DataHammer/scimrc huggingface dataset:
Question: What about the graphs with noise since the majority of the real-world graphs have errors or noise?
Evidence: In order to further investigate the robustness of GraphANGEL, we randomly generate some noises and add them into the training graphs. Concretely, for heterogeneous graphs and knowledge graphs shown in Tables A1 and A2, we randomly change the source or target node of each triplet h s, r, ti with probability 5%. Since the heterogeneous graphs have the node types, our changes are made by replacing the current source or target node with a randomly sampled node in the same node type. We compare our model with all the baselines and report the results for heterogeneous graph recommendation and knowledge graph completion tasks in Tables A13 and A14. These results show that our method can consistently outperform these baseline methods, sometimes, even are less inflfluenced by the noises. One explanation is that if we introduce noises in the labels y, then the learnable subgraph embeddings s+ and s− will simultaneously be inflfluenced by the noises. These inflfluences might be counteracted according to Eq. (3).
Answer: We devised the noisy graphs by modifying the source or target nodes with probability 5%, and compared GraphANGEL to baselines on the graphs. Results reported in Tables A12 and A13 show the superiority of GraphANGEL against all the baselines in the context of noisy graphs in both heterogeneous graph recommendation and knowledge graph completion tasks. Please see Appendix 6.5 for details.
Question: What teams were a part of the 2021 Valletta Cup?
Evidence: The 2021 Valletta Cup was a Twenty20 International (T20I) cricket tournament held in Malta between 21 and 24 October 2021. It was the second edition of the Valletta Cup. The matches were played at the Marsa Sports Club in Marsa. The participating teams were the hosts Malta along with Bulgaria, Gibraltar and Switzerland.

This was the first international tournament for Switzerland since the introduction of global T20I status in January 2019, and their re-admittance to the International Cricket Council (ICC) in July 2021. Cricket Switzerland (previously known as the Swiss Cricket Association), had been suspended by the ICC in 2012 due to non-compliance following the formation of a second organization also claiming to oversee cricket in the country. They were one of three nations to gain Associate membership after the ICC's annual general meeting in July 2021.

Switzerland finished on top of the round-robin stage with three wins. Malta defeated Switzerland in the final, after Bulgaria defeated Gibraltar in the third-place playoff. After the tournament, Malta and Gibraltar played a rain-affected two-match bilateral T20I series that was shared after one game was lost to the weather and the other finished as a DLS method tie.
Answer: Malta, Bulgaria, Gibraltar & Switzerland were of the participating teams in the 2021 Valletta Cup.
Question: How is performance measured?
Evidence: To assess the predictive capability of this and other models, we require some method by which we can compare the models. For that purpose, we use receiver operating characteristic (ROC) curves as a visual representation of predictive effectiveness. ROC curves compare the true positive rate (TPR) and false positive rate (FPR) of a model's predictions at different threshold levels. The area under the curve (AUC) (between 0 and 1) is a numerical measure, where the higher the AUC is, the better the model performs.
We cross-validate our model by first randomly splitting the corpus into a training set (95% of the corpus) and test set (5% of the corpus). We then fit the model to the training set, and use it to predict the response of the documents in the test set. We repeat this process 100 times. The threshold-averaged ROC curve BIBREF13 is found from these predictions, and shown in Figure 3 . Table 1 shows the AUC for each model considered.
To assess the predictive capability of this and other models, we require some method by which we can compare the models. For that purpose, we use receiver operating characteristic (ROC) curves as a visual representation of predictive effectiveness. ROC curves compare the true positive rate (TPR) and false positive rate (FPR) of a model's predictions at different threshold levels. The area under the curve (AUC) (between 0 and 1) is a numerical measure, where the higher the AUC is, the better the model performs.
We cross-validate our model by first randomly splitting the corpus into a training set (95% of the corpus) and test set (5% of the corpus). We then fit the model to the training set, and use it to predict the response of the documents in the test set. We repeat this process 100 times. The threshold-averaged ROC curve BIBREF13 is found from these predictions, and shown in Figure 3 . Table 1 shows the AUC for each model considered.
Answer: they use ROC curves and cross-validation
Question: Tell me whether these are movies about AI or other topics: , "Ex Machina", "Her", "Transcendence", "I, Robot", "Ghost in the Shell",  "The Godfather", "Pulp Fiction", "The Shawshank Redemption", "The Dark Knight", "12 Angry Men"
Answer: Out of the  ten films you mentioned, only "Ex Machina" ,  "Her", "Transcendence", "I, Robot" and "Ghost in the Shell" are about AI.
Question: Name different corporate messaging applications companies use.
Answer: Corporate messaging applications companies use include Slack, Teams, and Google Chat.
Question: How does the model compute the likelihood of executing to the correction semantic denotation?
Evidence: Since the training data consists only of utterance-denotation pairs, the ranker is trained to maximize the log-likelihood of the correct answer $z$ by treating logical forms as a latent variable:
It is impractical to rely solely on a neural decoder to find the most likely logical form at run time in the weakly-supervised setting. One reason is that although the decoder utilizes global utterance features for generation, it cannot leverage global features of the logical form since a logical form is conditionally generated following a specific tree-traversal order. To this end, we follow previous work BIBREF21 and introduce a ranker to the system. The role of the ranker is to score the candidate logical forms generated by the parser; at test time, the logical form receiving the highest score will be used for execution. The ranker is a discriminative log-linear model over logical form $y$ given utterance $x$ :
Since the training data consists only of utterance-denotation pairs, the ranker is trained to maximize the log-likelihood of the correct answer $z$ by treating logical forms as a latent variable
The role of the ranker is to score the candidate logical forms generated by the parser; at test time, the logical form receiving the highest score will be used for execution. The ranker is a discriminative log-linear model over logical form $y$ given utterance $x$ : 
Answer: By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x.
Question: The Bud Freeman All-Stars featuring Shorty Baker
Answer: AllMusic reviewer Scott Yanow stated: "Tenor-sax great Bud Freeman, who is often associated with the Eddie Condon school of Nicksieland, is heard heading an excellent swing quintet for this 1960 studio session. Trumpeter Harold "Shorty" Baker (best known for his periods with Duke Ellington) made too few small-group recordings throughout his life so this is one of his best ... the group plays superior standards and a couple of originals on this fine swing date"
Question: Was Alexander Skarsgård destined to become an actor?
Evidence: Skarsgård was born on 25 August 1976 in Stockholm, Sweden. He is the eldest son of actor Stellan Skarsgård and physician My Skarsgård. His parents are divorced. He has five younger siblings: Gustaf, Sam, Bill, Eija and Valter, and two half-brothers from his father's second wife, Megan Everett. Gustaf, Bill and Valter are also actors.

A friend of his father, a director, gave Skarsgård his first film role when he was seven years old, playing Kalle Nubb in Åke and His World (Åke och hans värld). In 1989, his lead role in the Swedish television production Hunden som log (The Dog That Smiled) earned him praise and brought him to prominence in Sweden at age 13. Uncomfortable with the fame, he quit acting at that same age.

At age 19, Skarsgård applied to perform his national service. He served in the Swedish Armed Forces, in the SäkJakt ("protect and hunt") unit that dealt with anti-sabotage and anti-terrorism in the Stockholm archipelago, for 18 months. After completing his service in 1996, he left Sweden and attended Leeds Metropolitan University in England for six months. He enrolled to study English but admits he did not study much and "had a blast" instead. While there, he considered pursuing an architecture career, but chose acting instead. In 1997, he enrolled in a theatre course at Marymount Manhattan College and moved to New York City. He returned to Stockholm after six months, but his time studying theatre confirmed to him that he wanted to act.
Answer: Alexander Skarsgård was born into an actors family, his father Stellan as well as several of his brothers are successful actors. Furthermore, he got off to an early start when a friend of his father got him a role at seven years old, which seemed to suggest he was always destined to become an actor. However, along the way, several diversions could have set him on another path. At 13 years old he quit acting as he got uncomfortable with the fame. Later on, he joined the Swedish national service, went off to study English in Leeds, and considered pursuing an architecture career. But in the end, he chooses the path of acting.
Question: What is Beggar's Chicken?
Answer: In Chinese, Beggar’s Chicken is called “叫化鸡” (Jiào huā jī). Made with a whole chicken, the bird is marinated, seasoned, and then stuffed, after which it is wrapped in lotus leaves, then coated in clay, and finally baked.

The best part of this recipe is that when it has finished cooking, you take a hammer and crack the clay to reveal a perfectly cooked chicken inside.
Question: When did Singapore gain independence, and who was the first President?
Evidence: After being expelled from Malaysia, Singapore became independent as the Republic of Singapore on 9 August 1965, with Lee Kuan Yew and Yusof bin Ishak as the first prime minister and president respectively. In 1967, the country co-founded the Association of Southeast Asian Nations (ASEAN). Race riots broke out once more in 1969. Lee Kuan Yew's emphasis on rapid economic growth, support for business entrepreneurship, and limitations on internal democracy shaped Singapore's policies for the next half-century. Economic growth continued throughout the 1980s, with the unemployment rate falling to 3% and real GDP growth averaging at about 8% up until 1999. During the 1980s, Singapore began to shift towards high-tech industries, such as the wafer fabrication sector, in order to remain competitive as neighbouring countries began manufacturing with cheaper labour. Singapore Changi Airport was opened in 1981 and Singapore Airlines was formed. The Port of Singapore became one of the world's busiest ports and the service and tourism industries also grew immensely during this period.
Answer: Singapore became independent on 9 August 1965, and the first president was Yusof bin Ishak.
Question: Given the reference text about Fidel Castro, tell me where he was born, where he studied, and when he was the leader of Cuba.
Evidence: Fidel Alejandro Castro Ruz (/ˈkæstroʊ/; American Spanish: [fiˈðel aleˈxandɾo ˈkastɾo ˈrus]; 13 August 1926 – 25 November 2016) was a Cuban revolutionary and politician who was the leader of Cuba from 1959 to 2008, serving as the prime minister of Cuba from 1959 to 1976 and president from 1976 to 2008. Ideologically a Marxist–Leninist and Cuban nationalist, he also served as the first secretary of the Communist Party of Cuba from 1961 until 2011. Under his administration, Cuba became a one-party communist state; industry and business were nationalized, and socialist reforms were implemented throughout society.

Born in Birán, the son of a wealthy Spanish farmer, Castro adopted leftist and anti-imperialist ideas while studying law at the University of Havana. After participating in rebellions against right-wing governments in the Dominican Republic and Colombia, he planned the overthrow of Cuban President Fulgencio Batista, launching a failed attack on the Moncada Barracks in 1953. After a year's imprisonment, Castro travelled to Mexico where he formed a revolutionary group, the 26th of July Movement, with his brother Raúl Castro and Ernesto "Che" Guevara. Returning to Cuba, Castro took a key role in the Cuban Revolution by leading the Movement in a guerrilla war against Batista's forces from the Sierra Maestra. After Batista's overthrow in 1959, Castro assumed military and political power as Cuba's prime minister. The United States came to oppose Castro's government and unsuccessfully attempted to remove him by assassination, economic embargo, and counter-revolution, including the Bay of Pigs Invasion of 1961. Countering these threats, Castro aligned with the Soviet Union and allowed the Soviets to place nuclear weapons in Cuba, resulting in the Cuban Missile Crisis – a defining incident of the Cold War – in 1962.
Answer: Fidel Castro was born in Birán and studied law at the University of Havana. He was the leader of Cuba from 1959 to 2008.
Question: Identify which instrument is string or percussion: Monkey stick, Tzouras
Answer: Monkey stick is percussion, Tzouras is string.
Question: Whose life is the 1965 film 'The Sound of Music' based on?
Evidence: The Sound of Music is a musical with music by Richard Rodgers, lyrics by Oscar Hammerstein II, and a book by Howard Lindsay and Russel Crouse. It is based on the 1949 memoir of Maria von Trapp, The Story of the Trapp Family Singers. Set in Austria on the eve of the Anschluss in 1938, the musical tells the story of Maria, who takes a job as governess to a large family while she decides whether to become a nun. She falls in love with the children, and eventually their widowed father, Captain von Trapp. He is ordered to accept a commission in the German navy, but he opposes the Nazis. He and Maria decide on a plan to flee Austria with the children
Answer: It is based on the 1949 memoir of Maria von Trapp
Question: Who was the guitarist and drummer of The Verve?
Evidence: The Verve were an English rock band formed in Wigan, Greater Manchester in 1990 by lead vocalist Richard Ashcroft, guitarist Nick McCabe, bass guitarist Simon Jones and drummer Peter Salisbury. Guitarist and keyboard player Simon Tong later became a member in their first reunion only.
Answer: Nick McCabe and Peter Salisbury
Question: Who was William P. Bolton
Evidence: William P. Bolton (July 2, 1885 – November 22, 1964) was a one-term U.S. Congressman who represented the second district of Maryland from 1949 to 1951 until defeated by Republican General James Devereux.

Born near Whiteford, Maryland, Bolton attended the public schools and St. Francis Parochial School in Baltimore County, Maryland. He graduated from the University of Baltimore Law School in 1909, and became a lawyer in private practice. He served as trial magistrate in Towson, Maryland from 1941-1946.

Bolton served in the Maryland State Senate from 1946 to 1948. He was elected as a Democrat to the Eighty-first Congress, serving from January 3, 1949 to January 3, 1951. He was an unsuccessful candidate for reelection to the Eighty-second Congress in 1950 and again in 1954.

During his short time in the Maryland Senate he proposed a far reaching amendment to the Maryland Constitution that was approved by the city of Baltimore voters to limit the city's ability to annex portions of Baltimore County, something that was previously a right of the city. This amendment, Question 5, was approved on November 2, 1948 by a majority of the city voters: 139,974 to 103,687.

After Congress, Bolton served as director of Baltimore County Civil Defense in 1951, and died in Baltimore in 1964. He is interred in Mount Maria Cemetery of Towson.
Answer: William P. Bolton (July 2, 1885 – November 22, 1964) was a one-term U.S. Congressman who represented the second district of Maryland from 1949 to 1951 until defeated by Republican General James Devereux.

After Congress, Bolton served as director of Baltimore County Civil Defense in 1951, and died in Baltimore in 1964. He is interred in Mount Maria Cemetery of Towson.
Question: What language is involved in the research of this article?
Evidence: The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases.
The main problems in BioIE are similar to those in Information Extraction:
This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain.
This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 .
In recent years, most of the research works related to QA are based on English language such as IBM Watson, Wolfram Alpha. Bengali speakers often fall in difficulty while communicating in English.
Answer: Bengali.
Question: What is Adventures of Tintin?
Evidence: The Adventures of Tintin (French: Les Aventures de Tintin [lez‿avɑ̃tyʁ də tɛ̃tɛ̃]) is a series of 24 bande dessinée albums created by Belgian cartoonist Georges Remi, who wrote under the pen name Hergé. The series was one of the most popular European comics of the 20th century. By 2007, a century after Hergé's birth in 1907, Tintin had been published in more than 70 languages with sales of more than 200 million copies, and had been adapted for radio, television, theatre, and film.
Answer: Adventures of Tinting is a very popular comic book series created by Belgian cartoonist Georges Remi who wrote under the pen name Herge'. It is a series of 24 comic books that have been published in 70 languages with sales of 200 million copies.
Question: What is the best Engineering college in Bangalore?
Answer: 1. RV college of Engineering
2. PEST University
3. Dayanand Sagar
Question: What is their baseline?
Evidence: As seen in figures, in most of the cases our model produces comparable result with human translator. Result for BLEU score for our model and Google's Neural Machine Translation is compared in table TABREF19:
 Result for BLEU score for our model and Google's Neural Machine Translation is compared in table TABREF19
Answer: The answers are shown as follows:
* Google's Neural Machine Translation
Question: Are there any limitations when trained systems on QuaRel?
Evidence: Despite rapid progress in general question-answering (QA), e.g., BIBREF1, and formal models for qualitative reasoning (QR), e.g., BIBREF2, BIBREF3, there has been little work on reasoning with textual qualitative knowledge, and no dataset available in this area. Although many datasets include a few qualitative questions, e.g., BIBREF4, BIBREF5, the only one directly probing QR is QuaRel BIBREF0. However, although QuaRel contains 2700 qualitative questions, its underlying qualitative knowledge was specified formally, using a small, fixed ontology of 19 properties. As a result, systems trained on QuaRel are limited to only questions about those properties. Likewise, although the QR community has performed some work on extracting qualitative models from text, e.g., BIBREF6, BIBREF7, and interpreting questions about identifying qualitative processes, e.g., BIBREF8, there is no dataset available for the NLP community to study textual qualitative reasoning. QuaRTz addresses this need.
However, although QuaRel contains 2700 qualitative questions, its underlying qualitative knowledge was specified formally, using a small, fixed ontology of 19 properties. As a result, systems trained on QuaRel are limited to only questions about those properties.
Answer: The underlying qualitative knowledge of qualitative questions in QuaRel was specified formally, thus systems trained on QuaRel are limited to only questions about those properties.
Question: Identify which animal species is alive or extinct: Stupendemys, Gray Whale
Answer: Gray Whale is alive, Stupendemys is extinct.
Question: How about the performance of the cross lingual latent variables approach?
Evidence: Semantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and other NLP tasks. SRL is the problem of finding predicate-argument structure in a sentence, as illustrated below:
 INLINEFORM0 
Here, the predicate WRITE has two arguments: `Mike' as A0 or the writer, and `a book' as A1 or the thing written. The labels A0 and A1 correspond to the PropBank annotations BIBREF0 .
As the need for SRL arises in different domains and languages, the existing manually annotated corpora become insufficient to build supervised systems. This has motivated work on unsupervised SRL BIBREF1 , BIBREF2 , BIBREF3 . Previous work has indicated that unsupervised systems could benefit from the word alignment information in parallel text in two or more languages BIBREF4 , BIBREF5 , BIBREF6 . For example, consider the German translation of sentence INLINEFORM0 :
 INLINEFORM0 
If sentences INLINEFORM0 and INLINEFORM1 have the word alignments: Mike-Mike, written-geschrieben, and book-Buch, the system might be able to predict A1 for Buch, even if there is insufficient information in the monolingual German data to learn this assignment. Thus, in languages where the resources are sparse or not good enough, or the distributions are not informative, SRL systems could be made more accurate by using parallel data with resource rich or more amenable languages.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages. The model consists of individual Bayesian models for each language BIBREF3 , and crosslingual latent variables to incorporate soft role agreement between aligned constituents. This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs BIBREF4 . We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
We begin by briefly describing the unsupervised SRL pipeline and the monolingual semantic role induction model we use, and then describe our multilingual model.
This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs (Naseem et al., 2009).
Answer: This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs (Naseem et al., 2009).