DataHammer/scimrc huggingface dataset:
Question: What are the different methods used for different corpora?
Evidence: We ran three groups of experiments, to assess both the effectiveness of our approach when compared with the approaches we found in literature and its capability of extracting features that are relevant for sarcasm in a cross-domain scenario. In both cases, we denote with the word model one of the possible combinations of classic/statistical LSA and a classifier. The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB).
For the first group of experiments, we evaluated the performance of each of our models in every corpus. We use 10-fold cross-validation and report the mean values of INLINEFORM0 -score, precision, and recall among all the folds. The proportion of the two classes in each fold is equal to the proportion in the whole corpus. Where applicable, we compare our results with existing results in the literature. Besides, we compare with the method presented in Poira et al. cambria2016.
In both cases, we denote with the word model one of the possible combinations of classic/statistical LSA and a classifier. The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB).
Where applicable, we compare our results with existing results in the literature.
Answer: The answers are shown as follows:
* Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB)
Question: Identify which animal species is alive or extinct: Seymouria, White Rhino
Answer: White Rhino is alive, Seymouria is extinct.
Question: What is LAPR?
Answer: This stands for life assurance premium relief. Before 14 March 1984 there was tax relief on life insurance premiums paid by policyholders for policies which qualified for tax relief. Policies which started
Question: What is Part-of-speech tagging?
Evidence: Part-of-speech (POS) tagging is a process to tag tokens in a string with their corresponding part-of-speech (e.g., noun, verb, etc). POS tagging is considered as one of the most basic tasks in NLP, as it is usually the first component in an NLP pipeline. This is because POS tags are shown to be useful features in various NLP tasks, such as named entity recognition BIBREF0 , BIBREF1 , machine translation BIBREF2 , BIBREF3 and constituency parsing BIBREF4 . Therefore, for any language, building a successful NLP system usually requires a well-performing POS tagger.
There are quite a number of research on Indonesian POS tagging BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . However, almost all of them are not evaluated on a common dataset. Even when they are, their train-test split are not the same. This lack of a common benchmark dataset makes a fair comparison among these works difficult. Moreover, despite the success of neural network models for English POS tagging BIBREF9 , BIBREF10 , the use of neural networks is generally unexplored for Indonesian. As a result, published results may not reflect the actual state-of-the-art performance of Indonesian POS tagger.
In this work, we explored different neural network architectures for Indonesian POS tagging. We evaluated our experiments on the IDN Tagged Corpus BIBREF11 . Our best model achieves 97.47 INLINEFORM0 score, a new state-of-the-art result for Indonesian POS tagging on the dataset. We release the dataset split that we used to serve as a benchmark for future work.
Part-of-speech (POS) tagging is a process to tag tokens in a string with their corresponding part-of-speech (e.g., noun, verb, etc).
Answer: Part-of-speech (POS) tagging is a process to tag tokens in a string with their corresponding part-of-speech (e.g., noun, verb, etc.).
Question: How to select the hyperparameters?
Evidence: To learn and leverage the mode slots, we introduce a novel recurrent unit named ModeCell, which follows a decoupling-aggregation framework with three modules, i.e., the state interaction and slot binding module, the adaptive slot fusion module, and the slot bus transition module.
To learn and leverage the mode slots, we introduce a novel recurrent unit named ModeCell, which follows a decoupling-aggregation framework with three modules, i.e., the state interaction and slot binding module, the adaptive slot fusion module, and the slot bus transition module.****We further conduct experiments on a complex human action dataset, Human3.6M (Ionescu et al., 2013). The Human3.6M dataset contains 2,220 sequences for training, 300 for validation, and 1,056 for testing, involving 17 different scenarios.****To approximate the multi-mode phenomenon of the real world, for each sequence, we randomly set the number of the flying digits in the range of 1 to 3. This setup is more challenging than previous convention, which requires the model to handle various spatiotemporal dynamics due to different frequencies of occlusions among sequences. We name this new dataset as the Mixed Moving MNIST, which contains 30,000 training sequences, 6,000 validation sequences, and 9,000 testing sequences.****Number of mode slots. In the right column of Table 4, we adjust the number of mode slots on RoboNet. We find that the performance first increases rapidly with the growth of the slot number and achieves the best performance at N = 4. Notably, using a single slot in each ModeCell achieves similar performance to SA-ConvLSTM (Lin et al., 2020), which incorporates self-attention in the recurrent state transitions but does not have a mode decoupling framework. We perform a similar grid search on other datasets and finally set N = 6 on KTH and N = 4 on the radar echo dataset.
Answer: There are not many hyperparameters to tune in ModeRNN, except for the number of ModeCells and the number of mode slots in each ModeCell. Basically, we set the hyperparameters that can lead to a comparable model size with the SOTA methods. For the number of mode slots, we follow the common practice of previous literature to use the validation set to tune the hyperparameters on RoboNet, Human3.6M, and Mixed Moving MNIST. For KTH, we follow the previous literature of SVG, PredRNN, and SA-ConvLSTM to tune the hyperparameters on the test set.
Question: Extract the current cash rate and basis point increase from the following using the format: {x} % increased by {x} basis points

At its meeting today, the Board decided to increase the cash rate target by 25 basis points to 3.60 per cent. It also increased the interest rate on Exchange Settlement balances by 25 basis points to 3.50 per cent.
Global inflation remains very high. In headline terms it is moderating, although services price inflation remains elevated in many economies. It will be some time before inflation is back to target rates. The outlook for the global economy remains subdued, with below average growth expected this year and next.
The monthly CPI indicator suggests that inflation has peaked in Australia. Goods price inflation is expected to moderate over the months ahead due to both global developments and softer demand in Australia. Services price inflation remains high, with strong demand for some services over the summer. Rents are increasing at the fastest rate in some years, with vacancy rates low in many parts of the country. The central forecast is for inflation to decline this year and next, to be around 3 per cent in mid-2025. Medium-term inflation expectations remain well anchored, and it is important that this remains the case.
Growth in the Australian economy has slowed, with GDP increasing by 0.5 per cent in the December quarter and 2.7 per cent over the year. Growth over the next couple of years is expected to be below trend. Household consumption growth has slowed due to the tighter financial conditions and the outlook for housing construction has softened. In contrast, the outlook for business investment remains positive, with many businesses operating at a very high level of capacity utilisation.
Answer: 3.60% increased by 25 basis points
Question: Classify the below states based on which coast they are located .
Connecticut, Delaware, Florida, Georgia, Maine, Maryland, California, Oregon
Answer: East Coast - Connecticut, Delaware, Florida, Georgia, Maine, Maryland
West Coast - California, Oregon
Question: Who did Outcault paint electric light displays for?
Evidence: Outcault painted electric light displays for Edison Laboratories for the 1888 Centennial Exposition of the Ohio Valley and Middle Atlantic States in Cincinnati. This led to full-time work with Edison in West Orange, New Jersey, doing mechanical drawings and illustrations. Edison appointed him official artist for the company's traveling exhibition in 1889–90, which included supervising the installation of Edison exhibits at the Exposition Universelle in Paris. While there, he studied art in the Latin Quarter and added the u to his surname.
Answer: Outclaut painted electric light displays for Edison Laboratories for the 1888 Centennial Exposition of the Ohio Valley and Middle Atlantic States in Cincinnati.
Question: Why is football/soccer called the beautiful game.
Answer: No one exactly knows where the phrase come from, but it was popularized by Pelé during his career. Football is played by 200 countries, and half of the world’s population turns in to watch the World Cup. The sport is beloved for its ability to bring fans from all over the world together, its unpredictable nature, the skill and artistry of the players footwork, goals and it's truly a team sport, where anyone has an opportunity to score the goal that takes your team to victory. It's an equalizer, no need for helmets, pads, and any other fancy equipment, all you need are your feet and a ball to play the beautiful game.
Question: What are the words of House Beesbury?
Answer: While the house motto is not confirmed in canon sources, it is suggested that the words of House Beesbury are "Beware Our Sting."
Question: Are encouragements to the harasser are subject to interpretation?
Evidence: The English and Dutch corpora were independently annotated for cyberbullying by trained linguists. All were Dutch native speakers and English second-language speakers. To demonstrate the validity of our guidelines, inter-annotator agreement scores were calculated using Kappa on a subset of each corpus. Inter-rater agreement for Dutch (2 raters) is calculated using Cohen's Kappa BIBREF53 . Fleiss' Kappa BIBREF54 is used for the English corpus ( INLINEFORM0 2 raters). Kappa scores for the identification of cyberbullying are INLINEFORM1 = 0.69 (Dutch) and INLINEFORM2 = 0.59 (English).
As shown in Table TABREF35 , inter-annotator agreement for the identification of the more fine-grained categories for English varies from fair to substantial BIBREF55 , except for defamation, which appears to be more difficult to recognise. No encouragements to the harasser were present in this subset of the corpus. For Dutch, the inter-annotator agreement is fair to substantial, except for curse and defamation. Analysis revealed that one of both annotators often annotated the latter as an insult, and in some cases even did not consider it as cyberbullying-related.
In short, the inter-rater reliability study shows that the annotation of cyberbullying is not trivial and that more fine-grained categories like defamation, curse and encouragements are sometimes hard to recognise. It appears that defamations were sometimes hard to distinguish from insults, whereas curses and exclusions were sometimes considered insults or threats. The analysis further reveals that encouragements to the harasser are subject to interpretation. Some are straightforward (e.g. `I agree we should send her hate'), whereas others are subject to the annotator's judgement and interpretation (e.g. `hahaha', `LOL').
The analysis further reveals that encouragements to the harasser are subject to interpretation.
Answer: Yes.
Question: How does the baseline CNN extract the inherent semantics from the sarcastic corpus?
Evidence: CNN can also be employed on the sarcasm datasets in order to identify sarcastic and non-sarcastic tweets. We term the features extracted from this network baseline features, the method as baseline method and the CNN architecture used in this baseline method as baseline CNN. Since the fully-connected layer has 100 neurons, we have 100 baseline features in our experiment. This method is termed baseline method as it directly aims to classify a sentence as sarcastic vs non-sarcastic. The baseline CNN extracts the inherent semantics from the sarcastic corpus by employing deep domain understanding. The process of using baseline features with other features extracted from the pre-trained model is described in Section SECREF24 .
The baseline CNN extracts the inherent semantics from the sarcastic corpus by employing deep domain understanding.
Answer: By employing deep domain understanding
Question: What paradigm does the authors' framework base on?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
Our framework is based on a bottom-up paradigm for verbalizing SW data.
Answer: The framework is based on a bottom-up paradigm for verbalizing SW data.
Question: Given a reference text about Former Indian Prime Minister Shri P.V Narasimha Rao, tell me when they were born, when they were Prime Minister and their most significant accomplishments in office
Evidence: Pamulaparthi Venkata Narasimha Rao (28 June 1921 – 23 December 2004), popularly known as P. V. Narasimha Rao, was an Indian lawyer, statesman and politician who served as the 9th prime minister of India from 1991 to 1996. He is known for introducing various liberal reforms to India's economy. His ascendancy to the prime ministership was politically significant because he was the second holder of this office from a non-Hindi-speaking region and the first from South India (United Andhra Pradesh). He led an important administration, overseeing a major economic transformation and several home incidents affecting national security of India. Rao, who held the Industries portfolio, was personally responsible for the dismantling of the Licence Raj, as this came under the purview of the Ministry of Commerce and Industry, reversing the economic policies of Rajiv Gandhi's government
Answer: Shir P.V. Narsimha Rao was born 28 June 1921 and was the 9th Prime Minister of India from 1991 to 1996. His most significant accomplishment was the major shift of the Indian Economy from one governed by fiscally conservative socialistic and protectionist policies ("License Raj") to a more liberal free market enterprise with lesser regulations and protectionism.

On the demographic front, Rao's ascendancy is significant because he was the first Prime Minister from South India and the second from non-Hindi speaking region.
Question: What color of light does the Haugsholmen Lighthouse emit?
Evidence: Haugsholmen Lighthouse (Norwegian: Haugsholmen fyr) is a coastal lighthouse located in the municipality of Sande in Møre og Romsdal county, Norway. It is located on the tiny island of Vestre Frekøy, in the mouth of the Vanylvsfjorden, about 2.5 kilometres (1.6 mi) southwest of the island of Kvamsøya. It was established in 1876 and automated in 1979.

The 10-metre (33 ft) tall tower sits at an elevation of 19.7 metres (65 ft) above sea level. The light emits white, red or green light, depending on direction, occulting twice every 10 seconds. The red cylindrical tower with lantern and gallery is attached to one corner of a 2+1⁄2-story white keeper's house.
Answer: The light emits white, red or green light, depending on direction, occulting twice every 10 seconds.
Question: What are the metrics for this multi-label classification?
Evidence: In many organizations, PA is done from a predefined set of perspectives, which we call attributes. Each attribute covers one specific aspect of the work done by the employees. This has the advantage that we can easily compare the performance of any two employees (or groups of employees) along any given attribute. We can correlate various performance attributes and find dependencies among them. We can also cluster employees in the workforce using their supervisor ratings for each attribute to discover interesting insights into the workforce. The HR managers in the organization considered in this paper have defined 15 attributes (Table TABREF20 ). Each attribute is essentially a work item or work category described at an abstract level. For example, FUNCTIONAL_EXCELLENCE covers any tasks, goals or activities related to the software engineering life-cycle (e.g., requirements analysis, design, coding, testing etc.) as well as technologies such as databases, web services and GUI.
In the example in Section SECREF4 , the first sentence (which has class STRENGTH) can be mapped to two attributes: FUNCTIONAL_EXCELLENCE and BUILDING_EFFECTIVE_TEAMS. Similarly, the third sentence (which has class WEAKNESS) can be mapped to the attribute INTERPERSONAL_EFFECTIVENESS and so forth. Thus, in order to answer the second question in Section SECREF1 , we need to map each sentence in each of the 3 classes to zero, one, two or more attributes, which is a multi-class multi-label classification problem.
We manually tagged the same 2000 sentences in Dataset D1 with attributes, where each sentence may get 0, 1, 2, etc. up to 15 class labels (this is dataset D2). This labelled dataset contained 749, 206, 289, 207, 91, 223, 191, 144, 103, 80, 82, 42, 29, 15, 24 sentences having the class labels listed in Table TABREF20 in the same order. The number of sentences having 0, 1, 2, or more than 2 attributes are: 321, 1070, 470 and 139 respectively. We trained several multi-class multi-label classifiers on this dataset. Table TABREF21 shows the results of 5-fold cross-validation experiments on dataset D2.
Precision, Recall and F-measure for this multi-label classification are computed using a strategy similar to the one described in BIBREF21 . Let INLINEFORM0 be the set of predicted labels and INLINEFORM1 be the set of actual labels for the INLINEFORM2 instance. Precision and recall for this instance are computed as follows: INLINEFORM3 
It can be observed that INLINEFORM0 would be undefined if INLINEFORM1 is empty and similarly INLINEFORM2 would be undefined when INLINEFORM3 is empty. Hence, overall precision and recall are computed by averaging over all the instances except where they are undefined. Instance-level F-measure can not be computed for instances where either precision or recall are undefined. Therefore, overall F-measure is computed using the overall precision and recall.
Precision, Recall and F-measure for this multi-label classification are computed using a strategy similar to the one described in [6].
Answer: Precision, Recall and F-measure.
Question: Is UBS a good bank
Evidence: UBS Group AG[nb 1] is a multinational investment bank and financial services company founded and based in Switzerland. Co-headquartered in the cities of Zürich and Basel, it maintains a presence in all major financial centres as the largest Swiss banking institution and the largest private bank in the world. UBS client services are known for their strict bank–client confidentiality and culture of banking secrecy.[nb 2] Because of the bank's large positions in the Americas, EMEA, and Asia Pacific markets, the Financial Stability Board considers it a global systemically important bank.
Answer: The UBS Group AG is a multinational investment bank and financial services company founded and based in Switzerland. Co-headquartered in the cities of Zürich and Basel, it maintains a presence in all major financial centres as the largest Swiss banking institution and the largest private bank in the world. UBS client services are known for their strict bank–client confidentiality and culture of banking secrecy.

Because of the bank's large positions in the Americas, EMEA, and Asia Pacific markets, the Financial Stability Board considers it a global systemically important bank.