Peace of Westphalia in 1648. Switzerland has maintained a policy of armed neutrality since the 16th century and has not fought an international war since 1815. It joined the United Nations only in 2002, but pursues an active foreign policy that include frequent involvement in peace-building processes worldwide.

Switzerland is the birthplace of the Red Cross, one of the world's oldest and best-known humanitarian organisations, and hosts the headquarters or offices of most major international institutions, including the WTO, the WHO, the ILO, FIFA, and the United Nations. It is a founding member of the European Free Trade Association (EFTA), but not part of the European Union (EU), the European Economic Area, or the Eurozone; however, it participates in the European single market and the Schengen Area through bilateral treaties. Switzerland is a federal republic composed of 26 cantons, with federal authorities based in Bern.

It has four main linguistic and cultural regions: German, French, Italian and Romansh. Although most Swiss are German-speaking, national identity is fairly cohesive, being rooted in a common historical background, shared values such as federalism and direct democracy, and Alpine symbolism. Swiss identity transcends language, ethnicity, and religion, leading to Switzerland being described as a Willensnation ("nation of volition") rather than a nation state.
Answer: German, French, Italian and Romansh
Question: Why did the authots conduct two extensive annotation studies?
Evidence: We create a new corpus which is, to the best of our knowledge, the largest corpus that has been annotated within the argumentation mining field to date. We choose several target domains from educational controversies, such as homeschooling, single-sex education, or mainstreaming. A novel aspect of the corpus is its coverage of different registers of user-generated Web content, such as comments to articles, discussion forum posts, blog posts, as well as professional newswire articles.
Since the data come from a variety of sources and no assumptions about its actual content with respect to argumentation can be drawn, we conduct two extensive annotation studies. In the first study, we tackle the problem of relatively high “noise” in the retrieved data. In particular, not all of the documents are related to the given topics in a way that makes them candidates for further deep analysis of argumentation (this study results into 990 annotated documents). In the second study, we discuss the selection of an appropriate argumentation model based on evidence in argumentation research and propose a model that is suitable for analyzing micro-level argumention in user-generated Web content. Using this model, we annotate 340 documents (approx. 90,000 tokens), reaching a substantial inter-annotator agreement. We provide a hand-analysis of all the phenomena typical to argumentation that are prevalent in our data. These findings may also serve as empirical evidence to issues that are on the spot of current argumentation research.
From the computational perspective, we experiment on the annotated data using various machine learning methods in order to extract argument structure from documents. We propose several novel feature sets and identify configurations that run best in in-domain and cross-domain scenarios. To foster research in the community, we provide the annotated data as well as all the experimental software under free license.
The rest of the article is structured as follows. First, we provide an essential background in argumentation theory in section SECREF2 . Section SECREF3 surveys related work in several areas. Then we introduce the dataset and two annotation studies in section SECREF4 . Section SECREF5 presents our experimental work and discusses the results and errors and section SECREF6 concludes this article.
Because the data come from a variety of sources and no assumptions about their actual content with respect to argumentation can be drawn, we conduct two extensive annotation studies.
Answer: Because the data come from a variety of sources and no assumptions about their actual content with respect to argumentation can be drawn.
Question: What is the BERT?
Evidence: Here, we analyze the BERT transformer model on the hate speech detection task. BERT is a multi-layer bidirectional transformer encoder trained on the English Wikipedia and the Book Corpus containing 2,500M and 800M tokens, respectively, and has two models named BERTbase and BERTlarge. BERTbase contains an encoder with 12 layers (transformer blocks), 12 self-attention heads, and 110 million parameters whereas BERTlarge has 24 layers, 16 attention heads, and 340 million parameters. Extracted embeddings from BERTbase have 768 hidden dimensions BIBREF11. As the BERT model is pre-trained on general corpora, and for our hate speech detection task we are dealing with social media content, therefore as a crucial step, we have to analyze the contextual information extracted from BERT' s pre-trained layers and then fine-tune it using annotated datasets. By fine-tuning we update weights using a labelled dataset that is new to an already trained model. As an input and output, BERT takes a sequence of tokens in maximum length 512 and produces a representation of the sequence in a 768-dimensional vector. BERT inserts at most two segments to each input sequence, [CLS] and [SEP]. [CLS] embedding is the first token of the input sequence and contains the special classification embedding which we take the first token [CLS] in the final hidden layer as the representation of the whole sequence in hate speech classification task. The [SEP] separates segments and we will not use it in our classification task. To perform the hate speech detection task, we use BERTbase model to classify each tweet as Racism, Sexism, Neither or Hate, Offensive, Neither in our datasets. In order to do that, we focus on fine-tuning the pre-trained BERTbase parameters. By fine-tuning, we mean training a classifier with different layers of 768 dimensions on top of the pre-trained BERTbase transformer to minimize task-specific parameters.
BERT is a multi-layer bidirectional transformer encoder trained on the English Wikipedia and the Book Corpus containing 2,500M and 800M tokens, respectively, and has two models named BERTbase and BERTlarge.
Answer: BERT is a multi-layer bidirectional transformer encoder trained on the English Wikipedia and the Book Corpus containing 2,500M and 800M tokens, respectively, and has two models named BERTbase and BERTlarge.
Question: What changes they did on input module?
Evidence: For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader, responsible only for encoding the words into a sentence embedding. The second component is the input fusion layer, allowing for interactions between sentences. This resembles the hierarchical neural auto-encoder architecture of BIBREF9 and allows content interaction between sentences. We adopt the bi-directional GRU for this input fusion layer because it allows information from both past and future sentences to be used. As gradients do not need to propagate through the words between sentences, the fusion layer also allows for distant supporting sentences to have a more direct interaction.
replacing this single GRU with two different components
first component is a sentence reader
second component is the input fusion layer
Answer: The answers are shown as follows:
* For the DMN+, we propose replacing this single GRU with two different components. The first component is a sentence reader
* The second component is the input fusion layer
Question: Do they report results only on English data?
Evidence: Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets. The Wikipedia and Toronto books sentences have been tokenized using the Stanford NLP library BIBREF10 , while for tweets we used the NLTK tweets tokenizer BIBREF11 . For training, we select a sentence randomly from the dataset and then proceed to select all the possible target unigrams using subsampling. We update the weights using SGD with a linearly decaying learning rate.
Unsupervised Similarity Evaluation Results. In Table TABREF19 , we see that our Sent2Vec models are state-of-the-art on the majority of tasks when comparing to all the unsupervised models trained on the Toronto corpus, and clearly achieve the best averaged performance. Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items. Also, C-PHRASE uses data three times the size of the Toronto book corpus. Interestingly, our model outperforms C-PHRASE when trained on Wikipedia, as shown in Table TABREF21 , despite the fact that we use no parse tree information. Official STS 2017 benchmark. In the official results of the most recent edition of the STS 2017 benchmark BIBREF35 , our model also significantly outperforms C-PHRASE, and in fact delivers the best unsupervised baseline method.
Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets. 
Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items. 
Answer: Yes.
Question: What countries have won the rugby world cup?
Evidence: The most important competition in rugby union is the Rugby World Cup, a men's tournament that has taken place every four years since the inaugural event in 1987. South Africa are the reigning champions, having defeated England in the final of the 2019 Rugby World Cup in Yokohama. New Zealand and South Africa have each won the title three times (New Zealand: 1987, 2011, 2015; South Africa: 1995, 2007, 2019), Australia have won twice (1991 and 1999), and England once (2003). England is the only team from the Northern Hemisphere to have won the Rugby World Cup.
Answer: - New Zealand
- Australia
- South Africa
- England
Question: What flavors of girl scout cookies are available?
Answer: There are currently nine available girl scout cookie flavors, along with a rotating flavor that may change every year or make it's way into the cookie rotation. Adventurefuls cookies are brownie-inspired cookies with caramel flavored creme. Lemon-Ups cookies are crispy lemon flavored cookies. Trefoils cookies are iconic shortbread cookies. Do-si-dos cookies are peanut butter sandwich cookies. Samoas cookies are chewy and rich, with chocolate, coconut, and caramel. Tagalongs cookies are peanut butter patties covered in chocolate. Thin Mints are thin, crisp, chocolate mint cookies. Girl Scout S'mores are chocolatey, marshmallow flavored graham cookies. Toffee-tastic cookies are gluten-free, rich, and buttery toffee cookies. The current rotating flavor is Rasberry Rally, which are thin, crispy, and raspberry infused cookies that are covered in chocolate.
Question: How is Toshiyuki written?
Evidence: Toshiyuki can be written using different combinations of kanji characters. Here are some examples:

敏幸, "agile, happiness"
敏行, "agile, go"
敏之, "agile, of"
敏志, "agile, determination"
敏恭, "agile, respectful"
俊幸, "talented, happiness"
俊行, "talented, go"
俊之, "talented, of"
俊志, "talented, determination"
俊恭, "talented, respectful"
利幸, "benefit, happiness"
利行, "benefit, go"
利之, "benefit, of"
寿幸, "long life, happiness"
寿行, "long life, go"
寿之, "long life, of"
年幸, "year, happiness"
年行, "year, go"
年之, "year, of"
The name can also be written in hiragana としゆき or katakana トシユキ.
Answer: Toshiyuki can be written using different combinations of kanji characters.
Question: What is a zerbert?
Answer: A zerbert is the act of touching lips on the skin of another person and exhaling in a way that makes a loud fart-like sound. It's likely that zerberts have been performed for as long as humans have evolved from the apes as a species with smooth enough skin to perform the task, but the term was popularized in the United States during the 1980s on a The Cosby Show, a popular 30-minute comedy that aired on the NBC television network. Most typically, adults perform zerberts (or zerbert someone, to use the term as a verb) on children in order to make them laugh.
Question: Why does Nadal play tennis left-handed?
Evidence: At age 8, Nadal won an under-12 regional tennis championship at a time when he was also a promising football player. This made Toni Nadal intensify training, and it was at that time that his uncle encouraged Nadal to play left-handed for a natural advantage on the tennis court, after studying Nadal's then two-handed forehand stroke.
Answer: When he was younger, Rafa Nadal used to play with a double handed forehand. His uncle then recommended that he switch to a left-handed stroke in order to have a natural advantage on court.
Question: What open source library are models implemented in?
Evidence: The decoder is the one used in BIBREF12, BIBREF13, BIBREF10 with the same hyper-parameters. For the encoder module, both the low-level and high-level encoders use a two-layers multi-head self-attention with two heads. To fit with the small number of record keys in our dataset (39), their embedding size is fixed to 20. The size of the record value embeddings and hidden layers of the Transformer encoders are both set to 300. We use dropout at rate 0.5. The models are trained with a batch size of 64. We follow the training procedure in BIBREF21 and train the model for a fixed number of 25K updates, and average the weights of the last 5 checkpoints (at every 1K updates) to ensure more stability across runs. All models were trained with the Adam optimizer BIBREF37; the initial learning rate is 0.001, and is reduced by half every 10K steps. We used beam search with beam size of 5 during inference. All the models are implemented in OpenNMT-py BIBREF38. All code is available at https://github.com/KaijuML/data-to-text-hierarchical
All the models are implemented in OpenNMT-py [14]. All code is available at https://github.com/KaijuML/data-to-text-hierarchical
Answer: OpenNMT-py.
Question: Give a bullet point list of all the names mentioned in this included paragraph.
Evidence: When Mohammad inherited the newly born sultanate from his father Alauddin, the land was still infested with thieves and robbers. As such he spent the entire part of his reign in establishing law in his land. He ordered his governors to never give refuge to anyone who defied his authority. This campaign was successful and at the end of his reign,the land became peaceful and law abiding. He also sat on the takht-ē-firoza or The Turquoise throne which was gifted to him by Kapaya Nayaka.
Answer: - Mohammad
- Alauddin
- Kapaya Nayaka
Question: Give me a title of one of the "Harry Potter" novels.
Answer: Harry Potter and the Order of the Phoenix
Question: Is all text in this dataset a question, or are there unrelated sentences in between questions?
Evidence: Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. 
Answer: the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences
Question: Name some of the most well known soccer players in the world
Answer: While the best soccer players are a topic of regular debate among fans of the sport, the general consensus is that the most popular players known throughout the world are: Lionel Messi (Argentina), Cristiano Ronaldo (Portugal),  Pele (Brazil) and Diego Maradona (Argentina). Diego Maradona and Pele were both known worldwide as top talents in an earlier era, helping drive the growth of the biggest sport in the world and consequently leading their club teams and nations to trophies. Lionel Messi and Cristiano Ronaldo also achieved incredible success, doing so during the rise of social media. Both Lionel and Cristiano have grown to 400+ million social media followers in different platforms-- putting them as some of the most followed individuals in the world.
Question: What are the words of House Hastwyck?
Answer: "None So Dutiful"
Question: Who won a gold medal alongside Waters in the coxed four?
Evidence: A member of the Hamilton Rowing Club, Waters began rowing as a 17-year-old. In 1929 he was a member of the Hamilton four that won the national championship. In March of the following year, he participated in a trial race for selection of the New Zealand team to compete at the 1930 British Empire Games in Hamilton, Ontario, but was not initially chosen for the 12-man squad. However, he was included in the final squad selected in late June, and competed in both the men's eight and coxed four at the 1930 Empire Games. He won a gold medal in the coxed four, alongside Mick Brough, Jack Macdonald, Bert Sandos, and Arthur Eastwood (cox), and a silver medal in the eights, finishing three-quarters of a boat length behind the victorious English crew.
Answer: Mick Brough, Jack Macdonald, Bert Sandos, and Arthur Eastwood won a gold medal alongside Waters in the coxed four.