DataHammer/scimrc huggingface dataset:
Question: Can the method answer multi-hop questions?
Evidence: Its worth mentioning that TupleInf only combines parallel evidence i.e. each tuple must connect words in the question to the answer choice. For reliable multi-hop reasoning using OpenIE tuples, we can add inter-tuple connections to the support graph search, controlled by a small number of rules over the OpenIE predicates. Learning such rules for the Science domain is an open problem and potential avenue of future work.
For reliable multi-hop reasoning using OpenIE tuples, we can add inter-tuple connections to the support graph search, controlled by a small number of rules over the OpenIE predicates. Learning such rules for the Science domain is an open problem and potential avenue of future work.
Answer: Yes.
Question: Why is pickleball growing more popular than tennis?
Answer: Pickleball is easy to learn. Compared to tennis, it requires less strength and skills to play. It also takes up less space to set up a pickleball court. It does not create forearm tightness like tennis. It is a good sport for all ages.
Question: Segregate the below numbers as palindrome or not.
1001, 202, 11, 65, 76, 36, 88, 999, 12345
Answer: Palindrome - 1001, 202, 11, 88, 999
Not a palindrome - 65, 76, 36, 12345
Question: From the passage below, extract the first five member countries of OPEC
Evidence: The Organization of the Petroleum Exporting Countries (OPEC, /ˈoʊpɛk/ OH-pek) is an organization enabling the co-operation of leading oil-producing countries, in order to collectively influence the global market and maximise profit. Founded on 14 September 1960 in Baghdad by the first five members (Iran, Iraq, Kuwait, Saudi Arabia, and Venezuela), it has, since 1965, been headquartered in Vienna, Austria, although Austria is not an OPEC member state. As of September 2018, the 13 member countries accounted for an estimated 44 percent of global oil production and 81.5 percent of the world's proven oil reserves, giving OPEC a major influence on global oil prices that were previously determined by the so-called "Seven Sisters" grouping of multinational oil companies.

The formation of OPEC marked a turning point toward national sovereignty over natural resources, and OPEC decisions have come to play a prominent role in the global oil market and international relations. The effect can be particularly strong when wars or civil disorders lead to extended interruptions in supply. In the 1970s, restrictions in oil production led to a dramatic rise in oil prices and in the revenue and wealth of OPEC, with long-lasting and far-reaching consequences for the global economy. In the 1980s, OPEC began setting production targets for its member nations; generally, when the targets are reduced, oil prices increase. This has occurred most recently from the organization's 2008 and 2016 decisions to trim oversupply.
Answer: The first five members of the Organization of the Petroleum Exporting Countries (OPEC) were Iran, Iraq, Kuwait, Saudi Arabia and Venezuela. As of September 2018, OPEC officially has 13 member countries.
Question: What are common florals found in Zigalga National Park?
Evidence: Zigalga National Park (Russian: Национальный парк «Зигальга») is located on the high Zigalga Ridge of the Southern Ural Mountains in Russia, on the transition between Europe and Siberia. Much of the territory is untouched by human activity and so supports Ice Age relict floral communities through all altitude zones - pine and birch forest, dark coniferous taiga, alpine meadows and mountain tundra. The park was officially created in 2019. The park is located in the Katav-Ivanovsky District of Chelyabinsk Oblast.

Topography
Ziglalga National Park connects two large protected areas on the main ridge of the Southern Urals, with Zyuratkul National Park to the northeast, and the South Ural Nature Reserve to the southwest. The park includes headwaters of the Yuryuzan River and the Kutkurka River. The highest point is at Mount Poperechnaya (1,387 metres (4,551 ft))
Answer: Zigalga National Park has the majority of its territory untouched by human activity and includes pine and birch forest, dark coniferous taiga, alpine meadows and mountain tundra.
Question: Under what settings do they propose to pre-train the encoder and the decoder of a sequence-to-sequence model?
Evidence: In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. We propose to pretrain the encoder and the decoder of a sequence-to-sequence model under both monolingual and cross-lingual settings. The pre-training objective encourages the model to represent different languages in the shared space, so that we can conduct zero-shot cross-lingual transfer. After the pre-training procedure, we use monolingual data to fine-tune the pre-trained model on downstream NLG tasks. Then the sequence-to-sequence model trained in a single language can be directly evaluated beyond that language (i.e., accepting multi-lingual input and producing multi-lingual output). Experimental results on question generation and abstractive summarization show that our model outperforms the machine-translation-based pipeline methods for zero-shot cross-lingual generation. Moreover, cross-lingual transfer improves NLG performance of low-resource languages by leveraging rich-resource language data. Our implementation and data are available at https://github.com/CZWin32768/xnlg.
In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. We propose to pretrain the encoder and the decoder of a sequence-to-sequence model under both monolingual and cross-lingual settings.
Answer: Monolingual and cross-lingual settings.
Question: Was John Clark Dore president of State Savings Institution in 1872?
Evidence: John Clark Dore (March 22, 1822 – December 14, 1900) was an American educator and politician from New Hampshire. A graduate of Dartmouth College, Dore briefly taught in Boston, Massachusetts before he was recruited to come to Chicago, Illinois to serve as its first superintendent of school. Dore was also a successful merchant, serving as the vice president of the Chicago Board of Trade. He was elected to the Illinois Senate in 1868, where he helped to establish the Illinois Humane Society.

He assumed the presidency of the Commercial Insurance Company, then was named president of the Chicago Board of Underwriters in 1869. Following the Great Chicago Fire of 1871, he was named the president of the State Savings Institution, resigning in 1873. Dore also presided over the Newsboys' and Bootblacks' Home and donated a building to them in 1884.
Answer: Yes, he was named president of the State Savings Institution in 1871 and did not resign until 1873.
Question: Are turtles amphibians or reptiles?
Answer: Turtles are reptiles. Some may assume Turtles are amphibian because they live in water. But that is not the critical factor. Many other animals live in waters, such as whales or crocodiles. To be an amphibian the animal has to have life stage in which it exclusively lives and breathes in water such as frogs.
Question: What are some sports that are played with a round ball?
Answer: Cricket, Baseball, Basketball, Volleyball, Tennis, Pickleball, Table Tennis, Soccer, Racquetball, Hockey, Squash, handball
Question: What does MPAD stand for？
Evidence: The concept of message passing over graphs has been around for many years BIBREF0, BIBREF1, as well as that of graph neural networks (GNNs) BIBREF2, BIBREF3. However, GNNs have only recently started to be closely investigated, following the advent of deep learning. Some notable examples include BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12. These approaches are known as spectral. Their similarity with message passing (MP) was observed by BIBREF9 and formalized by BIBREF13 and BIBREF14.
The MP framework is based on the core idea of recursive neighborhood aggregation. That is, at every iteration, the representation of each vertex is updated based on messages received from its neighbors. All spectral GNNs can be described in terms of the MP framework.
GNNs have been applied with great success to bioinformatics and social network data, for node classification, link prediction, and graph classification. However, a few studies only have focused on the application of the MP framework to representation learning on text. This paper proposes one such application. More precisely, we represent documents as word co-occurrence networks, and develop an expressive MP GNN tailored to document understanding, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Evaluation on 10 document classification datasets show that our architectures learn representations that are competitive with the state-of-the-art. Furthermore, ablation experiments shed light on the impact of various architectural choices.
In what follows, we first provide some background about the MP framework (in sec. SECREF2), thoroughly describe and explain MPAD (sec. SECREF3), present our experimental framework (sec. SECREF4), report and interpret our results (sec. SECREF5), and provide a review of the relevant literature (sec. SECREF6).
More precisely, we represent documents as word co-occurrence networks, and develop an expressive MP GNN tailored to document understanding, the Message Passing Attention network for Document understand_x001f_ing (MPAD).
Answer: It refers to Message Passing Attention network for Document understand_x001f_ing.
Question: Who is the best actor in the world
Answer: Shah Rukh Khan
Tom Cruise 
Keanu reeves
Question: is the dataset balanced across the four languages?
Evidence: The data stems from a joint ADAPT-Microsoft project. An overview of the provided dataset is given in Table TABREF16 . Notice that the available amount of data differs per language.
An overview of the provided dataset is given in Table TABREF16 . Notice that the available amount of data differs per language.
Answer: No.
Question: What do the models that they compare predict?
Evidence: The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. Parameters are tuned using the development data. Given the general robust performance of SVMs in the literature relative to other similar classifiers on variation tasks (c.f., Dunn, et al., 2016), we forego a systematic evaluation of classifiers.
This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model. The main experiments have focused on a dynamic syntactic feature set, showing that it is possible to predict dialect membership within-domain with only a small loss of performance against lexical models. This work raises two remaining problems:
The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features.
This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model. 
Answer: The answers are shown as follows:
* national dialects of English
Question: What are the most common languages in the world?
Answer: English, Spanish, Chinese, Arabic, Hindi
Question: How many triangles can be formed with 6 matchsticks of equal size without breaking or overlapping them?
Answer: 4 triangles if you arrange them in 3 dimension like a pyramid.
Question: What are some titles from George Orwell?
Answer: Nineteen Eighty-Four, Down and Out in Paris and London, Animal Farm, Burmese Days
Question: How many texts/datapoints are in the SemEval, TASS and SENTIPOLC datasets?
Evidence: FLOAT SELECTED: Table 3: Datasets details from each competition tested in this work
FLOAT SELECTED: Table 3: Datasets details from each competition tested in this work
Answer: Total number of annotated data:
Semeval'15: 10712
Semeval'16: 28632
Tass'15: 69000
Sentipol'14: 6428
Question: What is heavier? A pound of feathers or a pound of iron?
Answer: A pound of feathers and a pound of iron both weigh the same.
Question: How do they combine the models?
Evidence: Table TABREF24 shows performance of ensemble models by combining prediction results of the best context-aware logistic regression model and the best context-aware neural network model. We used two strategies in combining prediction results of two types of models. Specifically, the Max Score Ensemble model made the final decisions based on the maximum of two scores assigned by the two separate models; instead, the Average Score Ensemble model used the average score to make final decisions.
We used two strategies in combining prediction results of two types of models. Specifically, the Max Score Ensemble model made the final decisions based on the maximum of two scores assigned by the two separate models; instead, the Average Score Ensemble model used the average score to make final decisions.
Answer: The answers are shown as follows:
* maximum of two scores assigned by the two separate models
* average score
Question: Do they report results only on English data?
Evidence: A serious challenge in the field of AV is the lack of publicly available (and suitable) corpora, which are required to train and evaluate AV methods. Among the few publicly available corpora are those that were released by the organizers of the well-known PAN-AV competitions BIBREF11 , BIBREF5 , BIBREF12 . In regard to our experiments, however, we cannot use these corpora, due to the absence of relevant meta-data such as the precise time spans where the documents have been written as well as the topic category of the texts. Therefore, we decided to compile our own corpora based on English documents, which we crawled from different publicly accessible sources. In what follows, we describe our three constructed corpora, which are listed together with their statistics in Table TABREF23 . Note that all corpora are balanced such that verification cases with matching (Y) and non-matching (N) authorships are evenly distributed.
Therefore, we decided to compile our own corpora based on English documents, which we crawled from different publicly accessible sources.
Answer: Yes.
Question: Extract a list of all countries sharing a border with Germany from this passage.
Evidence: Germany is the seventh-largest country in Europe; bordering Denmark to the north, Poland and the Czech Republic to the east, Austria to the southeast, and Switzerland to the south-southwest. France, Luxembourg and Belgium are situated to the west, with the Netherlands to the northwest. Germany is also bordered by the North Sea and, at the north-northeast, by the Baltic Sea. German territory covers 357,022 km2 (137,847 sq mi), consisting of 348,672 km2 (134,623 sq mi) of land and 8,350 km2 (3,224 sq mi) of water.

Elevation ranges from the mountains of the Alps (highest point: the Zugspitze at 2,963 metres or 9,721 feet) in the south to the shores of the North Sea (Nordsee) in the northwest and the Baltic Sea (Ostsee) in the northeast. The forested uplands of central Germany and the lowlands of northern Germany (lowest point: in the municipality Neuendorf-Sachsenbande, Wilstermarsch at 3.54 metres or 11.6 feet below sea level) are traversed by such major rivers as the Rhine, Danube and Elbe. Significant natural resources include iron ore, coal, potash, timber, lignite, uranium, copper, natural gas, salt, and nickel.
Answer: Following countries share a border with Germany:
- Denmark
- Poland
- Czech Republic
- Austria
- Switzerland
- France
- Luxembourg
- Belgium
- Netherlands
Question: What are the baseline state of the art models?
Evidence: FLOAT SELECTED: Table 3: Evaluation results of different approaches compared to ours
FLOAT SELECTED: Table 3: Evaluation results of different approaches compared to ours
Answer: Stanford NER, BiLSTM+CRF, LSTM+CNN+CRF, T-NER and BiLSTM+CNN+Co-Attention
Question: What are some good items to keep in your pantry if you want to cook Asian food?
Answer: Pantry items that are commonly used in eastern Asian cuisine are Soy Sauce, Fish Sauce, Oyster Sauce, rice vinegar, sesame oil, rice cooking wine (mirin), rice, and MSG.
Question: Do the provided analyses help for better learning of cross-lingual word embeddings for translation purposes?
Evidence: Building a machine translation (MT) system requires lots of bilingual data. Neural MT models BIBREF0 , which become the current standard, are even more difficult to train without huge bilingual supervision BIBREF1 . However, bilingual resources are still limited to some of the selected language pairs—mostly from or to English.
A workaround for zero-resource language pairs is translating via an intermediate (pivot) language. To do so, we need to collect parallel data and train MT models for source-to-pivot and pivot-to-target individually; it takes a double effort and the decoding is twice as slow.
Unsupervised learning is another alternative, where we can train an MT system with only monolingual corpora. Decipherment methods BIBREF2 , BIBREF3 are the first work in this direction, but they often suffer from a huge latent hypothesis space BIBREF4 .
Recent work by unmt-artetxe and unmt-facebook train sequence-to-sequence MT models of both translation directions together in an unsupervised way. They do back-translation BIBREF5 back and forth for every iteration or batch, which needs an immensely long time and careful tuning of hyperparameters for massive monolingual data.
Here we suggest rather simple methods to build an unsupervised MT system quickly, based on word translation using cross-lingual word embeddings. The contributions of this paper are:
The proposed models can be efficiently trained with off-the-shelf softwares with little or no changes in the implementation, using only monolingual data. The provided analyses help for better learning of cross-lingual word embeddings for translation purpose. Altogether, our unsupervised MT system outperforms the sequence-to-sequence neural models even without training signals from the opposite translation direction, i.e. via back-translation.
The proposed models can be efficiently trained with off-the-shelf softwares with little or no changes in the implementation, using only monolingual data. The provided analyses help for better learning of cross-lingual word embeddings for translation purpose.
Answer: Yes.
Question: How is human evaluation performed, what were the criteria?
Evidence: Three annotators from a commercial annotation company are recruited to conduct our human evaluation. Responses from different models are shuffled for labeling. 300 test queries are randomly selected out, and annotators are asked to independently score the results of these queries with different points in terms of their quality: (1) Good (3 points): The response is grammatical, semantically relevant to the query, and more importantly informative and interesting; (2) Acceptable (2 points): The response is grammatical, semantically relevant to the query, but too trivial or generic (e.g.,“我不知道(I don't know)", “我也是(Me too)”, “我喜欢(I like it)" etc.); (3) Failed (1 point): The response has grammar mistakes or irrelevant to the query.
300 test queries are randomly selected out, and annotators are asked to independently score the results of these queries with different points in terms of their quality: (1) Good (3 points): The response is grammatical, semantically relevant to the query, and more importantly informative and interesting; (2) Acceptable (2 points): The response is grammatical, semantically relevant to the query, but too trivial or generic (e.g.,“我不知道(I don't know)", “我也是(Me too)”, “我喜欢(I like it)" etc.); (3) Failed (1 point): The response has grammar mistakes or irrelevant to the query.
Answer: The answers are shown as follows:
* (1) Good (3 points): The response is grammatical, semantically relevant to the query, and more importantly informative and interesting
* (2) Acceptable (2 points): The response is grammatical, semantically relevant to the query, but too trivial or generic
* (3) Failed (1 point): The response has grammar mistakes or irrelevant to the query
Question: What is cross-country skiing?
Answer: Cross-country (or "nordic") skiing, as opposed to downhill skiing, refer to the winter snow sport where the participant travels across relatively flat ground without the assistance of a chairlift or other machine aid in getting uphill. There are three main categories of cross-country ski available: classic, skate and touring, all of which come with specialized types of skies. Classic cross-country skiing involves a striding technique in which the skier keeps their skis parallel to each other and is best done in a narrow, groomed track. Skate skiing is considered more challenging to learn and involves a skiing technique that resembles the motion of ice skating and roller blading. Skate skiers also typically use a groomed trail but it is wider and smoother than the narrow grooves for classic skiing. Touring skis allows skiers to most easily ski off the trails on top of snow that has not been groomed.
Question: How was Liberum Veto mis-used?
Evidence: The liberum veto (Latin for "free veto") was a parliamentary device in the Polish–Lithuanian Commonwealth. It was a form of unanimity voting rule that allowed any member of the Sejm (legislature) to force an immediate end to the current session and to nullify any legislation that had already been passed at the session by shouting either Sisto activitatem! (Latin: "I stop the activity!") or Nie pozwalam! (Polish: "I do not allow!"). The rule was in place from the mid-17th century to the late 18th century in the Sejm's parliamentary deliberations. It was based on the premise that since all Polish noblemen were equal, every measure that came before the Sejm had to be passed unanimously. The liberum veto was a key part of the political system of the Commonwealth, strengthening democratic elements and checking royal power and went against the European-wide trend of having a strong executive (absolute monarchy).

Many historians hold that the liberum veto was a major cause of the deterioration of the Commonwealth political system, particularly in the 18th century, when foreign powers bribed Sejm members to paralyze its proceedings, and the Commonwealth's eventual destruction in the partitions of Poland and foreign occupation, dominance and manipulation of Poland. Piotr Stefan Wandycz wrote that the "liberum veto had become the sinister symbol of old Polish anarchy". In the period of 1573–1763, about 150 sejms were held, about a third failing to pass any legislation, mostly because of the liberum veto. The expression Polish parliament in many European languages originated from the apparent paralysis.
Answer: With Liberum veto,  it was only possible to pass any legislation with unanimous consent. So one member of parliament to stop any legislation. Adversaries would often influence or bribe just one member of Parliament to derail a legislation.
Question: What is Uście Gorlickie?
Evidence: Uście Gorlickie [ˈuɕt͡ɕɛ ɡɔrˈlʲit͡skʲɛ] (Ukrainian: Устя Руське, Ustia Rus’ke) is a village in Gorlice County, Lesser Poland Voivodeship, in southern Poland, close to the border with Slovakia. It is the seat of the gmina (administrative district) called Gmina Uście Gorlickie. It lies approximately 15 kilometres (9 mi) south of Gorlice and 106 km (66 mi) south-east of the regional capital Kraków.

The village has a population of 1,100.
Answer: Uscie Gorlicke is a small village of 1,100 people in southern Poland, close to the border of Slovakia. Is it the seat of the administrative district (gmina) called Gmina Uscie Gorlicke). The village lies 66 miles southeast of regional capital Krakow.