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1910.11471
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The main advantage of translating to a programming language is - it has a concrete and strict lexical and grammatical structure which human languages lack. The aim of this paper was to make the text-to-code framework work for general purpose programming language, primarily Python. In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.
In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.
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What additional techniques could be incorporated to further improve accuracy?
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The answers are shown as follows:
* phrase-based word embedding
* Abstract Syntax Tree(AST)
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null | false
| 109
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This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.
Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.
We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user", “URL" and “hashtag" references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
A two-step methodology has been employed in filtering the datasets used in our experiments. Firstly, we identified and removed all the “user”, “URL” and “hashtag” references present in the tweets using efficient regular expressions. Special emphasis was given to this step to avoid traces of hashtags, which might trigger the models to provide biased results. Secondly, we used NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons. Since our deep CNNs extract contextual information present in tweets, we include emoticons as part of the vocabulary. This enables the emoticons to hold a place in the word embedding space and aid in providing information about the emotions present in the sentence.
|
How do they filter the datasets in their experiments?
|
To use efficient regular expressions to delete three kinds of references and NLTK Twitter Tokenizer to ensure proper tokenization of words along with special symbols and emoticons.
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How spicy is the hottest peppers
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The hottest pepper is the ghost pepper with a measure of a million scoville units.
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1904.12087
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The focus of the aforementioned language and dialect identification competitions was diatopic variation and thus the data made available in these competitions was synchronic contemporary corpora. In the 2019 edition of the workshop, for the first time, a task including historical languages was organized. The CLI shared task provided participants with a dataset containing languages and dialects written in cuneiform script: Sumerian and Akkadian. Akkadian is divided into six dialects in the dataset: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian BIBREF14 .
Akkadian is divided into six dialects in the dataset: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian BIBREF14 .
|
Which language is divided into six dialects in the task mentioned in the paper?
|
The answers are shown as follows:
* Akkadian.
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1701.02877
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Since the goal of this study is to compare NER performance on corpora from diverse domains and genres, seven benchmark NER corpora are included, spanning newswire, broadcast conversation, Web content, and social media (see Table 1 for details). These datasets were chosen such that they have been annotated with the same or very similar entity classes, in particular, names of people, locations, and organisations. Thus corpora including only domain-specific entities (e.g. biomedical corpora) were excluded. The choice of corpora was also motivated by their chronological age; we wanted to ensure a good temporal spread, in order to study possible effects of entity drift over time.
FLOAT SELECTED: Table 1 Corpora genres and number of NEs of different classes.
Since the goal of this study is to compare NER performance on corpora from diverse domains and genres, seven benchmark NER corpora are included, spanning newswire, broadcast conversation, Web content, and social media (see Table 1 for details).
FLOAT SELECTED: Table 1 Corpora genres and number of NEs of different classes.
|
What web and user-generated NER datasets are used for the analysis?
|
MUC, CoNLL, ACE, OntoNotes, MSM, Ritter, UMBC
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What could you do on a Sunday?
|
Most people do not work on Sundays, so they are free to pursue leisure activities to relax and recharge. To some people this may mean pursuing a hobby or a sport. Others may see friends or family. Unfortunately, Sundays may not be entirely free of unenjoyable chores such doing the laundry or cleaning the house.
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Who founded the city of Detroit?
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The site that was to become the city of Detroit was established on July 24, 1701, by Antoine de la Mothe Cadillac, a French military leader and trader.
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I'm going to give you a passage from Marty Essen's book "Cool Creatures, Hot Planet: Exploring the Seven Continents" and want you to answer the following question: Where are western Hermann's tortoises found?
After spending the night at a large chain hotel, Deb and I traveled southeast with tortoises on our mind. Our plan for the day was to infiltrate the Massif des Maures area and search for one of France's most endangered reptiles-- the western Hermann's tortoise. The first stop of our quest was Le Village de Tortues (the Tortoise Village), a tortoise rehabilitation center near the town of Gonfaron. Our mission was to befriend a staff member, learn as much as we could, and get detailed information on tracking down the wily Hermann's tortoise in the wild. When we couldn't find an English-speaking staff member, our mission was nearly thwarted. Only through quick thinking and improvisation were we able to acquire the vital background information we needed. Okay - we utilized the signs provided for British visitors. For many people, the word tortoise invokes thoughts of giant four-foot-long Galapagos tortoises. Western Hermann's tortoises, on the other hand, are small, reaching a maximum length of eight inches. While both reptiles make the IUCN Red List of Threatened Species, the Galapagos tortoise is listed as "vulnerable" and the western Hermann's is listed as "endangered." In France, habitat destruction, egg pilfering, and fire are the primary factors for the tortoises decline.
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Western Hermann's tortoises are found in France, in a turtle rehabilitation village, Le Village de Tortues, near Gonfaron.
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Identify which instrument is string or percussion: Octoban, Socavon
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Socavon is string, Octoban is percussion.
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Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome (i.e., a meta-genome and/or meta-transcriptome, depending upon how it is sequenced); in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association. The OmicTools service lists more than 99 softwares related to multiomic data analysis, as well as more than 99 databases on the topic.
Systems biology approaches are often based upon the use of panomic analysis data. The American Society of Clinical Oncology (ASCO) defines panomics as referring to "the interaction of all biological functions within a cell and with other body functions, combining data collected by targeted tests ... and global assays (such as genome sequencing) with other patient-specific information."
Single-cell multiomics
A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics. This approach gives us an unprecedent resolution to look at multilevel transitions in health and disease at the single cell level. An advantage in relation to bulk analysis is to mitigate confounding factors derived from cell to cell variation, allowing the uncovering of heterogeneous tissue architectures.
Methods for parallel single-cell genomic and transcriptomic analysis can be based on simultaneous amplification or physical separation of RNA and genomic DNA. They allow insights that cannot be gathered solely from transcriptomic analysis, as RNA data do not contain non-coding genomic regions and information regarding copy-number variation, for example. An extension of this methodology is the integration of single-cell transcriptomes to single-cell methylomes, combining single-cell bisulfite sequencing to single cell RNA-Seq. Other techniques to query the epigenome, as single-cell ATAC-Seq and single-cell Hi-C also exist.
A different, but related, challenge is the integration of proteomic and transcriptomic data. One approach to perform such measurement is to physically separate single-cell lysates in two, processing half for RNA, and half for proteins. The protein content of lysates can be measured by proximity extension assays (PEA), for example, which use DNA-barcoded antibodies. A different approach uses a combination of heavy-metal RNA probes and protein antibodies to adapt mass cytometry for multiomic analysis.
Multiomics and machine learning
In parallel to the advances in highthroughput biology, machine learning applications to biomedical data analysis are flourishing. The integration of multi-omics data analysis and machine learning has led to the discovery of new biomarkers. For example, one of the methods of the mixOmics project implements a method based on sparse Partial Least Squares regression for selection of features (putative biomarkers).
https://en.wikipedia.org/wiki/Multiomics
|
Given these paragraphs about Multiomics, what is a typical advantage of single-cell multiomics versus bulk analysis?
|
An advantage of single-cell multiomics in relation to bulk analysis is to mitigate confounding factors derived from cell to cell variation, allowing the uncovering of heterogeneous tissue architectures.
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Russell begins by asserting that the standard model of AI research, in which the primary definition of success is getting better and better at achieving rigid human-specified goals, is dangerously misguided. Such goals may not actually reflect what human designers intend, such as by failing to take into account any human values not included in the goals. If an AI developed according to the standard model were to become superintelligent, it would likely not fully reflect human values and could be catastrophic to humanity. Russell asserts that precisely because the timeline for developing human-level or superintelligent AI is highly uncertain, safety research should be begun as soon as possible, as it is also highly uncertain how long it would take to complete such research.
Russell argues that continuing progress in AI capability is inevitable because of economic pressures. Such pressures can already be seen in the development of existing AI technologies such as self-driving cars and personal assistant software. Moreover, human-level AI could be worth many trillions of dollars. Russell then examines the current debate surrounding AI risk. He offers refutations to a number of common arguments dismissing AI risk and attributes much of their persistence to tribalism—AI researchers may see AI risk concerns as an "attack" on their field. Russell reiterates that there are legitimate reasons to take AI risk concerns seriously and that economic pressures make continued innovation in AI inevitable.
Russell then proposes an approach to developing provably beneficial machines that focuses on deference to humans. Unlike in the standard model of AI, where the objective is rigid and certain, this approach would have the AI's true objective remain uncertain, with the AI only approaching certainty about it as it gains more information about humans and the world. This uncertainty would, ideally, prevent catastrophic misunderstandings of human preferences and encourage cooperation and communication with humans. Russell concludes by calling for tighter governance of AI research and development as well as cultural introspection about the appropriate amount of autonomy to retain in an AI-dominated world.
|
Given the following summary of the book "Human Compatible" by Stuart Russell, what is the author's proposal to overcome the limitations of the standard approach for developing AI?
|
Russell suggests that the AIs should be trained in a way that makes them gradually gain knowledge of what is truly beneficial to humans. AIs developed in this fashion would cooperate with humans while leaving them with a high enough level of autonomy.
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What geographic area do Sephardic Jews originate from?
|
Sephardic Judaism is rooted in the Iberian peninsula
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Mikel Artetxe Guezuraga (born 24 September 1976) is a Spanish former road bicycle racer, who rode professionally between 1999 and 2007 for the Euskaltel–Euskadi, 3 Molinos Resort and Fuerteventura–Canarias teams.
Career
He turned professional in 1999 with Euskaltel–Euskadi, where he stayed for seven seasons. In his time with Euskaltel–Euskadi, Artetxe took two stages and the overall victory in the GP Jornal de Noticias in 2000, a stage victory in the 2001 Vuelta a Andalucía and a stage victory in the 2002 Troféu Joaquim Agostinho.
At the start of the 2006 season, Artetxe moved a division down to the newly formed 3 Molinos Resort team. Here he had the biggest wins of his career, with a stage win in the Vuelta a Asturias and the sprints classification in the Troféu Joaquim Agostinho. With the rapid demise of 3 Molinos Resort, Artetxe moved to the newly formed Fuerteventura–Canarias team for 2007.
|
Given a reference text about Mikel Artetxe Guezuraga, tell me when he turned professional and what his biggest wins are.
|
Mikel Artetxe Guezuraga turned professional in 1999 and had the biggest wins of his career in 2006 with a stage win in the Vuelta a Asturias and the sprints classification in the Troféu Joaquim Agostinho.
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| 267
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Simple word-based approaches, if used for blocking the posting of text or blacklisting users, not only fail to identify subtle offensive content, but they also affect the freedom of speech and expression. The word ambiguity problem – that is, a word can have different meanings in different contexts – is mainly responsible for the high false positive rate in such approaches. Ordinary NLP approaches on the other hand, are ineffective to detect unusual spelling, experienced in user-generated comment text. This is best known as the spelling variation problem, and it is caused either by unintentional or intentional replacement of single characters in a token, aiming to obfuscate the detectors.
In general, the complexity of the natural language constructs renders the task quite challenging. The employment of supervised learning classification methods for hate speech detection is not new. BIBREF6 reported performance for a simple LSTM classifier not better than an ordinary SVM, when evaluated on a small sample of Facebook data for only 2 classes (Hate, No-Hate), and 3 different levels of strength of hatred. BIBREF7 described another way of detecting offensive language in tweets, based on some supervised model. They differentiate hate speech from offensive language, using a classifier that involves naive Bayes, decision trees and SVM. Also, BIBREF8 attempted to discern abusive content with a supervised model combining various linguistic and syntactic features in the text, considered at character uni-gram and bi-gram level, and tested on Amazon data. In general, we can point out the main weaknesses of NLP-based models in their non-language agnostic nature and the low scores in detection.
Unsupervised learning approaches are quite common for detecting offensive messages in text by applying concepts from NLP to exploit the lexical syntactic features of sentences BIBREF9 , or using AI-solutions and bag-of-words based text-representations BIBREF10 . The latter is known to be less effective for automatic detection, since hatred users apply various obfuscation tricks, such as replacing a single character in offensive words. For instance, applying a binary classifier onto a paragraph2vec representation of words has already been attempted on Amazon data in the past BIBREF11 , but it only performed well on a binary classification problem. Another unsupervised learning based solution is the work by BIBREF12 , in which the authors proposed a set of criteria that a tweet should exhibit in order to be classified as offensive. They also showed that differences in geographic distribution of users have only marginal effect on the detection performance. Despite the above observation, we explore other features that might be possible to improve the detection accuracy in the solution outlined below.
The work by BIBREF5 applied a crowd-sourced solution to tackle hate-speech, with the creation of an additional dataset of annotations to extend the existing corpus. The impact of the experience of annotators in the classification performance was investigated. The work by BIBREF13 dealt with the classification problem of tweets, but their interest was on sexism alone, which they distinguished into `Hostile', `Benevolent' or `Other'. While the authors used the dataset of tweets from BIBREF12 , they treated the existing `Sexism' tweets as being of class `Hostile', while they collected their own tweets for the `Benevolent' class, on which they finally applied the FastText by BIBREF14 , and SVM classification.
BIBREF15 approached the issue with a supervised learning model that is based on a neural network. Their method achieved higher score over the same dataset of tweets than any unsupervised learning solution known so far. That solution uses an LSTM model, with features extracted by character n-grams, and assisted by Gradient Boosted Decision Trees. Convolution Neural Networks (CNN) has also been explored as a potential solution in the hate-speech problem in tweets, with character n-grams and word2vec pre-trained vectors being the main tools. For example, BIBREF16 transformed the classification into a 2-step problem, where abusive text first is distinguished from the non-abusive, and then the class of abuse (Sexism or Racism) is determined. BIBREF17 employed pre-trained CNN vectors in an effort to predict four classes. They achieved slightly higher F-score than character n-grams.
In spite of the high popularity of NLP approaches in hate-speech classification BIBREF3 , we believe there is still a high potential for deep learning models to further contribute to the issue. At this point it is also relevant to note the inherent difficulty of the challenge itself, which can be clearly noted by the fact that no solution thus far has been able to obtain an F-score above 0.93.
Davidson et al. (2017) described another way of detecting offensive language in tweets, based on some supervised model. They differentiate hate speech from offensive language, using a classifier that involves naive Bayes, decision trees and SVM.
|
In the method of Davidson et al., what kind of classifier was used to differentiate hate speech from offensive language?
|
A classifier that involves naive Bayes, decision trees and SVM.
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1811.01001
| false
| null |
BIBREF7 investigated the learning capabilities of simple RNNs to process and formalize a context-free grammar containing hierarchical (recursively embedded) dependencies: He observed that distinct parts of the networks were able to learn some complex representations to encode certain grammatical structures and dependencies of the context-free grammar. Later, BIBREF8 introduced an RNN with an external stack memory to learn simple context-free languages, such as $a^n b^m$ , $a^nb^ncb^ma^m$ , and $a^{n+m} b^n c^m$ . Similar studies BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 have explored the existence of stable counting mechanisms in simple RNNs, which would enable them to learn various context-free and context-sensitive languages, but none of the RNN architectures proposed in the early days were able to generalize the training set to longer (or more complex) test samples with substantially high accuracy.
BIBREF9 , on the other hand, proposed a variant of Long Short-Term Memory (LSTM) networks to learn two context-free languages, $a^n b^n$ , $a^n b^m B^m A^n$ , and one strictly context-sensitive language, $a^n b^n c^n$ . Given only a small fraction of samples in a formal language, with values of $n$ (and $m$ ) ranging from 1 to a certain training threshold $N$ , they trained an LSTM model until its full convergence on the training set and then tested it on a more generalized set. They showed that their LSTM model outperformed the previous approaches in capturing and generalizing the aforementioned formal languages. By analyzing the cell states and the activations of the gates in their LSTM model, they further demonstrated that the network learns how to count up and down at certain places in the sample sequences to encode information about the underlying structure of each of these formal languages.
Later, BIBREF8 introduced an RNN with an external stack memory to learn simple context-free languages, such as $a^n b^m$ , $a^nb^ncb^ma^m$ , and $a^{n+m} b^n c^m$ . Similar studies BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 have explored the existence of stable counting mechanisms in simple RNNs, which would enable them to learn various context-free and context-sensitive languages
BIBREF9 , on the other hand, proposed a variant of Long Short-Term Memory (LSTM) networks to learn two context-free languages, $a^n b^n$ , $a^n b^m B^m A^n$ , and one strictly context-sensitive language, $a^n b^n c^n$ .
|
How do they get the formal languages?
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These are well-known formal languages some of which was used in the literature to evaluate the learning capabilities of RNNs.
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null | false
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which country that has the most pyramids in the world?
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Sudan has more pyramids than any country in the world
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null | false
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Why was the model T only produced in black?
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Henry Ford produced the model T only in black because the black paint available at the time was the fastest to dry.
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null | false
| 356
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The classification accuracy for zero-shot transfer on the test set of our Multilingual Document Classification Corpus are summarized in Table 4 . The classifiers based on the MultiCCA embeddings perform very well on the development corpus (accuracies close or exceeding 90%). The system trained on English also achieves excellent results when transfered to a different languages, it scores best for three out of seven languages (DE, IT and ZH). However, the transfer accuracies are quite low when training the classifiers on other languages than English, in particular for Russian, Chinese and Japanese.
The systems using multilingual sentence embeddings seem to be overall more robust and less language specific. They score best for four out of seven languages (EN, ES, FR and RU). Training on German or French actually leads to better transfer performance than training on English. Cross-lingual transfer between very different languages like Chinese and Russian also achieves remarkable results.
The classifiers based on the MultiCCA embeddings perform very well on the development corpus (accuracies close or exceeding 90%).
|
Do the classifiers based on the MultiCCA embeddings perform very well on the development corpus?
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Yes, they do.
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Ouzo is a clear liquid. However, when water or ice is added, ouzo turns a milky-white colour. This is because anethole, the essential oil of anise, is completely soluble in alcohol at approximately 38% ABV and above, but not in water. Diluting the spirit causes it to separate, creating an emulsion whose fine droplets scatter the light. This process is called louching and is also seen while preparing absinthe.
The drinks with a similar flavour of Ouzu include sambuca from (Italy), pastis from (France), oghi (from Armenia), rakı from Turkey, and arak (from the Levant). Its aniseed flavour is also similar to the anise-flavoured liqueur of anís (Spain) and the stronger spirits of absinthe (France and Switzerland). Aguardiente (Latin America), made from sugar cane, is also similar. The Italian drink Pallini Mistra, named after the Greek city of Mystras in the Peloponnese is a version of ouzo made in Rome that closely resembles Greek and Cypriot ouzo.
|
What is the Turkish drink that tastes similar to
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Rakı is a drink from Turkey that has similar flavour with Ouzo.
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What is the most popular wine varietal produced in California?
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California is best known for its white wine, Chardonnay, and its red wine, Cabernet Sauvignon.
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1612.05310
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We had two human annotators who were trained on snippets (i.e., (suspected trolling attempt, responses) pairs) taken from 200 conversations and were allowed to discuss their findings. After this training stage, we asked them to independently label the four aspects for each snippet. We recognize that this limited amount of information is not always sufficient to recover the four aspects we are interested in, so we give the annotators the option to discard instances for which they couldn't determine the labels confidently. The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens. The distribution over the classes per trolling aspect is shown in the table TABREF19 in the column “Size”.
The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens.
|
What is the size of the dataset?
|
The answers are shown as follows:
* 1000 conversations composed of 6833 sentences and 88047 tokens
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Gandhi's first major achievement came in 1917 with the Champaran agitation in Bihar. The Champaran agitation pitted the local peasantry against largely Anglo-Indian plantation owners who were backed by the local administration. The peasants were forced to grow indigo (Indigofera sp.), a cash crop for Indigo dye whose demand had been declining over two decades, and were forced to sell their crops to the planters at a fixed price. Unhappy with this, the peasantry appealed to Gandhi at his ashram in Ahmedabad. Pursuing a strategy of nonviolent protest, Gandhi took the administration by surprise and won concessions from the authorities.
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When and where was the first major agitation led by Gandhi, tell me who this agitation was against, which crop led to this agitation and why this agitation happened.
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First major agitation was in 1917 in Champaran Bihar state. The agitation was against Anglo-Indian plantation owners. Indigo was the crop that led to this agitation and the reason was the lower fixed price for the Indigo crop.
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| 192
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Currently, probabilistic topic models are important tools for improving automatic text processing including information retrieval, text categorization, summarization, etc. Besides, they can be useful in supporting expert analysis of document collections, news flows, or large volumes of messages in social networks BIBREF0 , BIBREF1 , BIBREF2 . To facilitate this analysis, such approaches as automatic topic labeling and various visualization techniques have been proposed BIBREF1 , BIBREF3 .
Boyd-Graber et al. BIBREF4 indicate that to be understandable by humans, topics should be specific, coherent, and informative. Relationships between the topic components can be inferred. In BIBREF1 four topic visualization approaches are compared. The authors of the experiment concluded that manual topic labels include a considerable number of phrases; users prefer shorter labels with more general words and tend to incorporate phrases and more generic terminology when using more complex network graph. Blei and Lafferty BIBREF3 visualize topics with ngrams consisting of words mentioned in these topics. These works show that phrases and knowledge about hyponyms/hypernyms are important for topic representation.
In this paper we describe an approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics. The structure of the paper is as follows. In Section 2 we consider related works. Section 3 describes the proposed approach. Section 4 enumerates automatic quality measures used in experiments. Section 5 presents the results obtained on several text collections according to automatic measures. Section 6 describes the results of manual evaluation of combined topic models for Islam Internet-site thematic analysis.
In this paper we describe an approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics.
|
What method does the paper propose?
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An approach to integrate large manual lexical resources such as WordNet or EuroVoc into probabilistic topic models, as well as automatically extracted n-grams to improve coherence and informativeness of generated topics.
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Carbonara (Italian: [karboˈnaːra]) is a Roman pasta dish made with eggs, hard cheese, cured pork and black pepper. The dish took its modern form and name in the middle of the 20th century.
The cheese is usually Pecorino Romano, Parmigiano-Reggiano, or a combination of the two. Spaghetti is the most common pasta, but fettuccine, rigatoni, linguine, or bucatini are also used. Normally guanciale or pancetta are used for the meat component,] but lardons of smoked bacon are a common substitute outside Italy.
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Extract the ingredients needed to make pasta carbonara. Separate them with a comma.
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Eggs, hard cheese, cured pork, black pepper
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Tell me whether each of these dishes are served cold or hot: pizza, pasta, tiramisu, ice cream, ribs, steak, ceviche
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Pizza: Hot
Pasta: Hot
Tiramisu: Cold
Ice Cream: Cold
Ribs: Hot
Steak: Hot
Ceviche: Cold
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Provide a list of ways musicians can earn income from their music craft.
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Paid live music performances
Earning music royalties from your recorded music when it is commercially played, performed or streamed
Getting paid music sync licensing fees for your recorded music to appear in TV, film, or video games
Artist or band merchandise sales
Getting paid to provide voice or instrument lessons to others looking to learn
Paid artist endorsements or sponsorships for other products
Sale of no longer used or needed instruments or music equipment to others who can use them
Writing, recording and delivering a commissioned song for a particular buyer to commemorate a special person or occasion
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Name some famous rock bands from the San Francisco Bay Area.
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Many famous rock bands have come from the San Francisco Bay area. These bands include the Grateful Dead, Journey, The Jefferson Airplane, Metallica, Santana, and Green Day.
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What is hybrid working?
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Hybrid working is a term used for when you split your work time between an office building and from other locations, usually home. It became more widely used during the COVID19 pandemic when talking about where, when and how people work. Other names for Hybrid Working are Activity Based Working or Agile Working. Agile working is not to be confused with Agile project management which has different principles.
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What's the largest bird in the world?
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Ostrich
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List some cool places to go visit when you go to Prescott, AZ
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1. Whisky Row, Downtown Prescott
2. Jerome - copper mines
3. Sedona - Natural Waterslides
4. Off-roading
5. Lynx Lake
6. Watson Lake
7. Gold Water Lake
8. Prescott Camp Grounds
9. Mortimer Farms
10. Tim’s Toyota Center
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Virginia City is a census-designated place (CDP) that is the county seat of Storey County, Nevada, and the largest community in the county. The city is a part of the Reno–Sparks Metropolitan Statistical Area.
Virginia City developed as a boomtown with the 1859 discovery of the Comstock Lode, the first major silver deposit discovery in the United States, with numerous mines opening. The population peaked in the mid-1870s, with an estimated 25,000 residents. The mines' output declined after 1878, and the population declined as a result. As of the 2020 Census, the population of Virginia City was 787.
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Why is Virginia City famous?
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Virginia City is a census-designated place (CDP) and the county seat of Storey County, Nevada, and the largest community in the county. The city is a part of the Reno–Sparks Metropolitan Statistical Area.
In 1859, Virginia City developed as a boomtown with the discovery of the Comstock Lode. The Comstock Lode is the first major silver deposit discovery in the United States. Virginia City's population peaked in the mid-1870s, with an estimated 25,000 residents. The mines' output declined after 1878, and the population declined as a result. As of the 2020 Census, the population of Virginia City was only 787.
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Michael Flavin's Benjamin Disraeli: The Novel as a Political Discourse suggests that Venetia was largely a commercial endeavour for Disraeli, who was deep in debt at the time that he wrote it.
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Was Michael Flavin wealthy at the time he wrote Benjamin Disraeli?
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No, he was deep in debt at the time.
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What is Web3?
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Web3 (also known as Web 3.0) is an idea for a new iteration of the World Wide Web which incorporates concepts such as decentralization, blockchain technologies, and token-based economics. Some technologists and journalists have contrasted it with Web 2.0, wherein they say data and content are centralized in a small group of companies sometimes referred to as "Big Tech". The term "Web3" was coined in 2014 by Ethereum co-founder Gavin Wood, and the idea gained interest in 2021 from cryptocurrency enthusiasts, large technology companies, and venture capital firms.
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With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs).
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What are the feature spaces for classifiers defined by?
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Task-specific embeddings learned using FastText, Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs).
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Tell me if the following were originally board or computer games: Scrabble, Monopoly, Doom, Counter Strike, Sorry, Super Mario Bros?
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Scrabble, Monopoly, and Sorry were originally board games. Doom, Counter Strike, and Super Mario Bros were originally computer games.
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What are the top 3 TV shows you have binge watched and why?
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Hands down, my favorite series was The Americans. As someone who grew up in the 1980's, I could identify with the clothing and hair styles, the decor, and the dearth of real-time communication options (no internet, no email, no mobile phones, etc.). The limited communication modes is what led to secret information drop locations in this era, which were a common insurance in the show. But more importantly, I remember the feeling of growing up in the time of the Cold War. We were still having scheduled air raid drills in my grammar school as often as we'd have fire drills! As children, we were convinced that nuclear war was going to happen someday, even if the adults tried to tell us that it wasn't. Growing up about 25 miles from New York City, I was pretty sure that I'd be gone within seconds of a nuclear strike to the city. My next favorite has been Yellowstone. This is a show that seems to appeal to almost everyone because it has plenty of beautiful Western scenes (Montana), family drama, and battles between flawed characters. This series is still ongoing and I can't wait for the next set of episodes to be released! I'd have to say my third favorite is Billions. It's a series from several years ago that we just happened upon. We are in season 2 of 5 or 6 total seasons, and I am riveted by the characters, especially Bobby Axelrod (Ax), and Chuck and Wendy Rhoades. They are all so intense and engrossed in what they do, so it makes for very interesting interactions and plot twists! And I have to give an Honorable Mention to Perry Mason. While this is a very new series, I absolutely love this period drama so far. The creators have based many of the episodes on similar actual events from this period (the 1930s). It is very difficult to watch Della Street, who is insanely smart and charming, be relegated to the 'brains behind the guy' (Perry Mason) because it's the 1930s. Thanks for reading! I enjoyed brainstorming my favorites and sharing them.
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What is one of your favorite color?
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My favorite color is purple. Purple is a great color for many reasons. The hue and tone are quite pleasing to the eye. Additionally, purple is complementary to many other colors such as green, yellow, and gray. The color purple is often a color symbolizing richness and celebration, while it is a mature color it is also lively.
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Kieran Liam McAnespie (born 11 September 1979) is a football manager and former player. Capable of playing as a full-back or winger, he appeared in the Scottish Premier League for St Johnstone and Hearts. Born in England, he represented the Scotland U21 national team internationally
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Where does Kieran Liam McAnespie come from?
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Kieran Liam McAnespie was born in England.
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Name the famous place in Italy which was flooded for the 6th time in the last 1200 years.
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Saint Mark's Square in Venice
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Table TABREF5 shows the results of various methods on the hate speech detection task. Part A shows results for baseline methods. Parts B and C focus on the proposed methods where part B contains methods using neural networks only, while part C uses average of word embeddings learned by DNNs as features for GBDTs. We experimented with multiple classifiers but report results mostly for GBDTs only, due to lack of space.
As the table shows, our proposed methods in part B are significantly better than the baseline methods in part A. Among the baseline methods, the word TF-IDF method is better than the character n-gram method. Among part B methods, CNN performed better than LSTM which was better than FastText. Surprisingly, initialization with random embeddings is slightly better than initialization with GloVe embeddings when used along with GBDT. Finally, part C methods are better than part B methods. The best method is “LSTM + Random Embedding + GBDT” where tweet embeddings were initialized to random vectors, LSTM was trained using back-propagation, and then learned embeddings were used to train a GBDT classifier. Combinations of CNN, LSTM, FastText embeddings as features for GBDTs did not lead to better results. Also note that the standard deviation for all these methods varies from 0.01 to 0.025.
To verify the task-specific nature of the embeddings, we show top few similar words for a few chosen words in Table TABREF7 using the original GloVe embeddings and also embeddings learned using DNNs. The similar words obtained using deep neural network learned embeddings clearly show the “hatred” towards the target words, which is in general not visible at all in similar words obtained using GloVe.
We experimented with multiple classifiers but report results mostly for GBDTs only,due to lack of space.As the table shows, our proposed methods in part B are significantly better than the baseline methods in part A. Among the baseline methods, the word TF-IDF method is better than the character n-gram method.
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Can the proposed approach use multiple classifiers to detect hateful tweets effectively?
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Yes.
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| 41
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We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS. We used an implementation of sentiment analyzer from TextBlob to get a polarity score for each sentence. Table TABREF13 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION. It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem.
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS. We used an implementation of sentiment analyzer from TextBlob1 to get a polarity score for each sentence. Table 2 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION. It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem.
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Is the information about sentiments useful for their classification?
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No.
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Write a fairly boring short story about making coffee in the morning.
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6:00 AM. My alarm clocks rings and I hit the snooze. Yes, it's been "one of those weeks" and I just don't have the energy to wake up. I fall back asleep and the alarm clock rudely awakens me again at 6:10. I get out of bed and stumble into the kitchen. There is only one elixir that can rouse me from my sleepy state - coffee.
First, I grind the beans. The coffee grinder is a bit loud, but I like the taste of freshly ground beans. Next, I put a new coffee filter into my drip coffee maker. I pour the ground beans into the filter. Then, I fill the coffee maker with 6 cups of water. I start the machine, and my coffee starts brewing. A delicious aroma fills the air, and my mood starts to improve. After 5 minutes, my cup of joe is ready. I drink it and it tastes great.
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Recreational drug use is the use of one or more psychoactive drugs to induce an altered state of consciousness either for pleasure or for some other casual purpose or pastime. When a psychoactive drug enters the user's body, it induces an intoxicating effect. Generally, recreational drugs are divided into three categories: depressants (drugs that induce a feeling of relaxation and calmness), stimulants (drugs that induce a sense of energy and alertness), and hallucinogens (drugs that induce perceptual distortions such as hallucination).
In popular practice, recreational drug use generally is a tolerated social behaviour, rather than perceived as the medical condition of self-medication. However, drug use and drug addiction are severely stigmatized everywhere in the world. Many people also use prescribed and controlled depressants such as opioids, as well as opiates and benzodiazepines. What controlled substances are considered generally unlawful to possess varies by country, but usually includes cannabis, cocaine, opioids, MDMA, amphetamine, methamphetamine, psychedelics, benzodiazepines, and barbiturates. As of 2015, it is estimated that about 5% of people worldwide aged 15 to 65 (158 million to 351 million) had used controlled drugs at least once.
Common recreational drugs include caffeine, commonly found in coffee, tea, soft drinks, and chocolate; alcohol, commonly found in beer, wine, cocktails, and distilled spirits; nicotine, commonly found in tobacco, tobacco-based products, and electronic cigarettes; cannabis and hashish (with legality of possession varying inter/intra-nationally); and the controlled substances listed as controlled drugs in the Single Convention on Narcotic Drugs (1961) and the Convention on Psychotropic Substances (1971) of the United Nations (UN). Since the early 2000s, the European Union (EU) has developed several comprehensive and multidisciplinary strategies as part of its drug policy in order to prevent the diffusion of recreational drug use and abuse among the European population and raise public awareness on the adverse effects of drugs among all member states of the European Union, as well as conjoined efforts with European law enforcement agencies, such as the Europol and EMCDDA, in order to counter organized crime and illegal drug trade in Europe.
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Give me a summary of the paragraph in your own words and it should be short
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Recreational drug use refers to using certain drugs for enjoyment or leisure purposes instead of for medicinal reasons. Some common recreational drugs include caffeine, alcohol, nicotine, marijuana, and synthetic drugs like ecstasy. While many countries consider recreational drug use legal, there are still laws surrounding their consumption and distribution. The EU has implemented various policies aimed at reducing recreational drug use and addressing issues related to drug trafficking within its borders.
People often take recreational drugs because they want to experience feelings of euphoria, increased energy levels, or enhanced sensory perception. However, these drugs can have serious side effects, including dependence, withdrawal symptoms, impaired judgment and motor skills, and even death. Additionally, recreational drug use can lead to legal consequences, financial problems, strained relationships, and health issues such as mental illness, organ damage, and overdose. Despite these risks, many individuals continue to engage in recreational drug use due to peer pressure, stress relief, or simply for the thrill of it. Overall, recreational drug use poses significant dangers to both individual users and society as a whole.
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Reddit: To retrieve high-quality conversational data that would likely include hate speech, we referenced the list of the whiniest most low-key toxic subreddits. Skipping the three subreddits that have been removed, we collect data from ten subreddits: r/DankMemes, r/Imgoingtohellforthis, r/KotakuInAction, r/MensRights, r/MetaCanada, r/MGTOW, r/PussyPass, r/PussyPassDenied, r/The_Donald, and r/TumblrInAction. For each of these subreddits, we retrieve the top 200 hottest submissions using Reddit's API. To further focus on conversations with hate speech in each submission, we use hate keywords BIBREF6 to identify potentially hateful comments and then reconstructed the conversational context of each comment. This context consists of all comments preceding and following a potentially hateful comment. Thus for each potentially hateful comment, we rebuild the conversation where the comment appears. Figure FIGREF14 shows an example of the collected conversation, where the second comment contains a hate keyword and is considered as potentially hateful. Because a conversation may contain more than one comments with hate keywords, we removed any duplicated conversations.
Gab: We collect data from all the Gab posts in October 2018. Similar to Reddit, we use hate keywords BIBREF6 to identify potentially hateful posts, rebuild the conversation context and clean duplicate conversations.
Skipping the three subreddits that have been removed, we collect data from ten subreddits: r/DankMemes, r/Imgoingtohellforthis, r/KotakuInAction, r/MensRights, r/MetaCanada, r/MGTOW, r/PussyPass, r/PussyPassDenied, r/The Donald, and r/TumblrInAction.
|
What are the ten subreddits?
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r/DankMemes, r/Imgoingtohellforthis, r/KotakuInAction, r/MensRights, r/MetaCanada, r/MGTOW, r/PussyPass, r/PussyPassDenied, r/The Donald, and r/TumblrInAction.
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Trump lost the 2020 presidential election to Joe Biden but refused to concede defeat, falsely claiming widespread electoral fraud and attempting to overturn the results by pressuring government officials, mounting scores of unsuccessful legal challenges, and obstructing the presidential transition. On January 6, 2021, Trump urged his supporters to march to the U.S. Capitol, which many of them then attacked, resulting in multiple deaths and interrupting the electoral vote count.
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Did Trump lose the 2020 presidential election?
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Yes. Trump lost the 2020 presidential election, but refused to concede defeat. He furthermore urged his supporters to commit insurrection and attack the U.S. Capitol on January 6th 2021.
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What are few things that could impact your EV range on long trips
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1) Weather can be a big factor. For days that are extremely hot or cold you'll be running the AC or Heater respectively and that could be a big drain on the battery
2) And this could get unexpectedly get worse if you are stuck in a traffic jam since you are not able to gain much distance while draining battery to AC or Heather
3) Rain can also lead to faster battery drain where you'll need to run the defogger as well as windshield wipers.
4) Additional load. If you are planning on taking heavy load like camping equipment, bicycles it drains the battery faster.
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What are some of the best science fiction movies of all time?
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There have been a number of well received science fiction movies including classics such "2001: A Space Odyssey", "Star Wars", "Blade Runner", "Alien", "Inception", "The Martian" and "The Matrix". Often the most important factor of what makes a movie great is very dependent on the viewer themselves. Are they interested in movies that are based more on hard science like "The Martian" or more like a soft space opera or fantasy like "Star Wars". Movies can also be great commercial successes while not being critically acclaimed. Some movies can also be great action films or be very thought provoking or reflect on bigger themes like the nature of being.
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Which of the following are noble gases: helium, sodium, neon, oxygen, argon, hydrogen, calcium, magnesium, lithium, boron, krypton, nitrogen, xenon, aluminium, radon.
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The noble gases are - helium, neon, argon, krypton, xenon, radon.
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Danny Brannagan (born July 4, 1986) is a former professional Canadian football quarterback for the Toronto Argonauts of the Canadian Football League.
Early years
Brannagan attended high school at Assumption Catholic Secondary School in Burlington where in his 5th and final year in 2004, he led the Assumption Crusaders to the GHAC finals where they defeated St. Jean de Brebeuf in a thriller, with the final score 29-28. Trailing by 27 points with less than 8 minutes to play, Brannagan led Assumption's offense to an amazing comeback, taking the lead with under 2 minutes left to play.
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Where is Danny Brannagan from
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Danny Brannagan (born July 4, 1986) is a former professional Canadian football quarterback for the Toronto Argonauts of the Canadian Football League.
Brannagan attended high school at Assumption Catholic Secondary School in Burlington where in his 5th and final year in 2004, he led the Assumption Crusaders to the GHAC finals where they defeated St. Jean de Brebeuf in a thriller, with the final score 29-28. Trailing by 27 points with less than 8 minutes to play, Brannagan led Assumption's offense to an amazing comeback, taking the lead with under 2 minutes left to play.
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Think of things to do in a place with cold winters. Provide answer as a bullet list.
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- Build a snowman
- Have a snowball fight
- Go sledding
- Go skiing
- Go skating
- Go snowshoeing
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| 322
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In this paper, we presented LD2NL, a framework for verbalizing SW languages, especially on RDF and OWL while including the SPARQL verbalization provided by SPARQL2NL. Our evaluation with 86 persons revealed that our framework generates NL that can be understood by lay users. While the OWL verbalization was close to NL, the RDF was less natural but still sufficient to convey the meaning expressed by the corresponding set of triples. In future work, we aim to extend LD2NL to verbalize the languages SWRL BIBREF23 and SHACL BIBREF24.
In future work, we aim to extend LD2NL to verbalize the languages SWRL (Horrocks et al., 2004) and SHACL (Knublauch and Kontokostas, 2017).
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What does the author aim to do in future work?
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In future work, the authors aim to extend LD2NL to verbalize the languages SWRL and SHACL.
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Which of the following are perfect square roots? Classify them as 'perfect' and 'not perfect' - 1, 23, 4, 6, 9, 10, 42, 112, 81, 100, 55, 16, 32, 25.
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'perfect', 'not perfect', 'perfect', 'not perfect', 'perfect', 'not perfect', 'not perfect', 'not perfect', 'perfect', 'perfect', 'not perfect', 'perfect', 'not perfect', 'perfect'.
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| 80
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Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
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What is the biggest advantage of their SERA compared to ROUGE?
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SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.
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What is Fortran?
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Fortran is a general-purpose, compiled imperative programming language that is especially suited to numeric computation and scientific computing.
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1808.09920
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FLOAT SELECTED: Table 2: Accuracy of different models on WIKIHOP closed test set and public validation set. Our Entity-GCN outperforms recent prior work without learning any language model to process the input but relying on a pretrained one (ELMo – without fine-tunning it) and applying R-GCN to reason among entities in the text. * with coreference for unmasked dataset and without coreference for the masked one.
FLOAT SELECTED: Table 2: Accuracy of different models on WIKIHOP closed test set and public validation set. Our Entity-GCN outperforms recent prior work without learning any language model to process the input but relying on a pretrained one (ELMo – without fine-tunning it) and applying R-GCN to reason among entities in the text. * with coreference for unmasked dataset and without coreference for the masked one.
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What performance does the Entity-GCN get on WIKIHOP?
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During testing: 67.6 for single model without coreference, 66.4 for single model with coreference, 71.2 for ensemble of 5 models
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1910.03814
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Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
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How many tweats does MMHS150k contains, 150000?
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The answers are shown as follows:
* $150,000$ tweets
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| 111
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In Turkish, there do not exist well-established sentiment lexicons as in English. In this approach, we made use of the TDK (Türk Dil Kurumu - “Turkish Language Institution”) dictionary to obtain word polarities. Although it is not a sentiment lexicon, combining it with domain-specific polarity scores obtained from the corpus led us to have state-of-the-art results.
We first construct a matrix whose row entries are corpus words and column entries are the words in their dictionary definitions. We followed the boolean approach. For instance, for the word cat, the column words occurring in its dictionary definition are given a score of 1. Those column words not appearing in the definition of cat are assigned a score of 0 for that corresponding row entry.
When we performed clustering on this matrix, we observed that those words having similar meanings are, in general, assigned to the same clusters. However, this similarity fails in capturing the sentimental characteristics. For instance, the words happy and unhappy are assigned to the same cluster, since they have the same words, such as feeling, in their dictionary definitions. However, they are of opposite polarities and should be discerned from each other.
Therefore, we utilise a metric to move such words away from each other in the VSM, even though they have common words in their dictionary definitions. We multiply each value in a row with the corresponding row word's raw supervised score, thereby having more meaningful clusters. Using the training data only, the supervised polarity score per word is calculated as in (DISPLAY_FORM4).
Here, $ w_{t}$ denotes the sentiment score of word $t$, $N_{t}$ is the number of documents (reviews or tweets) in which $t$ occurs in the dataset of positive polarity, $N$ is the number of all the words in the corpus of positive polarity. $N^{\prime }$ denotes the corpus of negative polarity. $N^{\prime }_{t}$ and $N^{\prime }$ denote similar values for the negative polarity corpus. We perform normalisation to prevent the imbalance problem and add a small number to both numerator and denominator for smoothing.
As an alternative to multiplying with the supervised polarity scores, we also separately multiplied all the row scores with only +1 if the row word is a positive word, and with -1 if it is a negative word. We have observed it boosts the performance more compared to using raw scores.
The effect of this multiplication is exemplified in Figure FIGREF7, showing the positions of word vectors in the VSM. Those “x" words are sentimentally negative words, those “o" words are sentimentally positive ones. On the top coordinate plane, the words of opposite polarities are found to be close to each other, since they have common words in their dictionary definitions. Only the information concerned with the dictionary definitions are used there, discarding the polarity scores. However, when we utilise the supervised score (+1 or -1), words of opposite polarities (e.g. “happy" and “unhappy") get far away from each other as they are translated across coordinate regions. Positive words now appear in quadrant 1, whereas negative words appear in quadrant 3. Thus, in the VSM, words that are sentimentally similar to each other could be clustered more accurately. Besides clustering, we also employed the SVD method to perform dimensionality reduction on the unsupervised dictionary algorithm and used the newly generated matrix by combining it with other subapproaches. The number of dimensions is chosen as 200 again according to the $U$ matrix. The details are given in Section 3.4. When using and evaluating this subapproach on the English corpora, we used the SentiWordNet lexicon BIBREF13. We have achieved better results for the dictionary-based algorithm when we employed the SVD reduction method compared to the use of clustering.
We perform normalisation to prevent the imbalance problem and add a small number to both numerator and denominator for smoothing.
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How is the imbalance problem solved？
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They perform normalisation to prevent the imbalance problem.
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A death growl, or simply growl, is an extended vocal technique usually employed in extreme styles of music, particularly in death metal and other extreme subgenres of heavy metal music. Death growl vocals are sometimes criticized for their "ugliness", but their unintelligibility contributes to death metal's abrasive style and often dark and obscene subject matter.
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What criticism do people make about the death growl vocal technique?
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The death growl vocal technique is described as having a harsh tone which is called ugly.
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| 34
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Single-relation factoid questions are the most common form of questions found in search query logs and community question answering websites BIBREF1 , BIBREF2 . A knowledge-base (KB) such as Freebase, DBpedia, or Wikidata can help answer such questions after users reformulate them as queries. For instance, the question Where was Barack Obama born? can be answered by issuing the following KB query: $
\lambda (x).place\_of\_birth(Barack\_Obama, x)
$
However, automatically mapping a natural language question such as Where was Barack Obama born? to its corresponding KB query remains a challenging task.
There are three key issues that make learning this mapping non-trivial. First, there are many paraphrases of the same question. Second, many of the KB entries are unseen during training time; however, we still need to correctly predict them at test time. Third, a KB such as Freebase typically contains millions of entities and thousands of predicates, making it difficult for a system to predict these entities at scale BIBREF1 , BIBREF3 , BIBREF0 . In this paper, we address all three of these issues with a character-level encoder-decoder framework that significantly improves performance over state-of-the-art word-level neural models, while also providing a much more compact model that can be learned from less data.
First, we use a long short-term memory (LSTM) BIBREF4 encoder to embed the question. Second, to make our model robust to unseen KB entries, we extract embeddings for questions, predicates and entities purely from their character-level representations. Character-level modeling has been previously shown to generalize well to new words not seen during training BIBREF5 , BIBREF6 , which makes it ideal for this task. Third, to scale our model to handle the millions of entities and thousands of predicates in the KB, instead of using a large output layer in the decoder to directly predict the entity and predicate, we use a general interaction function between the question embeddings and KB embeddings that measures their semantic relevance to determine the output. The combined use of character-level modeling and a semantic relevance function allows us to successfully produce likelihood scores for the KB entries that are not present in our vocabulary, a challenging task for standard encoder-decoder frameworks.
Our novel, character-level encoder-decoder model is compact, requires significantly less data to train than previous work, and is able to generalize well to unseen entities in test time. In particular, without use of ensembles, we achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% BIBREF0 by 8.2% and 6.4% respectively. Moreover, we only use the training questions provided in SimpleQuestions to train our model, which cover about 24% of words in entity aliases on the test set. This demonstrates the robustness of the character-level model to unseen entities. In contrast, data augmentation is usually necessary to provide more coverage for unseen entities and predicates, as done in previous work BIBREF0 , BIBREF1 .
First, we use a long short-term memory (LSTM) encoder to embed the question.
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How do the authors embed the question?
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They use a long short-term memory (LSTM) encoder.
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Which is the best meal delivery service?
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It depends on what is important to you. Some people like Blue Apron because the ingredients are from sustainable sources and the recipe cards have pictures and are easy to follow. But some people don't like Blue Apron and other meal delivery services like Hello Fresh, Factor, and Dinnerly, because you only get the ingredients, not complete meals. So you have to spend time on meal prep. For completely assembled ready-to-eat meals, people like Freshly, Unity Kitchen, or Splendid Spoon. If you have young children, it's probably a good idea to order the ready-to-eat meals so you can save time. The pre-assembled meals may be more expensive, but it could really be worth it for a young family.
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null | false
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What year was Genghis Khan born?
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Genghis Khan was born in the year 1162.
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Datasets. We perform our experiments with two predetermined event categories: cyber security (CyberAttack) and death of politicians (PoliticianDeath). These event categories are chosen as they are representative of important event types that are of interest to many governments and companies. The need to create our own dataset was motivated by the lack of public datasets for event detection on microposts. The few available datasets do not suit our requirements. For example, the publicly available Events-2012 Twitter dataset BIBREF20 contains generic event descriptions such as Politics, Sports, Culture etc. Our work targets more specific event categories BIBREF21. Following previous studies BIBREF1, we collect event-related microposts from Twitter using 11 and 8 seed events (see Section SECREF2) for CyberAttack and PoliticianDeath, respectively. Unlabeled microposts are collected by using the keyword `hack' for CyberAttack, while for PoliticianDeath, we use a set of keywords related to `politician' and `death' (such as `bureaucrat', `dead' etc.) For each dataset, we randomly select 500 tweets from the unlabeled subset and manually label them for evaluation. Table TABREF25 shows key statistics from our two datasets.
Comparison Methods. To demonstrate the generality of our approach on different event detection models, we consider Logistic Regression (LR) BIBREF1 and Multilayer Perceptron (MLP) BIBREF2 as the target models. As the goal of our experiments is to demonstrate the effectiveness of our approach as a new model training technique, we use these widely used models. Also, we note that in our case other neural network models with more complex network architectures for event detection, such as the bi-directional LSTM BIBREF17, turn out to be less effective than a simple feedforward network. For both LR and MLP, we evaluate our proposed human-AI loop approach for keyword discovery and expectation estimation by comparing against the weakly supervised learning method proposed by BIBREF1 (BIBREF1) and BIBREF17 (BIBREF17) where only one initial keyword is used with an expectation estimated by an individual expert.
Parameter Settings. We empirically set optimal parameters based on a held-out validation set that contains 20% of the test data. These include the hyperparamters of the target model, those of our proposed probabilistic model, and the parameters used for training the target model. We explore MLP with 1, 2 and 3 hidden layers and apply a grid search in 32, 64, 128, 256, 512 for the dimension of the embeddings and that of the hidden layers. For the coefficient of expectation regularization, we follow BIBREF6 (BIBREF6) and set it to $\lambda =10 \times $ #labeled examples. For model training, we use the Adam BIBREF22 optimization algorithm for both models.
Evaluation. Following BIBREF1 (BIBREF1) and BIBREF3 (BIBREF3), we use accuracy and area under the precision-recall curve (AUC) metrics to measure the performance of our proposed approach. We note that due to the imbalance in our datasets (20% positive microposts in CyberAttack and 27% in PoliticianDeath), accuracy is dominated by negative examples; AUC, in comparison, better characterizes the discriminative power of the model.
Crowdsourcing. We chose Level 3 workers on the Figure-Eight crowdsourcing platform for our experiments. The inter-annotator agreement in micropost classification is taken into account through the EM algorithm. For keyword discovery, we filter keywords based on the frequency of the keyword being selected by the crowd. In terms of cost-effectiveness, our approach is motivated from the fact that crowdsourced data annotation can be expensive, and is thus designed with minimal crowd involvement. For each iteration, we selected 50 tweets for keyword discovery and 50 tweets for micropost classification per keyword. For a dataset with 80k tweets (e.g., CyberAttack), our approach only requires to manually inspect 800 tweets (for 8 keywords), which is only 1% of the entire dataset.
The need to create our own dataset was motivated by the lack of public datasets for event detection on microposts.The few available datasets do not suit our requirements.
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Why do they create their own dataset?
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Because there are few suitable datasets available.
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What was most important invention by Humans?
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Humans have invented many things and it really depends on each generation what was the most important invention. There are few that come to mind below
1) Fire: Ability to create fire led to significant increase in Humans ability to survive and thrive.
2) Wheel: Ability to create and control wheel led to Humans being able to cover more distances than they previously could do physically. This led to increased ability to hunt and also run away from predators
3) Farming: Ability to Farm food led to Humans not needing to hunt and led to creation of societies that led to more cooperation & more inventions
There are many others like Gun, Engine, Drugs, Electricity, Airplanes, Nuclear energy, Computer, iphone.
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1906.10551
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As a first corpus, we compiled INLINEFORM0 that represents a collection of 80 excerpts from scientific works including papers, dissertations, book chapters and technical reports, which we have chosen from the well-known Digital Bibliography & Library Project (DBLP) platform. Overall, the documents were written by 40 researchers, where for each author INLINEFORM1 , there are exactly two documents. Given the 80 documents, we constructed for each author INLINEFORM2 two verification problems INLINEFORM3 (a Y-case) and INLINEFORM4 (an N-case). For INLINEFORM5 we set INLINEFORM6 's first document as INLINEFORM7 and the second document as INLINEFORM8 . For INLINEFORM9 we reuse INLINEFORM10 from INLINEFORM11 as the known document and selected a text from another (random) author as the unknown document. The result of this procedure is a set of 80 verification problems, which we split into a training and test set based on a 40/60% ratio. Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms. The average time span between both documents of an author is 15.6 years. The minimum and maximum time span are 6 and 40 years, respectively. Besides the temporal aspect of INLINEFORM12 , another challenge of this corpus is the formal (scientific) language, where the usage of stylistic devices is more restricted, in contrast to other genres such as novels or poems.
Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms.
|
What is a self-compiled corpus?
|
The answers are shown as follows:
* restrict the content of each text to the abstract and conclusion of the original work
* considered other parts of the original works such as introduction or discussion sections
* extracted text portions are appropriate for the AV task, each original work was preprocessed manually
* removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms
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Tell me which of the following are body parts: head, ankle, banana, finger, earlobe, kiwi, toe, car, shoulder, bed
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head, ankle, finger, earlobe, toe, shoulder
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null | false
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Charles Henry Bromedge Caldwell was born in Hingham, Massachusetts on February 24, 1823. He entered the navy as midshipman February 27, 1838, and became lieutenant September 4, 1852. He commanded the USS Vandalia in the US Navy's second Fiji expedition, an 1859 punitive expedition against Waya Island warriors who had cannibalized two American traders. In 1862, he commanded the gun-boat Itasca, of the western gulf blockading squadron, and took part in the bombardment of Forts Jackson and St. Philip. On the night of April 20, his gun-boat, with the Pinola, was sent on an expedition under the command of Fleet-Capt. Bell, to make a passage for the fleet through the chain obstructions near the forts. Lieut. Caldwell and his party boarded one of the hulks that held the chains, and succeeded in detaching the latter, in spite of the heavy fire to which they were subjected. The Itasca was then swept on shore by the current, in full sight of the forts, and it was half an hour before she was afloat again. She was unable to pass the forts with the rest of the fleet, owing to a shot that penetrated her boiler. Lieut. Caldwell was in the action at Grand Gulf, June 9, 1862, and was promoted to commander on July 16 1862.
He commanded the iron-clad Essex, of the Mississippi squadron in 1862–63, and took part in the operations at Port Hudson, from March to July 1863, in command of the Essex and the mortar flotilla.
He commanded the Glaucus of the North Atlantic blockading squadron from 1863 till 1864, and the R. R. Cuyler, of the same squadron, from 1864 till 1865. He became captain, December 12, 1867, chief of staff of the North Atlantic fleet in 1870, and commodore on June 14, 1874.
He died on November 30, 1877.
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Given this reference text about the career of Charles Henry Bromedge Caldwell, name the ships he commanded after his promotion to the rank of Commander.
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Essex
Glaucus
R. R. Cuyler
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1705.03261
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In DDI extraction task, NLP methods or machine learning approaches are proposed by most of the work. Chowdhury BIBREF14 and Thomas et al. BIBREF11 proposed methods that use linguistic phenomenons and two-stage SVM to classify DDIs. FBK-irst BIBREF10 is a follow-on work which applies kernel method to the existing model and outperforms it.
Neural network based approaches have been proposed by several works. Liu et al. BIBREF9 employ CNN for DDI extraction for the first time which outperforms the traditional machine learning based methods. Limited by the convolutional kernel size, the CNN can only extracted features of continuous 3 to 5 words rather than distant words. Liu et al. BIBREF8 proposed dependency-based CNN to handle distant but relevant words. Sahu et al. BIBREF12 proposed LSTM based DDI extraction approach and outperforms CNN based approach, since LSTM handles sentence as a sequence instead of slide windows. To conclude, Neural network based approaches have advantages of 1) less reliance on extra NLP toolkits, 2) simpler preprocessing procedure, 3) better performance than text analysis and machine learning methods.
Chowdhury BIBREF14 and Thomas et al. BIBREF11 proposed methods that use linguistic phenomenons and two-stage SVM to classify DDIs. FBK-irst BIBREF10 is a follow-on work which applies kernel method to the existing model and outperforms it.
Neural network based approaches have been proposed by several works. Liu et al. BIBREF9 employ CNN for DDI extraction for the first time which outperforms the traditional machine learning based methods.
Sahu et al. BIBREF12 proposed LSTM based DDI extraction approach and outperforms CNN based approach, since LSTM handles sentence as a sequence instead of slide windows.
|
What are the existing methods mentioned in the paper?
|
The answers are shown as follows:
* Chowdhury BIBREF14 and Thomas et al. BIBREF11
* FBK-irst BIBREF10
* Liu et al. BIBREF9
* Sahu et al. BIBREF12
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1712.02121
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FLOAT SELECTED: Figure 1: Process involved in ConvKB (with the embedding size k = 4, the number of filters τ = 3 and the activation function g = ReLU for illustration purpose).
Our ConvKB uses different filters $\in \mathbb {R}^{1\times 3}$ to generate different feature maps. Let ${\Omega }$ and $\tau $ denote the set of filters and the number of filters, respectively, i.e. $\tau = |{\Omega }|$ , resulting in $\tau $ feature maps. These $\tau $ feature maps are concatenated into a single vector $\in \mathbb {R}^{\tau k\times 1}$ which is then computed with a weight vector ${w} \in \mathbb {R}^{\tau k\times 1}$ via a dot product to give a score for the triple $(h, r, t)$ . Figure 1 illustrates the computation process in ConvKB.
FLOAT SELECTED: Figure 1: Process involved in ConvKB (with the embedding size k = 4, the number of filters τ = 3 and the activation function g = ReLU for illustration purpose).
Our ConvKB uses different filters $\in \mathbb {R}^{1\times 3}$ to generate different feature maps. Let ${\Omega }$ and $\tau $ denote the set of filters and the number of filters, respectively, i.e. $\tau = |{\Omega }|$ , resulting in $\tau $ feature maps.
|
How many feature maps are generated for a given triple?
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3 feature maps for a given tuple
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| 395
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Natural language generation and document classification have been widely conducted using neural sequence models based on the encoder–decoder architecture. The underlying technique relies on the production of a context vector as the document representation, to estimate both tokens in natural language generation and labels in classification tasks. By combining recurrent neural networks with attention BIBREF0, the model is able to learn contextualized representations of words at the sentence level. However, higher-level concepts, such as discourse structure beyond the sentence, are hard for an RNN to learn, especially for longer documents. We hypothesize that NLP tasks such as summarization and document classification can be improved through the incorporation of discourse information.
In this paper, we propose to incorporate latent representations of discourse units into neural training. A discourse parser can provide information about the document structure as well as the relationships between discourse units. In a summarization scenario, for example, this information may help to remove redundant information or discourse disfluencies. In the case of document classification, the structure of the text can provide valuable hints about the document category. For instance, a scientific paper follows a particular discourse narrative pattern, different from a short story. Similarly, we may be able to predict the societal influence of a document such as a petition document, in part, from its discourse structure and coherence.
Specifically, discourse analysis aims to identify the organization of a text by segmenting sentences into units with relations. One popular representation is Rhetorical Structure Theory (RST) proposed by mann1988rhet, where the document is parsed into a hierarchical tree, where leaf nodes are the segmented units, known as Entity Discourse Units (EDUs), and non-terminal nodes define the relations.
As an example, in Figure FIGREF1 the two-sentence text has been annotated with discourse structure based on RST, in the form of 4 EDUs connected with discourse labels attr and elab. Arrows in the tree capture the nuclearity of relations, wherein a “satellite” points to its “nucleus”. The Nucleus unit is considered more prominent than the Satellite, indicating that the Satellite is a supporting sentence for the Nucleus. Nuclearity relationships between two EDUs can take the following three forms: Nucleus–Satellite, Satellite–Nucleus, and Nucleus–Nucleus. In this work, we use our reimplementation of the state of the art neural RST parser of BIBREF1, which is based on eighteen relations: purp, cont, attr, evid, comp, list, back, same, topic, mann, summ, cond, temp, eval, text, cause, prob, elab.
This research investigates the impact of discourse representations obtained from an RST parser on natural language generation and document classification. We primarily experiment with an abstractive summarization model in the form of a pointer–generator network BIBREF2, focusing on two factors: (1) whether summarization benefits from discourse parsing; and (2) how a pointer–generator network guides the summarization model when discourse information is provided. For document classification, we investigate the content-based popularity prediction of online petitions with a deep regression model BIBREF3. We argue that document structure is a key predictor of the societal influence (as measured by signatures to the petition) of a document such as a petition.
Our primary contributions are as follows: (1) we are the first to incorporate a neural discourse parser in sequence training; (2) we empirically demonstrate that a latent representation of discourse structure enhances the summaries generated by an abstractive summarizer; and (3) we show that discourse structure is an essential factor in modelling the popularity of online petitions.
This research investigates the impact of discourse representations obtained from an RST parser on natural language generation and document classification.
|
What does the paper investigate?
|
The impact of discourse representations obtained from an Rhetorical Structure Theory (RST) parser on natural language generation and document classification.
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What is an Italian Greyhound?
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Italian greyhounds are small dogs resembling racing greyhounds but are much smaller at around 15 pounds. Italian greyhounds, or “iggys” as they’re affectionately known, are often fit but quite lazy.
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1610.04377
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We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like “fire”, “earthquake”, “theft”, “robbery”, “drunk driving”, “drunk driving accident” etc. Later, we manually label tweets with <emergency>and <non-emergency>labels for classification as stage one. Our dataset contains 1313 tweet with positive label <emergency>and 1887 tweets with a negative label <non-emergency>. We create another dataset with the positively labeled tweets and provide them with category labels like “fire”, “accident”, “earthquake” etc.
We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like “fire”, “earthquake”, “theft”, “robbery”, “drunk driving”, “drunk driving accident” etc.
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Do the tweets come from any individual?
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Yes.
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Think up a comma separated list of tropical fruits
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Mangos, Pineapples, Coconuts, Papayas, Guavas, Pitayas, Kiwis, Passionfruits, Bananas
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Joseph Robinette Biden Jr. was born on November 20, 1942, at St. Mary's Hospital in Scranton, Pennsylvania, to Catherine Eugenia "Jean" Biden (née Finnegan) and Joseph Robinette Biden Sr. The oldest child in a Catholic family, he has a sister, Valerie, and two brothers, Francis and James. Jean was of Irish descent, while Joseph Sr. had English, Irish, and French Huguenot ancestry. Biden's paternal line has been traced to stonemason William Biden, who was born in 1789 in Westbourne, England, and emigrated to Maryland in the United States by 1820.
https://en.wikipedia.org/wiki/Joe_Biden
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Based on this paragraph, what was Joe Biden's mother's maiden's name, what city was he born in, how many siblings did he have growing up, and what religion was he as a child?
|
Based on this paragraph, Joe Biden's mother's maiden name was Finnegan, he was born in the city of Stranton, he had three siblings (a younger sister and two younger brothers), and he was raised as a member of the Catholic church.
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| 107
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This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/
Recent years have seen a surge of interest in automatic keyphrase extraction, thanks to the availability of the SemEval-2010 benchmark dataset BIBREF0 . This dataset is composed of documents (scientific articles) that were automatically converted from PDF format to plain text. As a result, most documents contain irrelevant pieces of text (e.g. muddled sentences, tables, equations, footnotes) that require special handling, so as to not hinder the performance of keyphrase extraction systems. In previous work, these are usually removed at the preprocessing step, but using a variety of techniques ranging from simple heuristics BIBREF1 , BIBREF2 , BIBREF3 to sophisticated document logical structure detection on richly-formatted documents recovered from Google Scholar BIBREF4 . Under such conditions, it may prove difficult to draw firm conclusions about which keyphrase extraction model performs best, as the impact of preprocessing on overall performance cannot be properly quantified.
While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. More precisely, our contributions are:
In this paper, we re-assess the performance of several state-of-the-art keyphrase extraction models at increasingly sophisticated levels of preprocessing. Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text.
|
What about the setting of preprocessing materials?
|
They prepared three incremental levels of document preprocessing for the experimented: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document.
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The masked language modeling (MLM) BIBREF13 task, also known as the Cloze task BIBREF23, aims at predicting the randomly masked words according to their context. The objective pretrains the bidirectional encoder to obtain contextual representations. Following BIBREF13, we randomly mask 15% of the tokens in a monolingual sentence. For each masked token, we substitute it with a special token M, a random token, or the unchanged token with a probability of 0.8, 0.1, and 0.1, respectively. Let $x$ denote a sentence from the monolingual training corpus, and $M_{x}$ the set of randomly masked positions. The monolingual MLM loss is defined as: MLM(x) = -i Mxp( xi | xMx) where $x_{\setminus M_{x}}$ is the masked version of input $x$. Notice that language tags are fed into the model for all pre-training tasks.
We use the denoising auto-encoding (DAE) objective BIBREF24 to pretrain the encoder-decoder attention mechanism. Given sentence $x$ from the monolingual corpus, we use three types of noise to obtain the randomly perturbed text $\hat{x}$. First, the word order is locally shuffled. Second, we randomly drop tokens of the sentence with a probability of $0.1$. Third, we substitute tokens with the special padding token P with a probability of $0.1$. The pre-training objective is to recover the original sentence $x$ by conditioning on $\hat{x}$. The DAE loss is computed via: DAE(x) = -p(x|x) = -i = 1|x|p(xi | x, x<i) where $x_{<i}$ represents the tokens of previous time steps $x_1,\cdots ,x_{i-1}$.
Similar to monolingual MLM, the masked token prediction task can be extended to cross-lingual settings BIBREF5. To be specific, given a parallel corpus, we concatenate the pair of bilingual sentences $(x,y)$ to a whole sequence, and use it as the input of MLM. The language tags are also fed into the model to indicate the languages of tokens. During training, we adopt the same masking strategy as monolingual MLM. Apart from using monolingual context to predict the masked tokens, XMLM encourages the model to utilize the alignment of bilingual sentences, so that the model learns to map cross-lingual texts into a shared vector space. Similar to eq:mlm, the cross-lingual MLM loss is: XMLM(x,y) = -i Mxp( xi | xMx , yMy)
-i Myp( yi | xMx , yMy) where $M_x, M_y$ represent the masked positions of $x$ and $y$, respectively.
If only DAE is used as the pre-training task for the decoder, we found that the model ignores the target language tag while generating just the same language as the input, caused by the spurious correlation issue BIBREF25. In other words, the DAE loss captures the spurious correlation between the source language tag and the target sentences, but we expect the language of generated sentences can be controlled by the target language tag. To solve the above problem, we use machine translation as the cross-lingual auto-encoding (XAE) task, which decreases mutual information between the target sentences and the source language tag. XAE can be viewed as the multilingual-version DAE task in the sense that both of them recover the sentence by conditioning on the encoded representations. The cross-lingual auto-encoding loss is defined as: XAE(x,y) = -p(y|x) - p(x|y) where $(x,y)$ is a pair of sentences in the parallel corpus.
Denoising Auto-Encoding (DAE) We use the denoising auto-encoding (DAE) objective (Vincent et al. 2008) to pretrain the encoder-decoder attention mechanism.
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What do they use to pre-train the encoder-decoder attention mechanism?
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The denoising auto-encoding (DAE) objective.
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What is a mirepoix?
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A mirepoix is rooted in French cooking but used widely across many cuisines. It consists of equal amounts of small, diced carrots, onions, and celery. A mirepoix if often used in soup making as a fundamental component that adds flavor while enhances other flavors.
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To study model sensitivity, for each sentence, we perturb one randomly-chosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 12 , we count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.
The neutral backoff variant has the lowest sensitivity (Table 5 ). This is expected, as it returns a fixed neutral word whenever the ScRNN predicts an UNK, therefore reducing the number of unique outputs it predicts. Open vocabulary (i.e. char-only, word+char, word-piece) downstream classifiers consider every unique combination of characters differently, whereas word-only classifiers internally treat all out of vocabulary (OOV) words alike. Hence, for char-only, word+char, and word-piece models, the pass-through version is more sensitive than the background variant, as it passes words as is (and each combination is considered uniquely). However, for word-only models, pass-through is less sensitive as all the OOV character combinations are rendered identical.
Ideally, a preferred defense is one with low sensitivity and word error rate. In practice, however, we see that a low error rate often comes at the cost of sensitivity. We see this trade-off in Figure 2 , where we plot WER and sensitivity on the two axes, and depict the robustness when using different backoff variants. Generally, sensitivity is the more dominant factor out of the two, as the error rates of the considered variants are reasonably low.
We verify if the sentiment (of the reviews) is preserved with char-level attacks. In a human study with 50 attacked (and subsequently misclassified), and 50 unchanged reviews, it was noted that 48 and 49, respectively, preserved the sentiment.
To study model sensitivity, for each sentence, we perturb one randomlychosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 1, we count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.
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How do they study the model sensitivity for each sentence?
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They perturb one randomlychosen word and replace it with all possible perturbations under a given attack type. The resulting set of perturbed sentences is then fed to the word recognizer (whose sensitivity is to be estimated). As described in equation 1, they count the number of unique predictions from the output sentences. Two corrections are considered unique if they are mapped differently by the downstream classifier.
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What is the best modern motorcycle for a beginning rider?
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If you have no or little experience with motorcycles, choosing a good first motorcycle can be challenging. While it is tempting to buy your dream supersport or touring cruiser, your first bike should be one that you can feel confident on so you can quickly master the skills necessary to ride a faster or larger bike more safely. Assess a new bike not from a place of desire, but from practical perspective, and consider these qualities:
- Seat height: For most new riders, confidence comes more quickly when you can place both feet on the ground with a little bend in the knees. If you have a 32" inseam, consider bikes with a 31" or less seat height. If you have a 28" inseam, consider bikes with a 27" or lower seat height. Note that some bikes have wider "waists" that require you to spread your legs farther apart, so visit a dealer and sit on a variety of bikes until you find ones that you can sit comfortable atop with both feet planted.
- Center of gravity: Bikes weigh from 250 lbs (dirt bikes) to over 800 lbs (large "dresser" cruiser bikes). Most bikes weigh between 400 and 600 lbs. However, weight isn't what makes a bike tip over easily -- rather, it's where the center of gravity is located. Sport bikes and adventure bikes carry their weight higher and more forward in the frame, where as standard and cruiser bikes carry it lower and closer to the rider. If you, as a new rider, grab a fistful of front brake during a low speed maneuver, what is going to cause the bike to start tipping over faster? Having a heavy weight high and forward. When assessing a first bike, look at where the engine and transmission mass seems to be centered relative to rider position. Sit on the bike with both feet planted and tilt it a little to the left and the right. Do you feel more or less strain on your shoulders as you bring it back it back to center? If you feel more, that sensation will become a danger when your learning low speed maneuvers and require more physical compensation, and will inhibit your ability to develop a good low speed riding technique.
- Ergonomics: Most people feel cool in a racer's lean on a supersport, or slung back in a feet-forward posture when riding a cruiser. However, starting in those positions as a new rider can lead to discomfort, or inhibit your ability to develop solid basic riding techniques. Ideally, you want to be upright, elbows loose and bent and even with the bars. Your back should be straight with about a 5 degree forward lean. Your feet should be below your hips with a comfortable bend to the knees. In this position, you have the flexibility to better compensate for a lack of experience and technique when you encounter challenging situations on the road.
- Engine displacement: Much ado is made about choosing the right displacement for a start bike. This is a bit of a myth: very few bikes, regardless of displacement, are fundamentally hostile to a new rider when it comes to engine performance: they are as fast or as slow as you treat the throttle. That said, smaller displacement bikes tend to be lighter, have lower seats, and are generally more newbie-friendly. However, some larger displacement bikes, including 650-900cc standards and 1100cc-plus cruisers can also be safe to learn on, as they favor low-revving torque over high-revving horsepower, with relatively gentle throttle response but plenty of grunt to push you forward from idle without extensive clutch feathering and rev management. Conversely, some smaller bikes, like the Kawasaki Ninja 250, require extensive revving and frequent gear shifting to get to street and highway speeds. Ask yourself: Do I want a light bike that will force me to learn good gear and rev management (a good idea if you have track aspirations), or a heavier bike that starts moving right off idle and requires less gear changing at street speeds? That's your answer to the question of displacement.
- Throttle and brake response. Modern fuel injection, ECU development, and environmental emissions regulations have led to a situation where many sportier bikes have notchy throttles at low speeds, where closing the throttle cuts fuel and causes the bike to lurch a bit. This can be unpleasant for new riders who have yet to learn partial clutch management skills. Additionally, powerful sport bikes with inline-4 or V4 engine configuration can stutter and lurch at very low revs, requiring careful clutch and rev management that might be unfamiliar or uncomfortable to new riders -- these are bikes designed to be ridden fast, high in their powerbands. Additionally, powerful supersports and "hooligan" supermotos have very powerful brakes, like Brembo's Stylema or M4 units, which can easily pitch a new rider over the bars if they haven't developed a light, progressive touch on the brakes. If you, as a new rider, are having to spend your riding time compensating for unpredictable throttle behaviors or aggressive brakes, you aren't in a place to quickly learn good techniques safely.
So with all that in mind, what are some good starter bikes?
- Kawasaki Ninja 400 or Z400: If your ambition is to ride a powerful supersport (like Kawasaki's own ZX-10R), start with a Ninja 400 or Z400. The Ninja is similar in style to supersport bikes, but has raised "clip-on" handle bars that convey the appearance of a racer but raise the incline of your posture to a more comfortable space. They also have a slightly lower seat height, and an accommodating 399cc parallel-twin motor with good throttle response and solid but unthreatening brakes. The Z400 is a more upright version of the Ninja 400 that shares the same engine and brakes, but lacks the plastic fairing and clip-on grips. Similar bikes include Yamaha's 321cc R3 and MT-03, Honda's CBR500F and CBR500R, KTM's RC390 and Duke 390, and BMW's G310. All of them perform very similarly, and are considered great gateway bikes to sport riding. Many track enthusiasts favor these bikes due to their light weight, engaging engines, and overall low cost of maintenance and insurance. If you really feel the need for speed so early in your riding career, Yamaha's MT-07 and Triumph's Trident 660 are relatively safe choices.
- Triumph Street Twin 900. While well over 400 lbs, Triumph's Street Twin 900 is a very newbie friendly bike with a smooth, slow revving 900cc parallel-twin engine that puts down a lot of grunt and requires very little rev management -- you can short shift it any gear and still have the torque to move away cleanly without any fuss. Its comfortable seat and neutral "standard" riding position, along with its traction control and ABS features, mean that riding it is effortless and you won't be sore when you step off it after a long day cruising the backroads. It carries its weight low and close to the rider, and has a low-ish seat height of approximately 30". Brakes are progressive without being intimidating, and a new rider will quickly learn good technique astride it. It also looks the part of a modern retro motorcycle, with clean curb appeal. Similar bikes include Moto Guzzi's V7 series, Kawasaki's Z650RS, and Royal Enfield's 650cc Interceptor.
- Honda's Rebel 500. While it may not be the most eye-catching cruiser at the local Starbucks, Honda's Rebel 500 is a great gateway to the cruiser world. The 500cc engine is tuned for torque, not speed, and its low seat height of around 26" means it is approachable by riders of all heights. It can keep up with larger bikes and serves as an effective commuter, with low costs of insurance and maintenance, and can be easily customized. Similar bikes include Royal Enfield's 350 Meteor and 650 Super Meteor, and Honda's 600cc Shadow line. For those not intimidated by weight, Harley-Davidson's 1700cc Heritage Softail is a surprisingly accommodating bike for new riders, with a low seat height and an impressive center of gravity that makes the bike feel a couple hundred pounds lighter once bumped off its kickstand. Avoid the older Sportster line, often inappropriately pitched as "the Harley for new riders" due to their relatively low displacements compared to KD's more upmarket models -- these bikes have very high-and-forward centers of gravity and weak front brakes that can easily inhibit your skills development.
Lastly, if you're looking at adventure bikes, Royal Enfield's 400cc Himalayan, Kawasaki's Versys 300X, and BMW's G310GS provide light weights, friendly engines, and reasonable seat heights all while being moderately well-equipped for gravel or graded dirt roads.
The most important thing, however, is to take the time to sit on the bikes at a dealer and think hard about the qualities discussed in this article. If you fall in love with the hobby, the bike you get today will not be the bike you have tomorrow. Focus on learning, not fashion, and make a choice that feels comfortable and safe to you.
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null | false
| null |
Give me a list of some of the famous stock market indices.
|
1. MSCI World: It comprises enterprises in developed countries based on market cap.
2. FTSE All World: It comprise of enterprises in both developed and developing economies with larger focus over large cap companies.
3. S&P 500: It is one of the most famous index comprise 500 big enterprises from United States
4. EURO STOXX 50: Top 50 enterprises present in Euro Zone.
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null | false
| null |
Why is the ice smooth during ice hockey games?
|
The ice hockey smoothing machine was invented in 1940 and is used to keep the ice rink smooth periodically between periods in the game.
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null | false
| null |
Give me ideas how I could become more productive.
|
Here are a few things you can try to become more productive:
1. Limit multitasking: While it may feel more productive, you can easily get distracted and spend more time on those tasks overall. Instead, try to complete one task to a higher standard in less time.
2. Set small goals: Large tasks can feel overwhelming which can in turn lead to procrastination. Try to break them down into smaller, manageable chunks and complete them one by one.
3. Take a break: It is scientifically proven that brain productivity increases after having a short break. Consider scheduling frequent short breaks of 10-15 minutes. Make sure you step away from your work environment. This will help your mind to have a rest and come up with new ideas after the break.
4. Complete the hardest task first: The most complex or hardest task can be intimidating to start with and we tend to avoid them therefore. Try to start with one of these tasks. It can energize and motivate you throughout the day, helping you accomplish a lot of smaller tasks.
5. Delegate: If you are part of a team, consider collaborating with team members more closely. Delegation is not about offloading work. Instead, it is about better task allocation ensuring each team member works on a task that is best suited to their skills and availability.
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| null |
How many episodes in season one of "Game of Thrones" did Brian Kirk direct?
|
Brian Kirk directed three episodes in the first season of Game of Thrones: Lord Snow, Cripples, Bastards, and Broken Things, and The Wolf and the Lion
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null | false
| null |
"I'm Just a Singer (In a Rock and Roll Band)" is a 1973 hit single by the English progressive rock band the Moody Blues, written by the band's bassist, John Lodge. It was first released in 1972 as the final track on the album Seventh Sojourn and was later released as a single in 1973, with "For My Lady" as its B-side. It was the second single released from Seventh Sojourn, with the first being "Isn't Life Strange", which was also written by Lodge.
The song reached number 12 on the Billboard Hot 100 in the US, becoming one of their highest-charting hits in that country, but fared less well in their native UK, where it managed number 36. It was also the final single released by the Moody Blues prior to their five-year hiatus, which was agreed upon so each of the band members could pursue their own solo careers. Their next single would not be until 1978, with "Steppin' in a Slide Zone".
|
When was "I'm Just a Singer (In a Rock and Roll Band)" released?
|
"I'm Just a Singer (In a Rock and Roll Band)" was originally released in 1972 as the final track on the album Seventh Sojourn and was later released as a single in 1973.
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null | false
| 383
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Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the mid-brain producing several motor and non-motor impairments in the patients BIBREF0. Motor symptoms include among others, bradykinesia, rigidity, resting tremor, micrographia, and different speech impairments. The speech impairments observed in PD patients are typically grouped as hypokinetic dysarthria, and include symptoms such as vocal folds rigidity, bradykinesia, and reduced control of muscles and limbs involved in the speech production. The effects of dysarthria in the speech of PD patients include increased acoustic noise, reduced intensity, harsh and breathy voice quality, increased voice nasality, monopitch, monoludness, speech rate disturbances, imprecise articulation of consonants BIBREF1, and involuntary introduction of pauses BIBREF2. Clinical observations in the speech of patients can be objectively and automatically measured by using computer aided methods supported in signal processing and pattern recognition with the aim to address two main aspects: (1) to support the diagnosis of the disease by classifying healthy control (HC) subjects and patients, and (2) to predict the level of degradation of the speech of the patients according to a specific clinical scale.
Most of the studies in the literature to classify PD from speech are based on computing hand-crafted features and using classifiers such as support vector machines (SVMs) or K-nearest neighbors (KNN). For instance, in BIBREF3, the authors computed features related to perturbations of the fundamental frequency and amplitude of the speech signal to classify utterances from 20 PD patients and 20 HC subjects, Turkish speakers. Classifiers based on KNN and SVMs were considered, and accuracies of up to 75% were reported. Later, in BIBREF4 the authors proposed a phonation analysis based on several time frequency representations to assess tremor in the speech of PD patients. The extracted features were based on energy and entropy computed from time frequency representations. Several classifiers were used, including Gaussian mixture models (GMMs) and SVMs. Accuracies of up to 77% were reported in utterances of the PC-GITA database BIBREF5, formed with utterances from 50 PD patients and 50 HC subjects, Colombian Spanish native speakers. The authors from BIBREF6 computed features to model different articulation deficits in PD such as vowel quality, coordination of laryngeal and supra-laryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing. The authors studied the rapid repetition of the syllables /pa-ta-ka/ pronounced by 24 Czech native speakers, and reported an accuracy of 88% discriminating between PD patients and HC speakers, using an SVM classifier. Additional articulation features were proposed in BIBREF7, where the authors modeled the difficulty of PD patients to start/stop the vocal fold vibration in continuous speech. The model was based on the energy content in the transitions between unvoiced and voiced segments. The authors classified PD patients and HC speakers with speech recordings in three different languages (Spanish, German, and Czech), and reported accuracies ranging from 80% to 94% depending on the language; however, the results were optimistic, since the hyper-parameters of the classifier were optimized based on the accuracy on the test set. Another articulation model was proposed in BIBREF8. The authors considered a forced alignment strategy to segment the different phonetic units in the speech utterances. The phonemes were segmented and grouped to train different GMMs. The classification was performed based on a threshold of the difference between the posterior probabilities from the models created for HC subjects and PD patients. The model was tested with Colombian Spanish utterances from the PC-GITA database BIBREF5 and with the Czech data from BIBREF9. The authors reported accuracies of up to 81% for the Spanish data, and of up to 94% for the Czech data.
In addition to the hand-crafted feature extraction models, there is a growing interest in the research community to consider deep learning models in the assessment of the speech of PD patients BIBREF10, BIBREF11, BIBREF12. Deep learning methods have the potential to extract more abstract and robust features than those manually computed. These features could help to improve the accuracy of different models to classify pathological speech, such as PD BIBREF13. A deep learning based articulation model was proposed in BIBREF11 to model the difficulties of the patients to stop/start the vibration of the vocal folds. Transitions between voiced and unvoiced segments were modeled with time-frequency representations and convolutional neural networks (CNNs). The authors considered speech recordings of PD patients and HC speakers in three languages: Spanish, German, and Czech, and reported accuracies ranging from 70% to 89%, depending on the language. However, in a language independent scenario, i.e., training the CNN with utterances from one language and testing with the remaining two, the results were not satisfactory (accuracy$<60\%$).
The classification of PD from speech in different languages has to be carefully conducted to avoid bias towards the linguistic content present in each language. For instance, Czech and German languages are richer than Spanish language in terms of consonant production, which may cause that it is easier to produce consonant sounds by Czech PD patients than by Spanish PD patients. Despite these language dependent issues, the results in the classification of PD in different languages could be improved using a transfer learning strategy among languages, i.e., to train a base model with utterances from one language, and then, to perform a fine-tuning of the weights with utterances from the target language BIBREF14. Similar approaches based on transfer learning have been recently considered to classify PD using handwriting BIBREF15. In the present study, we propose a methodology to classify PD via a transfer learning strategy with the aim to improve the accuracy in different languages. CNNs trained with utterances from one language are used to initialize a model to classify speech utterances from PD patients in a different language. The models are evaluated with speech utterances in Spanish, German, and Czech languages. The results suggest that the use of a transfer learning strategy improved the accuracy of the models over 8% with respect to those obtained when the model is trained only with utterance from the target language.
The extracted features were based on energy and entropy computed from time frequency representations. Several classifiers were used, including Gaussian mixture models (GMMs) and SVMs. Accuracies of up to 77% were reported in utterances of the PC-GITA database [6], formed with utterances from 50 PD patients and 50 HC subjects, Colombian Spanish native speakers. The authors from [7] computed features to model different articulation deficits in PD such as vowel quality, coordination of laryngeal and supra-laryngeal activity, precision of consonant articulation, tongue movement, occlusion weakening, and speech timing. The authors studied the rapid repetition of the syllables /pa-ta-ka/ pronounced by 24 Czech native speakers, and reported an accuracy of 88% discriminating between PD patients and HC speakers, using an SVM classifier.
|
What model and classifier are used to classify utterances from PD patients and healthy control subjects?
|
A Gaussian mixture model (GMMs) and SVMs.
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null | false
| null |
How do you make wine?
|
Wine is made from grapes. You first need to harvest or collect the grapes and then take them off the stem. Next you need to crush the grapes and get as much juice out of them as possible. Then you let the grapes ferment for many weeks or months and during the fermentation process alcohol will be released. Then you can siphon the wine in the barrels or let the wine age longer and then get it ready for bottling or corking / capping.
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null | false
| null |
How does one get accepted into a United States federal academy like West Point.
|
In order to get excepted into a United States Federal Academy, one has to receive a nomination either from a congressman, senator, or president. The process is long and requires patience, but can be rewarding.
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null | false
| null |
Which NFL franchise was ripped away from hits hometown and renamed the Baltimore Ravens?
|
Cleveland Browns
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null | false
| 31
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Traditional extractive summarization methods are mostly based on explicit surface features BIBREF10 , relying on graph-based methods BIBREF11 , or on submodular maximization BIBREF12 . Benefiting from the success of neural sequence models in other NLP tasks, chenglapata propose a novel approach to extractive summarization based on neural networks and continuous sentence features, which outperforms traditional methods on the DailyMail dataset. In particular, they develop a general encoder-decoder architecture, where a CNN is used as sentence encoder, a uni-directional LSTM as document encoder, with another uni-directional LSTM as decoder. To decrease the number of parameters while maintaining the accuracy, summarunner present SummaRuNNer, a simple RNN-based sequence classifier without decoder, outperforming or matching the model of BIBREF2 . They take content, salience, novelty, and position of each sentence into consideration when deciding if a sentence should be included in the extractive summary. Yet, they do not capture any aspect of the topical structure, as we do in this paper. So their approach would arguably suffer when applied to long documents, likely containing multiple and diverse topics.
While SummaRuNNer was tested only on news, EMNLP2018 carry out a comprehensive set of experiments with deep learning models of extractive summarization across different domains, i.e. news, personal stories, meetings, and medical articles, as well as across different neural architectures, in order to better understand the general pros and cons of different design choices. They find that non auto-regressive sentence extraction performs as well or better than auto-regressive extraction in all domains, where by auto-regressive sentence extraction they mean using previous predictions to inform future predictions. Furthermore, they find that the Average Word Embedding sentence encoder works at least as well as encoders based on CNN and RNN. In light of these findings, our model is not auto-regressive and uses the Average Word Embedding encoder.
They take content, salience, novelty, and position of each sentence into consideration when deciding if a sentence should be included in the extractive summary. Yet, they do not capture any aspect of the topical structure, as we do in this paper. So their approach would arguably suffer when applied to long documents, likely containing multiple and diverse topics.
|
What are the advantages and disadvantages of the SummaRuNNer classifier?
|
They take content, salience, novelty, and position of each sentence into consideration when deciding if a sentence should be included in the extractive summary. Yet, they do not capture any aspect of the topical structure, as they do in this paper. So their approach would arguably suffer when applied to long documents, likely containing multiple and diverse topics.
|
1910.08502
| false
| null |
However, we also showed difference in produced errors for each method and different impact at word-level depending of the approach or units. Thus, future work will focus on analysing the orthographic output of these systems in two ways: 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French and 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words.
However, we also showed difference in produced errors for each method and different impact at word-level depending of the approach or units. Thus, future work will focus on analysing the orthographic output of these systems in two ways: 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French and 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words.
|
What will be in focus for future work?
|
The answers are shown as follows:
* 1) investigate errors produced by the end-to-end methods and explore several approaches to correct common errors done in French
* 2) compare the end-to-end methods in a SLU context and evaluate the semantic value of the partially correct produced words
|
1909.11189
| false
| null |
The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3. It was mined from http://zeno.org and covers a time period from the mid 16th century up to the first decades of the 20th century. It contains many important texts that can be considered as part of the literary canon, even though it is far from complete (e.g. it contains only half of Rilke’s work). We find that around 51k texts are annotated with the label ’verse’ (TGRID-V), not distinguishing between ’lyric verse’ and ’epic verse’. However, the average length of these texts is around 150 token, dismissing most epic verse tales. Also, the poems are distributed over 229 authors, where the average author contributed 240 poems (median 131 poems). A drawback of TGRID-V is the circumstance that it contains a noticeable amount of French, Dutch and Latin (over 400 texts). To constrain our dataset to German, we filter foreign language material with a stopword list, as training a dedicated language identification classifier is far beyond the scope of this work.
The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3.
|
What is the corpus used in the study?
|
The answers are shown as follows:
* TextGrid Repository
|
null | false
| null |
Which is a species of fish? Herring or Hearing
|
Herring
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null | false
| 154
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We present a comprehensive comparison study of the existing corpora for selection-based question answering. Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora. Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning. Additionally, we create a silver-standard dataset for answer retrieval and triggering, which will be publicly available. In the future, we will explore different ways of improving the quality of our silver-standard datasets by fine-tuning the hyper-parameters.
Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning.
|
What is the contribution of the extrinsic analysis in this paper?
|
The extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning.
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null | false
| null |
The Treaty of Dunkirk was signed by France and the United Kingdom on 4 March 1947, during the aftermath of World War II and the start of the Cold War, as a Treaty of Alliance and Mutual Assistance in the event of possible attacks by Germany or the Soviet Union. In March 1948, this alliance was expanded in the Treaty of Brussels to include the Benelux countries, forming the Brussels Treaty Organization, commonly known as the Western Union. Talks for a wider military alliance, which could include North America, also began that month in the United States, where their foreign policy under the Truman Doctrine promoted international solidarity against actions they saw as communist aggression, such as the February 1948 coup d'état in Czechoslovakia. These talks resulted in the signature of the North Atlantic Treaty on 4 April 1949 by the member states of the Western Union plus the United States, Canada, Portugal, Italy, Norway, Denmark, and Iceland. Canadian diplomat Lester B. Pearson was a key author and drafter of the treaty.
|
Please give me a comma separated list of the countries that signed the North Atlantic Treaty in April 1949 given the text below
|
France, United Kingdom, Belgium, Luxembourg , Netherlands, United States, Canada, Portugal, Italy, Norway, Denmark, Iceland
|
1605.04655
| false
| null |
Argus Dataset
AI2-8grade/CK12 Dataset
We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0). However, we released it to the public and still included it in the comparison as these qualities reflect many realistic datasets of unknown qualities, so we find relative performances of models on such datasets instructive.
MCTest Dataset
The Machine Comprehension Test BIBREF8 dataset has been introduced to provide a challenge for researchers to come up with models that approach human-level reading comprehension, and serve as a higher-level alternative to semantic parsing tasks that enforce a specific knowledge representation. The dataset consists of a set of 660 stories spanning multiple sentences, written in simple and clean language (but with less restricted vocabulary than e.g. the bAbI dataset BIBREF9 ). Each story is accompanied by four questions and each of these lists four possible answers; the questions are tagged as based on just one in-story sentence, or requiring multiple sentence inference. We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers.
Argus Dataset
AI2-8grade/CK12 Dataset
We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0).
MCTest Dataset
We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers.
|
what datasets did they use?
|
The answers are shown as follows:
* Argus Dataset
* AI2-8grade/CK12 Dataset
* MCTest Dataset
|
null | false
| null |
What are some common mushrooms used in cooking?
|
Common mushrooms used in cooking include white button, cremini, portobello, shiitake, oyster, porcini, morel, and enoki.
|
|
null | false
| null |
What should I eat when I visit South India ?
|
South India is known for vegetarian delicacies. Here is a list of some foods that you should try :
1. Masala Dosa
2. Filter Coffee
3. Idly
4. Vada
5. Pongal
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null | false
| 114
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The discriminator INLINEFORM0 aims to distinguish real data and generated data like other GANs. Inspired by Least-Square GAN BIBREF25 , we devise the loss function as follows: DISPLAYFORM0
This loss function forces the discriminator to assign higher rewards to real data than to generated data, so the discriminator can learn to provide more proper rewards as the training proceeds.
This loss function forces the discriminator to assign higher rewards to real data than to generated data, so the discriminator can learn to provide more proper rewards as the training proceeds.
|
Why the discriminator can learn to provide more proper rewards as the training proceeds?
|
Because the loss function forces the discriminator to assign higher rewards to real data than to generated data.
|
null | false
| null |
On 17 March 2023, the International Criminal Court (ICC) issued arrest warrants for Vladimir Putin, the President of Russia, and Maria Lvova-Belova, Russian Commissioner for Children's Rights, alleging responsibility for the unlawful deportation and transfer of children during the Russian invasion of Ukraine. The warrant against Putin is the first against the leader of a permanent member of the United Nations Security Council.
The 123 member states of the ICC are obliged to detain and transfer Putin and Lvova-Belova if either sets foot on their territory.
|
Who is responsible for deportation of Ukrainian children to Russia?
|
President of Russian Federation Vladimir Putin and Maria Lvova-Belova, Russian Commissioner for Children's Rights are directly responsible the unlawful deportation of Ukrainian children to Russia. This crime against humanity along with many other atrocities (like Bucha massacre) has been committed by Russians during the Russian invasion of Ukraine that started on February 24, 2022.
|
1909.09524
| true
| null |
We evaluate the proposed transfer learning techniques in two non-English language pairs of WMT 2019 news translation tasks: French$\rightarrow $German and German$\rightarrow $Czech.
We evaluate the proposed transfer learning techniques in two non-English language pairs of WMT 2019 news translation tasks: French$\rightarrow $German and German$\rightarrow $Czech.
|
Are experiments performed with any other pair of languages, how did proposed method perform compared to other models?
|
No.
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null | false
| 149
|
Text classification has become an indispensable task due to the rapid growth in the number of texts in digital form available online. It aims to classify different texts, also called documents, into a fixed number of predefined categories, helping to organize data, and making easier for users to find the desired information. Over the past three decades, many methods based on machine learning and statistical models have been applied to perform this task, such as latent semantic analysis (LSA), support vector machines (SVM), and multinomial naive Bayes (MNB).
The first step in utilizing such methods to categorize textual data is to convert the texts into a vector representation. One of the most popular text representation models is the bag-of-words model BIBREF0 , which represents each document in a collection as a vector in a vector space. Each dimension of the vectors represents a term (e.g., a word, a sequence of words), and its value encodes a weight, which can be how many times the term occurs in the document.
Despite showing positive results in tasks such as language modeling and classification BIBREF1 , BIBREF2 , BIBREF3 , the BOW representation has limitations: first, feature vectors are commonly very high-dimensional, resulting in sparse document representations, which are hard to model due to space and time complexity. Second, BOW does not consider the proximity of words and their position in the text and consequently cannot encode the words semantic meanings.
To solve these problems, neural networks have been employed to learn vector representations of words BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . In particular, the word2vec representation BIBREF8 has gained attention. Given a training corpus, word2vec can generate a vector for each word in the corpus that encodes its semantic information. These word vectors are distributed in such a way that words from similar contexts are represented by word vectors with high correlation, while words from different contexts are represented by word vectors with low correlation.
One crucial aspect of the word2vec representation is that arithmetic and distance calculation between two word vectors can be performed, giving information about their semantic relationship. However, rather than looking at pairs of word vectors, we are interested in studying the relationship between sets of vectors as a whole and, therefore, it is desirable to have a text representation based on a set of these word vectors.
To tackle this problem, we introduce the novel concept of word subspace. It is mathematically defined as a low dimensional linear subspace in a word vector space with high dimensionality. Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces. Through this representation, most of the variability of the class is retained. Consequently, a word subspace can effectively and compactly represent the context of the corresponding text. We achieve this framework through the mutual subspace method (MSM) BIBREF9 .
The word subspace of each text class is modeled by applying PCA without data centering to the set of word vectors of the class. When modeling the word subspaces, we assume only one occurrence of each word inside the class.
However, as seen in the BOW approach, the frequency of words inside a text is an informative feature that should be considered. In order to introduce this feature in the word subspace modeling and enhance its performance, we further extend the concept of word subspace to the term-frequency (TF) weighted word subspace.
In this extension, we consider a set of weights, which encodes the words frequencies, when performing the PCA. Text classification with TF weighted word subspace can also be performed under the framework of MSM. We show the validity of our modeling through experiments on the Reuters database, an established database for natural language processing tasks. We demonstrate the effectiveness of the word subspace formulation and its extension, comparing our methods' performance to various state-of-art methods.
The main contributions of our work are:
The remainder of this paper is organized as follows. In Section "Related Work" , we describe the main works related to text classification. In Section "Word subspace" , we present the formulation of our proposed word subspace. In Section "Conventional text classification methods" , we explain how text classification with word subspaces is performed under the MSM framework. Then, we present the TF weighted word subspace extension in Section "TF weighted word subspace" . Evaluation experiments and their results are described in Section "Experimental Evaluation" . Further discussion is then presented in Section "Discussion" , and our conclusions are described in Section "Conclusions and Future Work" .
Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces.
|
What is the purpose of introducing the concept subspaces in this paper?
|
Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each text class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces.
|
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