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Text summarization is a task to generate a shorter and concise version of a text while preserving the meaning of the original text. The task can be divided into two subtask based on the approach: extractive and abstractive summarization. Extractive summarization is a task to create summaries by pulling out snippets of text form the original text and combining them to form a summary. Abstractive summarization asks to generate summaries from scratch without the restriction to use the available words from the original text. Due to the limitations of extractive summarization on incoherent texts and unnatural methodology BIBREF0 , the research trend has shifted towards abstractive summarization.
Sequence-to-sequence models BIBREF1 with attention mechanism BIBREF2 have found great success in generating abstractive summaries, both from a single sentence BIBREF3 and from a long document with multiple sentences BIBREF4 . However, when generating summaries, it is necessary to determine the main topic and to sift out unnecessary information that can be omitted. Sequence-to-sequence models have the tendency to include all the information, relevant or not, that are found in the original text. This may result to unconcise summaries that concentrates wrongly on irrelevant topics. The problem is especially severe when summarizing longer texts.
In this paper, we propose to use entities found in the original text to infer the summary topic, mitigating the aforementioned problem. Specifically, we leverage on linked entities extracted by employing a readily available entity linking system. The importance of using linked entities in summarization is intuitive and can be explained by looking at Figure 1 as an example. First (O1 in the Figure), aside from auxiliary words to construct a sentence, a summary is mainly composed of linked entities extracted from the original text. Second (O2), we can depict the main topic of the summary as a probability distribution of relevant entities from the list of entities. Finally (O3), we can leverage on entity commonsense learned from a separate large knowledge base such as Wikipedia.
To this end, we present a method to effectively apply linked entities in sequence-to-sequence models, called Entity2Topic (E2T). E2T is a module that can be easily attached to any sequence-to-sequence based summarization model. The module encodes the entities extracted from the original text by an entity linking system (ELS), constructs a vector representing the topic of the summary to be generated, and informs the decoder about the constructed topic vector. Due to the imperfections of current ELS's, the extracted linked entities may be too ambiguous and coarse to be considered relevant to the summary. We solve this issue by using entity encoders with selective disambiguation and by constructing topic vectors using firm attention.
We experiment on two datasets, Gigaword and CNN, with varying lengths. We show that applying our module to a sequence-to-sequence model with attention mechanism significantly increases its performance on both datasets. Moreover, when compared with the state-of-the-art models for each dataset, the model obtains a comparable performance on the Gigaword dataset where the texts are short, and outperforms all competing models on the CNN dataset where the texts are longer. Furthermore, we provide analysis on how our model effectively uses the extracted linked entities to produce concise and better summaries.
To this end, we present a method to effectively apply linked entities in sequence-to-sequence models, called Entity2Topic (E2T).
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What method do the authors present in the paper?
|
A method to effectively apply linked entities in sequence-to-sequence models, called Entity2Topic (E2T).
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Who was the prime minister of Spain when Spain joined NATO?
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Spain joined NATO by referendum in 1986. The prime minister of Spain at the time was Felipe Gonzalez.
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Sealing the food in sturdy plastic bags retains juices and aroma that otherwise would be lost in the process. Placing the packaged food in a water bath, with the temperature set at the desired final cooking temperature of the food, prevents overcooking, because the food cannot get hotter than the bath it is in, as in bain-marie.
As a result of precise temperature control of the bath and the fact that the bath temperature is the same as the target cooking temperature, very precise control of cooking can be achieved. Additionally, temperature, and thus cooking, can be very even throughout the food in sous vide cooking, even with irregularly shaped and very thick items, given enough time.
|
How do you cook sous vide?
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By contrast, in conventional high-heat cooking, such as oven roasting or grilling, the food is exposed to heat levels that are much higher than the desired internal cooking temperature, and it must be removed from the high heat prior to reaching the desired cooking temperature. If the food is removed from the heat too late, it becomes overcooked, and if it is removed too early, it is undercooked.
The use of temperatures much lower than those used for conventional cooking is an essential feature of sous vide. When cooking meat, tough collagen in connective tissue can be denatured into gelatin, without heating the meat's proteins high enough that they denature to a degree that the texture toughens and moisture is exuded from the meat.
In contrast, soft vegetables are often considered overcooked. Low-temperature cooking allows plant matter to remain more succulent, as the cell walls do not burst. Cooking vegetables at temperatures below the boiling point of water allows them to be thoroughly cooked (and pasteurized, if necessary) while maintaining a firm or somewhat crisp texture. While the cell walls will generally not burst, the depolymerization of the pectic polysaccharides that connect the vegetable cells together and the gelatinisation of starch in the vegetable can be achieved without overcooking.
From a culinary viewpoint, the exclusion of air is secondary, but this has practical importance. It allows cooked food to be stored, still sealed and refrigerated, for considerable times, which is especially useful for the catering industry, and it excludes oxygen from food that requires long cooking and is susceptible to oxidation, e.g., fat on meat, which may become rancid with prolonged exposure to air. It also improves the transfer of heat between the water bath and food, without the thermal insulating properties of any trapped air in the bag.
Apart from ensuring uniform cooking, sous vide cooking facilitates development of desired organoleptic flavors and limits off flavors due to oxidation
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The following are a mix of english and hindi words. separate them out logically:
your
mujhe
lamp
pyaar
tamanna
tomorrow
perhaps
kyunki
how
your
yaar
shade
|
English:
tomorrow
how
your
perhaps
lamp
shade
Hindi:
mujhe
kyunki
yaar
tamanna
pyaar
|
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Who is the most popular president ever in the USA?
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The most popular president ever in the USA is Franklin D. Roosevelt. He was president from 1933 to 1945 and is remembered for his New Deal programs that helped to improve the economy and the lives of Americans during the Great Depression.
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How much energy is released in a deuterium-tritium fusion reaction?
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A Deuterium-Tritium (DT) fusion reaction results in 17.6 MeV of energy, released in the form of a Helium nucleus and a free Neutron.
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null | false
| 81
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Writing errors can occur in many different forms – from relatively simple punctuation and determiner errors, to mistakes including word tense and form, incorrect collocations and erroneous idioms. Automatically identifying all of these errors is a challenging task, especially as the amount of available annotated data is very limited. Rei2016 showed that while some error detection algorithms perform better than others, it is additional training data that has the biggest impact on improving performance.
Being able to generate realistic artificial data would allow for any grammatically correct text to be transformed into annotated examples containing writing errors, producing large amounts of additional training examples. Supervised error generation systems would also provide an efficient method for anonymising the source corpus – error statistics from a private corpus can be aggregated and applied to a different target text, obscuring sensitive information in the original examination scripts. However, the task of creating incorrect data is somewhat more difficult than might initially appear – naive methods for error generation can create data that does not resemble natural errors, thereby making downstream systems learn misleading or uninformative patterns.
Previous work on artificial error generation (AEG) has focused on specific error types, such as prepositions and determiners BIBREF0 , BIBREF1 , or noun number errors BIBREF2 . Felice2014a investigated the use of linguistic information when generating artificial data for error correction, but also restricting the approach to only five error types. There has been very limited research on generating artificial data for all types, which is important for general-purpose error detection systems. For example, the error types investigated by Felice2014a cover only 35.74% of all errors present in the CoNLL 2014 training dataset, providing no additional information for the majority of errors.
In this paper, we investigate two supervised approaches for generating all types of artificial errors. We propose a framework for generating errors based on statistical machine translation (SMT), training a model to translate from correct into incorrect sentences. In addition, we describe a method for learning error patterns from an annotated corpus and transplanting them into error-free text. We evaluate the effect of introducing artificial data on two error detection benchmarks. Our results show that each method provides significant improvements over using only the available training set, and a combination of both gives an absolute improvement of 4.3% in INLINEFORM0 , without requiring any additional annotated data.
Our results show that each method provides significant improvements over using only the available training set, and a combination of both gives an absolute improvement of 4.3% in F0.5, without requiring any additional annotated data.
|
Compared with the available training set, what are the improvements of the method in this paper?
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Each method provides significant improvements and a combination of both gives an absolute improvement of 4.3% in F0.5, without requiring any additional annotated data.
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Classify the following elements as metal or noble gas: Argon, Iron, Copper, Krypton, Neon, Silver, Thorium, Radon, Helium, Xenon, Erbium, Tin, Protactinium
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Argon - Noble Gas
Iron - Metal
Copper - Metal
Krypton - Noble Gas
Neon - Noble Gas
Silver - Metal
Thorium - Metal
Radon - Noble Gas
Helium - Noble Gas
Xenon - Noble Gas
Erbium - Metal
Tin - Metal
Protactinium - Metal
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1603.00968
| false
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Stanford Sentiment Treebank Stanford Sentiment Treebank (SST) BIBREF14 . This concerns predicting movie review sentiment. Two datasets are derived from this corpus: (1) SST-1, containing five classes: very negative, negative, neutral, positive, and very positive. (2) SST-2, which has only two classes: negative and positive. For both, we remove phrases of length less than 4 from the training set.
Subj BIBREF15 . The aim here is to classify sentences as either subjective or objective. This comprises 5000 instances of each.
TREC BIBREF16 . A question classification dataset containing six classes: abbreviation, entity, description, human, location and numeric. There are 5500 training and 500 test instances.
Irony BIBREF17 . This dataset contains 16,006 sentences from reddit labeled as ironic (or not). The dataset is imbalanced (relatively few sentences are ironic). Thus before training, we under-sampled negative instances to make classes sizes equal. Note that for this dataset we report the Area Under Curve (AUC), rather than accuracy, because it is imbalanced.
Stanford Sentiment Treebank Stanford Sentiment Treebank (SST) BIBREF14 . This concerns predicting movie review sentiment. Two datasets are derived from this corpus: (1) SST-1, containing five classes: very negative, negative, neutral, positive, and very positive. (2) SST-2, which has only two classes: negative and positive. For both, we remove phrases of length less than 4 from the training set.
Subj BIBREF15 . The aim here is to classify sentences as either subjective or objective. This comprises 5000 instances of each.
TREC BIBREF16 . A question classification dataset containing six classes: abbreviation, entity, description, human, location and numeric. There are 5500 training and 500 test instances.
Irony BIBREF17 . This dataset contains 16,006 sentences from reddit labeled as ironic (or not). The dataset is imbalanced (relatively few sentences are ironic). Thus before training, we under-sampled negative instances to make classes sizes equal. Note that for this dataset we report the Area Under Curve (AUC), rather than accuracy, because it is imbalanced.
|
What dataset/corpus is this evaluated over?
|
The answers are shown as follows:
* SST-1
* SST-2
* Subj
* TREC
* Irony
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| 117
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In this section, we identify various linguistic activities on Quora and propose quantifications of the language usage patterns in this Q&A site. In particular, we show that there exists significant differences in the linguistic structure of the open and the answered questions. Note that most of the measures that we define are simple, intuitive and can be easily obtained automatically from the data (without manual intervention). Therefore the framework is practical, inexpensive and highly scalable.
Content of a question text is important to attract people and make them engage more toward it. The linguistic structure (i.e., the usage of POS tags, the use of Out-of-Vocabulary words, character usage etc.) one adopts are key factors for answerability of questions. We shall discuss the linguistic structure that often represents the writing style of a question asker.
In fig 1 (a), we observe that askers of open questions generally use more no. of words compared to answered questions. To understand the nature of words (standard English words or chat-like words frequently used in social media) used in the text, we compare the words with GNU Aspell dictionary to see whether they are present in the dictionary or not. We observe that both open questions and answered questions follow similar distribution (see fig 1 (b)). Part-of-Speech (POS) tags are indicators of grammatical aspects of texts. To observe how the Part-of-Speech tags are distributed in the question texts, we define a diversity metric. We use the standard CMU POS tagger BIBREF8 for identifying the POS tags of the constituent words in the question. We define the POS tag diversity (POSDiv) of a question $q_i$ as follows: $POSDiv(q_i) = -\sum _{j \in pos_{set}}p_j\times \log (p_j)$ where $p_j$ is the probability of the $j^{th}$ POS in the set of POS tags. Fig 1 (c) shows that the answered questions have lower POS tag diversity compared to open questions. Question texts undergo several edits so that their readability and the engagement toward them are enhanced. It is interesting to identify how far such edits can make the question different from the original version of it. To capture this phenomena, we have adopted ROUGE-LCS recall BIBREF9 from the domain of text summarization. Higher the recall value, lesser are the changes in the question text. From fig 1 (d), we observe that open questions tend to have higher recall compared to the answered ones which suggests that they have not gone through much of text editing thus allowing for almost no scope of readability enhancement.
Psycholinguistic analysis:
The way an individual talks or writes, give us clue to his/her linguistic, emotional, and cognitive states. A question asker's linguistic, emotional, cognitive states are also revealed through the language he/she uses in the question text. In order to capture such psycholinguistic aspects of the asker, we use Linguistic Inquiry and Word Count (LIWC) BIBREF10 that analyzes various emotional, cognitive, and structural components present in individuals' written texts. LIWC takes a text document as input and outputs a score for the input for each of the LIWC categories such as linguistic (part-of-speech of the words, function words etc.) and psychological categories (social, anger, positive emotion, negative emotion, sadness etc.) based on the writing style and psychometric properties of the document. In table 1 , we perform a comparative analysis of the asker's psycholinguistic state while asking an open question and an answered question.
Askers of open questions use more function words, impersonal pronouns, articles on an average whereas asker of answered questions use more personal pronouns, conjunctions and adverbs to describe their questions. Essentially, open questions lack content words compared to answered questions which, in turn, affects the readability of the question. As far as the psychological aspects are concerned, answered question askers tend to use more social, family, human related words on average compared to an open question asker. The open question askers express more positive emotions whereas the answered question asker tend to express more negative emotions in their texts. Also, answered question askers are more emotionally involved and their questions reveal higher usage of anger, sadness, anxiety related words compared to that of open questions. Open questions, on the other hand, contains more sexual, body, health related words which might be reasons why they do not attract answers.
In table 2 , we show a collection of examples of open questions to illustrate that many of the above quantities based on the linguistic activities described in this section naturally correspond to the factors that human judges consider responsible for a question remaining unanswered. This is one of the prime reasons why these quantities qualify as appropriate indicators of answerability.
To observe how the Part-of-Speech tags are distributed in the question texts, we define a diversity metric.
|
What is the diversity metric set for?
|
For observation of how the Part-of-Speech tags are distributed in the question texts,
|
1708.02267
| false
| null |
Another way to improve this method could be to select the samples which are more relevant to the target dataset. Based on the importance of the similarity between the datasets for transfer learning in the NLP tasks, this paper proposes to use the most relevant samples from the source dataset to train on the target dataset. One way to find the most similar samples is finding the pair-wise distance between all samples of the development set of the target dataset and source dataset.
To solve this problem, we propose using a clustering algorithm on the development set. The clustering algorithm used ihere is a hierarchical clustering algorithm. The cosine similarity is used as a criteria to cluster each question and answer. Therefore, these clusters are representative of the development set of the target dataset and the corresponding center for each cluster is representative of all the samples on that cluster. In the next step, the distance of each center is used to calculate the cosine similarity. Finally, the samples in the source dataset which are far from these centers are ignored. In other words, the outliers do not take part in transfer learning.
this paper proposes to use the most relevant samples from the source dataset to train on the target dataset. One way to find the most similar samples is finding the pair-wise distance between all samples of the development set of the target dataset and source dataset.
we propose using a clustering algorithm on the development set. The clustering algorithm used ihere is a hierarchical clustering algorithm. The cosine similarity is used as a criteria to cluster each question and answer. Therefore, these clusters are representative of the development set of the target dataset and the corresponding center for each cluster is representative of all the samples on that cluster. In the next step, the distance of each center is used to calculate the cosine similarity. Finally, the samples in the source dataset which are far from these centers are ignored. In other words, the outliers do not take part in transfer learning.
|
How do they transfer the model?
|
The answers are shown as follows:
* In the MULT method, two datasets are simultaneously trained, and the weights are tuned based on the inputs which come from both datasets. The hyper-parameter $\lambda \in (0,1)$ is calculated based on a brute-force search or using general global search. This hyper parameter is used to calculate the final cost function which is computed from the combination of the cost function of the source dataset and the target datasets.
* this paper proposes to use the most relevant samples from the source dataset to train on the target dataset. One way to find the most similar samples is finding the pair-wise distance between all samples of the development set of the target dataset and source dataset.
* we propose using a clustering algorithm on the development set. The clustering algorithm used ihere is a hierarchical clustering algorithm. The cosine similarity is used as a criteria to cluster each question and answer. Therefore, these clusters are representative of the development set of the target dataset and the corresponding center for each cluster is representative of all the samples on that cluster. In the next step, the distance of each center is used to calculate the cosine similarity. Finally, the samples in the source dataset which are far from these centers are ignored. In other words, the outliers do not take part in transfer learning.
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The Battle of Midway was a major naval battle in the Pacific Theater of World War II that took place from 4–7 June 1942, six months after Japan's attack on Pearl Harbor and one month after the Battle of the Coral Sea. The U.S. Navy under Admirals Chester W. Nimitz, Frank J. Fletcher, and Raymond A. Spruance defeated an attacking fleet of the Imperial Japanese Navy under Admirals Isoroku Yamamoto, Chūichi Nagumo, and Nobutake Kondō north of Midway Atoll, inflicting devastating damage on the Japanese fleet. Military historian John Keegan called it "the most stunning and decisive blow in the history of naval warfare", while naval historian Craig Symonds called it "one of the most consequential naval engagements in world history, ranking alongside Salamis, Trafalgar, and Tsushima Strait, as both tactically decisive and strategically influential"
|
What was the Battle of Midway?
|
The Battle of Midway was the turning point of the Pacific Naval Campaign of WW2 and occurred June 4-7, 1942. The US Navy, under Chester Nimitz defeated the Imperial Japanese Navy and sunk 4 of their aircraft carriers. This was a significant setback for the Japanese and marked the first major defeat of the Imperial Japanese fleet in WW2.
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Moret was born in 1953 in Vevey Switzerland, and did his undergraduate studies at the École Polytechnique Fédérale de Lausanne (EPFL), graduating in 1975. He went on to graduate studies at the University of Tennessee, earning a Ph.D. in 1980. He then joined the faculty of the University of New Mexico, where he remained until 2006, when he moved to EPFL. He retired from EPFL in December 2016.
In 1996, Moret founded the ACM Journal of Experimental Algorithmics, and he remained editor in chief of the journal until 2003. In 2001, Moret founded the Workshop in Algorithms for Bioinformatics (WABI) and remains on the Steering Committee for the conference.
In 2018, Moret was elected as a Fellow of the International Society for Computational Biology, for his outstanding contributions to the fields of computational biology and bioinformatics.
|
Extract universities Moret studied at and his year of graduation for each, and put them in a bulleted list.
|
* École Polytechnique Fédérale de Lausanne (EPFL) (graduated 1975)
* University of Tennessee (graduated 1980)
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1611.01400
| false
| null |
We compare our system to a variety of baselines. (1) Rank by the number of times a citation is mentioned in the document. (2) Rank by the number of times the citation is cited in the literature (citation impact). (3) Rank using Google Scholar Related Articles. (4) Rank by the TF*IDF weighted cosine similarity. (5) Rank using a learning-to-rank model trained on text similarity rankings. The first two baseline systems are models where the values are ordered from highest to lowest to generate the ranking. The idea behind them is that the number of times a citation is mentioned in an article, or the citation impact may already be good indicators of their closeness. The text similarity model is trained using the same features and methods used by the annotation model, but trained using text similarity rankings instead of the author's judgments.
We compare our system to a variety of baselines. (1) Rank by the number of times a citation is mentioned in the document. (2) Rank by the number of times the citation is cited in the literature (citation impact). (3) Rank using Google Scholar Related Articles. (4) Rank by the TF*IDF weighted cosine similarity. (5) Rank using a learning-to-rank model trained on text similarity rankings.
|
what were the baselines?
|
The answers are shown as follows:
* (1) Rank by the number of times a citation is mentioned in the document.
* (2) Rank by the number of times the citation is cited in the literature (citation impact).
* (3) Rank using Google Scholar Related Articles.
* (4) Rank by the TF*IDF weighted cosine similarity.
* (5) Rank using a learning-to-rank model trained on text similarity rankings.
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null | false
| 109
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Sarcasm is defined as “a sharp, bitter, or cutting expression or remark; a bitter gibe or taunt”. As the fields of affective computing and sentiment analysis have gained increasing popularity BIBREF0 , it is a major concern to detect sarcastic, ironic, and metaphoric expressions. Sarcasm, especially, is key for sentiment analysis as it can completely flip the polarity of opinions. Understanding the ground truth, or the facts about a given event, allows for the detection of contradiction between the objective polarity of the event (usually negative) and its sarcastic characteristic by the author (usually positive), as in “I love the pain of breakup”. Obtaining such knowledge is, however, very difficult.
In our experiments, we exposed the classifier to such knowledge extracted indirectly from Twitter. Namely, we used Twitter data crawled in a time period, which likely contain both the sarcastic and non-sarcastic accounts of an event or similar events. We believe that unambiguous non-sarcastic sentences provided the classifier with the ground-truth polarity of those events, which the classifier could then contrast with the opposite estimations in sarcastic sentences. Twitter is a more suitable resource for this purpose than blog posts, because the polarity of short tweets is easier to detect (as all the information necessary to detect polarity is likely to be contained in the same sentence) and because the Twitter API makes it easy to collect a large corpus of tweets containing both sarcastic and non-sarcastic examples of the same event.
Sometimes, however, just knowing the ground truth or simple facts on the topic is not enough, as the text may refer to other events in order to express sarcasm. For example, the sentence “If Hillary wins, she will surely be pleased to recall Monica each time she enters the Oval Office :P :D”, which refers to the 2016 US presidential election campaign and to the events of early 1990's related to the US president Clinton, is sarcastic because Hillary, a candidate and Clinton's wife, would in fact not be pleased to recall her husband's alleged past affair with Monica Lewinsky. The system, however, would need a considerable amount of facts, commonsense knowledge, anaphora resolution, and logical reasoning to draw such a conclusion. In this paper, we will not deal with such complex cases.
Existing works on sarcasm detection have mainly focused on unigrams and the use of emoticons BIBREF1 , BIBREF2 , BIBREF3 , unsupervised pattern mining approach BIBREF4 , semi-supervised approach BIBREF5 and n-grams based approach BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 with sentiment features. Instead, we propose a framework that learns sarcasm features automatically from a sarcasm corpus using a convolutional neural network (CNN). We also investigate whether features extracted using the pre-trained sentiment, emotion and personality models can improve sarcasm detection performance. Our approach uses relatively lower dimensional feature vectors and outperforms the state of the art on different datasets. In summary, the main contributions of this paper are the following:
The rest of the paper is organized as follows: Section SECREF2 proposes a brief literature review on sarcasm detection; Section SECREF4 presents the proposed approach; experimental results and thorough discussion on the experiments are given in Section SECREF5 ; finally, Section SECREF6 concludes the paper.
Sarcasm is defined as “a sharp, bitter, or cutting expression or remark; a bitter gibe or taunt”.
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What is sarcasm?
|
A sharp, bitter, or cutting expression or remark; a bitter gibe or taunt.
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What is Solidarity Tax?
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The IMF has proposed a Solidarity Tax on the Companies those prospered in Covid crises should pay additional tax to show solidarity with those who were hit hardest by pandemic
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null | false
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Systems development life cycle
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From Wikipedia, the free encyclopedia
This article is about systems development life cycle. For the IBM's computer communication protocol, see Synchronous Data Link Control.
Model of the software development life cycle, highlighting the maintenance phase
In systems engineering, information systems and software engineering, the systems development life cycle (SDLC), also referred to as the application development life cycle, is a process for planning, creating, testing, and deploying an information system. The SDLC concept applies to a range of hardware and software configurations, as a system can be composed of hardware only, software only, or a combination of both. There are usually six stages in this cycle: requirement analysis, design, development and testing, implementation, documentation, and evaluation.
Overview
A systems development life cycle is composed of distinct work phases that are used by systems engineers and systems developers to deliver information systems. Like anything that is manufactured on an assembly line, an SDLC aims to produce high-quality systems that meet or exceed expectations, based on requirements, by delivering systems within scheduled time frames and cost estimates. Computer systems are complex and often link components with varying origins. Various SDLC methodologies have been created, such as waterfall, spiral, agile, rapid prototyping, incremental, and synchronize and stabilize.
SDLC methodologies fit within a flexibility spectrum ranging from agile to iterative to sequential. Agile methodologies, such as XP and Scrum, focus on lightweight processes that allow for rapid changes. Iterative methodologies, such as Rational Unified Process and dynamic systems development method, focus on stabilizing project scope and iteratively expanding or improving products. Sequential or big-design-up-front (BDUF) models, such as waterfall, focus on complete and correct planning to guide larger projects and limit risks to successful and predictable results.[citation needed] Anamorphic development is guided by project scope and adaptive iterations.
In project management a project can include both a project life cycle (PLC) and an SDLC, during which somewhat different activities occur. According to Taylor (2004), "the project life cycle encompasses all the activities of the project, while the systems development life cycle focuses on realizing the product requirements".
SDLC is not a methodology per se, but rather a description of the phases that a methodology should address. The list of phases is not definitive, but typically includes planning, analysis, design, build, test, implement, and maintenance/support. In the Scrum framework, for example, one could say a single user story goes through all the phases of the SDLC within a two-week sprint. By contrast the waterfall methodology, where every business requirement[citation needed] is translated into feature/functional descriptions which are then all implemented typically over a period of months or longer.[citation needed]
History
According to Elliott & Strachan & Radford (2004), SDLC "originated in the 1960s, to develop large scale functional business systems in an age of large scale business conglomerates. Information systems activities revolved around heavy data processing and number crunching routines".
The structured systems analysis and design method (SSADM) was produced for the UK government Office of Government Commerce in the 1980s. Ever since, according to Elliott (2004), "the traditional life cycle approaches to systems development have been increasingly replaced with alternative approaches and frameworks, which attempted to overcome some of the inherent deficiencies of the traditional SDLC".
Models
A ten-phase version of the systems development life cycle
SDLC provides a set of phases/steps/activities for system designers and developers to follow. Each phase builds on the results of the previous one. Not every project requires that the phases be sequential. For smaller, simpler projects, phases may be combined/overlap.
Waterfall
The oldest and best known is the waterfall model, which uses a linear sequence of steps. Waterfall has different varieties. One variety is as follows:
Preliminary analysis
Conduct with a preliminary analysis, consider alternative solutions, estimate costs and benefits, and submit a preliminary plan with recommendations.
Conduct preliminary analysis: Identify the organization's objectives and define the nature and scope of the project. Ensure that the project fits with the objectives.
Consider alternative solutions: Alternatives may come from interviewing employees, clients, suppliers, and consultants, as well as competitive analysis.
Cost-benefit analysis: Analyze the costs and benefits of the project.
Systems analysis, requirements definition
Decompose project goals[clarification needed] into defined functions and operations. This involves gathering and interpreting facts, diagnosing problems, and recommending changes. Analyze end-user information needs and resolve inconsistencies and incompleteness:
Collect facts: Obtain end-user requirements by document review, client interviews, observation, and questionnaires.
Scrutinize existing system(s): Identify pros and cons.
Analyze the proposed system: Find solutions to issues and prepare specifications, incorporating appropriate user proposals.
Systems design
At this step, desired features and operations are detailed, including screen layouts, business rules, process diagrams, pseudocode, and other deliverables.
Development
Write the code.
Integration and testing
Assemble the modules in a testing environment. Check for errors, bugs, and interoperability.
Acceptance, installation, deployment
Put the system into production. This may involve training users, deploying hardware, and loading information from the prior system.
Maintenance
Monitor the system to assess its ongoing fitness. Make modest changes and fixes as needed.
Evaluation
The system and the process are reviewed. Relevant questions include whether the newly implemented system meets requirements and achieves project goals, whether the system is usable, reliable/available, properly scaled and fault-tolerant. Process checks include review of timelines and expenses, as well as user acceptance.
Disposal
At end of life, plans are developed for discontinuing the system and transitioning to its replacement. Related information and infrastructure must be repurposed, archived, discarded, or destroyed, while appropriately protecting security.
In the following diagram, these stages of the are divided into ten steps, from definition to creation and modification of IT work products:
Systems analysis and design
Systems analysis and design (SAD) can be considered a meta-development activity, which serves to set the stage and bound the problem. SAD can help balance competing high-level requirements. SAD interacts with distributed enterprise architecture, enterprise I.T. Architecture, and business architecture, and relies heavily on concepts such as partitioning, interfaces, personae and roles, and deployment/operational modeling to arrive at a high-level system description. This high-level description is then broken down into the components and modules which can be analyzed, designed, and constructed separately and integrated to accomplish the business goal. SDLC and SAD are cornerstones of full life cycle product and system planning.
Object-oriented analysis and design
Object-oriented analysis and design (OOAD) is the process of analyzing a problem domain to develop a conceptual model that can then be used to guide development. During the analysis phase, a programmer develops written requirements and a formal vision document via interviews with stakeholders.
The conceptual model that results from OOAD typically consists of use cases, and class and interaction diagrams. It may also include a user interface mock-up.
An output artifact does not need to be completely defined to serve as input of object-oriented design; analysis and design may occur in parallel. In practice the results of one activity can feed the other in an iterative process.
Some typical input artifacts for OOAD:
Conceptual model: A conceptual model is the result of object-oriented analysis. It captures concepts in the problem domain. The conceptual model is explicitly independent of implementation details.
Use cases: A use case is a description of sequences of events that, taken together, complete a required task. Each use case provides scenarios that convey how the system should interact with actors (users). Actors may be end users or other systems. Use cases may further elaborated using diagrams. Such diagrams identify the actor and the processes they perform.
System Sequence Diagram: A System Sequence diagrams (SSD) is a picture that shows, for a particular use case, the events that actors generate, their order, including inter-system events.
User interface document: Document that shows and describes the user interface.
Data model: A data model describes how data elements relate to each other. The data model is created before the design phase. Object-oriented designs map directly from the data model. Relational designs are more involved.
System lifecycle
The system lifecycle is a view of a system or proposed system that addresses all phases of its existence to include system conception, design and development, production and/or construction, distribution, operation, maintenance and support, retirement, phase-out, and disposal.
Conceptual design
The conceptual design stage is the stage where an identified need is examined, requirements for potential solutions are defined, potential solutions are evaluated, and a system specification is developed. The system specification represents the technical requirements that will provide overall guidance for system design. Because this document determines all future development, the stage cannot be completed until a conceptual design review has determined that the system specification properly addresses the motivating need.
Key steps within the conceptual design stage include:
Need identification
Feasibility analysis
System requirements analysis
System specification
Conceptual design review
Preliminary system design
During this stage of the system lifecycle, subsystems that perform the desired system functions are designed and specified in compliance with the system specification. Interfaces between subsystems are defined, as well as overall test and evaluation requirements. At the completion of this stage, a development specification is produced that is sufficient to perform detailed design and development.
Key steps within the preliminary design stage include:
Functional analysis
Requirements allocation
Detailed trade-off studies
Synthesis of system options
Preliminary design of engineering models
Development specification
Preliminary design review
For example, as the system analyst of Viti Bank, you have been tasked to examine the current information system. Viti Bank is a fast-growing bank in Fiji. Customers in remote rural areas are finding difficulty to access the bank services. It takes them days or even weeks to travel to a location to access the bank services. With the vision of meeting the customers' needs, the bank has requested your services to examine the current system and to come up with solutions or recommendations of how the current system can be provided to meet its needs.
Detail design and development
This stage includes the development of detailed designs that brings initial design work into a completed form of specifications. This work includes the specification of interfaces between the system and its intended environment, and a comprehensive evaluation of the systems logistical, maintenance and support requirements. The detail design and development is responsible for producing the product, process and material specifications and may result in substantial changes to the development specification.
Key steps within the detail design and development stage include:
Detailed design
Detailed synthesis
Development of engineering and prototype models
Revision of development specification
Product, process, and material specification
Critical design review
Production and construction
During the production and/or construction stage the product is built or assembled in accordance with the requirements specified in the product, process and material specifications, and is deployed and tested within the operational target environment. System assessments are conducted in order to correct deficiencies and adapt the system for continued improvement.
Key steps within the product construction stage include:
Production and/or construction of system components
Acceptance testing
System distribution and operation
Operational testing and evaluation
System assessment
Utilization and support
Once fully deployed, the system is used for its intended operational role and maintained within its operational environment.
Key steps within the utilization and support stage include:
System operation in the user environment
Change management
System modifications for improvement
System assessment
Phase-out and disposal
Effectiveness and efficiency of the system must be continuously evaluated to determine when the product has met its maximum effective lifecycle. Considerations include: Continued existence of operational need, matching between operational requirements and system performance, feasibility of system phase-out versus maintenance, and availability of alternative systems.
Phases
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System investigation
During this step, current priorities that would be affected and how they should be handled are considered. A feasibility study determines whether creating a new or improved system is appropriate. This helps to estimate costs, benefits, resource requirements, and specific user needs.
The feasibility study should address operational, financial, technical, human factors, and legal/political concerns.
Analysis
The goal of analysis is to determine where the problem is. This step involves decomposing the system into pieces, analyzing project goals, breaking down what needs to be created, and engaging users to define requirements.
Design
In systems design, functions and operations are described in detail, including screen layouts, business rules, process diagrams, and other documentation. Modular design reduces complexity and allows the outputs to describe the system as a collection of subsystems.
The design stage takes as its input the requirements already defined. For each requirement, a set of design elements is produced.
Design documents typically include functional hierarchy diagrams, screen layouts, business rules, process diagrams, pseudo-code, and a complete data model with a data dictionary. These elements describe the system in sufficient detail that developers and engineers can develop and deliver the system with minimal additional input.
Testing
The code is tested at various levels in software testing. Unit, system, and user acceptance tests are typically performed. Many approaches to testing have been adopted.
The following types of testing may be relevant:
Path testing
Data set testing
Unit testing
System testing
Integration testing
Black-box testing
White-box testing
Regression testing
Automation testing
User acceptance testing
Software performance testing
Training and transition
Once a system has been stabilized through testing, SDLC ensures that proper training is prepared and performed before transitioning the system to support staff and end users. Training usually covers operational training for support staff as well as end-user training.
After training, systems engineers and developers transition the system to its production environment.
Operations and maintenance
Maintenance includes changes, fixes, and enhancements.
Evaluation
The final phase of the SDLC is to measure the effectiveness of the system and evaluate potential enhancements.
Life cycle
Management and control
SDLC phases related to management controls
SDLC phase objectives are described in this section with key deliverables, a description of recommended tasks, and a summary of related control objectives for effective management. It is critical for the project manager to establish and monitor control objectives while executing projects. Control objectives are clear statements of the desired result or purpose and should be defined and monitored throughout a project. Control objectives can be grouped into major categories (domains), and relate to the SDLC phases as shown in the figure.
To manage and control a substantial SDLC initiative, a work breakdown structure (WBS) captures and schedules the work. The WBS and all programmatic material should be kept in the "project description" section of the project notebook.[clarification needed] The project manager chooses a WBS format that best describes the project.
The diagram shows that coverage spans numerous phases of the SDLC but the associated MCD[clarification needed] shows mappings to SDLC phases. For example, Analysis and Design is primarily performed as part of the Acquisition and Implementation Domain, and System Build and Prototype is primarily performed as part of delivery and support.
Work breakdown structured organization
Work breakdown structure
The upper section of the WBS provides an overview of the project scope and timeline. It should also summarize the major phases and milestones. The middle section is based on the SDLC phases. WBS elements consist of milestones and tasks to be completed rather than activities to be undertaken and have a deadline. Each task has a measurable output (e.g., analysis document). A WBS task may rely on one or more activities (e.g. coding). Parts of the project needing support from contractors should have a statement of work (SOW). The development of a SOW does not occur during a specific phase of SDLC but is developed to include the work from the SDLC process that may be conducted by contractors.
Baselines
Baselines[clarification needed] are established after four of the five phases of the SDLC, and are critical to the iterative nature of the model. Baselines become milestones.
functional baseline: established after the conceptual design phase.
allocated baseline: established after the preliminary design phase.
product baseline: established after the detail design and development phase.
updated product baseline: established after the production construction phase.
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What are the different phases in systems development life cycle?
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There are 7 phases in systems development life cycle. They are system investigation, Analysis, Design, Testing, Training and transition, Operations and maintenance and Evaluation
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Apple directly sub-contracts hardware production to external OEM companies, maintaining a high degree of control over the end product. The iPhone contains most of the hardware parts of a typical modern smartphone. Some hardware elements, such as 3D Touch and the Taptic Engine, are unique to the iPhone. The main hardware of the iPhone is the touchscreen, with current models offering screens of 4.7 inches and larger. All iPhones include a rear-facing camera; the front-facing camera dates back to the iPhone 4. The iPhone 7 Plus introduced multiple lenses to the rear-facing camera. A range of sensors are also included on the device, such as a proximity sensor, ambient light sensor, accelerometer, gyroscopic sensor, magnetometer, facial recognition sensor or fingerprint sensor (depending on the model) and barometer. In 2022, Apple added satellite communications to the iPhone, with the release of the iPhone 14 and iPhone 14 Pro.
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From the passage provided, extract the year when iPhones first came with satellite communications.
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iPhones first came with satellite communications in 2022 with the release of the iPhone 14 and iPhone 14 Pro.
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What is a genome-wide association study (GWAS)?
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In genomics, a genome-wide association study (GWA study, or GWAS), is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.
When applied to human data, GWA studies compare the DNA of participants having varying phenotypes for a particular trait or disease. These participants may be people with a disease (cases) and similar people without the disease (controls), or they may be people with different phenotypes for a particular trait, for example blood pressure. This approach is known as phenotype-first, in which the participants are classified first by their clinical manifestation(s), as opposed to genotype-first. Each person gives a sample of DNA, from which millions of genetic variants are read using SNP arrays. If there is significant statistical evidence that one type of the variant (one allele) is more frequent in people with the disease, the variant is said to be associated with the disease. The associated SNPs are then considered to mark a region of the human genome that may influence the risk of disease.
GWA studies investigate the entire genome, in contrast to methods that specifically test a small number of pre-specified genetic regions. Hence, GWAS is a non-candidate-driven approach, in contrast to gene-specific candidate-driven studies. GWA studies identify SNPs and other variants in DNA associated with a disease, but they cannot on their own specify which genes are causal.
The first successful GWAS published in 2002 studied myocardial infarction. This study design was then implemented in the landmark GWA 2005 study investigating patients with age-related macular degeneration, and found two SNPs with significantly altered allele frequency compared to healthy controls. As of 2017, over 3,000 human GWA studies have examined over 1,800 diseases and traits, and thousands of SNP associations have been found. Except in the case of rare genetic diseases, these associations are very weak, but while they may not explain much of the risk, they provide insight into genes and pathways that can be important.
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Which building is the most higher?
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The tallest building in the world, as of my knowledge cutoff of September 2021, is the Burj Khalifa, located in Dubai, United Arab Emirates. The Burj Khalifa stands at a height of 828 meters (2,716 feet) and has 163 floors above ground. It was completed in 2010 and has held the title of the world's tallest building since its construction.
However, there are always new skyscrapers and buildings being constructed around the world, so it's possible that another building may surpass the height of the Burj Khalifa in the future.
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Identify which instrument is string or percussion: Txalaparta, Clavinet
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Clavinet is string, Txalaparta is percussion.
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Write an essay describing why baseball should be considered America's pastime.
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Baseball is the oldest professional sport in America. It began in the late 1800s. The sport dates back to to the civil war era, which is where it started to become known as America's pastime. It coincides with a plethora of historical and cultural aspects of life in America. While the game has a vast amount of historical significance, it also seems to reflect changes that occur in our society. For example, over the past several decades we have seen a huge influx in players from all over the globe, with greater representation from countries like Dominican Republic, Venezuela, Puerto Rico, Japan and many others. America has always been considered a land of opportunity and this is evident with baseball. The MLB is the most prestigious league when it comes to baseball and is dream destination for any player. If you are good enough, you are sometime given the opportunity to play here. Recently we have seen a huge increase in the international representation of players in the league. As times have changed and the world has become more global, we also see this transpire in baseball. Baseball is America's pastime because it is a preservation of historical values, but also adapts and reflects current cultural values, which always keeps it relevant.
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iPhone 14 and 14 Plus are available in three internal storage configurations: 128, 256, and 512 GB. It has 6 GB of RAM, an increase over the iPhone 13 and 13 mini model's 4 GB of RAM. The iPhone 14 and 14 Plus have the same IP68 rating for dust and water resistance as their predecessors.
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Extract the different storage configurations available to the iPhone 14 that are mentioned in the text. Put them in a comma separated list.
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128 GB, 256 GB, 512 GB
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2002.10832
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To investigate whether our BERT-based model can transfer knowledge beyond language, we consider image features as simple visual tokens that can be presented to the model analogously to textual token embeddings. In order to make the $o_j$ vectors (of dimension $2048+4=2052$) comparable to BERT embeddings (of dimension 768), we use a simple linear cross-modal projection layer $W$ of dimensions $2052\hspace{-1.00006pt}\times \hspace{-1.00006pt}768$. The $N$ object regions detected in an image, are thus represented as $X_{img} = (W.o_1,...,W.o_N)$. Once mapped into the BERT embedding space with $W$, the image is seen by the rest of the model as a sequence of units with no explicit indication if it is a text or an image embedding.
To investigate whether our BERT-based model can transfer knowledge beyond language, we consider image features as simple visual tokens that can be presented to the model analogously to textual token embeddings. In order to make the $o_j$ vectors (of dimension $2048+4=2052$) comparable to BERT embeddings (of dimension 768), we use a simple linear cross-modal projection layer $W$ of dimensions $2052\hspace{-1.00006pt}\times \hspace{-1.00006pt}768$. The $N$ object regions detected in an image, are thus represented as $X_{img} = (W.o_1,...,W.o_N)$. Once mapped into the BERT embedding space with $W$, the image is seen by the rest of the model as a sequence of units with no explicit indication if it is a text or an image embedding.
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How are multimodal representations combined?
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The image feature vectors are mapped into BERT embedding dimensions and treated like a text sequence afterwards.
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Utilitarianism is a consequentialist approach to normative ethics that holds morally right actions are those that promote the most human happiness. Jeremy Bentham, who created his version of the theory in 1829, and John Stuart Mill who made his in 1861 are considered the founders of utilitarianism, though the basic concept predates either of the two philosophers. Utilitarianism remains as one of the more appealing and compelling approaches to normative ethics.
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What is the essence of utilitarianism?
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Utilitarianism evaluates any action in terms of the amount of human happiness delivered as the result of the action.
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At the 30th Academy Awards, Mehboob Khan's 1957 Hindi-language film Mother India was India's first submission for the Academy Award for Best International Feature Film category. It was nominated alongside four other films and lost to the Italian film Nights of Cabiria (1957) by one vote. In 1982, The National Film Development Corporation of India was instrumental in co-producing Richard Attenborough's biographical film Gandhi. At the 55th Academy Awards, Bhanu Athaiya became the first Indian to win an Academy Award for designing the costumes. Ravi Shankar was nominated for Best Original Score for the same film. As of 2023, three Indian films have been nominated for Best International Feature—Mother India, Salaam Bombay! (1988) and Lagaan (2001).
In 1992, legendary Bengali filmmaker Satyajit Ray was bestowed with an Honorary Academy Award, becoming the only Indian to date to receive the honour. Resul Pookutty and A. R. Rahman won the Academy Award for Best Sound Mixing and Best Original Score, respectively, for the 2008 British film Slumdog Millionaire. Rahman also won for Best Original Song alongside lyricist Gulzar for the song "Jai Ho", becoming the first Indian to date to have won more than one Academy Award. Rahman also holds the record for most nominations for an Indian with five total nominations to date, followed by Ismail Merchant with four nominations (three for Best Picture and once for Live Action Short).
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From the passage provided, extract the list of Indian winners of the Academy Awards. Separate them with a comma.
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Bhanu Athaiya, Resul Pookutty , A. R. Rahman
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Image restoration algorithms aim to recover a high-quality image from a contaminated input image by solving an ill-posed image restoration problem. There are various image restoration tasks depending on the type of corruptions, such as image denoising, demosaicing, single image super-resolution, and image compression artifacts reduction. To restore corrupted information from the contaminated image, a variety of image priors were proposed.
Recently, image restoration methods based on deep neural networks have achieved great success. Inspired by the widely used non-local prior, most recent approaches based on neural networks adapt non-local attention into their neural network to enhance the representation learning, following the non-local neural networks. In a nonlocal block, a response is calculated as a weighted sum over all pixel-wise features on the feature map to account for long-range information. Such a module was initially designed for high-level recognition tasks such as image classification, and it has been proven to be beneficial for low-level vision tasks.
Though attention modules have been shown to be effective in boosting the performance. Most attention modules only explore the correlation among features at the same layer. Actually, features at different intermediate layers encode variant information at different scales, and might be helpful to augment the information used in recovering the high-quality image. Motivated by the potential benefit of exploring feature correlation across intermediate layers, Holistic Attention Network (HAN) is proposed to find interrelationship among features at hierarchical levels with a Layer Attention Module (LAM). However, LAM assigns a single importance weight to all features at the same layer and neglects the difference of spatial positions of these features. Recent research in omnidirectional representation suggests that exploring the relationship among features at different layers can benefit the representation learning of neural networks. Nevertheless, calculating correlation among features at hierarchical layers is computationally expensive due to the quadratic complexity of dot product attention. The complexity of such cross-layer attention design is increased from (HW ) 2 L to (HW L) 2 , where H, W are the height and width of the feature map and L is the number of layers.
1.1 CONTRIBUTIONS Our contributions are listed as follows.
First, in order to address the problem caused by only referring to keys within the same layer in most attention modules, we propose a novel attention module termed Cross-Layer Attention (CLA), which searches for keys across different layers for each query feature. With the help of the deformation mechanism, CLA only attends to a small set of keys at different layers for each query feature.
Second, we propose an improved CLA termed Adaptive Cross-Layer Attention (ACLA) which selects an adaptive number of keys at each layer for each query, and searches for the optimal insert positions of ACLA modules. We deploy ACLA modules on commonly used neural network models, e.g. EDSR, for image restoration. Extensive experiments on single image super-resolution, image denoising, image compression artifacts reduction, and image demosaicing demonstrate the effectiveness of our approach.
To evaluate the effects of the depth and width in CapsGNN, we change them to observe the performance of the model on two graph datasets, ENZYMES and IMDB-MULTI (Kersting et al., 2016), in bioinformatics and social fields, respectively. It is shown in Figure 1 that if the model has small D and W, it can not sufficiently extract the features of graphs. On the contrary, when the model structure is too complicated, overfitting occurs frequently. Thus, CapsGNN is sensitive to changes in depth and width just because of the vectorized graph representation in it. Since the impacts of D
and W are distinct on different datasets, it is necessary to adaptively adjust the model architecture according to the latent characteristics of particular workloads. Figure 1: Results of classification accuracy when increasing D and W. The third subfigure depicts the changes of both D and W, and the x-axis represents the increase of either of them (for simplicity, the growth scales of D and W are set the same here).
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Why is the proposed method coupled with CapsGCN if the RL module is to tune depth and width of convolutional layers?
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CapsGNN extracts multi-scale node features from different convolutional layers and represents the extracted features in the form of capsules. Compared to classic GCN models, CapsGNN has been proven to have better representation ability on graph data. Nevertheless, CapsGNN is sensitive to changes in depth and width just because of the vectorized graph representation in it, as shown in Figure 1. Therefore, based on CapsGNN, we propose an RL-based assistant module (AM) to find optimal depth-width settings on different datasets. Note that we also added an experiment on the effectiveness of AM on GIN, the model with the best classification performance in all the baseline methods, and recorded corresponding accuracy results in Table 3.
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Word embeddings are fixed-length vector representations for words BIBREF0 , BIBREF1 . In recent years, the morphology of words is drawing more and more attention BIBREF2 , especially for Chinese whose writing system is based on logograms.
UTF8gbsn With the gradual exploration of the semantic features of Chinese, scholars have found that not only words and characters are important semantic carriers, but also stroke feature of Chinese characters is crucial for inferring semantics BIBREF3 . Actually, a Chinese word usually consists of several characters, and each character can be further decomposed into a stroke sequence which is certain and changeless, and this kind of stroke sequence is very similar to the construction of English words. In Chinese, a particular sequence of strokes can reflect the inherent semantics. As shown in the upper half of Figure FIGREF3 , the Chinese character “驾" (drive) can be decomposed into a sequence of eight strokes, where the last three strokes together correspond to a root character “马" (horse) similar to the root “clar" of English word “declare" and “clarify".
Moreover, Chinese is a language originated from Oracle Bone Inscriptions (a kind of hieroglyphics). Its character glyphs have a spatial structure similar to graphs which can convey abundant semantics BIBREF4 . Additionally, the critical reason why Chinese characters are so rich in morphological information is that they are composed of basic strokes in a 2-D spatial order. However, different spatial configurations of strokes may lead to different semantics. As shown in the lower half of Figure 1, three Chinese characters “入" (enter), “八" (eight) and “人" (man) share exactly a common stroke sequence, but they have completely different semantics because of their different spatial configurations.
In addition, some biological investigations have confirmed that there are actually two processing channels for Chinese language. Specifically, Chinese readers not only activate the left brain which is a dominant hemisphere in processing alphabetic languages BIBREF5 , BIBREF6 , BIBREF7 , but also activate the areas of the right brain that are responsible for image processing and spatial information at the same time BIBREF8 . Therefore, we argue that the morphological information of characters in Chinese consists of two parts, i.e., the sequential information hidden in root-like strokes order, and the spatial information hidden in graph-like character glyphs. Along this line, we propose a novel Dual-channel Word Embedding (DWE) model for Chinese to realize the joint learning of sequential and spatial information in characters. Finally, we evaluate DWE on two representative tasks, where the experimental results exactly validate the superiority of DWE in capturing the morphological information of Chinese.
Finally, we evaluate DWE on two representative tasks, where the experimental results exactly validate the superiority of DWE in capturing the morphological information of Chinese.
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How many representative tasks do the authors evaluate DWE on?
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Two.
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What is the major source of income in Ethiopia and why its important?
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Agriculture is the backbone of the economy and the main source of livelihood for the majority of the population. However, traditional agricultural practices have led to soil degradation and deforestation. Farmers in Ethiopia often rely on a few staple crops, such as teff, wheat, and maize, which are grown repeatedly on the same plot of land without proper rotation or replenishment of soil nutrients (International Labour Organization). This leads to reduced yields, which can make it difficult for farmers to produce enough food to feed their families and earn a living. Deforestation, which is also a major problem, has been driven by the need for more land to grow crops. Ethiopia has one of the highest deforestation rates in the world, with an estimated loss of 1.1 million hectares of forest every year (World Bank).
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What medicine is inside of an EpiPen?
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Epinephrine (adrenaline)
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What is core banking?
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Core banking is a banking service provided by a group of networked bank branches where customers may access their bank account and perform basic transactions from any of the member branch offices.
Core banking is often associated with retail banking and many banks treat the retail customers as their core banking customers. Businesses are usually managed via the corporate banking division of the institution. Core banking covers basic depositing and lending of money.
Core banking functions will include transaction accounts, loans, mortgages and payments. Banks make these services available across multiple channels like automated teller machines, Internet banking, mobile banking and branches.
Banking software and network technology allows a bank to centralise its record keeping and allow access from any location.
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Classify each of the following as a new world or old world crop: Potato, Corn, Tomato, Wheat, Oat, Cacao, Apple, Olive
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Potato: New World, Corn: New World, Tomato: New World, Wheat: Old World, Oat: Old World, Cacao: New World, Apple: Old World, Olive: Old World
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Li Xueju (simplified Chinese: 李学举; traditional Chinese: 李學舉; pinyin: Lǐ Xuéjǔ; born April 1945) is a politician of the People's Republic of China, and the former Minister of Civil Affairs of PRC.
Born in Gongzhuling, Jilin Province, Li joined the Chinese Communist Party in January 1966, and started working in May 1966. In early years, he served in various posts in local commune. In 1975, he was promoted to Secretary of Communist Youth League committee in Siping region of Jilin. From 1978, he served as vice section chief, vice director and later, director of organization department of CYL central committee. From 1984 to 1986, he studied at CCP Central Party School. In 1986, he was appointed as the director of general office of CYL central committee. He was a member of 10th CYL central committee, and a standing member of 11th and 12th CYL central committee.
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Who is the Chinese Li Xueju
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Li Xueju born April 1945 is a politician of the People's Republic of China, and the former Minister of Civil Affairs of PRC.
Born in Gongzhuling, Jilin Province, Li joined the Chinese Communist Party in January 1966, and started working in May 1966. In early years, he served in various posts in local commune. In 1975, he was promoted to Secretary of Communist Youth League committee in Siping region of Jilin.
From 1978, he served as vice section chief, vice director and later, director of organization department of CYL central committee. From 1984 to 1986, he studied at CCP Central Party School. In 1986, he was appointed as the director of general office of CYL central committee. He was a member of 10th CYL central committee, and a standing member of 11th and 12th CYL central committee.
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Which is an imperial or a metric measurement: ton, tonne
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ton is imperial, tonne is metric
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The current coverage of the political landscape in the press and in social media has led to an unprecedented situation. Like never before, a statement in an interview, a press release, a blog note, or a tweet can spread almost instantaneously and reach the public in no time. This proliferation speed has left little time for double-checking claims against the facts, which has proven critical in politics, e.g., during the 2016 presidential campaign in the USA, which was arguably impacted by fake news in social media and by false claims.
Investigative journalists and volunteers have been working hard trying to get to the root of a claim and to present solid evidence in favor or against it. Manual fact-checking has proven very time-consuming, and thus automatic methods have been proposed as a way to speed-up the process. For instance, there has been work on checking the factuality/credibility of a claim, of a news article, or of an information source BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. However, less attention has been paid to other steps of the fact-checking pipeline, which is shown in Figure FIGREF1.
The process starts when a document is made public. First, an intrinsic analysis is carried out in which check-worthy text fragments are identified. Then, other documents that might support or rebut a claim in the document are retrieved from various sources. Finally, by comparing a claim against the retrieved evidence, a system can determine whether the claim is likely true or likely false. For instance, BIBREF8 do this on the basis of a knowledge graph derived from Wikipedia. The outcome could then be presented to a human expert for final judgment.
In this paper, we focus on the first step: predicting check-worthiness of claims. Our contributions can be summarized as follows:
New dataset: We build a new dataset of manually-annotated claims, extracted from the 2016 US presidential and vice-presidential debates, which we gathered from nine reputable sources such as CNN, NPR, and PolitiFact, and which we release to the research community.
Modeling the context: We develop a novel approach for automatically predicting which claims should be prioritized for fact-checking, based on a rich input representation. In particular, we model not only the textual content, but also the context: how the target claim relates to the current segment, to neighboring segments and sentences, and to the debate as a whole, and also how the opponents and the public react to it.
State-of-the-art results: We achieve state-of-the-art results, outperforming a strong rivaling system by a margin, while also demonstrating that this improvement is due primarily to our modeling of the context.
We model the problem as a ranking task, and we train both Support Vector Machines (SVM) and Feed-forward Neural Networks (FNN) obtaining state-of-the-art results. We also analyze the relevance of the specific feature groups and we show that modeling the context yields a significant boost in performance. Finally, we also analyze whether we can learn to predict which facts are check-worthy with respect to each of the individual media sources, thus capturing their biases. It is worth noting that while trained on political debates, many features of our model can be potentially applied to other kinds of information sources, e.g., interviews and news.
The rest of the paper is organized as follows: Section SECREF2 discusses related work. Section SECREF3 describes the process of gathering and annotating our political debates dataset. Section SECREF4 presents our supervised approach to predicting fact-checking worthiness, including the explanation of the model and the information sources we use. Section SECREF5 presents the evaluation setup and discusses the results. Section SECREF6 provides further analysis. Finally, Section SECREF7 presents the conclusions and outlines some possible directions for future research.
In particular, we model not only the textual content, but also the context: how the target claim relates to the current segment, to neighboring segments and sentences, and to the debate as a whole, and also how the opponents and the public react to it.
|
How do the authors model the context?
|
How the target claim relates to the current segment, to neighboring segments and sentences, and to the debate as a whole, and also how the opponents and the public react to it.
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null | false
| null |
Which institute is known as International money laundering watch dog?
|
Financial Action Task Force (FATF)
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null | false
| 3
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Consider the task of translating from an extremely low-resource language (source) to a target language. The parallel corpus between the two languages if available may be too small to train a NMT model. Similar to existing works BIBREF3 , BIBREF10 , BIBREF12 , we use transfer learning to overcome data sparsity and train a NMT model between the source and the target languages. Specifically, the NMT model (parent model) is trained on the assisting language and target language pairs. We choose English as the assisting language in all our experiments. In our resource-scarce scenario, we have no parallel corpus for the child task. Hence, at test time, the source language sentence is translated using the parent model after performing a word-by-word translation into the assisting language.
Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the contextual representations generated by the encoder for the two languages. In this paper, we propose to pre-order English sentences (assisting language sentences) to match the word-order of the source language and train the parent model on this pre-ordered corpus. In our experiments, we look at scenarios where the assisting language has SVO word order and the source language has SOV word order.
For instance, consider the English sentence Anurag will meet Thakur. One of the pre-ordering rule swaps the position of the noun phrase followed by a transitive verb with the transitive verb. The original and the resulting re-ordered parse tree will be as shown in the Table TABREF5 . Applying this reordering rule to the above sentence Anurag will meet Thakur will yield the reordered sentence Anurag Thakur will meet. Additionally, the Table TABREF5 shows the parse trees for the above sentence with and without pre-ordering.
Pre-ordering should also be beneficial for other word order divergence scenarios (e.g., SOV to SVO), but we leave verification of these additional scenarios for future work.
Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the con textual representations generated by the encoder for the two languages.
|
What do the authors hypothesize due to the source language and the assisting language (English) having different word orders?
|
The authors hypothesize that it leads to inconsistencies in the con textual representations generated by the encoder for the two languages.
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null | false
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How can I make the paint color purple from primary colors?
|
By combining Red and Blue primary colors, you can make purple.
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null | false
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Which items on this list are scientific instruments and which are musical instruments: guitar, microscope, drums, petri dish, piano, trumpet, and bunsen burner.
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The scientific instruments on this list include the microscope, petri dish and bunsen burner, and the musical instruments are the guitar, drums, piano, and trumpet.
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null | false
| null |
give me a list of ways some one can warm up
|
- go under the covers
- start a fire
- drink a hot beverage
- snuggle up with someone else
- take a dip in a hot spring
- do a few jumping jacks
- put more clothes on
- go in a sauna
- take a trip to a tropical destination
- turn on an electric heater
- turn on the car heater
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|
1610.04377
| true
| null |
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 and labels for classification as stage one. Our dataset contains 1313 tweet with positive label and 1887 tweets with a negative label . We create another dataset with the positively labeled tweets and provide them with category labels like “fire”, “accident”, “earthquake” etc.
|
Are the tweets specific to a region?
|
No.
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1710.00341
| false
| null |
We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN.
We further use as features the embeddings of the claim, of the best-scoring snippet, and of the best-scoring sentence triplet from a Web page. We calculate these embeddings (i) as the average of the embeddings of the words in the text, and also (ii) using LSTM encodings, which we train for the task as part of a deep neural network (NN). We also use a task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN.
|
What algorithm and embedding dimensions are used to build the task-specific embeddings?
|
The answers are shown as follows:
* task-specific embedding of the claim together with all the above evidence about it, which comes from the last hidden layer of the NN
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null | false
| 267
|
Social media is a very popular way for people to express their opinions publicly and to interact with others online. In aggregation, social media can provide a reflection of public sentiment on various events. Unfortunately, many users engaging online, either on social media, forums or blogs, will often have the risk of being targeted or harassed via abusive language, which may severely impact their online experience and the community in general. The existence of social networking services creates the need for detecting user-generated hateful messages prior to publication. All published text that is used to express hatred towards some particular group with the intention to humiliate its members is considered a hateful message.
Although hate speech is protected under the free speech provisions in the United States, there are other countries, such as Canada, France, United Kingdom, and Germany, where there are laws prohibiting it as being promoting violence or social disorder. Social media services such as Facebook and Twitter have been criticized for not having done enough to prohibit the use of their services for attacking people belonging to some specific race, minority etc. BIBREF0 . They have announced though that they would seek to battle against racism and xenophobia BIBREF1 . Nevertheless, the current solutions deployed by them have attempted to address the problem with manual effort, relying on users to report offensive comments BIBREF2 . This not only requires a huge effort by human annotators, but it also has the risk of applying discrimination under subjective judgment. Moreover, a non-automated task by human annotators would have strong impact on system response times, since a computer-based solution can accomplish this task much faster than humans. The massive rise in the user-generated content in the above social media services, with manual filtering not being scalable, highlights the need for automating the process of on-line hate-speech detection.
Despite the fact that the majority of the solutions for automated detection of offensive text rely on Natural Language Processing (NLP) approaches, there is lately a tendency towards employing pure machine learning techniques like neural networks for that task. NLP approaches have the drawback of being complex, and to a large extent dependent on the language used in the text. This provides a strong motivation for employing alternative machine learning models for the classification task. Moreover, the majority of the existing automated approaches depend on using pre-trained vectors (e.g. Glove, Word2Vec) as word embeddings to achieve good performance from the classification model. That makes the detection of hatred content unfeasible in cases where users have deliberately obfuscated their offensive terms with short slang words.
There is a plethora of unsupervised learning models in the existing literature to deal with hate-speech BIBREF3 , as well as in detecting the sentiment polarity in tweets BIBREF4 . At the same time, the supervised learning approaches have not been explored adequately so far. While the task of sentence classification seems similar to that of sentiment analysis; nevertheless, in hate-speech even negative sentiment could still provide useful insight. Our intuition is that the task of hate-speech detection can be further benefited by the incorporation of other sources of information to be used as features into a supervised learning model. A simple statistical analysis on an existing annotated dataset of tweets by BIBREF5 , can easily reveal the existence of significant correlation between the user tendency in expressing opinions that belong to some offensive class (Racism or Sexism), and the annotation labels associated with that class. More precisely, the correlation coefficient value that describes such user tendency was found to be 0.71 for racism in the above dataset, while that value reached as high as 0.76 for sexism. In our opinion, utilizing such user-oriented behavioural data for reinforcing an existing solution is feasible, because such information is retrieva2ble in real-world use-case scenarios like Twitter. This highlights the need to explore the user features more systematically to further improve the classification accuracy of a supervised learning system.
Our approach employs a neural network solution composed of multiple Long-Short-Term-Memory (LSTM) based classifiers, and utilizes user behavioral characteristics such as the tendency towards racism or sexism to boost performance. Although our technique is not necessarily revolutionary in terms of the deep learning models used, we show in this paper that it is quite effective.
Our main contributions are: INLINEFORM0 ) a deep learning architecture for text classification in terms of hateful content, which incorporates features derived form the users' behavioural data, INLINEFORM1 ) a language agnostic solution, due to no-use of pre-trained word embeddings, for detecting hate-speech, INLINEFORM2 ) an experimental evaluation of the model on a Twitter dataset, demonstrating the top performance achieved on the classification task. Special focus is given to investigating how the additional features concerning the users' tendency to utter hate-speech, as expressed by their previous history, could leverage the performance. To the best of our knowledge, there has not been done any previous study on exploring features related to the users tendency in hatred content that used a deep learning model.
The rest of the paper is organized as follows. In Section SECREF2 we describe the problem of hate speech in more detail, and we refer to the existing work in the field in Section SECREF3 . In Section SECREF4 we present our proposed model, while in Section SECREF5 we refer to the dataset used, the evaluation tests we performed and we discuss the results received. Finally, in Section SECREF6 we summarize our contributions and discuss the future work.
Our main contributions are: INLINEFORM0 ) a deep learning architecture for text classification in terms of hateful content, which incorporates features derived form the users' behavioural data, INLINEFORM1 ) a language agnostic solution, due to no-use of pre-trained word embeddings, for detecting hate-speech, INLINEFORM2 ) an experimental evaluation of the model on a Twitter dataset, demonstrating the top performance achieved on the classification task.
|
What learning architecture is proposed in the paper?
|
A supervised learning architecture for text classification in terms of hateful content.
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null | false
| 377
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The FraCaS test suite is an NLI data set consisting of 346 inference problems. Each problem contains one or more premises followed by one yes/no-question. There is a three way classification: YES, NO or UNK (unknown, see Figure 1 for an example from FraCaS). The FraCaS test suite was later on turned into machine-readable format by Bill McCartney
Expansions of FraCaS include: a) MultiFraCaS, in effect a multilingual FraCaS, and b) JSem, the Japanese counterpart to FraCaS, which expands the original FraCaS in a number of ways.
Even though the FraCaS test suite contains a rather small number of examples (346), it covers a lot of NLI cases and is, at least to some extent, multilingual. It is to some extent precise, even though there are test cases that do not involve a clear answer and thus are dubbed as undefined in Bill MacCartney's XML version. A further drawback of the FraCaS test stuite is that it involves constructed examples, rather than real text.
An UNK example from the FraCaS test suite.
A Scandinavian won the Nobel Prize.
Every Swede is Scandinavian.
Did a Swede win the Nobel prize?
A Swede won the Nobel prize.
UNK [FraCaS 065]
The FraCaS test suite is an NLI data set consisting of 346 inference problems.
|
What does the FraCaS test suite consist of?
|
The FraCaS test suite is an NLI data set consisting of 346 inference problems.
|
null | false
| null |
Who directed the Game of Thrones season three episode titled "Kissed by Fire"?
|
Alex Graves directed "Kissed by Fire." He also directed the season three episode "And Now His Watch Is Ended."
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null | false
| null |
Prince introduced his iconic symbol on the cover of which single?
|
1999.
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null | false
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Largest Earthquakes ever recorded
The 10 largest magnitudes in recorded history
1. Valdivia, Chile 22 May 1960 (magnitude 9.5)
This earthquake killed 1655 people, injured 3000 and displaced two million. It caused US$550 million damage in Chile, while the tsunami that it spawned caused deaths and damage as far away as Hawaii, Japan and the Philippines. The 'rupture zone' of the quake was more than 1000 km long. Two days after the initial quake, the nearby volcano Puyehue erupted, sending ash and steam up to 6 km into the atmosphere over a period of several weeks.
2. Prince William Sound, Alaska 28 March 1964 (magnitude 9.2)
Compared to the Chilean earthquake, this earthquake was less damaging: the resulting tsunami took 128 lives and caused overall US$311 million in damage. The earthquake was felt mainly over Alaska, as well as some places in Canada, while the tsunami created by it caused damage as far away as Hawaii. The most damage was sustained by the city of Anchorage, 120 km north-west of the epicentre. Shaking from the quake itself is reported to have lasted for three minutes.
3. Sumatra, Indonesia 26 December 2004 (magnitude 9.1)
In terms of damage and loss of life, the scale of the disaster caused by the resulting Boxing Day Tsunami was enormous. In total, 227,900 people were killed or presumed dead, with around 1.7 million displaced over 14 countries in South Asia and East Africa. The epicentre was 250 km south-east of Band Aceh, Indonesia, at a depth of 30 km. Several days later on 28 December, a mud volcano began erupting near Baratang, Andamar Islands, which is thought to have been associated with the earthquake.
4. Sendai, Japan 11 March 2011 (magnitude 9.0)
So far the official death toll stands at several thousand from the combined effect of the powerful earthquake, aftershocks and the tsunami. However, the total is expected to rise, with some estimates of a final toll of over 10,000. Economic impacts are expected to be huge, with the shutting down of nuclear reactors which many industries rely on for power.
5. Kamchatka, Russia 4 November 1952 (magnitude 9.0)
This earthquake generated a tsunami that caused widespread damage in the Hawaiian Islands. Property damage was estimated at around US$1,000,000. Some reports describe waves of over 9 m high at Kaena Point, Oahu. A farmer on Oahu reported the loss of six cows to the tsunami, but no people were reported killed.
6. Bio-bio, Chile 27 February 2010 (magnitude 8.8)
This earthquake and subsequent tsunami killed at least 521 people, with 56 missing and 12,000 injured. More than 800,000 people were displaced with a total of 1.8m people affected across Chile, where damage was estimated at US$30 billion. The epicentre was 335 km south-west of Santiago, at a depth of 35 km. A minor tsunami travelled across the Pacific causing damage to boats as far away as San Diego, California.
7. Off the coast of Ecuador 31 January 1906 (magnitude 8.8)
This earthquake caused a tsunami that is reported to have killed between 500 and 1,500 in Ecuador and Colombia. The tsunami travelled as far north as San Francisco, on the west coast of the US, and west to Hawaii and Japan. The tsunami took roughly 12 hours to cross the Pacific to Hilo, Hawaii.
8. Rat Islands, Alaska 2 April 1965 (magnitude 8.7)
The worst of the damage attributed to this earthquake was caused by a tsunami, reported to be about 10 m high on Shemya Island. The wave caused flooding on Amchitka Island, causing US$10,000 in property damage. No deaths or injuries were reported.
9. Sumatra, Indonesia 28 March 2005 (magnitude 8.6)
This earthquake killed 1313, with over 400 people injured by the tsunami as far away as Sri Lanka. The epicentre was 205 km north-west of Sibolga, Sumatra, at a depth of 30 km. This region, also the site of the 2004 Boxing Day Tsunami, is particularly geologically active, with three of the 15 biggest known earthquakes having happened here.
10. Assam - Tibet 15 August 1950 (magnitude 8.6)
This inland earthquake caused widespread damages to buildings as well as large landslides. 780 people were killed in eastern Tibet, with many villages and towns affected across Assam, China, Tibet and India. Oscillations to lake levels occurred as far away as Norway. The total death toll is likely to be higher, as no definitive total was ever estimated. While the earthquake itself is known as the Assam Earthquake, it is believed the epicentre may have been in Tibet.
|
Which of the 10 largest earthquakes was the deadliest?
|
The 2004 earthquake on Sumatra, Indonesia was the deadliest. It caused a tsunami that killed more than 200,000 people.
|
1707.06878
| true
| null |
Figure 1 presents architecture of the WSD system. As one may observe, no human labor is used to learn interpretable sense representations and the corresponding disambiguation models. Instead, these are induced from the input text corpus using the JoBimText approach BIBREF8 implemented using the Apache Spark framework, enabling seamless processing of large text collections. Induction of a WSD model consists of several steps. First, a graph of semantically related words, i.e. a distributional thesaurus, is extracted. Second, word senses are induced by clustering of an ego-network of related words BIBREF9 . Each discovered word sense is represented as a cluster of words. Next, the induced sense inventory is used as a pivot to generate sense representations by aggregation of the context clues of cluster words. To improve interpretability of the sense clusters they are labeled with hypernyms, which are in turn extracted from the input corpus using Hearst:92 patterns. Finally, the obtained WSD model is used to retrieve a list of sentences that characterize each sense. Sentences that mention a given word are disambiguated and then ranked by prediction confidence. Top sentences are used as sense usage examples. For more details about the model induction process refer to BIBREF10 . Currently, the following WSD models induced from a text corpus are available: Word senses based on cluster word features. This model uses the cluster words from the induced word sense inventory as sparse features that represent the sense.
Word senses based on context word features. This representation is based on a sum of word vectors of all cluster words in the induced sense inventory weighted by distributional similarity scores.
Super senses based on cluster word features. To build this model, induced word senses are first globally clustered using the Chinese Whispers graph clustering algorithm BIBREF9 . The edges in this sense graph are established by disambiguation of the related words BIBREF11 , BIBREF12 . The resulting clusters represent semantic classes grouping words sharing a common hypernym, e.g. “animal”. This set of semantic classes is used as an automatically learned inventory of super senses: There is only one global sense inventory shared among all words in contrast to the two previous traditional “per word” models. Each semantic class is labeled with hypernyms. This model uses words belonging to the semantic class as features.
Super senses based on context word features. This model relies on the same semantic classes as the previous one but, instead, sense representations are obtained by averaging vectors of words sharing the same class.
Currently, the following WSD models induced from a text corpus are available: Word senses based on cluster word features. This model uses the cluster words from the induced word sense inventory as sparse features that represent the sense.
Word senses based on context word features. This representation is based on a sum of word vectors of all cluster words in the induced sense inventory weighted by distributional similarity scores.
Super senses based on cluster word features. To build this model, induced word senses are first globally clustered using the Chinese Whispers graph clustering algorithm BIBREF9 . The edges in this sense graph are established by disambiguation of the related words BIBREF11 , BIBREF12 . The resulting clusters represent semantic classes grouping words sharing a common hypernym, e.g. “animal”. This set of semantic classes is used as an automatically learned inventory of super senses: There is only one global sense inventory shared among all words in contrast to the two previous traditional “per word” models. Each semantic class is labeled with hypernyms. This model uses words belonging to the semantic class as features.
Super senses based on context word features. This model relies on the same semantic classes as the previous one but, instead, sense representations are obtained by averaging vectors of words sharing the same class.
|
Do they use a neural model for their task?
|
No.
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In August 1902, he was charged under the Offences against the Person Act 1861 with incitement to murder - during the course of a speech against an eviction - of Major-General Devinish Meares. That December, he was found guilty and sentenced to eight months' imprisonment. After the passing of the Evicted Tenants Act, 1908, some of this holding, but not his house, was returned to him
|
Who was charged in August 1902, under the Offences against the Person Act 1861 with incitement to murder?
|
Denis Kilbride (September 1848 – 24 October 1924) was an Irish nationalist politician, who as member of the Irish Parliamentary Party represented South Kerry (1887–1895), and North Galway (1895–1900) and South Kildare (1903–1918) as a Member of Parliament (MP) in the House of Commons of the United Kingdom of Great Britain and Ireland.
He was educated at Clongowes Wood. He was evicted, along with other subtenants of the 5th Marquess of Lansdowne, was evicted from his 500-acre (2.0 km2) holding during the Luggacurran evictions, Stradbally, County Laois, in March 1887.
Kilbride's rent was 760 pounds a year, although the holding was valued at only 450. Although he was a large tenant, he and other tenants adopted the Plan of Campaign.
To raise awareness of the evictions, Kilbride travelled with William O'Brien to Canada, where the Marquess of Lansdowne was governor general, and the USA.
In August 1902, he was charged under the Offences against the Person Act 1861 with incitement to murder - during the course of a speech against an eviction - of Major-General Devinish Meares. That December, he was found guilty and sentenced to eight months' imprisonment. After the passing of the Evicted Tenants Act, 1908, some of this holding, but not his house, was returned to him.
He died at his residence in Luggacurran in 1924 aged 76 and was buried in Clopook cemetery.
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The Macquarie University Real Tennis Club (formerly the Sydney Real Tennis Club) played real tennis at Macquarie University in Sydney, Australia. Its only court closed on 31 December 2005, converted to other athletic uses. However, the university has agreed to provide land for another court as well as A$350,000 toward its construction. The club is working to provide additional funds required to begin construction.
|
Given this paragraph on Macquarie University Real Tennis Club, please tell me how many tennis courts the club had.
|
Based on this paragraph, it appears that the Macquarie University Real Tennis Club had only one tennis court.
|
1608.03902
| false
| null |
To compare our neural models with the traditional approaches, we experimented with a number of existing models including: Support Vector Machine (SVM), a discriminative max-margin model; Logistic Regression (LR), a discriminative probabilistic model; and Random Forest (RF), an ensemble model of decision trees. We use the implementation from the scikit-learn toolkit BIBREF19 . All algorithms use the default value of their parameters.
To compare our neural models with the traditional approaches, we experimented with a number of existing models including: Support Vector Machine (SVM), a discriminative max-margin model; Logistic Regression (LR), a discriminative probabilistic model; and Random Forest (RF), an ensemble model of decision trees.
|
what was their baseline comparison?
|
The answers are shown as follows:
* Support Vector Machine (SVM)
* Logistic Regression (LR)
* Random Forest (RF)
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Does Bolivia have a wine scene?
|
Bolivia and more specifically, Tarija, have a wonderful wine scene that has continued to develop in the shadow of its southern neighbor, Mendoza. The high altitudes and clay soil produce many excellent varietals of grape, of which they are most predominantly known for their, Tannat, a full bodied red wine originating from France and thriving in South America.
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Why is the sky blue?
|
Blue light is scattered in all directions by the tiny molecules of air in Earth's atmosphere. Blue is scattered more than other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time.
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| 33
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Measures of semantic similarity can be classified into three broad categories : path–based, feature–based and information content (IC). Path–based similarity measures use the structure of a taxonomy to measure similarity – concepts positioned close to each other are more similar than those further apart. Feature–based methods rely on set theoretic measures of overlap between features (union and intersection). The information content measures quantify the amount of information that a concept provides – more specific concepts have a higher amount of information content.
RadaMBB89 introduce the Conceptual Distance measure. This measure is simply the length of the shortest path between two concepts ( INLINEFORM0 and INLINEFORM1 ) in the MeSH hierarchy. Paths are based on broader than (RB) and narrower than (RN) relations. CaviedesC04 extends this measure to use parent (PAR) and child (CHD) relations. Our INLINEFORM2 measure is simply the reciprocal of this shortest path value (Equation EQREF3 ), so that larger values (approaching 1) indicate a high degree of similarity. DISPLAYFORM0
While the simplicity of INLINEFORM0 is appealing, it can be misleading when concepts are at different levels of specificity. Two very general concepts may have the same path length as two very specific concepts. WuP94 introduce a correction to INLINEFORM1 that incorporates the depth of the concepts, and the depth of their Least Common Subsumer (LCS). This is the most specific ancestor two concepts share. In this measure, similarity is twice the depth of the two concept's LCS divided by the product of the depths of the individual concepts (Equation EQREF4 ). Note that if there are multiple LCSs for a pair of concepts, the deepest of them is used in this measure. DISPLAYFORM0
ZhongZLY02 take a very similar approach and again scale the depth of the LCS by the sum of the depths of the two concepts (Equation EQREF5 ), where INLINEFORM0 . The value of INLINEFORM1 was set to 2 based on their recommendations. DISPLAYFORM0
PekarS02 offer another variation on INLINEFORM0 , where the shortest path of the two concepts to the LCS is used, in addition to the shortest bath between the LCS and the root of the taxonomy (Equation EQREF6 ). DISPLAYFORM0
Feature–based methods represent each concept as a set of features and then measure the overlap or sharing of features to measure similarity. In particular, each concept is represented as the set of their ancestors, and similarity is a ratio of the intersection and union of these features.
MaedcheS01 quantify the similarity between two concepts as the ratio of the intersection over their union as shown in Equation EQREF8 . DISPLAYFORM0
BatetSV11 extend this by excluding any shared features (in the numerator) as shown in Equation EQREF9 . DISPLAYFORM0
Information content is formally defined as the negative log of the probability of a concept. The effect of this is to assign rare (low probability) concepts a high measure of information content, since the underlying assumption is that more specific concepts are less frequently used than more common ones.
Resnik95 modified this notion of information content in order to use it as a similarity measure. He defines the similarity of two concepts to be the information content of their LCS (Equation EQREF11 ). DISPLAYFORM0
JiangC97, Lin98, and PirroE10 extend INLINEFORM0 by incorporating the information content of the individual concepts in various different ways. Lin98 defines the similarity between two concepts as the ratio of information content of the LCS with the sum of the individual concept's information content (Equation EQREF12 ). Note that INLINEFORM1 has the same form as INLINEFORM2 and INLINEFORM3 , and is in effect using information content as a measure of specificity (rather than depth). If there is more than one possible LCS, the LCS with the greatest IC is chosen. DISPLAYFORM0
JiangC97 define the distance between two concepts to be the sum of the information content of the two concepts minus twice the information content of the concepts' LCS. We modify this from a distance to a similarity measure by taking the reciprocal of the distance (Equation EQREF13 ). Note that the denominator of INLINEFORM0 is very similar to the numerator of INLINEFORM1 . DISPLAYFORM0
PirroE10 define the similarity between two concepts as the information content of the two concept's LCS divided by the sum of their individual information content values minus the information content of their LCS (Equation EQREF14 ). Note that INLINEFORM0 can be viewed as a set–theoretic version of INLINEFORM1 . DISPLAYFORM0
Feature–based methods rely on set theoretic measures of overlap between features (union and intersection).
|
What do feature–based methods rely on?
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Feature–based methods rely on set theoretic measures of overlap between features (union and intersection).
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Why practicing sport is important ?
|
Practicing sport is important for your physical health and mental health. It teaches you to set goals and and how to reach them. You will learn discipline, consistency and regularity. But it's also very important for you physical health because of the endorphins and dopamina it secretes. It regulates your blood pressure level. It will reduce your stress, improve your sleep and you will feel happier.
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What are the steps I should take to become a data engineer for absolute beginner?
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1. Learn about Database systems and how data is store. (Get to know some history too of early Database Management Systems (DBMS))
2. Learn SQL.
3. Learn tuning and how to write efficient queries
4. Learn Python.
5. Learn a distributed data processing framework like Spark.
6. Do some personal projects.
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| 287
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Adverse drug reactions (ADR)—unwanted or harmful reactions resulting from correct medical drug use—present a significant and costly public health problem. BIBREF0 Detecting, assessing, and preventing these events are the tasks of pharmacovigilance. In the pre-trial and trial stages of drug development, the number of people taking a drug is carefully controlled, and the collection of ADR data is centralized. However, after the drug is available widely, post-marketing surveillance often requires the collection and merging of data from disparate sources, BIBREF1 including patient-initiated spontaneous reporting. Unfortunately, adverse reactions to drugs are grossly underreported to health professionals. BIBREF2 , BIBREF3 Considerable issues with patient-initiated reporting have been identified, including various types of reporting biases and causal attributions of adverse events. BIBREF4 , BIBREF5 , BIBREF6 Nevertheless, a large number of people, freely and spontaneously, report ADRs on social media. The potential availability of inexpensive, large-scale, and real-time data on ADRs makes social media a valuable resource for pharmacovigilance.
Information required for pharmacovigilance includes a reported adverse drug reaction, a linked drug referred to by its full, abbreviated, or generic name, and an indication whether it was the social media post author that experienced the adverse event. However, there are considerable challenges in automatically extracting this information from free-text social media data. Social media texts are often short and informal, and include non-standard abbreviations and creative language. Drug names or their effects may be mis-spelled; they may be used metaphorically (e.g., Physics is like higher level maths on steroids). Drug names might have other non-drug related meanings (e.g., ecstasy). An adverse event may be negated or only expected (e.g., I bet I'll be running to the bathroom all night), or it may not apply to the author of the post at all (e.g., a re-tweet of a press release).
The shared task challenge organized as part of the AMIA-2017 Workshop on Social Media Mining for Health Applications (SMM4H) focused on Twitter data and had three tasks: Task 1 - recognizing whether a tweet is reporting an adverse drug reaction, Task 2 - inferring whether a tweet is reporting the intake of a medication by the tweeter, and Task 3 - mapping a free-text ADR to a standardized MEDDRA term. Our team made submissions for Task 1 and Task 2. For both tasks, we trained Support Vector Machine classifiers using a variety of surface-form, sentiment, and domain-specific features. Handling class imbalance with under-sampling was particularly helpful. Our submissions obtained F-scores of 0.435 on Task 1 and 0.673 on Task 2, resulting in a rank of first and third, respectively. (Nine teams participated in each task.) We make the resources created as part of this project freely available at the project webpage: http://saifmohammad.com/WebPages/tweets4health.htm.
The shared task challenge organized as part of the AMIA-2017 Workshop on Social Media Mining for Health Applications (SMM4H) focused on Twitter data and had three tasks: Task 1 - recognizing whether a tweet is reporting an adverse drug reaction, Task 2 - inferring whether a tweet is reporting the intake of a medication by the tweeter, and Task 3 - mapping a free-text ADR to a standardized MEDDRA term.
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What are the three tasks in the shared task challenge organized as part of the AMIA-2017 Workshop on Social Media Mining for Health Applications?
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Task 1 - recognizing whether a tweet is reporting an adverse drug reaction, Task 2 - inferring whether a tweet is reporting the intake of a medication by the tweeter, and Task 3 - mapping a free-text ADR to a standardized MEDDRA term.
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The greater blue-ringed octopus (Hapalochlaena lunulata) is one of four species of extremely venomous blue-ringed octopuses belonging to the family Octopodidae. This particular species of blue-ringed octopus is known as one of the most toxic marine animals in the world.
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How many arms does Hapalochlaena lunulata have?
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Hapalochlaena lunulata is an octopus, so it must have eight arms.
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In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array. If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. If the search ends with the remaining half being empty, the target is not in the array.
The binary search runs in logarithmic time in the worst case, making
O(log n) comparisons, where
n is the number of elements in the array. Binary search is faster than linear search except for small arrays. However, the array must be sorted first to apply binary search. There are specialized data structures designed for fast searching, such as hash tables, that can be searched more efficiently than binary search. However, binary search can solve a wider range of problems, such as finding the next-smallest or next-largest element in the array relative to the target even if it is absent from the array.
There are numerous variations of binary search. In particular, fractional cascading speeds up binary searches for the same value in multiple arrays. Fractional cascading efficiently solves several search problems in computational geometry and numerous other fields. Exponential search extends binary search to unbounded lists. The binary search tree and B-tree data structures are based on binary search.
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What is binary search ?
|
Binary search is a search algorithm that finds the position of a target value in a sorted array. It eliminates the half in which the target value cannot lie. It searches the value in the other half. This continues by repeating the middle element to compare to the target value until the target value is found.
Binary search is faster than linear search except for small arrays.
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Think of 5 different ways I can cook a chicken breast without a traditional oven or stove.
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Here are 5 ways you could get creative and cook chicken without a stove:
1. Make a small fire and roast the chicken. Be sure to be outdoors!
2. Travel to a location in a desert climate (like Arizona) and place the chicken on the hood of your car on a hot day.
3. Put the chicken in the microwave for a really really long time.
4. Refract the sun through a magnifying glass at the chicken for a very long time.
5. And my personal favorite, bring your chicken to a science lab and roast it over a Bunsen burner!
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The standard approach to multilingual NLP is to design a single architecture, but tune and train a separate model for each language. While this method allows for customizing the model to the particulars of each language and the available data, it also presents a problem when little data is available: extensive language-specific annotation is required. The reality is that most languages have very little annotated data for most NLP tasks.
ammar2016malopa found that using training data from multiple languages annotated with Universal Dependencies BIBREF1 , and represented using multilingual word vectors, outperformed monolingual training. Inspired by this, we apply the idea of training one model on multiple languages—which we call polyglot training—to PropBank-style semantic role labeling (SRL). We train several parsers for each language in the CoNLL 2009 dataset BIBREF0 : a traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset. To our knowledge, this is the first multilingual SRL approach to combine supervision from several languages.
The CoNLL 2009 dataset includes seven different languages, allowing study of trends across the same. Unlike the Universal Dependencies dataset, however, the semantic label spaces are entirely language-specific, making our task more challenging. Nonetheless, the success of polyglot training in this setting demonstrates that sharing of statistical strength across languages does not depend on explicit alignment in annotation conventions, and can be done simply through parameter sharing. We show that polyglot training can result in better labeling accuracy than a monolingual parser, especially for low-resource languages. We find that even a simple combination of data is as effective as more complex kinds of polyglot training. We include a breakdown into label categories of the differences between the monolingual and polyglot models. Our findings indicate that polyglot training consistently improves label accuracy for common labels.
We find that even a simple combination of data is as effective as more complex kinds of polyglot training.
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Is a simple combination of data as effective as more complex kinds of polyglot training?
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Yes.
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| 339
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For an ever increasing spectrum of applications (e.g., medical text analysis, opinion mining, sentiment analysis, social media text analysis, customer intelligence, fraud analytics etc.) mining and analysis of unstructured natural language text data is necessary BIBREF0, BIBREF1, BIBREF2.
One of key challenge while designing such text analytics (TA) applications is to identify right set of features. For example, for text classification problem, different sets of features have been considered in different works (spanning a history of more than twenty years) including `bag of words', `bag of phrases', `bag of n-grams', `WordNet based word generalizations', and `word embeddings' BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. Even for recent end-to-end designs using deep neural networks, specification of core features remains manually driven BIBREF8, BIBREF9. During feature engineering, often data scientists manually determine which features to use based upon their experience and expertise with respect to the underlying application domain as well as state-of-the-art tools and techniques. Different tools (e.g., NLTK BIBREF10, Mallet BIBREF11, Stanford CoreNLP BIBREF12, Apache OpenNLP BIBREF13, Apache Lucene BIBREF14, etc.) available to a NLP data scientist for TA application design and development often differ in terms of support towards extraction of features, level of granularity at which feature extraction process is to be specified; and these tools often use different programing vocabularies to specify semantically equivalent features.
Currently, there is no generic method or approach, which can be applied during TA application's design process to define and extract features for any arbitrary application in an automated or semi-automated manner. Even there is no single way to express wide range of NLP features, resulting into increased efforts during feature engineering which has to start new for each data scientist and automated reuse of features across semantically similar or related applications designed by different data scientists is difficult. This also hinders foundational studies on NLP feature engineering including why certain features are more critical than others.
In this paper, we aim to present an approach towards automating NLP feature engineering. We start with an outline of a language for expressing NLP features abstracting over the feature extraction process, which often implicitly captures intent of the NLP data scientist to extract specific features from given input text. We next discuss a method to enable automated reuse of features across semantically related applications when a corpus of feature specifications for related applications is available. Proposed language and system would help achieving reduction in manual effort towards design and extraction of features, would ensure standardization in feature specification process, and could enable effective reuse of features across similar and/or related applications.
One of key challenge while designing such text analytics (TA) applications is to identify right set of features.
|
What is one of the key challenges while designing such text analytics (TA) applications?
|
One of key challenge while designing such text analytics (TA) applications is to identify right set of features.
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Classify the cities based on whether they are located in Eritrea or Gabon.
Keren, Franceville, Asmara, Port-Gentil, Assab, Libreville
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Eritrea - Asmara, Keren, Assab
Gabon - Libreville, Port-Gentil, Franceville
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What is a trumpet?
|
A trumpet is horned instrument that is made out of brass. It is used in classical music and it is also very prevalent in jazz. You have trumpets like the piccolo that have a very high sound and a bass trumpet which has a very low sound. Trumpets can be played in a solo or in a group with other instruments.
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| 388
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The current coverage of the political landscape in the press and in social media has led to an unprecedented situation. Like never before, a statement in an interview, a press release, a blog note, or a tweet can spread almost instantaneously and reach the public in no time. This proliferation speed has left little time for double-checking claims against the facts, which has proven critical in politics, e.g., during the 2016 presidential campaign in the USA, which was arguably impacted by fake news in social media and by false claims.
Investigative journalists and volunteers have been working hard trying to get to the root of a claim and to present solid evidence in favor or against it. Manual fact-checking has proven very time-consuming, and thus automatic methods have been proposed as a way to speed-up the process. For instance, there has been work on checking the factuality/credibility of a claim, of a news article, or of an information source BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. However, less attention has been paid to other steps of the fact-checking pipeline, which is shown in Figure FIGREF1.
The process starts when a document is made public. First, an intrinsic analysis is carried out in which check-worthy text fragments are identified. Then, other documents that might support or rebut a claim in the document are retrieved from various sources. Finally, by comparing a claim against the retrieved evidence, a system can determine whether the claim is likely true or likely false. For instance, BIBREF8 do this on the basis of a knowledge graph derived from Wikipedia. The outcome could then be presented to a human expert for final judgment.
In this paper, we focus on the first step: predicting check-worthiness of claims. Our contributions can be summarized as follows:
New dataset: We build a new dataset of manually-annotated claims, extracted from the 2016 US presidential and vice-presidential debates, which we gathered from nine reputable sources such as CNN, NPR, and PolitiFact, and which we release to the research community.
Modeling the context: We develop a novel approach for automatically predicting which claims should be prioritized for fact-checking, based on a rich input representation. In particular, we model not only the textual content, but also the context: how the target claim relates to the current segment, to neighboring segments and sentences, and to the debate as a whole, and also how the opponents and the public react to it.
State-of-the-art results: We achieve state-of-the-art results, outperforming a strong rivaling system by a margin, while also demonstrating that this improvement is due primarily to our modeling of the context.
We model the problem as a ranking task, and we train both Support Vector Machines (SVM) and Feed-forward Neural Networks (FNN) obtaining state-of-the-art results. We also analyze the relevance of the specific feature groups and we show that modeling the context yields a significant boost in performance. Finally, we also analyze whether we can learn to predict which facts are check-worthy with respect to each of the individual media sources, thus capturing their biases. It is worth noting that while trained on political debates, many features of our model can be potentially applied to other kinds of information sources, e.g., interviews and news.
The rest of the paper is organized as follows: Section SECREF2 discusses related work. Section SECREF3 describes the process of gathering and annotating our political debates dataset. Section SECREF4 presents our supervised approach to predicting fact-checking worthiness, including the explanation of the model and the information sources we use. Section SECREF5 presents the evaluation setup and discusses the results. Section SECREF6 provides further analysis. Finally, Section SECREF7 presents the conclusions and outlines some possible directions for future research.
Finally, we also analyze whether we can learn to predict which facts are check-worthy with respect to each of the individual media sources, thus capturing their biases.
|
Do the authors analyze whether they can learn to predict which facts are check-worthy with respect to each of the individual media sources?
|
Yes, they do.
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| 27
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SVM: A linear support vector machine with bag-of-words sentence representations.
MLP: A multi-layer perceptron with USE embeddings BIBREF4 as input.
FastText: A shallow neural network that averages embeddings of n-grams BIBREF5.
CNN: A convolutional neural network with non-static word embeddings initialized with GloVe BIBREF6.
BERT: A neural network that is trained to predict elided words in text and then fine-tuned on our data BIBREF1.
Platforms: Several platforms exist for the development of task-oriented agents. We consider Google's DialogFlow and Rasa NLU with spacy-sklearn.
SVM: A linear support vector machine with bagof-words sentence representations. MLP: A multi-layer perceptron with USE embeddings (Cer et al., 2018) as input. FastText: A shallow neural network that averages embeddings of n-grams (Joulin et al., 2017). CNN: A convolutional neural network with nonstatic word embeddings initialized with GloVe (Pennington et al., 2014). BERT: A neural network that is trained to predict elided words in text and then fine-tuned on our data (Devlin et al., 2019). Platforms: Several platforms exist for the development of task-oriented agents. We consider Google’s DialogFlow4 and Rasa NLU5 with spacy-sklearn.
|
What are the Classifier Models in this paper?
|
They are SVM, MLP, FastText, CNN, BERT and Platforms.
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| 13
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A perfect data collection scenario for our dataset would use real running dialog system providing general information from the knowledge base to real users. This system could then ask for explanations and answers for questions which it is not able to answer.
However, getting access to systems with real users is usually hard. Therefore, we used the crowdsourcing platform CrowdFlower (CF) for our data collection.
A CF worker gets a task instructing them to use our chat-like interface to help the system with a question which is randomly selected from training examples of Simple questions BIBREF7 dataset. To complete the task user has to communicate with the system through the three phase dialog discussing question paraphrase (see Section "Interactive Learning Evaluation" ), explanation (see Section "Future Work" ) and answer of the question (see Section "Conclusion" ). To avoid poor English level of dialogs we involved CF workers from English speaking countries only. The collected dialogs has been annotated (see Section "Acknowledgments" ) by expert annotators afterwards.
The described procedure leads to dialogs like the one shown in the Figure 2 .
However, getting access to systems with real users is usually hard. Therefore, we used the crowdsourcing platform CrowdFlower (CF) for our data collection.
|
What approach does the author take to the problem that it is often difficult to access the system with real users?
|
They used the crowdsourcing platform CrowdFlower (CF) for their data collection.
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| 424
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We briefly review continuous modern Hopfield networks that are used for deep learning architectures.
They are continuous and differentiable, therefore they a work with gradient descent in deep architec_x005f_x0002_tures. They retrieve with one update only, therefore they can be activated like other deep learning layers. They have exponential storage capacity, therefore they can tackle large problems. Hopfield networks are energy-based, binary associative memories, which popularized artificial neural networks in the 1980s (Hopfield, 1982; 1984). Associative memory networks have been designed to store and
retrieve samples. Their storage capacity can be considerably increased by polynomial terms in the energy function (Chen et al., 1986; Psaltis & Cheol, 1986; Baldi & Venkatesh, 1987; Gardner, 1987; Abbott & Arian, 1987; Horn & Usher, 1988; Caputo & Niemann, 2002; Krotov & Hopfield, 2016). In contrast to these binary memory networks, we use continuous associative memory networks with very high storage capacity. These modern Hopfield networks for deep learning architectures have an energy function with continuous states and can retrieve samples with only one update (Ramsauer et al., 2021; 2020). Modern Hopfield Networks have been successfully applied to immune repertoire classification (Widrich et al., 2020) and chemical reaction prediction (Seidl et al., 2021).
|
What exactly is a modern Hopfield network?
|
The reviewer is absolutely right. The explanation of modern Hopfield networks was very superficial to non-existing. We now write in the paper: “Hopfield networks are energy-based, binary associative memories, which popularized artificial neural networks in the 1980s (Hopfield,1982; 1984). Associative memory networks have been designed to store and retrieve samples. Their storage capacity can be considerably increased by polynomial terms in the energy function (Chen et al., 1986; Psaltis & Cheol, 1986; Baldi & Venkatesh, 1987; Gardner, 1987; Abbott & Arian, 1987; Horn & Usher, 1988; Caputo & Niemann, 2002; Krotov & Hopfield, 2016). In contrast to these binary memory networks, we use continuous associative memory networks with very high storage capacity. These modern Hopfield networks for deep learning architectures have an energy function with continuous states and can retrieve samples with only one update (Ramsauer et al., 2021; 2020). Modern Hopfield Networks have already been successfully applied to immune repertoire classification (Widrich et al., 2020) and chemical reaction prediction (Seidl et al., 2021)”
We also added to the paper: “Modern Hopfield networks is a new concept for contrastive learning. In bioinformatics the covariance structure in a sequence is reinforced by first retrieving similar sequences from a database and then aligning them. Conserved regions are characterized by high local covariance in the alignment (Dickson & Gloor, 2012; Kreth & Fodor, 2014). Modern Hopfield networks detect high covariances of embedded features, which is conveyed by the retrieved sample that corresponds to an alignment.”
Furthermore, we give a review of modern Hopfield networks in the Appendix A.4.
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Are the following books fantasy or science fiction: Red Mars, The City We Became, The Three-Body Problem, the Harry Potter series, A Game of Thrones, Dune, The Fifth Season.
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These books are science fiction: Red Mars, The Three-Body Problem, and Dune, because they involve space travel and life on other planets. In addition, Red Mars and The Three-Body Problem include discussions of hard science.
These books are fantasy: The City We Became, the Harry Potter series, and A Game of Thrones, because they involve magic and beings with supernatural powers. In addition, A Game of Thrones takes place in a different world that is not related to space travel.
The Fifth Season is considered "science fantasy", because it includes elements of both science fiction and fantasy. It takes place on a planet that is not Earth, but is not reached by space travel. Some of its characters have special powers. However, the plot depends on the hard science of plate tectonics.
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How do you do meal prep efficiently and make cooking easy?
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Doing meal prep efficiently is mainly about getting things prepared before your cooking time and use the right kitchen appliances to free you up. You will want to prepare the ingratiations for multiple meals together. Bulk cutting and seasoning your vegetables and meats first. Put them in containers with the same dimensions like silicon bags in your freezer or refrigerator. Then, when you need to cook, you can start cooking right away. Also, use kitchen appliances like Instant Pot and oven can help you free up your hands to do other things. There are lots of Instant Pot or one pan recipes online to help you get inspired.
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How to introduce a new cat to the household if you already have cats?
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Confine the new cat to a single room. Make sure the confined cat have access to everything he or she needs, including water, food, bedding, litterbox and toys. Feed your new and existing cats across the closed door, so that the cats can smell each other and associate each other's presence with food and happiness. Use synthetic feline facial pheromone in both the new cat’s room and in the area where the resident cat spends the most time will help to create feelings of familiarity and security within the physical environment. After a while, exchange pieces of bedding between the resident cats and the new cat so that the cats start to recognize each others scents. Slowly allow cats to interact with each other under supervision to ensure the cats build early positive experiences together.
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Identify which instrument is string or percussion: Bougarabou, Haegeum
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Haegeum is string, Bougarabou is percussion.
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Give me 3 ideas for an instant serotonin boost.
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If you want an instant boost of serotonin, try the following things:
1. Work in a few minutes of exercise, even a short walk can help boost your mood.
2. Take yourself out for your favorite treat, whether it's a meal at your favorite restaurant, going to your favorite coffee shop, or picking up your favorite ice cream.
3. Think of a happy memory and remember the events of that day.
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1909.11467
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KTC is composed of 31 educational textbooks published from 2011 to 2018 in various topics by the MoE. We received the material from the MoE partly in different versions of Microsoft Word and partly in Adobe InDesign formats. In the first step, we categorized each textbook based on the topics and chapters. As the original texts were not in Unicode, we converted the content to Unicode. This step was followed by a pre-processing stage where the texts were normalized by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography based on the reference orthography of the Kurdistan Region of Iraq. In the normalization process, we did not remove punctuation and special characters so that the corpus can be easily adapted our current task and also to future tasks where the integrity of the text may be required.
This step was followed by a pre-processing stage where the texts were normalized by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography based on the reference orthography of the Kurdistan Region of Iraq.
|
How is the corpus normalized?
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The answers are shown as follows:
* by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography
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classify each of the following strings as positive or negative: ':)', ':(', 'i love you', 'i do not like you', 'happy', 'sad'
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positive: ':)', 'i love you', 'happy'
negative: ':(', 'i do not like you', 'sad'
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Ortiz was born on November 18, 1975, in Santo Domingo, Dominican Republic, as the oldest of four children of Enrique (Leo) Ortiz and Ángela Rosa Arias. As a boy, he followed the careers of standout pitcher Ramón Martinez and his younger brother Pedro, attending games whenever he could and building a friendship with Pedro that would only grow over the years. Ortiz graduated from Estudia Espaillat High School in the Dominican Republic, and was a standout baseball and basketball player there.
https://en.wikipedia.org/wiki/David_Ortiz
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Given this reference text, what sport other than baseball did David Ortiz play growing up, how many children did his parents have, and what age is he today?
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David Ortiz played basketball in addition to baseball. His parents had five total children, and today (on April 5, 2023) he is 47 years old.
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1905.10810
| false
| null |
PlEWi BIBREF20 is an early version of WikEd BIBREF21 error corpus, containing error type annotations allowing us to select only non-word errors for evaluation. Specifically, PlEWi supplied 550,755 [error, correction] pairs, from which 298,715 were unique. The corpus contains data extracted from histories of page versions of Polish Wikipedia. An algorithm designed by the corpus author determined where the changes were correcting spelling errors, as opposed to expanding content and disagreements among Wikipedia editors.
Specifically, PlEWi supplied 550,755 [error, correction] pairs, from which 298,715 were unique.
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How is PIEWi annotated?
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The answers are shown as follows:
* [error, correction] pairs
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Takiko Mizunoe (水の江 瀧子, Mizunoe Takiko), born Umeko Miura (三浦 ウメ子, Miura Umeko, 20 February 1915 – 16 November 2009), was a Japanese actress, film producer, and radio and TV presenter. She was born in Otaru, Hokkaido, and began her career by acting in Shochiku's musical theatre troupe. Later she became one of Japan’s first female film producers, working with the actors Yujiro Ishihara and Masumi Okada and the director Koreyoshi Kurahara at Nikkatsu during the studio’s golden age. Films she produced include Season of the Sun and Crazed Fruit. She also twice hosted the New Year’s Eve music show Kōhaku Uta Gassen.
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What are some films produced by Takiko Mizunoe?
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Season of the Sun and Crazed Fruit
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| 495
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With the implementation details described in Appendix C, we found that the MnM dynamics model trained stably, despite resembling a GAN. In Fig. 10, we plot the validation MSE of the MnM model throughout training, observing that it decreases monotonically.****Figure 10: MnM trains stably. Despite resembling a GAN, the MnM dynamics model trains stably, with the validation MSE decreasing steadily throughout training. Different colors correspond to different random seeds of MnM. The dashed line corresponds to the minimum validation MSE of a maximum likelihood dynamics model. With the implementation details described in Appendix C, we found that the MnM dynamics model trained stably, despite resembling a GAN. In Fig. 10, we plot the validation MSE of the MnM model throughout training, observing that it decreases monotonically. Different lines correspond to different random seeds, and the dashed line corresponds to the minimum MSE of a maximum likelihood model (a MBPO model). Note that the MnM model is not trained with this MSE objective, but with the GAN-like objective in Eq. 10. It is therefore not surprising that MnM does not perform as well on this objective as the maximum likelihood model.
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Is the dynamics model learned well?
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The MnM dynamics model does train stably and its MSE decreases throughout training. As expected, the MSE of the MnM dynamics model is larger than the MSE of a maximum likelihood model. So, while the MnM dynamics model may not be as accurate as the maximum likelihood model, it may be more useful, as it sometimes results in higher return policies. We have revised the paper to add a figure (Figure 10) showing the MSE of the MnM dynamics model throughout training.
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Identify the bird from the list: Shrimp, Barnacle goose, Polar Bear
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Barnacle goose
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What is the difference between blanc de noirs and blanc de blancs champagne?
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In champagne, three grape varieties are commonly used. Two of these varieties are red grapes, pinot noir and pinot meunier, and the other is a white grape, chardonnay. Blanc de blancs, meaning white of whites, represents a version of champagne that only uses white grapes, while blanc de noirs, meaning white of blacks, represents a version that uses only red grapes.
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Day for Night is the fourth studio album by the Canadian rock band The Tragically Hip. It is named for the film of the same name.
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What is the fourth album from The Tragically Hip?
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Day for Night is the Tragically Hip's 4th studio album released in 1994
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1909.07863
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We used the Visual storytelling (VIST) dataset comprising of image sequences obtained from Flickr albums and respective annotated descriptions collected through Amazon Mechanical Turk BIBREF1. Each sequence has 5 images with corresponding descriptions that together make up for a story. Furthermore, for each Flickr album there are 5 permutations of a selected set of its images. In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
We used the Visual storytelling (VIST) dataset comprising of image sequences obtained from Flickr albums and respective annotated descriptions collected through Amazon Mechanical Turk BIBREF1. Each sequence has 5 images with corresponding descriptions that together make up for a story. Furthermore, for each Flickr album there are 5 permutations of a selected set of its images. In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
|
What statistics on the VIST dataset are reported?
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The answers are shown as follows:
* In the overall available data there are 40,071 training, 4,988 validation, and 5,050 usable testing stories.
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| 60
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We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion. In a conventional Gaussian Mixture Model - Hidden Markov Model (GMM-HMM) acoustic model, the state emission log-likelihood of the observation feature vector $o_t$ for certain tied state $s_j$ of HMMs at time $t$ is computed as
where M is the number of Gaussian mixtures in the GMM for state $j$ and $\pi _{jm}$ is the mixing weight. As the outputs from DNNs represent the state posteriors $p(s_j|\mathbf {o}_t)$, a DNN-HMM hybrid system uses pseudo log-likelihood as the state emissions that is computed as
where the state priors log $p(s_j )$ can be estimated using the state alignments on the training speech data.
We adopt a DNN-based acoustic model [1] with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion.
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How many hidden layers are there in their acoustic model?
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11 hidden layers.
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In SQA, each sample is a triple, INLINEFORM0 , where INLINEFORM1 is a question in either spoken or text form, INLINEFORM2 is a multi-sentence spoken-form document, and INLINEFORM3 is the answer in text from. The task of this work is extractive SQA; that means INLINEFORM4 is a word span from the reference transcription of INLINEFORM5 . An overview framework of SQA is shown in Figure FIGREF1 . In this paper, we frame the source domain as reference transcriptions and the target domain as ASR hypotheses. Hence, we can collect source domain data more easily, and adapt the model to the target domain.
In this task, when the machine is given a spoken document, it needs to find the answer of a question from the spoken document. SQA can be solved by the concatenation of an ASR module and a question answering module. Given the ASR hypotheses of a spoken document and a question, the question answering module can output a text answer.
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer. We used the standard evaluation script from SQuAD BIBREF1 to evaluate the performance.
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer.
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What's the most intuitive way to evaluate the text answer?
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To directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer.
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What decade is the Great Gatsby set in, and what is the nickname for that time period?
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The Great Gatsby is set in the 1920s, known as the Roaring Twenties
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Electronics deals with electrical circuits that involve active electrical components such as vacuum tubes, transistors, diodes, sensors and integrated circuits, and associated passive interconnection technologies. The nonlinear behaviour of active components and their ability to control electron flows makes digital switching possible, and electronics is widely used in information processing, telecommunications, and signal processing. Interconnection technologies such as circuit boards, electronics packaging technology, and other varied forms of communication infrastructure complete circuit functionality and transform the mixed components into a regular working system.
Today, most electronic devices use semiconductor components to perform electron control. The underlying principles that explain how semiconductors work are studied in solid state physics, whereas the design and construction of electronic circuits to solve practical problems are part of electronics engineering.
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Extract 3 areas based on the following passage where electronics is widely used.
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Electronics is widely used in
1. information processing,
2. telecommunications, and
3. signal processing.
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| 490
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In setting i, we use an untrained CNN as the dense model, and solve the DIP optimization (Ulyanov et al., 2018) θ ∗ = arg min θ E(fθ(z); ˜x), x∗ = fθ ∗ (z), (1) where E(; ) denotes the Mean Square Error (MSE), x˜ is the corrupted version of the image x ∈ R 3×H×W , fθ represents the dense model f with initial parameter θ and z is the random tensor that z ∈ R C×H×W . We choose the same hourglass architecture with skip connections as in (Ulyanov et al., 2018), to be the dense model by default. In setting ii, we follow Bora et al. (2017) to use a pre-trained GAN generator, to reconstruct the
unknown vector x∗ ∈ Rn, after observing m < n linear measurements of its entries with noise:y = Ax∗ + η, where A ∈ R
m×n is the measurement matrix and η ∈ R
m is the noise. Within the range of the pre-trained generator prior, GANs could reconstruct the vector x∗ with high perceptual quality. Following the recommendation of Jalal et al. (2020), we use the official pre-trained model PGAN (Karras et al., 2017).
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In the modified IMP, when using clean labels for training, where is the randomness coming from? How is the expectation defined?
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We added more explanation to Section 3.1-”Neural Network as Priors: Two Settings” for a stronger logistic link between the two settings considered in this work, DIP and GAN CS, and a clearer presentation.
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how to survive on Mars
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Surviving on Mars would be a challenging and dangerous endeavor. However, here are some steps that could increase your chances of survival:
Oxygen: Mars' atmosphere is mostly carbon dioxide, so you'll need to find a way to generate oxygen. One way is to use a process called electrolysis to extract oxygen from water.
Water: Water is essential for survival, so you'll need to find a source of water on Mars. This could be in the form of ice or underground reserves.
Food: Growing your own food on Mars would be difficult, but not impossible. You could use hydroponics or aeroponics to grow plants indoors, or find ways to grow crops outdoors.
Shelter: Mars' harsh environment would require a well-built shelter to protect you from the extreme cold, radiation, and dust storms. The shelter would need to be well insulated and sealed to prevent air leaks.
Energy: You'll need a reliable source of energy to power your shelter and equipment. Solar panels could be used to generate electricity, or nuclear reactors could be used for more efficient energy production.
Communications: Communication with Earth would be crucial for survival. You would need a reliable communication system to send and receive messages from Earth, as well as to coordinate with other astronauts on Mars.
Medical Supplies: Medical emergencies could arise on Mars, so having a well-stocked medical kit and the knowledge to treat basic medical issues would be important.
Mental Health: Living in isolation on Mars could take a toll on your mental health. Maintaining social connections with other astronauts, regular exercise, and engaging in activities to combat boredom would be important for mental health.
Surviving on Mars would require a great deal of preparation, resources, and expertise. It would also require a strong will to persevere through the challenges of living on an inhospitable planet.
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1906.03538
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FLOAT SELECTED: Table 2: A summary of PERSPECTRUM statistics
We now provide a brief summary of [wave]390P[wave]415e[wave]440r[wave]465s[wave]485p[wave]525e[wave]535c[wave]595t[wave]610r[wave]635u[wave]660m. The dataset contains about INLINEFORM0 claims with a significant length diversity (Table TABREF19 ). Additionally, the dataset comes with INLINEFORM1 perspectives, most of which were generated through paraphrasing (step 2b). The perspectives which convey the same point with respect to a claim are grouped into clusters. On average, each cluster has a size of INLINEFORM2 which shows that, on average, many perspectives have equivalents. More granular details are available in Table TABREF19 .
FLOAT SELECTED: Table 2: A summary of PERSPECTRUM statistics
The dataset contains about INLINEFORM0 claims with a significant length diversity (Table TABREF19 ).
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What is the average length of the claims?
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Average claim length is 8.9 tokens.
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| 82
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We collected a simplified dataset from Simple English Wikipedia that are freely available, which has been previously used for many text simplification methods BIBREF0 , BIBREF10 , BIBREF3 . The simple English Wikipedia is pretty easy to understand than normal English Wikipedia. We downloaded all articles from Simple English Wikipedia. For these articles, we removed stubs, navigation pages and any article that consisted of a single sentence. We then split them into sentences with the Stanford CorNLP BIBREF21 , and deleted these sentences whose number of words are smaller than 10 or large than 40. After removing repeated sentences, we chose 600K sentences as the simplified data with 11.6M words, and the size of vocabulary is 82K.
We collected a simplified dataset from Simple English Wikipedia that are freely available1, which has been previously used for many text simplification methods (Biran et al., 2011; Coster and Kauchak, 2011; Zhu et al., 2010).
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Where did the team collect the dataset in their research?
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From Simple English Wikipedia.
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Hot pot or hotpot (simplified Chinese: 火锅; traditional Chinese: 火鍋; pinyin: huǒguō; lit. 'fire pot'), also known as soup-food or steamboat, is a dish whereby a heat source placed on the dining table keeps a pot of soup stock simmering, and a variety of Chinese foodstuffs and ingredients are served beside the pot for the diners to put into the hot stock.
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What is hot pot?
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Hot pot is a traditional Chinese dish that you put all kinds of food such as sliced beef/lamp, seafood, vegetables, meatballs into a boiled seasoned soup until they are fully cooked
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| 55
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Various neural networks have been proposed for sentence pair modeling, all of which fall into two types of approaches. The sentence encoding approach encodes each sentence into a fixed-length vector and then computes sentence similarity directly. The model of this type has advantages in the simplicity of the network design and generalization to other NLP tasks. The sentence pair interaction approach takes word alignment and interactions between the sentence pair into account and often show better performance when trained on in-domain data. Here we outline the two types of neural networks under the same general framework:
•The Input Embedding Layer takes vector representations of words as input, where pretrained word embeddings are most commonly used, e.g. GloVe (Pennington et al., 2014) or Word2vec (Mikolov et al., 2013).
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What approach is most commonly used for the input embedding layer?
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Pretrained word embeddings are most commonly used, e.g. GloVe (Pennington et al., 2014) or Word2vec (Mikolov et al., 2013).
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Which of the following are exercise equipment: bicycle, book, dumbbell, treadmill, spoon, television
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A bicycle, dumbbell, and treadmill are all pieces of exercise equipment. Bicycles can be used to ride around outside or even indoors, they are a great way to improve your cardio health. Dumbbells are often used at a gym but can be used at home, they help to improve muscle mass through strength training. A treadmill is a great way to improve work on your cardio health by allowing you to run inside. A book is not a piece of exercise equipment as it does not require much physical effort to engage with. A spoon will help you in eating meals, but does little to improve your cardio health or strength. A television, or TV, can be watched while exercising, but cannot be exercised with.
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Name some of the bravolebrities from Married to Medicine
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Anila Sajja, Dr. Contessa Metcalfe, Dr. Heavenly Kimes, Dr. Jacqueline Walters, Dr. Simone Whitmore, Toya Bush-Harris and Quad Webb
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| null |
In what language do multilingual people dream?
|
This answer really depends on whom you are speaking to. Some multilingual people dream in different languages and this depends on who they are interacting with within their dreams. For example, if in the dream they are talking to their French mother they would reply in French, however when speaking to their English teacher they speak in English. Other multilingual people, in their dreams, speak in any language they know without a particular distinction. Some people just speak 1 language all the way through. Anyways, I’m sure there are even more variances that we are not aware of!
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The British Empire was composed of the dominions, colonies, protectorates, mandates, and other territories ruled or administered by the United Kingdom and its predecessor states. It began with the overseas possessions and trading posts established by England between the late 16th and early 18th centuries. At its height it was the largest empire in history and, for over a century, was the foremost global power. By 1913, the British Empire held sway over 412 million people, 23 per cent of the world population at the time, and by 1920, it covered 35.5 million km2 (13.7 million sq mi), 24 per cent of the Earth's total land area. As a result, its constitutional, legal, linguistic, and cultural legacy is widespread. At the peak of its power, it was described as "the empire on which the sun never sets", as the Sun was always shining on at least one of its territories.
|
What percentage of the world's population did the British Empire hold sway over?
|
23 per cent of the world population
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| null |
The following is an Abstract of a paper on Autism Spectrum Disorder. Based on this abstract would it be a good idea to enroll a child with ASD into group sports?
"Given the characteristics of people with Autism Spectrum Disorder, it is evident the difficulties they show in the development of social skills. The scarce participation of people with Autism Spectrum Disorder in group sports can be taken as a reference. The aim of this study was to analyse the impact of football on the development of social skills in children with Autism Spectrum Disorder. In order to measure the sporting impact, it was necessary to implement a football training programme with the intention of evaluating different social skills. Thirteen children participated in the programme, all of them with a diagnosis of Autism Spectrum Disorder and with a severity level of 1. The study was based on a pre-experimental, pre-test/post-test design. Non-parametric tests were used for the statistical analysis, applying the Wilcoxon test. Two specific tools on social skills were used for data collection. The results showed a generalised improvement in the dimensions linked to the social skills assessed. This highlights the possibility of considering group sport as an alternative to be taken into account to work on and enhance social skills in children with Autism Spectrum Disorder"
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In this study, the authors assess 13 children with Autism Spectrum Disorder (ASD-Level 1) on their social skills, before and after being enrolled in a football training program. The authors claim that the results showed an overall improvement in participant's social skills.
This suggests that participating in group sports maybe considered as a method to improve children with ASD's social and communication skills.
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Documentaries
Title Premiere Runtime Language
Mumbai Mafia: Police vs The Underworld January 6, 2023 1 h 27 min English
The Hatchet Wielding Hitchhiker January 10, 2023 1 h 25 min English
Big Mäck: Gangsters and Gold January 20, 2023 1 h 30 min German
Pamela, a Love Story January 31, 2023 1 h 52 min English
Money Shot: The Pornhub Story March 15, 2023 1 h 34 min English
Caught Out: Crime. Corruption. Cricket. March 17, 2023 1 h 17 min English
Lewis Capaldi: How I'm Feeling Now April 5, 2023 1 h 36 min English
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Total # of Documentary in Netflix since 2023
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There are 8 Documentaries on netflix since 2023
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What is Taj Mahal?
|
The Taj Mahal 'Crown of the Palace' is an ivory-white marble mausoleum on the right bank of the river Yamuna in Agra, Uttar Pradesh, India. The Taj Mahal attracts more than 6 million visitors a year and in 2007, it was declared a winner of the New 7 Wonders of the World.
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