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David Moses ben Meir Dyte (c. 1770–1830) was an English Jewish quill merchant who distinguished himself by preventing the assassination attempt on George III by James Hadfield on 15 May 1800.
Dyte was attending Colley Cibber's play She Would and She Would Not at the Theatre Royal, Drury Lane when Hadfield fired a horsepistol at the King. Dyte, seated directly behind the shooter, struck the assailant and deflected the shot. It is said that Dyte asked as his sole reward the right to sell opera tickets, then a monopoly at the royal disposal. In 1802 he was appointed 'Purveyor of Pens and Quills to the Royal Household'. The incident was immortalised in the play Jew Dyte by Harold Rubinstein.
Dyte was the father of Henry Dyte, who served as Honorary Secretary to the Blind Society; and the grandfather of D. H. Dyte, Surgeon to the Jewish Board of Guardians, and Charles Dyte, a parliamentarian in the colony of Victoria.
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Given a reference text about David Moses ben Meir Dyte, tell me what he is known for.
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David Moses ben Meir Dyte is known for preventing an assassination attempt by James Hadfield on George III.
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How many times Fernando Alonso won the F1 championship ?
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2 times
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Classify the below natural satellites based on the whether they are moon of Mars or Jupiter.
Phobos, Deimos, Europa, Ganymede
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Mars - Phobos, Deimos
Jupiter - Europa, Ganymede
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1908.06151
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Last, we analyze the importance of our second encoder ($enc_{src \rightarrow mt}$), compared to the source encoder ($enc_{src}$) and the decoder ($dec_{pe}$), by reducing and expanding the amount of layers in the encoders and the decoder. Our standard setup, which we use for fine-tuning, ensembling etc., is fixed to 6-6-6 for $N_{src}$-$N_{mt}$-$N_{pe}$ (cf. Figure FIGREF1), where 6 is the value that was proposed by Vaswani:NIPS2017 for the base model. We investigate what happens in terms of APE performance if we change this setting to 6-6-4 and 6-4-6. To handle out-of-vocabulary words and reduce the vocabulary size, instead of considering words, we consider subword units BIBREF19 by using byte-pair encoding (BPE). In the preprocessing step, instead of learning an explicit mapping between BPEs in the $src$, $mt$ and $pe$, we define BPE tokens by jointly processing all triplets. Thus, $src$, $mt$ and $pe$ derive a single BPE vocabulary. Since $mt$ and $pe$ belong to the same language (German) and $src$ is a close language (English), they naturally share a good fraction of BPE tokens, which reduces the vocabulary size to 28k.
The number of layers ($N_{src}$-$N_{mt}$-$N_{pe}$) in all encoders and the decoder for these results is fixed to 6-6-6. In Exp. 5.1, and 5.2 in Table TABREF5, we see the results of changing this setting to 6-6-4 and 6-4-6. This can be compared to the results of Exp. 2.3, since no fine-tuning or ensembling was performed for these three experiments. Exp. 5.1 shows that decreasing the number of layers on the decoder side does not hurt the performance. In fact, in the case of test2016, we got some improvement, while for test2017, the scores got slightly worse. In contrast, reducing the $enc_{src \rightarrow mt}$ encoder block's depth (Exp. 5.2) does indeed reduce the performance for all four scores, showing the importance of this second encoder.
FLOAT SELECTED: Table 1: Evaluation results on the WMT APE test set 2016, and test set 2017 for the PBSMT task; (±X) value is the improvement over wmt18smtbest (x4). The last section of the table shows the impact of increasing and decreasing the depth of the encoders and the decoder.
Last, we analyze the importance of our second encoder ($enc_{src \rightarrow mt}$), compared to the source encoder ($enc_{src}$) and the decoder ($dec_{pe}$), by reducing and expanding the amount of layers in the encoders and the decoder. Our standard setup, which we use for fine-tuning, ensembling etc., is fixed to 6-6-6 for $N_{src}$-$N_{mt}$-$N_{pe}$ (cf. Figure FIGREF1), where 6 is the value that was proposed by Vaswani:NIPS2017 for the base model. We investigate what happens in terms of APE performance if we change this setting to 6-6-4 and 6-4-6.
The number of layers ($N_{src}$-$N_{mt}$-$N_{pe}$) in all encoders and the decoder for these results is fixed to 6-6-6. In Exp. 5.1, and 5.2 in Table TABREF5, we see the results of changing this setting to 6-6-4 and 6-4-6. This can be compared to the results of Exp. 2.3, since no fine-tuning or ensembling was performed for these three experiments. Exp. 5.1 shows that decreasing the number of layers on the decoder side does not hurt the performance. In fact, in the case of test2016, we got some improvement, while for test2017, the scores got slightly worse. In contrast, reducing the $enc_{src \rightarrow mt}$ encoder block's depth (Exp. 5.2) does indeed reduce the performance for all four scores, showing the importance of this second encoder.
FLOAT SELECTED: Table 1: Evaluation results on the WMT APE test set 2016, and test set 2017 for the PBSMT task; (±X) value is the improvement over wmt18smtbest (x4). The last section of the table shows the impact of increasing and decreasing the depth of the encoders and the decoder.
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What experiment result led to conclussion that reducing the number of layers of the decoder does not matter much?
|
The answers are shown as follows:
* Exp. 5.1
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How do you thicken bolognese sauce?
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You can use flour or cornstarch to thicken your bolognese or tomato-based spaghetti sauce using starch (flour or cornstarch). Additionally, you can reduce your sauce's liquid content — this is also a practical option for thickening any sauce.
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Who was given the Booker Prize 2020?
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Douglas Stuart
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Identify which instrument is string or percussion: Chime bar, Pinaka vina
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Pinaka vina is string, Chime bar is percussion.
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1911.11698
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Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW). The hs option defines whether hierarchical softmax or negative sampling is used during the training. Finally, the vector_size parameter affects the number of dimensions composing the resulting vector.
Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW). The hs option defines whether hierarchical softmax or negative sampling is used during the training. Finally, the vector_size parameter affects the number of dimensions composing the resulting vector.
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What six parameters were optimized with grid search?
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The answers are shown as follows:
* window_size
* alpha
* sample
* dm
* hs
* vector_size
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Who am I?
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Ah, an existential question! Only you can answer who you are. Studying philosophy can offer guidance on the meaning of life and existence, but only you can determine how your nature, the way you were nurtured, and the decisions you've made will ultimately shape your identity.
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Casandra Brené Brown (born November 18, 1965) is an American professor, lecturer, author, and podcast host. Brown is known in particular for her research on shame, vulnerability, and leadership, and for her widely viewed TEDx talk in 2010. Since then she has written six number-one New York Times bestselling books, hosts two podcasts, and has filmed a lecture for Netflix as well as a series about her latest book, Atlas of the Heart on HBO Max.
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Based on the passage provided, what does Brene Brown's research focus on?
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Brene Brown's research focuses on shame, vulnerability, and leadership.
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Swami Vivekananda (/ˈswɑːmi ˌvɪveɪˈkɑːnəndə/; Bengali: [ʃami bibekanɔndo] (listen); 12 January 1863 – 4 July 1902), born Narendranath Datta (Bengali: [nɔrendronatʰ dɔto]), was an Indian Hindu monk, philosopher, author, religious teacher, and the chief disciple of the Indian mystic Sri Ramakrishna Paramahansa. He was a key figure in the introduction of Vedanta and Yoga to the Western world and is credited with raising interfaith awareness, and bringing Hinduism to the status of a major world religion. Vivekananda became a popular figure after the 1893 Parliament of Religions in Chicago, where he began his famous speech with the words, "Sisters and brothers of America...," before introducing Hinduism to Americans. He was so impactful at the Parliament that an American newspaper described him as “an orator by divine right and undoubtedly the greatest figure at the Parliament”. After great success at the Parliament, in the subsequent years, Vivekananda delivered hundreds of lectures across the United States, England and Europe, disseminating the core tenets of Hindu philosophy, and founded the Vedanta Society of New York and the Vedanta Society of San Francisco (now Vedanta Society of Northern California), both of which became the foundations for Vedanta Societies in the West.
In 1881, Narendra first met Ramakrishna, who became his spiritual focus after his own father had died in 1884.
Narendra's first introduction to Ramakrishna occurred in a literature class at General Assembly's Institution when he heard Professor William Hastie lecturing on William Wordsworth's poem, The Excursion. While explaining the word "trance" in the poem, Hastie suggested that his students visit Ramakrishna of Dakshineswar to understand the true meaning of trance. This prompted some of his students (including Narendra) to visit Ramakrishna.
They probably first met personally in November 1881,[note 1] though Narendra did not consider this their first meeting, and neither man mentioned this meeting later. At this time, Narendra was preparing for his upcoming F. A. examination, when Ram Chandra Datta accompanied him to Surendra Nath Mitra's, house where Ramakrishna was invited to deliver a lecture. According to Makarand Paranjape, at this meeting Ramakrishna asked young Narendra to sing. Impressed by his singing talent, he asked Narendra to come to Dakshineshwar.
In late 1881 or early 1882, Narendra went to Dakshineswar with two friends and met Ramakrishna. This meeting proved to be a turning point in his life. Although he did not initially accept Ramakrishna as his teacher and rebelled against his ideas, he was attracted by his personality and began to frequently visit him at Dakshineswar. He initially saw Ramakrishna's ecstasies and visions as "mere figments of imagination" and "hallucinations". As a member of Brahmo Samaj, he opposed idol worship, polytheism and Ramakrishna's worship of Kali. He even rejected the Advaita Vedanta of "identity with the absolute" as blasphemy and madness, and often ridiculed the idea. Narendra tested Ramakrishna, who faced his arguments patiently: "Try to see the truth from all angles", he replied.
Narendra's father's sudden death in 1884 left the family bankrupt; creditors began demanding the repayment of loans, and relatives threatened to evict the family from their ancestral home. Narendra, once a son of a well-to-do family, became one of the poorest students in his college. He unsuccessfully tried to find work and questioned God's existence, but found solace in Ramakrishna and his visits to Dakshineswar increased.
One day, Narendra requested Ramakrishna to pray to goddess Kali for their family's financial welfare. Ramakrishna instead suggested him to go to the temple himself and pray. Following Ramakrishna's suggestion, he went to the temple thrice, but failed to pray for any kind of worldly necessities and ultimately prayed for true knowledge and devotion from the goddess. Narendra gradually grew ready to renounce everything for the sake of realising God, and accepted Ramakrishna as his Guru.
In 1885, Ramakrishna developed throat cancer, and was transferred to Calcutta and (later) to a garden house in Cossipore. Narendra and Ramakrishna's other disciples took care of him during his last days, and Narendra's spiritual education continued. At Cossipore, he experienced Nirvikalpa samadhi. Narendra and several other disciples received ochre robes from Ramakrishna, forming his first monastic order. He was taught that service to men was the most effective worship of God. Ramakrishna asked him to care of the other monastic disciples, and in turn asked them to see Narendra as their leader. Ramakrishna died in the early-morning hours of 16 August 1886 in Cossipore.
|
Who asked Swami Vivekananda to come to Dakshineswar and in which year?
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In 1881, Sri Ramakrishna Paramahansa asked Swami Vivekananda to come to Dakshineswar
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Classify each of the following as either characters from Star Wars or Star Trek: Jean Luc Picard, Grogu, Han Solo, Luke Skywalker, Data, Q, Darth Vader, Ahsoka, William Riker, James Kirk, Spock
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Star Wars: Grogu, Han Solo, Luke Skywalker, Darth Vader, Ahsoka
Star Trek: Jean Luc Picard, Data, Q, William Riker, James Kirk, Spock
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Tell which of these musicians is a guitarist or a bassist: Eddie Van Halen, Nikki Sixx, Steve Harris, Billy Sheehan, Steve Vai, Joe Satriani, Matthias Jabs, Jimmy Bain, Mick Mars, Vivian Campbell, Angus Young
|
Eddie Van Halen - Guitarist
Nikki Sixx - Bassist
Steve Harris - Bassist
Billy Sheehan - Bassist
Steve Vai - Guitarist
Joe Satriani - Guitarist
Matthias Jabs - Guitarist
Jimmy Bain - Bassist
Mick Mars - Guitarist
Vivian Campbell - Guitarist
Angus Young - Guitarist
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| 112
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The dev data set BIBREF19 in the competition was small hence, the train and dev sets were merged to perform 10-fold cross validation. On each fold, a model was trained and the predictions were collected on the remaining dataset. The predictions are averaged across all the folds to generalize the solution and prevent over-fitting. As described in Section SECREF6 , different combinations of feature extractors were used. After performing feature extraction, the data was then passed to various regressors Support Vector Regression, AdaBoost, RandomForestRegressor, and, BaggingRegressor of sklearn BIBREF20 . Finally, the chosen top performing models had the least error on evaluation metrics namely Pearson's Correlation Coefficient and Spearman's rank-order correlation.
The dev data set (Mohammad and BravoMarquez, 2017b) in the competition was small hence, the train and dev sets were merged to perform 10-fold cross validation. On each fold, a model was trained and the predictions were collected on the remaining dataset.
|
How did the author select the data set?
|
The train and dev sets were merged to perform 10-fold cross validation. On each fold, a model was trained and the predictions were collected on the remaining dataset.
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| 88
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Knowledge graphs have been proved to benefit many artificial intelligence applications, such as relation extraction, question answering and so on. A knowledge graph consists of multi-relational data, having entities as nodes and relations as edges. An instance of fact is represented as a triplet (Head Entity, Relation, Tail Entity), where the Relation indicates a relationship between these two entities. In the past decades, great progress has been made in building large scale knowledge graphs, such as WordNet BIBREF0 , Freebase BIBREF1 . However, most of them have been built either collaboratively or semi-automatically and as a result, they often suffer from incompleteness and sparseness.
The knowledge graph completion is to predict relations between entities based on existing triplets in a knowledge graph. Recently, a new powerful paradigm has been proposed to encode every element (entity or relation) of a knowledge graph into a low-dimensional vector space BIBREF2 , BIBREF3 . The representations of entities and relations are obtained by minimizing a global loss function involving all entities and relations. Therefore, we can do reasoning over knowledge graphs through algebraic computations.
Although existing methods have good capability to learn knowledge graph embeddings, it remains challenging for entities with few or no facts BIBREF4 . To solve the issue of KB sparsity, many methods have been proposed to learn knowledge graph embeddings by utilizing related text information BIBREF5 , BIBREF6 , BIBREF7 . These methods learn joint embedding of entities, relations, and words (or phrases, sentences) into the same vector space. However, there are still three problems to be solved. (1) The combination methods of the structural and textual representations are not well studied in these methods, in which two kinds of representations are merely aligned on word level or separate loss function. (2) The text description may represent an entity from various aspects, and various relations only focus on fractional aspects of the description. A good encoder should select the information from text in accordance with certain contexts of relations. Figure 1 illustrates the fact that not all information provided in its description are useful to predict the linked entities given a specific relation. (3) Intuitively, entities with many facts depend more on well-trained structured representation while those with few or no facts might be largely determined by text descriptions. A good representation should learn the most valuable information by balancing both sides.
In this paper, we propose a new deep architecture to learn the knowledge representation by utilizing the existing text descriptions of entities. Specifically, we learn a joint representation of each entity from two information sources: one is structure information, and another is its text description. The joint representation is the combination of the structure and text representations with a gating mechanism. The gate decides how much information from the structure or text representation will carry over to the final joint representation. In addition, we also introduce an attention mechanism to select the most related information from text description under different contexts. Experimental results on link prediction and triplet classification show that our joint models can handle the sparsity problem well and outperform the baseline method on all metrics with a large margin.
Our contributions in this paper are summarized as follows.
The joint representation is the combination of the structure and text representations with a gating mechanism.
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How structure and text representations are combined?
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A gating mechanism is used to combine the structure and text representations.
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| 264
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Recently there has been a considerable interest in language modeling due to various academic and commercial demands. Academically, many studies have investigated this domain such as machine translation, chat-bot, message generation, image tagging and other language-related areas. Commercially, it can be used as a core technology for providing a new application on consumer products or services. For instance, an automatic message-reply prediction service can be launched in mobile devices, thus helping a user to send a reply message when he/she is not provided with a proper input interface.
To model the language of human dialogue, a recurrent neural network (RNN) structure is known to show the state of the arts performance with its ability to learn a sequential pattern of the data BIBREF0 . Among the RNN structures, a Long Short-Term Memory RNN (LSTM-RNN) and its variants are successfully used for language modeling tasks BIBREF1 , BIBREF2 . However, as a kind of deep learning technique, the LSTM-RNN and the RNN structure requires both a large number of data and huge computing power to train the model properly. Hence any attempts for applying the RNN structure to personalized language modeling are mainly constrained by the following two limitations. First, personal mobile devices contain private message data among close acquaintances, so users seldom agree to transfer their log out of the devices. This causes a limitation of gathering the whole user data to common computing spaces, where high-performance machines are available. Second, in relatively small computing machines, i.e., smart phone, it is not always-guaranteed to have enough resources to train a deep model within the devices.
To resolve these limitations, we propose fast transfer learning schemes. It trains a base model with a large dataset and copies its first n-many layers to the first n-many layers of a target model. Then the target model is fine-tuned with relatively small target data. Several learning schemes such as freezing a certain layer or adding a surplus layer are proposed for achieving the result. In experiments, we trained a general language model with huge corpus such as an Workshop on Statistical Machine Translation (WMT) data and a movie script data by using powerful computing machines, and then transferred the model to target environment for updating to be a personalized language model. With this approach, the final model can mimic target user's language style with proper syntax.
In the experiments, we trained the general language model with literary-style data and applied the transfer learning with spoken-style data. Then we evaluated the model output for sentence completion task in a qualitative and a quantitative manner. The test result showed that the model learned the style of the target language properly. Another test was conducted by training the general language model with the script of the drama, “Friends," and by applying transfer learning with main character corpora from the script to generate the personalized language model. The message-reply prediction task was evaluated with this model. The test result shows higher similarity between the output of the personalized language model and the same user dialogue than the one between the output of the personalized language model and other users' dialogues.
The contributions of this paper are as follows. First, we propose efficient transfer learning schemes for personalized language modeling, which is the first research on transfer learning for RNN based language models with privacy preserving. Second, we show the applicability of our research to the target scenario in the short message reply application by training the model in the similar environment to that of the mobile device, and highlight its test results.
In experiments, we trained a general language model with huge corpus such as an Workshop on Statistical Machine Translation (WMT) data1 and a movie script data by using powerful computing machines, and then transferred the model to target environment for updating to be a personalized language model.
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What data has been used by them for training a general language model?
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Huge corpus such as an Workshop on Statistical Machine Translation (WMT) data and a movie script data.
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What motivates a chief executive officer?
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Revenue and platform or product consumption and scalability motivate chief technology officers (CEOs). It is the quickest way to achieve company exponential growth. Strategic vision with laser focus execution focus items that drive company growth also motivate CEOs. Having a team of world-class employees who help to deliver the strategic vision, and product platform scalability and consumption that increases revenue also motivates CEOs.
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| 274
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Internet “trolls” are users of an online community who quarrel and upset people, seeking to sow discord by posting inflammatory content. More recently, organized “troll farms” of political opinion manipulation trolls have also emerged.
Such farms usually consist of state-sponsored agents who control a set of pseudonymous user accounts and personas, the so-called “sockpuppets”, which disseminate misinformation and propaganda in order to sway opinions, destabilize the society, and even influence elections BIBREF0.
The behavior of political trolls has been analyzed in different recent circumstances, such as the 2016 US Presidential Elections and the Brexit referendum in UK BIBREF0, BIBREF1. However, this kind of analysis requires painstaking and time-consuming manual labor to sift through the data and to categorize the trolls according to their actions. Our goal in the current paper is to automate this process with the help of machine learning (ML). In particular, we focus on the case of the 2016 US Presidential Elections, for which a public dataset from Twitter is available. For this case, we consider only accounts that post content in English, and we wish to divide the trolls into some of the functional categories identified by BIBREF0: left troll, right troll, and news feed.
We consider two possible scenarios. The first, prototypical ML scenario is supervised learning, where we want to learn a function from users to categories {left, right, news feed}, and the ground truth labels for the troll users are available. This scenario has been considered previously in the literature by BIBREF2. Unfortunately, a solution for such a scenario is not directly applicable to a real-world use case. Suppose a new troll farm trying to sway the upcoming European or US elections has just been discovered. While the identities of the accounts might be available, the labels to learn from would not be present. Thus, any supervised machine learning approach would fall short of being a fully automated solution to our initial problem.
A more realistic scenario assumes that labels for troll accounts are not available. In this case, we need to use some external information in order to learn a labeling function. Indeed, we leverage more persistent entities and their labels: news media. We assume a learning scenario with distant supervision where labels for news media are available. By combining these labels with a citation graph from the troll accounts to news media, we can infer the final labeling on the accounts themselves without any need for manual labeling.
One advantage of using distant supervision is that we can get insights about the behavior of a newly-discovered troll farm quickly and effortlessly. Differently from troll accounts in social media, which usually have a high churn rate, news media accounts in social media are quite stable. Therefore, the latter can be used as an anchor point to understand the behavior of trolls, for which data may not be available.
We rely on embeddings extracted from social media. In particular, we use a combination of embeddings built on the user-to-user mention graph, the user-to-hashtag mention graph, and the text of the tweets of the troll accounts. We further explore several possible approaches using label propagation for the distant supervision scenario.
As a result of our approach, we improve the classification accuracy by more than 5 percentage points for the supervised learning scenario. The distant supervision scenario has not previously been considered in the literature, and is one of the main contributions of the paper. We show that even by hiding the labels from the ML algorithm, we can recover 78.5% of the correct labels.
The contributions of this paper can be summarized as follows:
We predict the political role of Internet trolls (left, news feed, right) in a realistic, unsupervised scenario, where labels for the trolls are not available, and which has not been explored in the literature before.
We propose a novel distant supervision approach for this scenario, based on graph embeddings, BERT, and label propagation, which projects the more-commonly-available labels for news media onto the trolls who cited these media.
We improve over the state of the art in the traditional, fully supervised setting, where training labels are available.
We further explore several possible approaches using label propagation for the distant supervision scenario.
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The authors further explore several possible approaches using what?
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Label propagation for the distant supervision scenario.
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2004.01878
| false
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We use the public financial news dataset released by BIBREF4, which is crawled from Reuters and Bloomberg over the period from October 2006 to November 2013. We conduct our experiments on predicting the Standard & Poor’s 500 stock (S&P 500) index and its selected individual stocks, obtaining indices and prices from Yahoo Finance. Detailed statistics of the training, development and test sets are shown in Table TABREF8. We report the final results on test set after using development set to tune some hyper-parameters.
We use the public financial news dataset released by BIBREF4, which is crawled from Reuters and Bloomberg over the period from October 2006 to November 2013. We conduct our experiments on predicting the Standard & Poor’s 500 stock (S&P 500) index and its selected individual stocks, obtaining indices and prices from Yahoo Finance. Detailed statistics of the training, development and test sets are shown in Table TABREF8. We report the final results on test set after using development set to tune some hyper-parameters.
|
What is dataset used for news-driven stock movement prediction?
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The answers are shown as follows:
* the public financial news dataset released by BIBREF4
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Classify the following celebrities as sports or acting personalities or both : Arnold Schwarzenegger, Brad Pitt, Kevin Durant, Shohei Ohtani, Harrison Ford, Dwayne Johnson, Jason Statham, Stephen Curry, Robin Williams, Scarlett Johansson, Tom Brady, Patrick Mahomes
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Many athletes are known to do cameos in movies and a have a couple of their own sports related movies. Ignoring those few roles, I would classify the celebrities mentioned as :
Actors who were not sports personalities include Brad Pitt, Harrison Ford, Robin Williams, Scarlett Johansson.
Sports personalities who have not taken up acting as a major career (yet) are :
Kevin Durant - Acted in Thunderstuck and other movies and documentaries, but not a mainstream actor.
Stephen Curry - Did a few cameos and documentaries.
Shohei Ohtani and Patrick Mahomes only appear in sports documentaries and tv series.
Tom Brady had a major plot role in '80 for Brady' and cameos in other movies, but he is still largely a sports personality.
Actors with successful sports background are : Arnold Schwarzenegger (body building), Dwayne Johnson (Wrestling) and Jason Statham (Diving)
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Tell me in which country are the following cities: Zaragoza, Albufeira, Bari, Amfissa and Targoviste.
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Zaragoza is a city in Spain, Albufeira is in Portugal, Bari is located in Italy, Amfissa is part of Greece and Targoviste is in Romania.
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Who is considered the G.O.A.T. in basketball?
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Michael Jordan, Lebron James, Kobe Bryant
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What are the best hiking trails near Seattle, Washington?
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Washington state is famous for breathtaking nature with scenic lakes, enormous mountains, and picturesque ocean views. Some of the best hiking trails near Seattle, Washington are:
- Rattlesnake Ledge Trail: in North Bend, Washington only 30 mins away from Seattle. Given how close and beautiful this trail is to the city, no wonder this is one of the most popular hiking trails in Washington. The trail is moderately challenging with 5.3 miles roundtrip and 1,459 ft of elevation. The mountain trail provides 3 levels of panoramic views of Mount Si and Mount Washington looming above RattleSnake lake, which is also a popular swimming and kayaking destination in the summer.
- Wallace Falls via Woody Trail: a 4.9 miles out and back trail near Gold Bar, Washington, less than an hour away from Seattle. It's a scenic trail meandering along the Wallace River, with the lush vegetation and forest moss. You can see the Wallace Falls from 3 view points at Lower Falls, Middle Falls and Upper Falls. Most of the trail to Middle Falls doesn't have much elevation, the substantial elevation gain is between Middle Falls and Upper Falls.
- Lake 22: in Mount Baker Snoqualmie National Forest, about 2 hours drive from Seattle. Lake Twenty-Two Trail is a beautiful hike to an alpine lake through old growth forests. The trail is challenging with 6.8 miles roundtrip and 1,473 ft of elevation. When you reach the destination, you can loop around the turquoise lake and take in the enormous view of Mount Pilchuck.
- Colchuck Lake: in Okanogan-Wenatchee National Forest, near Leavenworth about 3 hours drive from Seattle. It features a beautiful scenery in the Alpine Lakes area of Washington state. The trail is challenging with 8.5 miles round trip and 2,349 ft of elevation. The hike can be technical with steep terrain over boulders and water streams. However, you will be rewarded with a breathtaking view of a emerald blue glacier lake and towering snow mountains.
- From Seattle, visitors can also go to 3 beautiful National Parks with hundreds of trails such as Mount Rainier National Park (ranked 6/59), Olympic National Park (ranked 12/59), and North Cascades National Park (ranked 36/59)
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What is Mr. Burn's assistant name on the Simpsons?
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Smithers
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| 391
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The goal of Machine Reading Comprehension (MRC) is to have machines read a text passage and then generate an answer (or select an answer from a list of given candidates) for any question about the passage. There has been a growing interest in the research community in exploring neural MRC models in an end-to-end fashion, thanks to the availability of large-scale datasets, such as CNN/DM BIBREF0 and SQuAD BIBREF1 .
Despite the variation in model structures, most state-of-the-art models perform reading comprehension in two stages. First, the symbolic representations of passages and questions are mapped into vectors in a neural space. This is commonly achieved via embedding and attention BIBREF2 , BIBREF3 or fusion BIBREF4 . Then, reasoning is performed on the vectors to generate the right answer.
Ideally, the best attention and reasoning strategies should adapt organically in order to answer different questions. However, most MRC models use a static attention and reasoning strategy indiscriminately, regardless of various question types. One hypothesis is because these models are optimized on those datasets whose passages and questions are domain-specific (or of a single type). For example, in CNN/DM, all the passages are news articles, and the answer to each question is an entity in the passage. In SQuAD, the passages came from Wikipedia articles and the answer to each question is a text span in the article. Such a fixed-strategy MRC model does not adapt well to other datasets. For example, the exact-match score of BiDAF BIBREF2 , one of the best models on SQuAD, drops from 81.5 to 55.8 when applied to TriviaQA BIBREF5 , whereas human performance is 82.3 and 79.7 on SQuAD and TriviaQA, respectively.
In real-world MRC tasks, we must deal with questions and passages of different types and complexities, which calls for models that can dynamically determine what attention and reasoning strategy to use for any input question-passage pair on the fly. In a recent paper, BIBREF6 proposed dynamic multi-step reasoning, where the number of reasoning steps is determined spontaneously (using reinforcement learning) based on the complexity of the input question and passage. With a similar intuition, in this paper we propose a novel MRC model which is dynamic not only on the number of reasoning steps it takes, but also on the way it performs attention. To the best of our knowledge, this is the first MRC model with this dual-dynamic capability.
The proposed model is called a Dynamic Fusion Network (DFN). In this paper, we describe the version of DFN developed on the RACE dataset BIBREF7 . In RACE, a list of candidate answers is provided for each passage-question pair. So DFN for RACE is a scoring model - the answer candidate with the highest score will be selected as the final answer.
Like other MRC models, DFNs also perform machine reading in two stages: attention and reasoning. DFN is unique in its use of a dynamic multi-strategy attention process in the attention stage. Here “attention” refers to the process that texts from different sources (passage, question, answers) are combined in the network. In literature, a fixed attention mechanism is usually employed in MRC models. In DFN, the attention strategy is not static; instead, the actual strategy for drawing attention among the three text sources are chosen on the fly for each sample. This lends flexibility to adapt to various question types that require different comprehension skills. The output of the attention stage is then fed into the reasoning module to generate the answer score. The reasoning module in DFN uses dynamic multi-step reasoning, where the number of steps depends on the complexity of the question-passage pair and varies from sample to sample.
Inspired by ReasoNet BIBREF6 and dynamic neural module networks BIBREF8 , we use deep reinforcement learning methods BIBREF9 , BIBREF10 to dynamically choose the optimal attention strategy and the optimal number of reasoning steps for a given sample. We use RL in favor of other simpler methods (like cascading, pooling or weighted averaging) mainly because we intend to learn a policy that constructs an instance of DFN of a sample-specific structure. Given an input sample consisting of a question, a passage and a list of candidate answers in RACE, an instance of DFN can be constructed via RL step by step on the fly. Such a policy is particularly appealing as it also provides insights on how the model performs on different types of questions. At each decision step, the policy maps its “state”, which represents an input sample, and DFN's partial knowledge of the right answer, to the action of assembling proper attention and reasoning modules for DFN.
Experiments conducted on the RACE dataset show that DFN significantly outperforms previous state-of-the-art MRC models and has achieved the best result reported on RACE. A thorough empirical analysis also demonstrates that DFN is highly effective in understanding passages of a wide variety of styles and answering questions of different complexities.
With a similar intuition, in this paper we propose a novel MRC model which is dynamic not only on the number of reasoning steps it takes, but also on the way it performs attention. To the best of our knowledge, this is the first MRC model with this dual-dynamic capability.
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Is the model the first MRC model with this dual-dynamic capability?
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Yes.
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null | false
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Is Missouri a city or a state?
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Both. Missouri is a state, and a city in texas
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null | false
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What is a Security Information Event Management platform (SIEM)?
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Security Information Event Management (pronounced SIM) is a combination of security information management (SIM) and security event management (SEM) technologies. It allows security operations teams to collect, parse, alert, and investigate security events. Modern SIEMs can leverage machine learning and SOAR capabilities to offer more significant detection and response capabilities.
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null | false
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Where were the 13th South Asian Games held?
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They were held in 3 cities of Nepal-Kathmandu, Pokhara & Janakpur
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1911.07228
| false
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We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:
Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps. His word embedding is the vector of 100 dimension and it has about 10k words.
Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia. The format is the same at Kyubyong's, but their embedding is the vector of 300 dimension, and they have about 200k words
We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:
Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps.
Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia.
|
What word embeddings were used?
|
The answers are shown as follows:
* Kyubyong Park
* Edouard Grave et al BIBREF11
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null | false
| 389
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We introduce an open domain question answering system based on Chinese knowledge graph in this paper. We analyze the questions and find that most of the answers to the questions are within two hops. Therefore, we only solve the problem within two hops to reduce the complexity of the system. The system consists of a topic entity selection module, a relationship recognition module and an answer selection module. Firstly, we construct a scoring mechanism to select the core entity of the question; Then we score the relationship in the two-hop subgraph of the topic entity; Finally, we build a classier to judge whether a question is simple or complicated, so that we can choose the final relationship and generate sparql query.
The system consists of a topic entity selection module, a relationship recognition module and an answer selection module.
|
What module does the system consist of?
|
A topic entity selection module, a relationship recognition module and an answer selection module.
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null | false
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Which countries are part of the five eyes alliance and what is its purpose?
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The Five Eyes is an intelligence alliance between the countries of Australia, Canada, New Zealand, United Kingdom and the United States.The goal is to share intelligence between one another in one of the worlds most unilateral agreement
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2001.05493
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Exploiting psycho-linguistic features with basic linguistic features as meta-data. The main aim is to minimize the direct dependencies on in-depth grammatical structure of the language (i.e., to support code-mixed data). We have also included emoticons, and punctuation features with it. We use the term "NLP Features" to represent it in the entire paper.
We have identified a novel combination of features which are highly effective in aggression classification when applied in addition to the features obtained from the deep learning classifier at the classification layer. We have introduced two new features in addition to the previously available features. The first one is the Emotion Sensor Feature which use a statistical model to classify the words into 7 different classes based on the sentences obtained from twitter and blogs which contain total 1,185,540 words. The second one is the collection of selected topical signal from text collected using Empath (see Table 1.).
FLOAT SELECTED: Table 1: Details of NLP features
Exploiting psycho-linguistic features with basic linguistic features as meta-data. The main aim is to minimize the direct dependencies on in-depth grammatical structure of the language (i.e., to support code-mixed data). We have also included emoticons, and punctuation features with it. We use the term "NLP Features" to represent it in the entire paper.
We have identified a novel combination of features which are highly effective in aggression classification when applied in addition to the features obtained from the deep learning classifier at the classification layer. We have introduced two new features in addition to the previously available features. The first one is the Emotion Sensor Feature which use a statistical model to classify the words into 7 different classes based on the sentences obtained from twitter and blogs which contain total 1,185,540 words. The second one is the collection of selected topical signal from text collected using Empath (see Table 1.).
FLOAT SELECTED: Table 1: Details of NLP features
|
Which psycholinguistic and basic linguistic features are used?
|
Emotion Sensor Feature, Part of Speech, Punctuation, Sentiment Analysis, Empath, TF-IDF Emoticon features
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null | false
| 383
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Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8\% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy.
The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set.
|
Why do they use a transfer learning strategy?
|
The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set.
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null | false
| 70
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Named Entity Recognition (NER) has been studied for many languages like English, German, Spanish, and others but virtually no studies have focused on the Nepali language. One key reason is the lack of an appropriate, annotated dataset. In this paper, we describe a Nepali NER dataset that we created. We discuss and compare the performance of various machine learning models on this dataset. We also propose a novel NER scheme for Nepali and show that this scheme, based on grapheme-level representations, outperforms character-level representations when combined with BiLSTM models. Our best models obtain an overall F1 score of 86.89, which is a significant improvement on previously reported performance in literature.
Our novel neural based model gained relative improvement of 33% to 50% compared to feature based SVM model and up to 10% improvement over state-of-the-art neural based models developed for languages beside Nepali.
|
How much does the model outperform the state-of-the-art?
|
Their novel neural based model gained up to 10% improvement over state-of-the-art neural based models developed for languages beside Nepali.
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1707.07048
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| null |
For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,
$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)
Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization.
For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,
$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)
Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization.
|
How is the discriminative training formulation different from the standard ones?
|
The answers are shown as follows:
* the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$
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null | false
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William Henry Harrison (February 9, 1773 – April 4, 1841) was an American military officer and politician who served as the ninth president of the United States. Harrison died just 31 days after his inauguration in 1841, and had the shortest presidency in United States history. He was also the first United States president to die in office, and a brief constitutional crisis resulted as presidential succession was not then fully defined in the United States Constitution. Harrison was the last president born as a British subject in the Thirteen Colonies and was the paternal grandfather of Benjamin Harrison, the 23rd president of the United States.
|
From the passage below, please locate the birthdate of William Harrison.
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William Henry Harrison was born on February 9, 1773.
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null | false
| 434
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Table 15: Optimized objective values (i.e., latency in milliseconds) under zero-shot setting when the test applications include all the nine evaluated models (e.g. MobileNet (Edge, V2, V3), M4, M5, M6, t-RNN Dec, t-RNN Enc, U-Net). Lower latency is better. From left to right: the applications used to train the surrogate model in PRIME the target applications for which the accelerator is optimized for, the area constraint of the accelerator, PRIME’s (best, median) latency, and best online method’s (best, median) latency. The best accelerator configurations identified is highlighted in bold.
|
How should PRIME choose the training applications?What is the minimal training application subset to get the best results on all benchmarks?
|
For the conservative surrogates trained by PRIME in Table 4, we are evaluating them on all the applications we study. A subset of the results which are complete are now shown in Appendix A.8., Table 15, Page 20, and depict that PRIME can outperform online evolutionary methods even when evaluated over all applications in a zero-shot manner. Additionally, we experimented with a few more subsets of training applications, with the goal to identify the minimal subset of applications that gives rise to good results on all benchmarks. We are running these experiments and will update the reviewer and the paper before the end of the rebuttal period.
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| 252
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Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%, 1.31%, respectively.
Recently, neural networks based on multitask learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks.
|
Are there any limitations in the existing approaches?
|
The shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks.
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null | false
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What is the tallest building in Chicago?
|
The tallest building in Chicago is the Sears Tower
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1910.10408
| false
| null |
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). While our main goal is to verify our hypotheses on a large data condition, thus the need to include proprietary data, for the sake of reproducibility in both languages we also provide results with systems only trained on TED Talks (small data condition). When training on large scale data we use Transformer with layer size of 1024, hidden size of 4096 on feed forward layers, 16 heads in the multi-head attention, and 6 layers in both encoder and decoder. When training only on TED talks, we set layer size of 512, hidden size of 2048 for the feed forward layers, multi-head attention with 8 heads and again 6 layers in both encoder and decoder.
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De).
|
What dataset do they use?
|
The answers are shown as follows:
* English$\rightarrow $Italian/German portions of the MuST-C corpus
* As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)
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null | false
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I follow you all the time and copy your every move, but you can’t touch me or catch me. What am I?
|
I am your shadow.
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null | false
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Jean Marie Ralph Féthière is a Haitian politician. He is a Senator from the north and a member of the ruling PHTK party. On September 23, 2019, while trying to leave Parliament amid a crowd as the government was voting to confirm a new prime minister, Féthière drew a handgun and fired toward the crowd. Chery Dieu-Nalio, a photographer for the Associated Press, suffered injuries to his face from bullet fragments, while a security guard named Leon Leblanc was also injured.
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In which country is Jean Marie Ralph Féthière a politician?
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Haiti
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null | false
| null |
Joanne Rowling CH OBE FRSL (/ˈroʊlɪŋ/ "rolling"; born 31 July 1965), also known by her pen name J. K. Rowling, is a British author and philanthropist. She wrote Harry Potter, a seven-volume children's fantasy series published from 1997 to 2007. The series has sold over 600 million copies, been translated into 84 languages, and spawned a global media franchise including films and video games. The Casual Vacancy (2012) was her first novel for adults. She writes Cormoran Strike, an ongoing crime fiction series, under the alias Robert Galbraith.
|
Who is J.K.Rowling?
|
Joanne Rowling a.k.a J.K.Rowling is the famous author of Harry Potter books.
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What is the best kind of shade tree for a backyard in growing region 6?
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There are many great trees homeowners in growing region 6 may want to consider for their backyard depending on their priorities.
First, the two main types of trees are deciduous, trees that lose their leaves each winter, and evergreen, which retain their foliage year round. Many of the most popular shade trees are deciduous trees, however there are some great evergreen options as well.
Starting with deciduous trees, there are flowering trees and traditional shade trees. Of the traditional shade trees, you may want to consider maples and oaks, of which there are many varieties. Red maples grow a bit slower than silver maples, but have hardier bark, beautiful red leaves in the fall, and a more pleasing traditional branching habit that many prefer vs, Silver maples. Sugar maples growth slower still, but are beautiful, prized trees that turn glowing orange in the fall and produce the sap used in maple syrup come spring. These trees can be 40-70 feet tall at maturity. Oaks are incredibly beautiful trees that are typically a little slower growing than maples, but have strong bark, enabling them to survive harsher conditions and have a long lifespan. They can handle being used as street trees as they do fine with moderate pollution levels and salt from de-icing. You really can't go wrong with maples or oaks as a summer shade tree, with their tall canopies providing wonderful shade once their leaves fill in during the spring.
Some popular flowering options that provide ample shade include cherry and magnolia trees, both of which have beautiful showy spring flowers and reach a height of 20-60 feet at maturity depending on which of the many variants you choose (be careful with this as some are best suited to warmer climates and don't tolerate the cold as well).
For evergreens, the options are a bit more limited as many of these trees have low canopies that extend all the way to the ground. Many species of spruce and fir tree can have their canopies raised as they mature, creating space underneath for shade. Some pine trees have ample canopy under them, however their thin needle density can limit the amount of shade they provide.
Whichever you ultimately choose, Shade trees add a ton of value to a home and are a great investment. Consider working with an arborist to make the decision based on your lighting and soil conditions. Enlisting a professional's help with planting will ensure your new tree is set up to grow healthy and strong.
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1807.11714
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We use the English coreference resolution dataset from the CoNLL-2012 shared task BIBREF15 , the benchmark dataset for the training and evaluation of coreference resolution. The training dataset contains 2408 documents with 1.3 million words. We use two state-of-art neural coreference resolution models described by BIBREF2 and BIBREF1 . We report the average F1 value of standard MUC, B INLINEFORM0 and CEAF INLINEFORM1 metrics for the original test set.
We use two state-of-art neural coreference resolution models described by BIBREF2 and BIBREF1 .
|
What is the state-of-the-art neural coreference resolution model?
|
The answers are shown as follows:
* BIBREF2
* BIBREF1
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null | false
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who won the oscar 2023 for best original song music
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M.M.Keeravani , Chnadrabose won the oscar for Naatu Naatu song from film RRR. RRR is one the biggest and top grosser in Indian films and got various applauds internationally. RRR was directed by S.S.Rajamouli one of the greatest Director who directed films like Baahubali which is the one of the biggest periodic drama. Ramcharan and NTR are the lead actors in RRR.
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null | false
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What is a computer network?
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A computer network are groups of computers than can communicate with each other through a LAN or Local Area Network. In the early days of networking they all had to be hard wired and with the development of newer technology networks could communicate and be setup over wireless. Computer networks also share data and devices with each other. For example a shared printer can be used by multiple devices on the same computer network.
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null | false
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Tell me whether these are indoor or outdoor games.
Cricket, Table Tennis, Soccer, Carom
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Cricket- Outdoor Game, Table Tennis - Indoor Game, Soccer- Outdoor Game, Carom - Indoor Games
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null | false
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Classify each of the following workouts as upper body or lower body: bench press, pull-up, squat, rows, leg extension, calf raise
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Bench Press: Upper Body
Pull-up: Upper Body
Squat: Lower Body
Rows: Upper Body
Leg Extension: Lower Body
Calf Raise: Lower Body
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1910.11790
| true
| null |
BERT BIBREF6 is a state-of-the-art model, which has been pre-trained on a large corpus and is suitable to be fine-tuned for various downstream NLP tasks. The main innovation between this model and existing language models is in how the model is trained. For BERT, the text conditioning happens on both the left and right context of every word and is therefore bidirectional. In previous models BIBREF7, a unidirectional language model was usually used in the pre-training. With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).
For this study, the NSP is used as a proxy for the relevance of response. Furthermore, in order to improve performance, we fine-tune on a customized dataset which achieved an accuracy of 82.4%. For the main analysis, we used the single-turn dataset, which gave us a correlation of 0.28 between the mean of the AMT evaluation and the BERT NSP. Next, we put each score into a category. For example, if the average score is 2.3, this would be placed in category 2. We then displayed the percentage of positive and negative predictions in a histogram for each of the categories. As seen in Figure FIGREF5, a clear pattern is seen between the higher scores and the positive prediction, and the lower scores and the negative predictions. details of how they are combined to create a final classification layer.
With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).
For this study, the NSP is used as a proxy for the relevance of response.
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was bert used?
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Yes.
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null | false
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What is the most effective way to clean your floors?
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Using the O-Cedar EasyWring Microfiber Spin Mop and Bucket Cleaning System, fill the bucket with hot water and 1 TSP of powdered tide. It will leave your floors super clean and smelling good. For deeper cleans, you can add a bit of distilled white vinegar.
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| 339
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Semantic similarity can be estimated between words, between phrases, between sentences, and between documents in a corpus. Estimation could either be based upon corpus text alone by applying approaches like vector space modeling BIBREF16, latent semantic analysis BIBREF17, topic modeling BIBREF18, or neural embeddings (e.g., Word2Vec BIBREF19 or Glove BIBREF20) and their extensions to phrase, sentence, and document levels. Otherwise it can be estimated based upon ontological relationships (e.g., WordNet based BIBREF21) among concept terms appearing in the corpus.
Semantic similarity can be estimated between words, between phrases, between sentences, and between documents in a corpus.
|
How to estimate the semantic similarity?
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Semantic similarity can be estimated between words, between phrases, between sentences, and between documents in a corpus.
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What is the best bike setup for Zwift's Three Little Sisters tour?
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I recommend a lightweight bike with good climbing capabilities for this route, such as the Specialized Tarmac, Canyon Ultimate, or Trek Emonda. These bikes offer a good balance of speed and climbing ability, which can be helpful on the steep ascents of the Three Little Sisters tour. The route will first tackle the Hilly KOM, Titan's Grove KOM, and finally, finish on the Volcano KOM. In terms of wheels, a set of lightweight climbing wheels such as the DT Swiss disc or Zipp 858's could be a good option, as they offer reduced weight and improved aerodynamics.
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null | false
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Which is a species of fish? Bass or Treble
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Bass
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null | false
| null |
In historical linguistics, transphonologization (also known as rephonologization or cheshirization, see below) is a type of sound change whereby a phonemic contrast that used to involve a certain feature X evolves in such a way that the contrast is preserved, yet becomes associated with a different feature Y.
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For what is known as Transphonologization?
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In historical linguistics, transphonologization (also known as rephonologization or cheshirization, see below) is a type of sound change whereby a phonemic contrast that used to involve a certain feature X evolves in such a way that the contrast is preserved, yet becomes associated with a different feature Y.
For example, a language contrasting two words */sat/ vs. */san/ may evolve historically so that final consonants are dropped, yet the modern language preserves the contrast through the nature of the vowel, as in a pair /sa/ vs. /sã/. Such a situation would be described by saying that a former contrast between oral and nasal consonants has been transphonologized into a contrast between oral and nasal vowels.
The term transphonologization was coined by André-Georges Haudricourt. The concept was defined and amply illustrated by Hagège & Haudricourt; it has been mentioned by several followers of panchronic phonology, and beyond.
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Tell me whether each of these is a drug or food: marijuana, fried chicken, alcohol, caffeine, sushi, mango
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Marijuana: Drug
Fried Chicken: Food
Alcohol: Drug
Caffeine: Drug
Sushi: Food
Mango: Food
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null | false
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Gangga (or Tingri according to name of region) (Tibetan: སྒང་དགའ་, Wylie: sgang dga'; Chinese: 岗嘎镇; pinyin: Gǎnggā Zhèn) is a town in Tingri County, in the south of the Tibet Autonomous Region. It has a population of around 523, and is often used as a base by mountain climbers preparing to ascend Mount Everest or Cho Oyu. It is known[citation needed] for its views of Mount Everest, Mount Lhotse, Mount Makalu, and Cho Oyu, which comprise four of the six highest mountains in the world.
Tingri used to be an important trading post where Sherpas from Nepal exchanged rice, grain and iron for Tibetan wool, livestock and salt. It gives its name to the more than 4,500-meter (14,800 ft)-high broad upland basin that is known as the Tingri Plain. One must cross the pass known as the Lak Pa La (el. 5,220 meters (17,130 ft)) to the north to reach the Tsangpo Valley system. Shallow, fast-flowing rivers of melted snow water make its grassy meadowland ideal for grazing by Tibetan animals. The plain used to abound with gazelles, blue sheep, antelopes and khyang or wild asses but most of the animals are gone now. The Chinese army has a small base nearby.
Tingri Lankor (Ding ri glang 'khor) — Padampa's Residence was founded in 1097 CE by the South Indian Buddhist adept, Padampa Sangye (died 1117) who made five visits to Tibet and was an important person in the re-establishment of the Buddhadharma in Tibet. His consort was the Tibetan dakini Machik Labdron. The gompa or temple was built in the Padampa Sange's meditation cave and became the seat of the Dampapa School of Tibetan Buddhism. It is in the process of restoration.
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Why was Tingri important for Nepalese people based on the following text?
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Tingri was used as a trading post by Sherpas from Nepal as a means for exchanging rice, grain and iron in return for Tibetan wool, livestock and salt.
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Dixie Violet Egerickx (born 31 October 2005) is an English actress. Egerickx has appeared three times on the London stage, as Iphigenia in Robert Icke's adaptation of Oresteia, Rosalind in Alexi Kay Campbell's Sunset at the Villa Thalia at the National Theatre and as Jenny Caroline 'Qui Qui' Marx in Richard Bean and Clive Coleman's Young Marx at The Bridge Theatre, directed by Sir Nicholas Hytner. She appeared in the 2017 National Geographic series Genius and the 2018 television series Patrick Melrose making her feature film debut in the 2018 film The Little Stranger. Egerickx was named in Screen International's Screen Stars of Tomorrow list in 2019 at the age of 13. She starred as Edie in the 2020 drama film Summerland, and portrayed Mary Lennox in the 2020 film The Secret Garden. In 2019 she filmed HBO's Unaired Game of Thrones Prequel Pilot and most recently has completed filming the role of Jo Ransome in the Apple TV miniseries of The Essex Serpent, directed by Clio Barnard.
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Extract the names of the stage shows Dixie Violet Egerickx starred in and the role she played in them. List them using bullets in the format {Stage} - {Role}
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Oresteia - Iphigenia
Sunset at the Villa Thalia - Rosalind
Young Marx - Jenny Caroline 'Qui Qui' Marx
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Tell me which of these is a Country, a City, a River, or a Mountain: New York, Nile, Amazon, Kilimanjaro, USA, Spain, Italy, Rome, Prague, Zurich, France, San Francisco, K2, Buenos Aires, Brazil, Mount Everest, London, Mekong
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New York - City
Nile - River
Amazon - River
Kilimanjaro - Mountain
USA - Country
Spain - Country
Italy - Country
Rome - City
Prague - City
Zurich - City
France - Country
San Francisco - City
K2 - Mountain
Buenos Aires - City
Brazil - Country
Mount Everest - Mountain
London - City
Mekong - River
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What kind of milk is Vietnamese coffee often served with?
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Vietnamese coffee is often brewed over sweetened condensed milk.
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Waring was born John Roderick Rodriguez in Lewisham, London, into a respectable family of Portuguese ancestry, and attended a Catholic school in Southwark.
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Where was his Catholic school?
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Southwark
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Stanwell-Fletcher Lake is the largest lake on Somerset Island, the tenth-largest island of the Canadian Arctic Archipelago. The lake, along with most of Somerset Island, is located within the Qikiqtaaluk Region of the Canadian territory of Nunavut.
The harsh tundral climate and minimal soil cover of the highlands surrounding the lake allows only a few types of plants to grow there, mostly lichens, mosses, and arctic willow. Vegetation is more prominent in the thermokarst lowlands to the north of the lake, which are dominated by sedges during the summer. The low-lying areas to the southeast of the lake, and the nearby Creswell Bay, host a Canadian Important Bird Area. The area supports large bird populations during the summer, especially breeding populations of various shorebirds. 13 shorebird species have been observed in the area, while 11 are known to breed there. These species includes the white-rumped sandpiper, red phalarope, black-bellied plover, sanderling, American golden plover, ruddy turnstone, Baird's sandpiper, buff-breasted sandpiper, pectoral sandpiper, semipalmated sandpiper, and red knot. Other birds with populations in the area include the snow goose, king eider, long-tailed duck, northern fulmar, black-legged kittiwake, and peregrine falcon. Stanwell-Fletcher Lake is also a large and important habitat for anadromous populations of arctic char, which spend summer in coastal areas including Creswell Bay and surrounding waters, but migrate via the Union River to in the lake before it freezes over. Fourhorn sculpin are also present within the lake.
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Write a bulleted list of the plants that grow around Stanwell-Fletcher Lake.
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* lichens
* mosses
* arctic willow
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Casting took place virtually through Zoom due to the COVID-19 pandemic. Casting director Victoria Thomas wanted to honor the game without being limited by it. Mazin and Thomas sought high-profile guest stars; Thomas said many of the actors "don't usually do one-episode guest spots". On February 10, 2021, Pascal and Ramsey were cast as Joel and Ellie. Earlier that day, it was reported Mahershala Ali was offered the role of Joel after Matthew McConaughey turned it down; The Hollywood Reporter noted Ali "did circle a role" in the show, but a deal was never formed. Any actresses considered for Ellie for the canceled film adaptation—such as Maisie Williams and Kaitlyn Dever—had aged out of consideration by the time the series was in production, resulting in a reset of candidates. The producers primarily sought actors who could embody Joel and Ellie individually as well as imitate their relationship.: 14:42 Though both were previously featured on HBO's Game of Thrones, Pascal and Ramsey had not met before the filming of The Last of Us began but found they had instant chemistry, which developed over the course of production.
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Given this paragraph about the Last of Us TV Series, who was not cast in the show?
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Matthew McConaughey turned down the role of Joel which was also reportedly offered to and turned down by Mahershala Ali afterwards. Maisie Williams and Kaitlyn Dever were considered for the role of Ellie but aged out of consideration by the time of production.
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What is beta oxidation?
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In biochemistry and metabolism, beta oxidation (also β-oxidation) is the catabolic process by which fatty acid molecules are broken down in the cytosol in prokaryotes and in the mitochondria in eukaryotes to generate acetyl-CoA, which enters the citric acid cycle, and NADH and FADH2, which are co-enzymes used in the electron transport chain. It is named as such because the beta carbon of the fatty acid undergoes oxidation to a carbonyl group. Beta-oxidation is primarily facilitated by the mitochondrial trifunctional protein, an enzyme complex associated with the inner mitochondrial membrane, although very long chain fatty acids are oxidized in peroxisomes.
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William Jefferson Clinton (né Blythe III; born August 19, 1946) is an American politician who served as the 42nd president of the United States from 1993 to 2001. He previously served as governor of Arkansas from 1979 to 1981 and again from 1983 to 1992, and as attorney general of Arkansas from 1977 to 1979. A member of the Democratic Party, Clinton became known as a New Democrat, as many of his policies reflected a centrist "Third Way" political philosophy. He is the husband of Hillary Clinton, who was a U.S. senator from New York from 2001 to 2009, secretary of state from 2009 to 2013 and the Democratic nominee for president in the 2016 presidential election.
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Who is Bill Clinton?
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William Jefferson Clinton (né Blythe III; born August 19, 1946) is an American politician who served as the 42nd president of the United States from 1993 to 2001. He previously served as governor of Arkansas from 1979 to 1981 and again from 1983 to 1992, and as attorney general of Arkansas from 1977 to 1979. A member of the Democratic Party, Clinton became known as a New Democrat, as many of his policies reflected a centrist "Third Way" political philosophy. He is the husband of Hillary Clinton, who was a U.S. senator from New York from 2001 to 2009, secretary of state from 2009 to 2013 and the Democratic nominee for president in the 2016 presidential election.
Clinton was born and raised in Arkansas and attended Georgetown University. He received a Rhodes Scholarship to study at University College, Oxford, and later graduated from Yale Law School. He met Hillary Rodham at Yale; they married in 1975. After graduating from law school, Clinton returned to Arkansas and won election as state attorney general, followed by two non-consecutive tenures as Arkansas governor. As governor, he overhauled the state's education system and served as chairman of the National Governors Association. Clinton was elected president in the 1992 presidential election, defeating incumbent Republican president George H. W. Bush and independent businessman Ross Perot. At 46 years old, he became the third-youngest president of the United States and the first president to be born in the Baby Boomer generation.
Clinton presided over the longest period of peacetime economic expansion in American history. He signed into law the North American Free Trade Agreement (NAFTA) and the Violent Crime Control and Law Enforcement Act, but failed to pass his plan for national health care reform. The Republican Party won unified control of Congress for the first time in 40 years in the 1994 elections, but Clinton was still comfortably re-elected in 1996, becoming the first Democrat since Franklin D. Roosevelt to win a second full term. Starting in the mid-1990s, he began an ideological evolution as he became much more conservative in his domestic policy, advocating for and signing the Personal Responsibility and Work Opportunity Act, the State Children's Health Insurance Program and financial deregulation measures. He appointed Ruth Bader Ginsburg and Stephen Breyer to the U.S. Supreme Court. During the last three years of Clinton's presidency, the Congressional Budget Office reported a budget surplus—the first such surplus since 1969. In foreign policy, Clinton ordered U.S. military intervention in the Bosnian and Kosovo wars, eventually signing the Dayton Peace agreement. He also called for the expansion of NATO in Eastern Europe and many former Warsaw Pact members joined NATO during his presidency. Clinton's foreign policy in the Middle East saw him sign the Iraq Liberation Act which gave aid to groups against Saddam Hussein. He also participated in the Oslo I Accord and Camp David Summit to advance the Israeli–Palestinian peace process, and assisted the Northern Ireland peace process.
Clinton's second term was dominated by the Monica Lewinsky scandal, which began in 1996, when he had a sexual relationship with 22-year-old Monica Lewinsky, an intern at the White House. In January 1998, news of the affair made tabloid headlines. This scandal escalated throughout the year, culminating on December 19 when Clinton was impeached by the House of Representatives, becoming the second U.S. president—the first since Andrew Johnson—to be impeached. The two impeachment articles that the House passed were centered around him using the powers of the presidency to obstruct the investigation and lying under oath. In 1999, Clinton's impeachment trial began in the Senate. He was acquitted on both charges as the Senate failed to cast 67 votes against him, which was necessary to meet the two-thirds conviction threshold prescribed by Article I, section 3, clause 6 of the U.S. Constitution.
Clinton left office in 2001 with the joint-highest approval rating of any U.S. president in the modern era, alongside Franklin D. Roosevelt and Ronald Reagan. His presidency has been ranked among the upper tier in historical rankings of U.S. presidents. However, his personal conduct and allegations of sexual assault have made him the subject of substantial scrutiny. Since leaving office, Clinton has been involved in public speaking and humanitarian work. He created the Clinton Foundation to address international causes such as the prevention of HIV/AIDS and global warming. In 2009, he was named the United Nations Special Envoy to Haiti. After the 2010 Haiti earthquake, Clinton and George W. Bush formed the Clinton Bush Haiti Fund. He has remained active in Democratic Party politics, campaigning for his wife's 2008 and 2016 presidential campaigns.
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1705.09665
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Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable.
. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 .
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How do the authors measure how temporally dynamic a community is?
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The answers are shown as follows:
* the average volatility of all utterances
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Who created the game of basketball?
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Basketball was invented by Dr. James Naismith, a canadian physical education teacher in winter of 1891. The game was invented to keep his students active during the Winter months.
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What is some advice for planning an Alaskan vacation?
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First, you need to determine what are your goals for visiting Alaska. Do you enjoy summer activities or winter activities? Do you want to see the Northern Lights or would you rather vacation under the Midnight Sun? Once you have decided when to go, you can build your itinerary around these desired activities. It's important to note that if you want to work within a budget, it's more expensive during the summer. Plane tickets, hotels, and car rentals will be pricey and oftentimes harder to reserve. On the other hand, it will be cheaper to visit Alaska in the winter, as you will have more hotel options available to you, but fewer flight options, and you will need to have winter driving experience if you rent a car.
In the summertime, you will get to experience the state in full bloom -- pun intended. Because Alaska gets more direct sunlight during this time of year, everything grows big and fast. The best months for a summertime visit are late May, June, and July. The further north you go, the more sunlight you will have, with 24 hours of full sun in Utqiagvik (formerly known as Barrow). There are plenty of places to go hiking, fishing, camping, and hunting. Don't forget to bring bug spray, as the mosquitoes are huge and plentiful; you'll also likely encounter "no-see-ums" which are tiny biting midges that can swarm you. In Anchorage, the biggest city, you can rent a bicycle to ride the hundreds of miles of trails within the municipality. Keep in mind that despite the extra sun exposure, you'll likely encounter cold and rainy weather, so bring a rain jacket.
In the wintertime, you will have the opportunity to see the Northern Lights. The further north you are, the chances are higher that you will see them. When it's very cold and clear out, and if you're away from city light pollution, you're almost guaranteed to see a beautiful display in the night sky. While you might not have much success camping or fishing, you can still do other activities like skiing and snowboarding at the world-famous Alyeska resort, ice skating on many public-use ponds and lakes, and perhaps catch the ceremonial start of the Iditarod dog race in February. Don't forget to bring lots of warm clothes and to layer up from head-to-toe. Average December temperatures across Alaska vary from a low of 27F in Juneau to -20F in Tok, but it isn't unusual for temperatures to drop much, much further.
Many people don't realize that Alaska is a very, very big state. Don't try to cram too many activities into a short amount of time and give yourself plenty of time to travel to your intended destinations. There most reliable transportation is either a car or plane; there is not a well-developed rail system nor other public transport system. Some car rental company won't let you drive to certain areas because of inherent highway dangers, such as unpaved, gravel roads, isolation, and animal dangers. You'll encounter delays on roads in the summer due to road construction and delays in the winter due to accidents, ice, and blizzards.
No matter what time of year you go, there will be plenty to see and do in Alaska. Just remember to plan ahead, budget accordingly, dress appropriately for the weather, and build some flexibility into your itinerary to accommodate for delays or unexpected changes.
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America’s cup comeback
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In September, 2013 one of sports most iconic comeback took place on the San Francisco Bay.
The incredible comeback was when Oracle Team USA overturned an 8-1 deficit to retain the Auld Mug.
Winning eight straight races has to be ranked the best and most unlikely of all such sporting turnarounds.
With the Kiwis requiring just one more race for victory, the Americans called up Ben Ainslie who was the driver for the reserve boat. The Brit’s influence helped shift the momentum that the Kiwis were unable to stop.
San Francisco Bay is extremely challenging to sail with currents and winds above 25knts. The regatta will forever be remembered as the best comeback of sailing all time.
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What is the purpose of using Terraform modules ?
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In order to easily maintain, manage and scale their infrastructure, DevOps teams design their infrastructure using modular and reusable components called Terraform modules. Terraform modules allow you to easily share and reuse the same components across multiple use cases and environments. It also helps enforce a standardised approach of defining resources and adopting best practices across your organisation. Not only does consistency ensure best practices are followed, it also helps to enforce compliant deployment, avoid accidental misconfigurations which could lead to costly errors.
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Should I become an influencer?
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It is important to consider several factors when thinking about becoming an influencer. Influencers usually have a defined niche, and can make up to six figures or more creating content about it. However, at least in the beginning, your income will likely not come in a steady manner, and you may even be investing more into getting set up than you may be earning in return. The work happens in a freelance manner - if you like setting your own schedule this could appeal to you, but the flip side would be that your work bleeds more into your weekends and evenings. Some people also would love the idea of pursuing their hobbies and interest full time, but when they have to treat it as their job, soon become disenchanted.
In summary, your decision to become an influencer should be based on your financial situation, risk tolerance, and personal working style and interests.
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Identify which animal is domesticated or wild: Fancy rat, Sugar glider
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Fancy rat is domesticated, Sugar glider is wild.
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Samuel Barry Jones (8 July 1923 – 26 January 1999) was an Australian politician. He was a member of the New South Wales Legislative Assembly from 1965 until 1984 and a member of the ALP.
Early life
Samuel Barry Jones was born in Newcastle. He was the son of a steelworker and a brother of Charlie Jones. He was educated to intermediate level at Newcastle Boys' High School and trained as a fitter and turner. He was an office-holder in the Amalgamated Engineering Union.
Political career
Sam Jones joined the Labor Party in 1945 and served as vice president and secretary of Tighes Hill branch, vice president of Newcastle and Waratah State Electorate Councils and secretary of Newcastle Federal Electorate Council.
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Which party Sam Jones was part of?
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Sam Jones (Samuel Barry Jones) served as vice president and secretary of the Labor Party in 1945.
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What's the hardest mineral?
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Diamond is the hardest mineral on earth.
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| 34
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Single-relation factoid questions are the most common form of questions found in search query logs and community question answering websites BIBREF1 , BIBREF2 . A knowledge-base (KB) such as Freebase, DBpedia, or Wikidata can help answer such questions after users reformulate them as queries. For instance, the question Where was Barack Obama born? can be answered by issuing the following KB query: $
\lambda (x).place\_of\_birth(Barack\_Obama, x)
$
However, automatically mapping a natural language question such as Where was Barack Obama born? to its corresponding KB query remains a challenging task.
There are three key issues that make learning this mapping non-trivial. First, there are many paraphrases of the same question. Second, many of the KB entries are unseen during training time; however, we still need to correctly predict them at test time. Third, a KB such as Freebase typically contains millions of entities and thousands of predicates, making it difficult for a system to predict these entities at scale BIBREF1 , BIBREF3 , BIBREF0 . In this paper, we address all three of these issues with a character-level encoder-decoder framework that significantly improves performance over state-of-the-art word-level neural models, while also providing a much more compact model that can be learned from less data.
First, we use a long short-term memory (LSTM) BIBREF4 encoder to embed the question. Second, to make our model robust to unseen KB entries, we extract embeddings for questions, predicates and entities purely from their character-level representations. Character-level modeling has been previously shown to generalize well to new words not seen during training BIBREF5 , BIBREF6 , which makes it ideal for this task. Third, to scale our model to handle the millions of entities and thousands of predicates in the KB, instead of using a large output layer in the decoder to directly predict the entity and predicate, we use a general interaction function between the question embeddings and KB embeddings that measures their semantic relevance to determine the output. The combined use of character-level modeling and a semantic relevance function allows us to successfully produce likelihood scores for the KB entries that are not present in our vocabulary, a challenging task for standard encoder-decoder frameworks.
Our novel, character-level encoder-decoder model is compact, requires significantly less data to train than previous work, and is able to generalize well to unseen entities in test time. In particular, without use of ensembles, we achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% BIBREF0 by 8.2% and 6.4% respectively. Moreover, we only use the training questions provided in SimpleQuestions to train our model, which cover about 24% of words in entity aliases on the test set. This demonstrates the robustness of the character-level model to unseen entities. In contrast, data augmentation is usually necessary to provide more coverage for unseen entities and predicates, as done in previous work BIBREF0 , BIBREF1 .
In particular, without use of ensembles, we achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% (Bordes et al., 2015) by 8.2% and 6.4% respectively.
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Does the authors' model outperform the previous state-of-arts on the SimpleQuestions dataset?
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Yes.
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2002.00317
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Statistical significance is assessed for select results using bootstrapping with 1000 samples in each of 100 iterations. This test shows that conditioning on the introduction of the source document improves performance compared to conditioning on the abstract when using the SciGPT2 model. However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used.
However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used.
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Which baseline performs best?
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The answers are shown as follows:
* IR methods perform better than the best neural models
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1704.04539
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Cross-lingual techniques can cope with the lack of labeled data on languages when this data is available in at least one language, usually English. The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 . Another common thread of cross-lingual work is model transfer, where parameters are shared across languages BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37 .
The annotation projection method, which we follow in this work, is one way to address this problem. It was introduced for POS tagging, base noun phrase bracketing, NER tagging, and inflectional morphological analysis BIBREF29 but it has also been used for dependency parsing BIBREF30 , role labeling BIBREF31 , BIBREF32 and semantic parsing BIBREF26 .
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Do the authors test their annotation projection techniques on tasks other than AMR?
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No.
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Mumbai Meri Jaan (translation: Mumbai, My Life) is a 2008 Indian drama film directed by Nishikant Kamat and produced by Ronnie Screwvala. It stars R. Madhavan, Irrfan Khan, Soha Ali Khan, Paresh Rawal and Kay Kay Menon. It deals with the aftermath of the 11 July 2006 Mumbai train bombings, where 209 people lost their lives and over 700 were injured. It won multiple Filmfare Awards.Rupali Joshi (Soha Ali Khan) is a successful reporter who is getting married in two months. Nikhil Agrawal (Madhavan) is an environmentally conscious executive who rides the train to work every day and is expecting his first child. Suresh (Kay Kay Menon) is a struggling computer tech who spends his time loafing at a local cafe and criticizing Muslims. Meanwhile, Sunil Kadam (Vijay Maurya) struggles with the corruption and inefficiency of the Mumbai police force and his boss, Tukaram Patil (Paresh Rawal), who is nearing retirement.On 11 July Nikhil and Suresh are in the second class compartment of a train when a bomb goes off in the first class compartment. The two survive, but Nikhil is too afraid to take the train again and is diagnosed with acute stress disorder. Suresh becomes obsessed with punishing the city's Muslims and is only stopped from antagonising them by Kadam and Patil on patrol. Kadam and Patil abuse a street vendor named Thomas (Irrfan Khan) who begins calling in fake bomb scares at malls to relieve his feelings. After an elderly man suffers a heart attack while the police are evacuating one mall, Thomas feels guilty and decides to stop.Rupali, who rushed to the scene of the bombings to cover the story, is devastated when she discovers that her fiancé died in the blasts. Her grief is augmented when the news channel she works for tries to exploit her story for ratings. Meanwhile, Suresh pursues a Muslim that he suspects of being a terrorist. However, after Patil stops him and lectures him on communal harmony, Suresh befriends the man.After Nikhil's wife goes into labour, he is forced to take the train to get to the hospital. Mumbai stops for two minutes while the city observes a moment of silence for those killed in the bombings. Patil finally retires from the police force and Kadam forgives him for his corrupt actions. Nikhil overcomes his fear of trains and Thomas gives a rose to the elderly man whose heart attack he caused.Filmfare Critics Award for Best Movie - WinnerFilmfare Best Screenplay Award - WinnerFilmfare Best Editing Award - WinnerNational Film Award for Best Special Effects for Govardhan (Tata Elxsi) - WinnerBest Feature Film at the New Generation Cinema Lyon Film Festival - WinnerBest Screenplay award at the 2009 Asia Pacific Screen Awards - NominationThe end titles are accompanied by the song \Aye Dil Hain Mushkil\" (also known as \"Bombay Meri Jaan\") from the 1956 film C.I.D.
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Which film won multiple Filmfare Awards?
A. Mumbai Meri Jaan
B. Govardhan
C. C.I.D.
D. The end titles are accompanied by the song Aye Dil Hain Mushkil.
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The film from this prompt that won multiple Filmfare Awards was Mumbai Merj Jaan. It won the award for Best Screenplay, Best Editing, and Best Special Effects at the 2009 Asian Pacific Screen Awards.
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What is the history of Tax Day in the United States?
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Tax Day exists as a deadline for many United States residents to report earnings in order to pay income taxes.
The United States introduced the income tax in 1861 in order to fund the American Civil War. This was later deemed unconstitutional and repealed in 1872. This was later reenacted in 1894 as part of the Revenue Act with its constitutionality settled in the Sixteenth Amendment to the Constitution in 1913. Tax day was initially set for March in 1913 and was eventually moved to April 15th in 1955.
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Who owns the Dallas Cowboys?
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Jerry Jones
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The geologic time scale is a way of representing deep time based on events that have occurred throughout Earth's history, a time span of about 4.54 ± 0.05 Ga (4.54 billion years). It chronologically organizes strata, and subsequently time, by observing fundamental changes in stratigraphy that correspond to major geological or paleontological events. For example, the Cretaceous–Paleogene extinction event, marks the lower boundary of the Paleogene System/Period and thus the boundary between the Cretaceous and Paleogene Systems/Periods. For divisions prior to the Cryogenian, arbitrary numeric boundary definitions (Global Standard Stratigraphic Ages, GSSAs) are used to divide geologic time. Proposals have been made to better reconcile these divisions with the rock record.
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How old is the Earth?
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Earth is about 4.54 billion years old.
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Breaking Bad is an American neo-Western crime drama television series created and produced by Vince Gilligan for AMC. Set and filmed in Albuquerque, New Mexico, the series follows Walter White (Bryan Cranston), an underpaid, overqualified, and dispirited high-school chemistry teacher who is struggling with a recent diagnosis of stage-three lung cancer. White turns to a life of crime and partners with a former student, Jesse Pinkman (Aaron Paul), to produce and distribute methamphetamine to secure his family's financial future before he dies, while navigating the dangers of the criminal underworld. Breaking Bad premiered on AMC on January 20, 2008, and concluded on September 29, 2013, after five seasons consisting of 62 episodes.
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What is "Breaking Bad"?
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Breaking Bad is an American neo-Western crime drama television series created and produced by Vince Gilligan for AMC. Set and filmed in Albuquerque, New Mexico, the series follows Walter White (Bryan Cranston), an underpaid, overqualified, and dispirited high-school chemistry teacher who is struggling with a recent diagnosis of stage-three lung cancer. White turns to a life of crime and partners with a former student, Jesse Pinkman (Aaron Paul), to produce and distribute methamphetamine to secure his family's financial future before he dies, while navigating the dangers of the criminal underworld. Breaking Bad premiered on AMC on January 20, 2008, and concluded on September 29, 2013, after five seasons consisting of 62 episodes.
Among the show's co-stars are Anna Gunn and RJ Mitte as Walter's wife Skyler and son Walter Jr., and Betsy Brandt and Dean Norris as Skyler's sister Marie Schrader and her husband Hank, a DEA agent. Others include Bob Odenkirk as Walter's and Jesse's lawyer Saul Goodman, Jonathan Banks as private investigator and fixer Mike Ehrmantraut, and Giancarlo Esposito as drug kingpin Gus Fring. The final season introduces Jesse Plemons as the criminally ambitious Todd Alquist, and Laura Fraser as Lydia Rodarte-Quayle, a cunning business executive secretly managing Walter's global meth sales for her company.
Breaking Bad's first season received generally positive reviews, while the rest of its run received unanimous critical acclaim, with praise for the performances, direction, cinematography, screenplay, story, and character development. Since its conclusion, the show has been lauded by critics as one of the greatest television series of all time. It had fair viewership in its first three seasons, but the fourth and fifth seasons saw a moderate rise in viewership when it was made available on Netflix just before the fourth season premiere. Viewership increased more drastically upon the premiere of the second half of the fifth season in 2013. By the time that the series finale aired, it was among the most-watched cable shows on American television. The show received numerous awards, including 16 Primetime Emmy Awards, eight Satellite Awards, two Golden Globe Awards, two Peabody Awards, two Critics' Choice Awards, and four Television Critics Association Awards. Cranston won the Primetime Emmy Award for Outstanding Lead Actor in a Drama Series four times, while Aaron Paul won the Primetime Emmy Award for Outstanding Supporting Actor in a Drama Series three times; Anna Gunn won the Primetime Emmy Award for Outstanding Supporting Actress in a Drama Series twice. In 2013, Breaking Bad entered the Guinness World Records as the most critically acclaimed TV show of all time.
The series gave rise to the larger Breaking Bad franchise. Better Call Saul, a prequel series featuring Odenkirk, Banks, and Esposito reprising their Breaking Bad roles, as well as many others in guest and recurring appearances, debuted on AMC on February 8, 2015, and concluded on August 15, 2022. A sequel film, El Camino: A Breaking Bad Movie, starring Paul was released on Netflix and in theaters on October 11, 2019.
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Machine washing puts great mechanical stress on textiles, particularly natural fibers such as cotton and wool. The fibers at the fabric surface are squashed and frayed, and this condition hardens while drying the laundry in air, giving the laundry a harsh feel. Adding a liquid fabric softener to the final rinse (rinse-cycle softener) results in laundry that feels softer.
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Why should I use fabric softener?
|
Machine washing puts great mechanical stress on textiles, so adding a liquid fabric softener to the final rinse results in laundry that feels softer.
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2001.00137
| true
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We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT.
The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words.
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Should their approach be applied only when dealing with incomplete data?
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No.
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What are some games you can play in the middle of nature and a bunch of people?
|
There are the classic games you can always play, like tag, or I spy, or Truth or Dare, but there are plenty of ways to be more creative.
You can collect a bunch of rocks. Then about 10 feet away create a small stack of rocks. Each person takes turns with their own pebbles and tries to knock the stack of rocks over.
If you are near moving water, you can have a "stick boat" race. Everyone chooses a stick and places it at one point in the river, and you cheer and goad each other to see who's stick wins the race.
You can divide into teams and create "nature art" like Andy Goldsworthy who assembles leaves and sticks into beautiful images.
You can simulate games like Bocce or Frisbee Golf with things you find; rocks can be bocce balls, sticks can be frisbees.
You can make a game out of anything.
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Identify which car manufacturer is Japanese or American: Jeep, Suzuki
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Jeep is American, Suzuki is Japanese
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Who came in first place in Formula One the last ten years?
|
There have been three drivers that have won the world championship over the last ten seasons of Formula One. In 2013, it was Sebastian Vettel (Germany) racing for the RedBull team. Then, Lewis Hamilton (United Kingdom) won seven seasons in a row from 2014-2017 racing for Mercedes. Then, Max Verstappen (Netherlands) won the championship the last two years from 2021-2022 racing for Redbull.
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1901.02262
| false
| null |
FLOAT SELECTED: Table 2: Performance of our and competing models on the MS MARCO V2 leaderboard (4 March 2019). aSeo et al. (2017); bYan et al. (2019); cShao (unpublished), a variant of Tan et al. (2018); dLi (unpublished), a model using Devlin et al. (2018) and See et al. (2017); eQian (unpublished); fWu et al. (2018). Whether the competing models are ensemble models or not is unreported.
FLOAT SELECTED: Table 5: Performance of our and competing models on the NarrativeQA test set. aSeo et al. (2017); bTay et al. (2018); cBauer et al. (2018); dIndurthi et al. (2018); eHu et al. (2018). fResults on the NarrativeQA validation set.
FLOAT SELECTED: Table 2: Performance of our and competing models on the MS MARCO V2 leaderboard (4 March 2019). aSeo et al. (2017); bYan et al. (2019); cShao (unpublished), a variant of Tan et al. (2018); dLi (unpublished), a model using Devlin et al. (2018) and See et al. (2017); eQian (unpublished); fWu et al. (2018). Whether the competing models are ensemble models or not is unreported.
FLOAT SELECTED: Table 5: Performance of our and competing models on the NarrativeQA test set. aSeo et al. (2017); bTay et al. (2018); cBauer et al. (2018); dIndurthi et al. (2018); eHu et al. (2018). fResults on the NarrativeQA validation set.
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What are the baselines that Masque is compared against?
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BiDAF, Deep Cascade QA, S-Net+CES2S, BERT+Multi-PGNet, Selector+CCG, VNET, DECAPROP, MHPGM+NOIC, ConZNet, RMR+A2D
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Provide a list of things a young felon can do when they get out of jail to turn their life around
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1. Move in with family, they are the only ones that actually want the best for you
2. Completely disconnect from your past “friends”, you want them to think you’re dead
3. Get a job right away, a restaurant will be the easiest as they don’t do background checks so yes, lie on the application in this case
4. Go to school, start at a jr college with a plan to transfer to a university, not a bs for profit school but an actual well know university
5. Do not pick a major out of passion….pick a major to get you out of the hole you dug yourself into….anything business related like finance or tech related like data science is a great place to start
6. Get on the honor roll, do whatever it takes, yes, you need to actually read the textbooks
7. Stay on top of your mental health and recovery, seek therapy attend meetings do what you have to do to stay on the right path
8. Avoid all drugs and alcohol to stay focused…especially during your 5 year probation and especially if you have a joint suspension
9. Accelerate your studies, take 21 units if you can while maintaining straight A’s (this is possible)
10. Stay as busy as you can, boredom leads to trouble
11. Apply to transfer to your local university, you can’t leave your town until you’re off probation anyways so might as well stay local
12. When you get in, work even harder, stay on the honor roll and try to get in any honors programs you can
13. Pay all your restitution and recovery in full
14. Probation starts when you’re sentenced at the beginning of serving your time….so if you time this right you can go up for expungement at the same time you will be graduating, so be time conscious
15. Assuming you are broke and you can’t afford $10 - $15 k for an expungement attorney….start looking into Californian penal code 1203.4.
16. Go down to the courthouse…get the forms if you haven’t printed them already and ask questions
17. Get all your case information gathered and fill out the forms
18. Get your court date
19. Collect letters of recommendation from any important person you know, business letterhead is nice to have but not everyone will be comfortable having their company vouch for you. Go to professors, bosses, family, “good” friends, mentors etc. These conversations may be difficult, especially if these people don’t know your past…but you have to do this
20. Write a letter to the judge, short but powerful, something that would shed a tear
21. On the day of your expungement hearing…dress your best
22. Invite everyone who will come to support you at your hearing, the more support the more the judge believes you’re not a total POS and you just were a little lost at some point
23. Be humble and respectful on that stand. Show grace and composure.
24. There’s only about 6% of expungement requests that get granted so if the judge gives it to you….be grateful. You may not be able to help dropping to your knees and crying
25. If you don’t get it, listen to what the judge says and try again
26. Regardless of what happens just know that there is no such thing as luck, you got in this mess from making stupidly ridiculous decisions, and the only way to get out of it is by making extremely smart good decisions.
27. Once you’re expunged….it takes about three months for your record to be expunged from both California and the FBI National database, so hang tight.
28. Start applying for jobs, stay away from schools or anywhere that requires a Livescan. You can pass all background checks but those….and if you end up working for a defense contractor which you totally can….do not ever go for a role that requires a secret or top secret clearance
29. If you’ve made it this far you've just gotten started, get your masters degree next, and keep going from there, get two or even a Doctorate, there is no limit where you will go
30. Work as hard as you can day in and day out. Just remember….it took a whole lot more for you to be sitting where you’re at than the person next to you. Value it, appreciate it and always remember where you came from, but never look back
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| 19
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We analyse the incremental effect of the different features in our system, focusing on the low-resource setting in Track 1 and using development data.
Encoding the entire context with an LSTM highly increases the variance of the observed results. So we trained fifty models for each language and each architecture. Figure FIGREF23 visualises the means and standard deviations over the trained models. In addition, we visualise the average accuracy for the five best models for each language and architecture, as these are the models we use in the final ensemble prediction. Below we refer to these numbers only.
The results indicate that encoding the full context with an LSTM highly enhances the performance of the model, by 11.15% on average. This observation explains the high results we obtain also for Track 2.
Adding the auxiliary objective of MSD prediction has a variable effect: for four languages (de, en, es, and sv) the effect is positive, while for the rest it is negative. We consider this to be an issue of insufficient data for the training of the auxiliary component in the low resource setting we are working with.
We indeed see results improving drastically with the introduction of multilingual training, with multilingual results being 7.96% higher than monolingual ones on average.
We studied the five best models for each language as emerging from the multilingual training (listed in Table TABREF27 ) and found no strong linguistic patterns. The en–sv pairing seems to yield good models for these languages, which could be explained in terms of their common language family and similar morphology. The other natural pairings, however, fr–es, and de–sv, are not so frequent among the best models for these pairs of languages.
Finally, monolingual finetuning improves accuracy across the board, as one would expect, by 2.72% on average.
The final observation to be made based on this breakdown of results is that the multi-tasking approach paired with multilingual training and subsequent monolingual finetuning outperforms the other architectures for five out of seven languages: de, en, fr, ru and sv. For the other two languages in the dataset, es and fi, the difference between this approach and the approach that emerged as best for them is less than 1%. The overall improvement of the multilingual multi-tasking approach over the baseline is 18.30%.
So we trained fifty models for each language and each architecture.
|
How many models were trained for each language and each architecture?
|
Fifty models.
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What is Jenkins?
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Jenkins is an open source automation server. It helps automate the parts of software development related to building, testing, and deploying, facilitating continuous integration and continuous delivery.
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Classify each of these as 'useful' vs 'not useful' when applying for college admissions:
GRE scores, SAT scores, dresses, prom dates, admission letter, recommendation letters, interview prep, networking, fees, headphones, number of friends, social media influencer
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Useful: GRE scores, SAT scores, admission letter, recommendation letters, interview prep, networking, fees
Not useful: dresses, prom dates, headphones, number of friends, social media influencer score
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What's funny about the phrase "Why was Dumbo sad? He felt irrelephant."
|
The phrase "Why was Dumbo sad? He felt irrelephant" is funny because Dumbo is an elephant and that word sounds like irrelevant. The phrase is a pun.
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Swimming under ice. On 16 March 2000, Hof set the Guinness World Record for farthest swim under ice on his second attempt, with a distance of 57.5 metres (188.6 feet). Hof's first attempt the day before failed when he began his swim without goggles and his corneas froze solid and blinded him. A rescue diver pulled him to the surface after he passed out. The record has been broken several times since and is 265 feet (81 m) as of 2022."
|
Who set the first Guinness World Record for swimming under ice?
|
On 16 March 2000, Wim Hof set the Guinness World Record for thre farthest swim under ice, with a distance of 57.5 metres (188.6 feet).
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| 37
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Our resource has the potential to be the basis of computational research in Mapudungun across several areas. Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis. The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation. We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction.
Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community. In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems. The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun. Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications.
The potential applications of our resource, however, are not exhausted in language technologies. The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture.
In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8. As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work.
Our resource has the potential to be the basis of computational research in Mapudungun across several areas.
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What is the important function of the resource collected by the authors?
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The resource has the potential to be the basis of computational research in Mapudungun across several areas.
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| 150
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Language modelling in its inception had one-hot vector encoding of words. However, it captures only alphabetic ordering but not the word semantic similarity. Vector space models helps to learn word representations in a lower dimensional space and also captures semantic similarity. Learning word embedding aids in natural language processing tasks such as question answering and reasoning BIBREF0, stance detection BIBREF1, claim verification BIBREF2.
Recent models BIBREF3, BIBREF4 work on the basis that words with similar context share semantic similarity. BIBREF4 proposes a neural probabilistic model which models the target word probability conditioned on the previous words using a recurrent neural network. Word2Vec models BIBREF3 such as continuous bag-of-words (CBOW) predict the target word given the context, and skip-gram model works in reverse of predicting the context given the target word. While, GloVe embeddings were based on a Global matrix factorization on local contexts BIBREF5. However, the aforementioned models do not handle words with multiple meanings (polysemies).
BIBREF6 proposes a neural network approach considering both local and global contexts in learning word embeddings (point estimates). Their multiple prototype model handles polysemous words by providing apriori heuristics about word senses in the dataset. BIBREF7 proposes an alternative to handle polysemous words by a modified skip-gram model and EM algorithm. BIBREF8 presents a non-parametric based alternative to handle polysemies. However, these approaches fail to consider entailment relations among the words. BIBREF9 learn a Gaussian distribution per word using the expected likelihood kernel. However, for polysemous words, this may lead to word distributions with larger variances as it may have to cover various senses.
BIBREF10 proposes multimodal word distribution approach. It captures polysemy. However, the energy based objective function fails to consider asymmetry and hence entailment. Textual entailment recognition is necessary to capture lexical inference relations such as causality (for example, mosquito $\rightarrow $ malaria), hypernymy (for example, dog $\models $ animal) etc.
In this paper, we propose to obtain multi-sense word embedding distributions by using a variant of max margin objective based on the asymmetric KL divergence energy function to capture textual entailment. Multi-sense distributions are advantageous in capturing polysemous nature of words and in reducing the uncertainty per word by distributing it across senses. However, computing KL divergence between mixtures of Gaussians is intractable, and we use a KL divergence approximation based on stricter upper and lower bounds. While capturing textual entailment (asymmetry), we have also not compromised on capturing symmetrical similarity between words (for example, funny and hilarious) which will be elucidated in Section $3.1$. We also show the effectiveness of the proposed approach on the benchmark word similarity and entailment datasets in the experimental section.
In this paper, we propose to obtain multi-sense word embedding distributions by using a variant of max margin objective based on the asymmetric KL divergence energy function to capture textual entailment.
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How textual entailment is captured?
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They use a variant of max margin objective based on the asymmetric KL divergence energy function to capture textual entailment.
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What is the law of sines?
|
In geometry, the law of sines is a formula to find the triangle measurements based on knowing two angles and a side of it
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1912.01679
| false
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More recently, acoustic representation learning has drawn increasing attention BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23 in speech processing. For example, an autoregressive predictive coding model (APC) was proposed in BIBREF20 for unsupervised speech representation learning and was applied to phone classification and speaker verification. WaveNet auto-encoders BIBREF21 proposed contrastive predictive coding (CPC) to learn speech representations and was applied on unsupervised acoustic unit discovery task. Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks.
Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion.
Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks.
Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion.
|
What are baseline models on WSJ eval92 and LibriSpeech test-clean?
|
The answers are shown as follows:
* Wav2vec BIBREF22
* a fully-supervised system using all labeled data
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What dog breed is a mix between a Cocker Spaniel and a Poodle?
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Cockapoo
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null | false
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Give me a list of the last five european golden boots winner And tell me how many goals they scored.
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1. Robert Lewandowski, 2022-2021, 35 goals
2. Robert Lewandowski, 2021-2020, 41 goals
3. Ciro Immobile, 2020-2019, 36 goals
4. Lionel Messi, 2019-2018, 36 goals
5. Lionel Messi, 2018-2017, 34 goals
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