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Several methods exist for storing hydrogen. These include mechanical approaches such as using high pressures and low temperatures, or employing chemical compounds that release H2 upon demand. While large amounts of hydrogen are produced by various industries, it is mostly consumed at the site of production, notably for the synthesis of ammonia. For many years hydrogen has been stored as compressed gas or cryogenic liquid, and transported as such in cylinders, tubes, and cryogenic tanks for use in industry or as propellant in space programs. Interest in using hydrogen for on-board storage of energy in zero-emissions vehicles is motivating the development of new methods of storage, more adapted to this new application. The overarching challenge is the very low boiling point of H2: it boils around 20.268 K (−252.882 °C or −423.188 °F). Achieving such low temperatures requires expending significant energy.
https://en.wikipedia.org/wiki/Hydrogen_storage
|
Given this paragraph about Hydrogen Storage, what is the hydrogen boiling point?
|
It boils around 20.268 K (−252.882 °C or −423.188 °F).
|
null | false
| 28
|
PARENT evaluates each instance INLINEFORM0 separately, by computing the precision and recall of INLINEFORM1 against both INLINEFORM2 and INLINEFORM3 .
Hence, we adopt a smoothing technique from Chen and Cherry (2014) that assigns a small positive value ε to any of Epn, Ern(Ri) and Er(Ti) which are 0. When multiple references are available for a table, we compute PARENT against each reference and take the maximum as its overall score, similar to METEOR (Denkowski and Lavie, 2014).
|
Have the team adopted any solutions to avoid the danger with geometric averages?
|
Yes.
|
null | false
| null |
Name few motivation tips and techniques for learning
|
Set clear goals: Provide someone with clear and specific goals that are challenging but achievable. This can give them a sense of purpose and direction, and help them stay focused on what they need to accomplish.
Offer feedback and recognition: Regularly provide feedback and recognition to let someone know that their efforts are noticed and appreciated. This can help boost their confidence and morale, and motivate them to continue performing at a high level.
Provide opportunities for growth and development: Offer someone opportunities to learn new skills, take on new challenges, and grow professionally. This can help them stay engaged and motivated, and feel like they are progressing in their career.
Create a positive work environment: Foster a positive work environment that is supportive, collaborative, and respectful. This can help someone feel valued and connected to their work, and motivated to contribute to the team's success.
Provide autonomy and trust: Give someone the autonomy and trust they need to make decisions and take ownership of their work. This can help them feel empowered and invested in their work, and motivated to take on new challenges.
Offer incentives and rewards: Provide someone with incentives and rewards, such as bonuses, promotions, or other perks, to recognize and reward their hard work and achievements.
Encourage teamwork and collaboration: Foster a culture of teamwork and collaboration, where people are encouraged to work together and support one another. This can help build camaraderie and motivation, and create a sense of shared purpose.
|
|
1908.04531
| false
| null |
We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark. In light of the General Data Protection Regulations in Europe (GDPR) and the increased concern for online privacy, we applied some necessary pre-processing steps on our dataset to ensure the privacy of the authors of the comments that were used. Personally identifying content (such as the names of individuals, not including celebrity names) was removed. This was handled by replacing each name of an individual (i.e. author or subject) with @USER, as presented in both BIBREF0 and BIBREF2 . All comments containing any sensitive information were removed. We classify sensitive information as any information that can be used to uniquely identify someone by the following characteristics; racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic data, and bio-metric data.
We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark.
|
How large was the dataset of Danish comments?
|
The answers are shown as follows:
* 3600 user-generated comments
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null | false
| 249
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In this paper, we explore the feasibility of automatically recognising signals of cyberbullying. A crucial difference with state-of-the-art approaches to cyberbullying detection is that we aim to model bullying attacks, as well as reactions from victims and bystanders (i.e., all under one binary label `signals of cyberbullying'), since these could likewise indicate that cyberbullying is going on. The experiments described in this paper focus on the detection of such posts, which are signals of a potential cyberbullying event to be further investigated by human moderators.
The English and Dutch corpus contain 113,698 and 78,387 posts, respectively. As shown in Table TABREF36 , the experimental corpus features a heavily imbalanced class distribution with the large majority of posts not being part of cyberbullying. In classification, this class imbalance can lead to decreased performance. We apply cost-sensitive SVM as a possible hyperparameter in optimisation to counter this. The cost-sensitive SVM reweighs the penalty parameter INLINEFORM0 of the error term by the inverse class-ratio. This means that misclassifications of the minority positive class are penalised more than classification errors on the majority negative class. Other pre-processing methods to handle data imbalance in classification include feature filtering metrics and data resampling BIBREF56 . These methods were omitted as they were found to be too computationally expensive given our high-dimensional dataset.
For the automatic detection of cyberbullying, we performed binary classification experiments using a linear kernel support vector machine (SVM) implemented in LIBLINEAR BIBREF57 by making use of Scikit-learn BIBREF58 , a machine learning library for Python. The motivation behind this is twofold: i) support vector machines (SVMs) have proven to work well for tasks similar to the ones under investigation BIBREF38 and ii) LIBLINEAR allows fast training of large-scale data which allow for a linear mapping (which was confirmed after a series of preliminary experiments using LIBSVM with linear, RBF and polynomial kernels).
The classifier was optimised for feature type (cf. Section SECREF38 ) and hyperparameter combinations (cf. Table TABREF37 ). Model selection was done using 10-fold cross validation in grid search over all possible feature types (i.e., groups of similar features, like different orders of INLINEFORM0 -gram bag-of-words features) and hyperparameter configurations. The best performing hyperparameters are selected by F INLINEFORM1 -score on the positive class. The winning model is then retrained on all held-in data and subsequently tested on a hold-out test set to assess whether the classifier is over- or under-fitting. The holdout represents a random sample ( INLINEFORM2 ) of all data. The folds were randomly stratified splits over the hold-in class distribution. Testing all feature type combinations is a rudimentary form of feature selection and provides insight into which types of features work best for this particular task.
Feature selection over all individual features was not performed because of the large feature space (NL: 795,072 and EN: 871,296 individual features). BIBREF59 , among other researchers, demonstrated the importance of joint optimisation, where feature selection and hyperparameter optimisation are performed simultaneously, since the techniques mutually influence each other.
The optimised models are evaluated against two baseline systems: i) an unoptimised linear-kernel SVM (configured with default parameter settings) based on word INLINEFORM0 -grams only and, ii) a keyword-based system that marks posts as positive for cyberbullying if they contain a word from existing vocabulary lists composed by aggressive language and profanity terms.
We apply cost-sensitive SVM as a possible hyperparameter in optimisation to counter this.
|
What is applied to encounter class imbalance?
|
Cost-sensitive SVM is applied to enconter class imbalance.
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null | false
| null |
What is Thomas Anderson's hacker name?
|
Thomas Anderson, in The Matrix, called himself "Neo."
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null | false
| null |
Identify which instrument is string or percussion: Nagara, Sape
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Sape is string, Nagara is percussion
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null | false
| null |
In a website browser address bar, what does “www” stand for?
|
World Wide Web
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null | false
| 123
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Language models can be optimized to recognize syntax and semantics with great accuracy BIBREF0. However, the output generated can be repetitive and generic leading to monotonous or uninteresting responses (e.g “I don't know”) regardless of the input BIBREF1. While application of attention BIBREF2, BIBREF3 and advanced decoding mechanisms like beam search and variation sampling BIBREF4 have shown improvements, it does not solve the underlying problem. In creative text generation, the objective is not strongly bound to the ground truth—instead the objective is to generate diverse, unique or original samples. We attempt to do this through a discriminator which can give feedback to the generative model through a cost function that encourages sampling of creative tokens. The contributions of this paper are in the usage of a GAN framework to generate creative pieces of writing. Our experiments suggest that generative text models, while very good at encapsulating semantic, syntactic and domain information, perform better with external feedback from a discriminator for fine-tuning objectiveless decoding tasks like that of creative text. We show this by evaluating our model on three very different creative datasets containing poetry, metaphors and lyrics.
Previous work on handling the shortcomings of MLE include length-normalizing sentence probability BIBREF5, future cost estimation BIBREF6, diversity-boosting objective function BIBREF7, BIBREF1 or penalizing repeating tokens BIBREF8. When it comes to poetry generation using generative text models, Zhang and Lapata BIBREF9, Yi et al. BIBREF10 and Wang et al. BIBREF11 use language modeling to generate Chinese poems. However, none of these methods provide feedback on the quality of the generated sample and hence, do not address the qualitative objective required for creative decoding. For the task of text generation, MaskGAN BIBREF12 uses a Reinforcement Learning signal from the discriminator, FMD-GAN BIBREF13 uses an optimal transport mechanism as an objective function. GumbelGAN BIBREF14 uses Gumbel-Softmax distribution that replaces the non-differentiable sample from a categorical distribution with a differentiable sample to propagate stronger gradients. Li et al. BIBREF1 use a discriminator for a diversity promoting objective. Yu et al. BIBREF15 use SeqGAN to generate poetry and comment on the performance of SeqGAN over MLE in human evaluations, encouraging our study of GANs for creative text generation. However, these studies do not focus solely on creative text.
Previous work on handling the shortcomings of MLE include length-normalizing sentence probability [6], future cost estimation [7], diversity-boosting objective function [8, 2] or penalizing repeating tokens [9].
|
Is there any work done before on handling the shortcomings of MLE?
|
Yes. It includes length-normalizing sentence probability, future cost estimation, diversity-boosting objective function or penalizing repeating tokens .
|
1910.02789
| false
| null |
Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representations methods. It can be seen that the NLP representation outperforms the other methods. This is contrary to the fact that it contains the same information as the semantic segmentation maps. More interestingly, comparing the vision-based and feature-based representations render inconsistent conclusions with respect to their relative performance. NLP representations remain robust to changes in the environment as well as task-nuisances in the state. As depicted in fig: nuisance scenarios, inflating the state space with task-nuisances impairs the performance of all representations. There, a large amount of unnecessary objects were spawned in the level, increasing the state's description length to over 250 words, whilst retaining the same amount of useful information. Nevertheless, the NLP representation outperformed the vision and feature based representations, with high robustness to the applied noise.
Results of the DQN-based agent are presented in fig: scenario comparison. Each plot depicts the average reward (across 5 seeds) of all representations methods. It can be seen that the NLP representation outperforms the other methods.
NLP representations remain robust to changes in the environment as well as task-nuisances in the state.
|
What result from experiments suggest that natural language based agents are more robust?
|
Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances
|
null | false
| null |
As established by law and government policy, the two official forms of written Norwegian are Bokmål (literally 'book tongue') and Nynorsk ('new Norwegian'). The official Norwegian Language Council (Språkrådet) is responsible for regulating the two forms, and recommends the terms Norwegian Bokmål and Norwegian Nynorsk in English.[citation needed] Two other written forms without official status also exist. One, called Riksmål ('national language'), is today to a large extent the same language as Bokmål though somewhat closer to the Danish language. It is regulated by the unofficial Norwegian Academy, which translates the name as 'Standard Norwegian'. The other is Høgnorsk ('High Norwegian'), a more purist form of Nynorsk, which maintains the language in an original form as given by Ivar Aasen and rejects most of the reforms from the 20th century; this form has limited use.
|
What are the official forms of written Norwegian?
|
The two official forms of written Norwegian are Bokmål and Nynorsk.
|
null | false
| null |
What are the best things to do when visiting Los Angeles?
|
Some popular tourist attractions in Los Angeles are:
- Eating out in K-Town
- Visiting the Huntington Library
- Riding rollercoasters at Universal Studios
- Finding your favorite celebrities at the Hollywood Walk of Fame
- Getting a cool selfie in front of the Hollywood sign
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null | false
| null |
Mercury-Redstone 1 (MR-1) was the first Mercury-Redstone uncrewed flight test in Project Mercury and the first attempt to launch a Mercury spacecraft with the Mercury-Redstone Launch Vehicle. Intended to be an uncrewed sub-orbital spaceflight, it was launched on November 21, 1960 from Cape Canaveral Air Force Station, Florida. The launch failed in abnormal fashion: immediately after the Mercury-Redstone rocket started to move, it shut itself down and settled back on the pad, after which the capsule jettisoned its escape rocket and deployed its recovery parachutes. The failure has been referred to as the "four-inch flight", for the approximate distance traveled by the launch vehicle.
|
Given this paragraph on Mercury-Redstone 1, how many people were on the MR-1 when the launched failed, and when was the launch?
|
MR-1 was launched on November 21, 1960, and MR-1 was an uncrewed flight, so there were no people on the spacecraft.
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1909.00997
| false
| null |
We compare the performance of the following models:
- IMG-only: This is a simple baseline where we just pass the image through a VGG19 and use the embedding of the image to predict the answer from a fixed vocabulary.
- QUES-only: This is a simple baseline where we just pass the question through a LSTM and use the embedding of the question to predict the answer from a fixed vocabulary.
- SANBIBREF2: This is a state of the art VQA model which is an encoder-decoder model with a multi-layer stacked attention BIBREF26 mechanism. It obtains a representation for the image using a deep CNN and a representation for the query using LSTM. It then uses the query representation to locate relevant regions in the image and uses this to pick an answer from a fixed vocabulary.
- SANDYBIBREF1: This is the best performing model on the DVQA dataset and is a variant of SAN. Unfortunately, the code for this model is not available and the description in the paper was not detailed enough for us to reimplement it. Hence, we report the numbers for this model only on DVQA (from the original paper).
- VOES: This is our model as described in section SECREF3 which is specifically designed for questions which do not have answers from a fixed vocabulary.
- VOES-Oracle: blackThis is our model where the first three stages of VOES are replaced by an Oracle, i.e., the QA model answers questions on a table that has been generated using the ground truth annotations of the plot. With this we can evaluate the performance of the WikiTableQA model when it is not affected by the VED model's errors.
- SAN-VOES: Given the complementary strengths of SAN-VQA and VOES, we train a hybrid model with a binary classifier which given a question decides whether to use the SAN or the VOES model. The data for training this binary classifier is generated by comparing the predictions of a trained SAN model and a trained VOES model on the training dataset. For a given question, the label is set to 1 (pick SAN) if the performance of SAN was better than that of VOES. We ignore questions where there is a tie. The classifier is a simple LSTM based model which computes a representation for the question using an LSTM and uses this representation to predict 1/0. At test time, we first pass the question through this model and depending on the output of this model use SAN or VOES.
We compare the performance of the following models:
- IMG-only: This is a simple baseline where we just pass the image through a VGG19 and use the embedding of the image to predict the answer from a fixed vocabulary.
- QUES-only: This is a simple baseline where we just pass the question through a LSTM and use the embedding of the question to predict the answer from a fixed vocabulary.
- SANBIBREF2: This is a state of the art VQA model which is an encoder-decoder model with a multi-layer stacked attention BIBREF26 mechanism. It obtains a representation for the image using a deep CNN and a representation for the query using LSTM. It then uses the query representation to locate relevant regions in the image and uses this to pick an answer from a fixed vocabulary.
- SANDYBIBREF1: This is the best performing model on the DVQA dataset and is a variant of SAN. Unfortunately, the code for this model is not available and the description in the paper was not detailed enough for us to reimplement it. Hence, we report the numbers for this model only on DVQA (from the original paper).
- VOES: This is our model as described in section SECREF3 which is specifically designed for questions which do not have answers from a fixed vocabulary.
- VOES-Oracle: blackThis is our model where the first three stages of VOES are replaced by an Oracle, i.e., the QA model answers questions on a table that has been generated using the ground truth annotations of the plot. With this we can evaluate the performance of the WikiTableQA model when it is not affected by the VED model's errors.
- SAN-VOES: Given the complementary strengths of SAN-VQA and VOES, we train a hybrid model with a binary classifier which given a question decides whether to use the SAN or the VOES model. The data for training this binary classifier is generated by comparing the predictions of a trained SAN model and a trained VOES model on the training dataset. For a given question, the label is set to 1 (pick SAN) if the performance of SAN was better than that of VOES. We ignore questions where there is a tie. The classifier is a simple LSTM based model which computes a representation for the question using an LSTM and uses this representation to predict 1/0. At test time, we first pass the question through this model and depending on the output of this model use SAN or VOES.
|
What models other than SAN-VOES are trained on new PlotQA dataset?
|
The answers are shown as follows:
* IMG-only
* QUES-only
* SAN
* SANDY
* VOES-Oracle
* VOES
|
1611.01400
| false
| null |
Support Vector Machine (SVM) ( BIBREF25 ) is a commonly used supervised classification algorithm that has shown good performance over a range of tasks. SVM can be thought of as a binary linear classifier where the goal is to maximize the size of the gap between the class-separating line and the points on either side of the line. This helps avoid over-fitting on the training data. SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below. SVMRank has previously been used in the task of document retrieval in ( BIBREF27 ) for a more traditional short query task and has been shown to be a top-performing system for ranking.
SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below.
|
what is the supervised model they developed?
|
The answers are shown as follows:
* SVMRank
|
null | false
| null |
What did Apple co-founder, Steve Jobs, die from?
|
Steve Jobs died in 2011 from pancreatic cancer.
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1908.08419
| false
| null |
To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history.
|
How does the scoring model work?
|
The answers are shown as follows:
* the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history
|
null | false
| null |
C. Shannon Bacon (born 1971/1972) is an American attorney, legal scholar, and jurist serving as the chief justice of the New Mexico Supreme Court.
Early life and education
Bacon was raised in North Valley, New Mexico. She earned a Bachelor of Arts from Creighton University in Omaha and a Juris Doctor from the Creighton University School of Law.
Career
After graduating from law school, Bacon returned to Albuquerque, where she was admitted to the State Bar of New Mexico in 1997. She was formerly partner at a pair of Albuquerque law firms and clerked under Judge A. Joseph Alarid at the New Mexico Court of Appeals. Bacon is an adjunct professor at the University of New Mexico School of Law, where she teaches courses on evidence and trial practice.
Bacon identifies as a lesbian. She is the first openly LGBT member of the New Mexico Supreme Court.[citation needed]
|
What made C. Shannon Bacon famous
|
C. Shannon Bacon (born 1971/1972) is an American attorney, legal scholar, and jurist serving as the chief justice of the New Mexico Supreme Court. Bacon identifies as a lesbian. She is the first openly LGBT member of the New Mexico Supreme Court.
Bacon was raised in North Valley, New Mexico. She earned a Bachelor of Arts from Creighton University in Omaha and a Juris Doctor from the Creighton University School of Law.
After graduating from law school, Bacon returned to Albuquerque, where she was admitted to the State Bar of New Mexico in 1997. She was formerly partner at a pair of Albuquerque law firms and clerked under Judge A. Joseph Alarid at the New Mexico Court of Appeals. Bacon is an adjunct professor at the University of New Mexico School of Law, where she teaches courses on evidence and trial practice.
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1806.03191
| false
| null |
Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. For this task, we evaluate all models on five benchmark datasets: First, we employ the noun-noun subset of bless, which contains hypernymy annotations for 200 concrete, mostly unambiguous nouns. Negative pairs contain a mixture of co-hyponymy, meronymy, and random pairs. This version contains 14,542 total pairs with 1,337 positive examples. Second, we evaluate on leds BIBREF13 , which consists of 2,770 noun pairs balanced between positive hypernymy examples, and randomly shuffled negative pairs. We also consider eval BIBREF14 , containing 7,378 pairs in a mixture of hypernymy, synonymy, antonymy, meronymy, and adjectival relations. eval is notable for its absence of random pairs. The largest dataset is shwartz BIBREF2 , which was collected from a mixture of WordNet, DBPedia, and other resources. We limit ourselves to a 52,578 pair subset excluding multiword expressions. Finally, we evaluate on wbless BIBREF15 , a 1,668 pair subset of bless, with negative pairs being selected from co-hyponymy, random, and hyponymy relations. Previous work has used different metrics for evaluating on BLESS BIBREF11 , BIBREF5 , BIBREF6 . We chose to evaluate the global ranking using Average Precision. This allowed us to use the same metric on all detection benchmarks, and is consistent with evaluations in BIBREF4 .
Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. For this task, we evaluate all models on three datasets described by BIBREF16 : On bless, the task is to predict the direction for all 1337 positive pairs in the dataset. Pairs are only counted correct if the hypernymy direction scores higher than the reverse direction, i.e. INLINEFORM0 . We reserve 10% of the data for validation, and test on the remaining 90%. On wbless, we follow prior work BIBREF17 , BIBREF18 and perform 1000 random iterations in which 2% of the data is used as a validation set to learn a classification threshold, and test on the remainder of the data. We report average accuracy across all iterations. Finally, we evaluate on bibless BIBREF16 , a variant of wbless with hypernymy and hyponymy pairs explicitly annotated for their direction. Since this task requires three-way classification (hypernymy, hyponymy, and other), we perform two-stage classification. First, a threshold is tuned using 2% of the data, identifying whether a pair exhibits hypernymy in either direction. Second, the relative comparison of scores determines which direction is predicted. As with wbless, we report the average accuracy over 1000 iterations.
Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds. For this task, we follow prior work BIBREF19 , BIBREF18 and use the noun part of hyperlex BIBREF20 , consisting of 2,163 noun pairs which are annotated to what degree INLINEFORM0 is-a INLINEFORM1 holds on a scale of INLINEFORM2 . For all models, we report Spearman's rank correlation INLINEFORM3 . We handle out-of-vocabulary (OOV) words by assigning the median of the scores (computed across the training set) to pairs with OOV words.
Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. For this task, we evaluate all models on five benchmark datasets: First, we employ the noun-noun subset of bless, which contains hypernymy annotations for 200 concrete, mostly unambiguous nouns.
Second, we evaluate on leds BIBREF13 , which consists of 2,770 noun pairs balanced between positive hypernymy examples, and randomly shuffled negative pairs.
Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. For this task, we evaluate all models on three datasets described by BIBREF16 : On bless, the task is to predict the direction for all 1337 positive pairs in the dataset. Pairs are only counted correct if the hypernymy direction scores higher than the reverse direction, i.e. INLINEFORM0 . We reserve 10% of the data for validation, and test on the remaining 90%. On wbless, we follow prior work BIBREF17 , BIBREF18 and perform 1000 random iterations in which 2% of the data is used as a validation set to learn a classification threshold, and test on the remainder of the data. We report average accuracy across all iterations. Finally, we evaluate on bibless BIBREF16 , a variant of wbless with hypernymy and hyponymy pairs explicitly annotated for their direction.
Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds. For this task, we follow prior work BIBREF19 , BIBREF18 and use the noun part of hyperlex BIBREF20 , consisting of 2,163 noun pairs which are annotated to what degree INLINEFORM0 is-a INLINEFORM1 holds on a scale of INLINEFORM2 .
|
Which benchmark datasets are used?
|
The answers are shown as follows:
* noun-noun subset of bless
* leds BIBREF13
* bless
* wbless
* bibless
* hyperlex BIBREF20
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null | false
| null |
How many keys are in a standard piano?
|
There are 88 keys in a standard piano. It has been the standard since Steinway built their pianos in the 1880s. There are 52 white keys and 36 black keys.
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null | false
| null |
Give me a bulleted list of ways to protect a house from hurricane damage
|
The following are ways to protect a house from hurricane damage:
1. Ensure all exterior windows are impact windows
2. Build the house elevated above ground level
3. Use sandbags to reduce water entering exterior doors
4. Cover windows with plywood or hurricane shutters
5. Don't build a house in hurricane and flood prone areas
|
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null | false
| null |
Tell me whether these are TV shows or movies
|
Game of Thrones, Titanic, Smallville, Ozark, Peaky Blinders
|
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null | false
| null |
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
RBMs were initially invented under the name Harmonium by Paul Smolensky in 1986, and rose to prominence after Geoffrey Hinton and collaborators invented fast learning algorithms for them in the mid-2000. RBMs have found applications in dimensionality reduction, classification, collaborative filtering, feature learning, topic modelling and even many body quantum mechanics. They can be trained in either supervised or unsupervised ways, depending on the task.
As their name implies, RBMs are a variant of Boltzmann machines, with the restriction that their neurons must form a bipartite graph: a pair of nodes from each of the two groups of units (commonly referred to as the "visible" and "hidden" units respectively) may have a symmetric connection between them; and there are no connections between nodes within a group. By contrast, "unrestricted" Boltzmann machines may have connections between hidden units. This restriction allows for more efficient training algorithms than are available for the general class of Boltzmann machines, in particular the gradient-based contrastive divergence algorithm.
Restricted Boltzmann machines can also be used in deep learning networks. In particular, deep belief networks can be formed by "stacking" RBMs and optionally fine-tuning the resulting deep network with gradient descent and backpropagation.
|
Without quoting directly from the text give me a summary of what a restricted Boltzmann machine is?
|
A restricted Boltzmann machine (RBM) is a category of an artificial neural network invented by Paul Smolensky and popularized by Geoffrey Hinton. RBM's have been useful in various applications within the field of machine learning and can be trained in supervised and unsupervised ways.
RBMS is a restricted variant of Boltzmann machines that constrains their neurons; a pair of nodes from each of the two groups of neurons, visible and hidden forming a bipartite graph. Connection can only be between two groups and not within groups (visible and hidden). This constrained formulation results in the ability to more efficiently train algorithms.
RBM's can also be integrated into deep learning networks such as deep belief networks with options to fine tune those networks using gradient descent and backpropagation.
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null | false
| null |
How can I choose a favorite professional basketball team?
|
There is no wrong answer, but here are a few things you can consider in making that choice. You can determine which team is the closest to where you live and become a fan of the local team. Alternatively, you can determine which player or players are your favorites to watch and cheer for the teams that they play for. Another option is to cheer for a team that wins often, as it is more fun to root for a winning team.
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What is the nature of god, from a logical perspective?
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Assuming god exists, one can think of the nature of god in two dimensions: is god active or passive, and is god benevolent or indifferent? Therefore, god is either active and benevolent, active and indifferent, passive and benevolent, or passive and indifferent. If god is active and benevolent, then all the bad things that happen would not happen, so this combination is unlikely. If god is active, god would likely be benevolent, so an indifferent active god seems a low probability. If god is benevolent, one would think they would be active, so a benevolent passive god seems a remote possibility. That leaves as the likeliest scenario a god that is passive and indifferent.
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Which of these forms of Bay Area transit are AC Transit bus lines and which are trains: Caltrain, BART, 51B, 36, 51A, 79, VTA Light Rail.
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The trains are the Caltrain, BART, and VTA Light Rail, and the bus lines are the 51B, 36, 51A, and 79.
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What is the oldest film set in ancient Egypt?
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The oldest film set in ancient Egypt is from 1899! Cléopâtre from director Georges Méliès.
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A lithophane is a thin plaque of translucent material, normally porcelain, which has been moulded to varying thickness, such that when lit from behind the different thicknesses show as different shades, forming an image. Only when lit from behind does the image display properly. They were invented in the 19th century and became very popular, typically for lampshades, nightlights, or to be hung on windows. They could also be given stands, to be placed in front of a light source. The longest side of a lithophane is typically between 6 and 10 inches.
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Given this paragraph about material, what is lithophane?
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A lithophane is a thin plaque of translucent material, normally porcelain, which has been moulded to varying thickness, such that when lit from behind the different thicknesses show as different shades, forming an image.
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Out of these animals, categorize them as either 'small' or 'large: A horse, a mouse, an elephant, a snake, and a whale.
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The horse, elephant, and whale would all be categorized as large animals in this example. A snake and a mouse are much smaller by comparison, so these would be considered small animals.
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Hyperparameter tuning for firefly: Since the online optimization algorithms we run have access to querying the simulator over the course of training, we can simply utilize the value of the latest proposed design as a way to perform early stopping and hyperparameter tuning. A naïve way to perform hyperparameter tuning for such evolutionary methods is to run the algorithm for multiple rounds with multiple hyperparameters, however this is compute and time intensive. Therefore, we adopted a dynamic hyperparameter tuning strategy. Our implementation of the firefly optimizer tunes hyperparameters by scoring a set of hyperparameters based on its best performance over a sliding window of T data points. This allows us to adapt to the best hyperparameters on the fly, within the course of optimization, effectively balancing the number of runs that need to be run in the simulator and hyperparameter tuning. This dynamic hyperparameter tuning strategy requires some initial coverage of the hyperparameter space before hyperparameter tuning begins, and therefore, this tuning begins only after 750 datapoints. After this initial phase, every T = 50 iterations, the parameters γ and β0 are updated via an evolutionary scoring strategy towards their best value.
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Are the hyperparameters for other methods tuned using the validation set?
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For the online methods we study, we directly utilize the online performance evaluated against the simulator for cross-validation and choosing hyperparameters, as it is the groundtruth performance metric that we wish to optimize. We have now added the details of tuning the Firefly optimizer in Appendix B.1.1. (paragraph titled “Hyperparameter tuning for firefly”). Briefly, we adaptively adjust hyperparameters for firefly over the course of online optimization, which gives us a favorable balance between time and compute requirements for running firefly against the simulator and tuning hyperparameters. For COMs baseline in Appendix A, which is offline, we use the validation set + Kendall’s rank correlation metric as well for tuning.
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A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks.
Properties
Though the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design.
Though trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to "memorize" a great quantity of facts during training.
Hallucinations
Main article: Hallucination (artificial intelligence)
In artificial intelligence in general, and in large language models in particular, a "hallucination" is a confident response that does not seem to be justified by the model's training data.
Emergent abilities
On a number of natural language benchmarks involving tasks such as question answering, models perform no better than random chance until they reach a certain scale (in this case, measured by training computation), at which point their performance sharply increases. These are examples of emergent abilities.
Unpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called "emergent abilities". Researchers note that such abilities "cannot be predicted simply by extrapolating the performance of smaller models". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed. Hundreds of emergent abilities have been described. Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs.
Architecture and training
Large language models have most commonly used the transformer architecture, which, since 2018, has become the standard deep learning technique for sequential data (previously, recurrent architectures such as the LSTM were most common). LLMs are trained in an unsupervised manner on unannotated text. A left-to-right transformer is trained to maximize the probability assigned to the next word in the training data, given the previous context. Alternatively, an LLM may use a bidirectional transformer (as in the example of BERT), which assigns a probability distribution over words given access to both preceding and following context. In addition to the task of predicting the next word or "filling in the blanks", LLMs may be trained on auxiliary tasks which test their understanding of the data distribution such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear side-by-side in the training corpus.
The earliest LLMs were trained on corpora having on the order of billions of words. The first model in OpenAI's GPT series was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totalling 3.3 billion words. In the years since then, training corpora for LLMs have increased by orders of magnitude, reaching up to hundreds of billions or trillions of tokens.
LLMs are computationally expensive to train. A 2020 study estimated the cost of training a 1.5 billion parameter model (1-2 orders of magnitude smaller than the state of the art at the time) at $1.6 million.
A 2020 analysis found that neural language models' capability (as measured by training loss) increased smoothly in a power law relationship with number of parameters, quantity of training data, and computation used for training. These relationships were tested over a wide range of values (up to seven orders of magnitude) and no attenuation of the relationship was observed at the highest end of the range (including for network sizes up to trillions of parameters).
Application to downstream tasks
Between 2018 and 2020, the standard method for harnessing an LLM for a specific natural language processing (NLP) task was to fine tune the model with additional task-specific training. It has subsequently been found that more powerful LLMs such as GPT-3 can solve tasks without additional training via "prompting" techniques, in which the problem to be solved is presented to the model as a text prompt, possibly with some textual examples of similar problems and their solutions.
Fine-tuning
Main article: Fine-tuning (machine learning)
Fine-tuning is the practice of modifying an existing pretrained language model by training it (in a supervised fashion) on a specific task (e.g. sentiment analysis, named entity recognition, or part-of-speech tagging). It is a form of transfer learning. It generally involves the introduction of a new set of weights connecting the final layer of the language model to the output of the downstream task. The original weights of the language model may be "frozen", such that only the new layer of weights connecting them to the output are learned during training. Alternatively, the original weights may receive small updates (possibly with earlier layers frozen).
Prompting
See also: Prompt engineering and Few-shot learning (natural language processing)
In the prompting paradigm, popularized by GPT-3, the problem to be solved is formulated via a text prompt, which the model must solve by providing a completion (via inference). In "few-shot prompting", the prompt includes a small number of examples of similar (problem, solution) pairs. For example, a sentiment analysis task of labelling the sentiment of a movie review could be prompted as follows:
Review: This movie stinks.
Sentiment: negative
Review: This movie is fantastic!
Sentiment:
If the model outputs "positive", then it has correctly solved the task. In zero-shot prompting, no solve examples are provided. An example of a zero-shot prompt for the same sentiment analysis task would be "The sentiment associated with the movie review 'This movie is fantastic!' is".
Few-shot performance of LLMs has been shown to achieve competitive results on NLP tasks, sometimes surpassing prior state-of-the-art fine-tuning approaches. Examples of such NLP tasks are translation, question answering, cloze tasks, unscrambling words, and using a novel word in a sentence. The creation and optimisation of such prompts is called prompt engineering.
Instruction tuning
Instruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions. Given a text input, a pretrained language model will generate a completion which matches the distribution of text on which it was trained. A naive language model given the prompt "Write an essay about the main themes of Hamlet." might provide a completion such as "A late penalty of 10% per day will be applied to submissions received after March 17." In instruction tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. Various techniques for instruction tuning have been applied in practice. OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function was learned based on a dataset of human preferences. Another technique, "self-instruct", fine-tunes the language model on a training set of examples which are themselves generated by an LLM (bootstrapped from a small initial set of human-generated examples).
https://en.wikipedia.org/wiki/Large_language_model
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Given these paragraphs about Large language models, what is hallucination?
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In artificial intelligence in general, and in large language models in particular, a "hallucination" is a confident response that does not seem to be justified by the model's training data.
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What are the primary causes of inflation?
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Inflation is caused by imbalance of supply and demand.
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Where is Hesse's Demi Bastion
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Hesse's Demi Bastion is a demi-bastion in the British Overseas Territory of Gibraltar. It is part of the Northern Defences of Gibraltar. The bastion forms a link in a chain of fortifications which ascend the lower north-west slopes of the Rock of Gibraltar, below the King's Lines Battery and Bombproof Battery. The Moorish Castle's Tower of Homage is at the top of the same incline.
History
It was originally the Baluado de S. Pedro (St. Peter's Bastion) during the Spanish period prior to the Capture of Gibraltar in 1704 under Prince George of Hesse-Darmstadt, after whom the bastion is named. The old Spanish bastion was rebuilt and renamed by the British in 1730. In 1762, during the Seven Years' War, it was armed with two 18-pdrs and four 4-pdrs to flank the ditch in front of the Landport Gate. By 1885 the bastion's guns were all 32-pdrs.
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What is your favorite teaching from Stoic philosophy?
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The Discipline of Ascent trains you to pay attention to your thought process and cultivate a healthy mind. You must pause and think about your responses to life, rather than allowing instincts, habits, and default behavior to run your life. Preventing negative thinking is an active process, you will improve with more practice. Most importantly, learning to guide your thought processes will allow you to put more energy into what you control.
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Knowledge graphs (KGs) containing relationship triples (subject, relation, object), denoted as (s, r, o), are the useful resources for many NLP and especially information retrieval applications such as semantic search and question answering BIBREF0 . However, large knowledge graphs, even containing billions of triples, are still incomplete, i.e., missing a lot of valid triples BIBREF1 . Therefore, much research efforts have focused on the knowledge graph completion task which aims to predict missing triples in KGs, i.e., predicting whether a triple not in KGs is likely to be valid or not BIBREF2 , BIBREF3 , BIBREF4 . To this end, many embedding models have been proposed to learn vector representations for entities (i.e., subject/head entity and object/tail entity) and relations in KGs, and obtained state-of-the-art results as summarized by BIBREF5 and BIBREF6 . These embedding models score triples (s, r, o), such that valid triples have higher plausibility scores than invalid ones BIBREF2 , BIBREF3 , BIBREF4 . For example, in the context of KGs, the score for (Melbourne, cityOf, Australia) is higher than the score for (Melbourne, cityOf, United Kingdom).
Triple modeling is applied not only to the KG completion, but also for other tasks which can be formulated as a triple-based prediction problem. An example is in search personalization, one would aim to tailor search results to each specific user based on the user's personal interests and preferences BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 . Here the triples can be formulated as (submitted query, user profile, returned document) and used to re-rank documents returned to a user given an input query, by employing an existing KG embedding method such as TransE BIBREF3 , as proposed by BIBREF12 . Previous studies have shown the effectiveness of modeling triple for either KG completion or search personalization. However, there has been no single study investigating the performance on both tasks.
Conventional embedding models, such as TransE BIBREF3 , DISTMULT BIBREF13 and ComplEx BIBREF14 , use addition, subtraction or simple multiplication operators, thus only capture the linear relationships between entities. Recent research has raised interest in applying deep neural networks to triple-based prediction problems. For example, BIBREF15 proposed ConvKB—a convolutional neural network (CNN)-based model for KG completion and achieved state-of-the-art results. Most of KG embedding models are constructed to modeling entries at the same dimension of the given triple, where presumably each dimension captures some relation-specific attribute of entities. To the best of our knowledge, however, none of the existing models has a “deep” architecture for modeling the entries in a triple at the same dimension.
BIBREF16 introduced capsule networks (CapsNet) that employ capsules (i.e., each capsule is a group of neurons) to capture entities in images and then uses a routing process to specify connections from capsules in a layer to those in the next layer. Hence CapsNet could encode the intrinsic spatial relationship between a part and a whole constituting viewpoint invariant knowledge that automatically generalizes to novel viewpoints. Each capsule accounts for capturing variations of an object or object part in the image, which can be efficiently visualized. Our high-level hypothesis is that embedding entries at the same dimension of the triple also have these variations, although it is not straightforward to be visually examined.
To that end, we introduce CapsE to explore a novel application of CapsNet on triple-based data for two problems: KG completion and search personalization. Different from the traditional modeling design of CapsNet where capsules are constructed by splitting feature maps, we use capsules to model the entries at the same dimension in the entity and relation embeddings. In our CapsE, INLINEFORM0 , INLINEFORM1 and INLINEFORM2 are unique INLINEFORM3 -dimensional embeddings of INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , respectively. The embedding triple [ INLINEFORM7 , INLINEFORM8 , INLINEFORM9 ] of (s, r, o) is fed to the convolution layer where multiple filters of the same INLINEFORM10 shape are repeatedly operated over every row of the matrix to produce INLINEFORM11 -dimensional feature maps. Entries at the same dimension from all feature maps are then encapsulated into a capsule. Thus, each capsule can encode many characteristics in the embedding triple to represent the entries at the corresponding dimension. These capsules are then routed to another capsule which outputs a continuous vector whose length is used as a score for the triple. Finally, this score is used to predict whether the triple (s, r, o) is valid or not.
In summary, our main contributions from this paper are as follows:
INLINEFORM0 We propose an embedding model CapsE using the capsule network BIBREF16 for modeling relationship triples. To our best of knowledge, our work is the first consideration of exploring the capsule network to knowledge graph completion and search personalization.
INLINEFORM0 We evaluate our CapsE for knowledge graph completion on two benchmark datasets WN18RR BIBREF17 and FB15k-237 BIBREF18 . CapsE obtains the best mean rank on WN18RR and the highest mean reciprocal rank and highest Hits@10 on FB15k-237.
INLINEFORM0 We restate the prospective strategy of expanding the triple embedding models to improve the ranking quality of the search personalization systems. We adapt our model to search personalization and evaluate on SEARCH17 BIBREF12 – a dataset of the web search query logs. Experimental results show that our CapsE achieves the new state-of-the-art results with significant improvements over strong baselines.
Triple modeling is applied not only to the KG completion, but also for other tasks which can be formulated as a triple-based prediction problem.
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Is triple modeling applied for other tasks which can be formulated as a triple-based prediction problem?
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Yes, it is.
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| 488
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In this experiment we sought to ascertain if ROSA is robust to the component used to generate exploration bonus L. We ran two versions of ROSA, one where L is computed using RND and one where L is computed using a simple count-based measure 1/Count(s) where s ∈ S (the function Count(s) simply tallies the number of times state s has been visited). Figure 8 shows performance of these two versions of ROSA on the Maze environment shown in Figure 1. ROSA performs equally well with both components, indicating that it is not dependent on a fine-tuned exploration bonus. The additional machinery of switching controls and choices of intrinsic rewards to add mean that ROSA can work with basic exploration bonuses. Note that we generally use RND since it is simple to implement and works equally well on discrete and continuous state spaces.****Proposition 2 The policy g2 is given by the following expression: g2(st) = H(Mπ,π2Vπ,π2 − Vπ,π2 )(st,It), ∀(st, It) ∈ S × {0, 1}, where V is the solution in Theorem 1 and H is the Heaviside function, moreover Shaper’s switching times are τk = inf{τ > τk−1|Mπ,π2Vπ,π2 = Vπ,π2 }.
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Is the proposed method really learning what are the relevant states?
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We refer the reviewer to the visualisation in Exp. 1 which shows exactly where the Shaper adds intrinsic rewards. There it is shown that the Shaper adds intrinsic rewards at precisely (and only) the set of states that guide the controller to the optimal goal state (and away from the suboptimal goal state). This is formally asserted in our theoretical result in Prop. 2.
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What is a newspaper?
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A newspaper is a daily printed publication delivered to your house or place of business. It is made our of folded sheets of printed paper, and it contains current and prior day events also called news. You will also find advertisements and different correspondences in it as well. It is a great way to stay up to date with current events, and you will have to pay to get a newspaper subscription delivered daily or weekly.
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Why are people migrating in the US from the NE and Mid-West to the South and SE?
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Remote work has eliminated the requirement to live close to the office
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Name 5 popular musicians
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Taylor Swift, Vance Joy, The Script, Coldplay, Lady Gaga
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What's the best country in the world?
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There is no single answer. The United States of America has the largest economy of any country, but Qatar has the highest GDP per capita. Finland, on the other hand, is the happiest country in the world. Russia is the largest country by landmass. Many consider New Zealand to be the most beautiful country. Which country is best depends on what you are looking for.
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Classify the below based on whether continent or ocean-
Asia, Africa, North America, Atlantic, Pacific, Indian, Arctic, South America, Antarctica, Europe, Australia
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Continent - Asia, Africa, North America, South America, Antarctica, Europe and Australia
Ocean - Atlantic, Pacific, Indian, and Arctic
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Write a story about a budding romance between two people. Talk about their going on an adventure together where they sneak into a fancy garden in Washington DC and get into just the right amount of trouble.
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Natalie and I woke up the morning after a fun night out. I was in DC that week for a work trip, and had extended my stay the weekend before and after, so we could spend time together. We'd only met a month prior, and after more than four weeks of long-distance text message exchanges, it was exciting to finally be together in person. Our first date had been the weekend before, when we'd gone to a Jimmy Buffett concert. It was a magical night.
It was now 10am on the Saturday after the work week, and we were ready for another adventure. Natalie, being from DC, wanted to show me the Dumbarton Oaks Gardens, so off we went. Unfortunately, when we got there, the front gates were closed. Still feeling adventurous, we decided to walk around the property, convinced we'd find an unlocked gate, door, or other way to get in. Towards the back end of the property, while we didn't find an open gate, we did find one that was scale-able. And that's exactly what we did. We climbed over that fence, and welcomed ourselves to the beautiful gardens. It was early august, and so much was still in bloom. The gardens were empty, and we thought we had the whole place to ourselves. But, we soon found that that wasn't the case. While exploring a small cottage that also seemed to double as an art gallery, we noticed a small pool right next to it. We also noticed someone swimming in that pool. Presumably that person noticed us too, because a few minutes later a security guard came walking up to us while we were inspecting some fine smelling roses nearby. It was too late to run away, so instead we walked up to the security guard, and thanked him for finding us. As they escorted us out, we complimented them on the great state of the garden, though that didn't seem to calm him down much. He was far too frustrated with the situation. As they were about to lead us out through the front gate, we offered to pay for our admission. They rather sternly refused, and insisted we leave immediately, which we did.
And that's how Natalie and I got our free tour of the gardens, and took one step closer to falling in love. Three years later, we were married.
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What's the world looks like in 1000 years?
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I don't know the exact answer but one thing I do know is that human beings was extinct on the earth. Enjoy :)
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What generation is the best generation?
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None, all are flawed.
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2002.00652
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To conduct a thorough comparison, we evaluate 13 different context modeling methods upon the same parser, including 6 methods introduced in Section SECREF2 and 7 selective combinations of them (e.g., Concat+Action Copy). The experimental results are presented in Figure FIGREF37. Taken as a whole, it is very surprising to observe that none of these methods can be consistently superior to the others. The experimental results on BERT-based models show the same trend. Diving deep into the methods only using recent questions as context, we observe that Concat and Turn perform competitively, outperforming Gate by a large margin. With respect to the methods only using precedent SQL as context, Action Copy significantly surpasses Tree Copy and SQL Attn in all metrics. In addition, we observe that there is little difference in the performance of Action Copy and Concat, which implies that using precedent SQL as context gives almost the same effect with using recent questions. In terms of the combinations of different context modeling methods, they do not significantly improve the performance as we expected.
FLOAT SELECTED: Figure 5: Question Match, Interaction Match and Turn i Match on SPARC and COSQL development sets. The numbers are averaged over 5 runs. The first column represents absolute values. The rest are improvements of different context modeling methods over CONCAT.
To conduct a thorough comparison, we evaluate 13 different context modeling methods upon the same parser, including 6 methods introduced in Section SECREF2 and 7 selective combinations of them (e.g., Concat+Action Copy). The experimental results are presented in Figure FIGREF37. Taken as a whole, it is very surprising to observe that none of these methods can be consistently superior to the others. The experimental results on BERT-based models show the same trend. Diving deep into the methods only using recent questions as context, we observe that Concat and Turn perform competitively, outperforming Gate by a large margin. With respect to the methods only using precedent SQL as context, Action Copy significantly surpasses Tree Copy and SQL Attn in all metrics. In addition, we observe that there is little difference in the performance of Action Copy and Concat, which implies that using precedent SQL as context gives almost the same effect with using recent questions. In terms of the combinations of different context modeling methods, they do not significantly improve the performance as we expected.
FLOAT SELECTED: Figure 5: Question Match, Interaction Match and Turn i Match on SPARC and COSQL development sets. The numbers are averaged over 5 runs. The first column represents absolute values. The rest are improvements of different context modeling methods over CONCAT.
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What context modelling methods are evaluated?
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Concat
Turn
Gate
Action Copy
Tree Copy
SQL Attn
Concat + Action Copy
Concat + Tree Copy
Concat + SQL Attn
Turn + Action Copy
Turn + Tree Copy
Turn + SQL Attn
Turn + SQL Attn + Action Copy
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In June 1955, Gorbachev graduated with a distinction; his final paper had been on the advantages of "socialist democracy" (the Soviet political system) over "bourgeois democracy" (liberal democracy). He was subsequently assigned to the Soviet Procurator's office, which was then focusing on the rehabilitation of the innocent victims of Stalin's purges, but found that they had no work for him. He was then offered a place on an MSU graduate course specializing in kolkhoz law, but declined. He had wanted to remain in Moscow, where Raisa was enrolled in a PhD program, but instead gained employment in Stavropol; Raisa abandoned her studies to join him there.
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Based on this passage, in what field did Raisa earn her PhD?
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Based on this passage, Raisa did not earn a PhD. She abandoned her graduate studies in Moscow in order to join Gorbachev in Stavropol.
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Is "1984" a book or a movie?
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Both. George Orwell's book "Nineteen Eighty-Four" was published in 1949. Films adapted from the book came out in 1956 and 1984; another adaptation is to be released in 2023 and will be the first adaptation in the Russian language.
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Connie Passalacqua Hayman is an American journalist and educator. Passalacqua Hayman has written on a number of topics, but is noted for her reporting and critical analysis of daytime soap operas.
Career
Passalacqua Hayman started her soap journalism career in 1980 as an assistant to newspaper columnist Jon-Michael Reed, "the first soap journalist to review soaps as theater and edit soap magazines for intelligent readers." She has since written about the daytime industry for various publications and news organizations, including Newsday, United Features Syndicate, The New York Times, Variety, the New York Daily News, TV Guide and USA Today. She was the editor of Afternoon TV magazine from 1980 to 1983, an editor for Soap Opera World and a contributor for Soap Opera Digest, Soap Opera People and others.
Between 1995 and 2001, Passalacqua Hayman was an adjunct professor of journalism at New York University, and in 2001 she left writing full-time to earn a master's degree in education at NYU. She taught journalism at Marymount Manhattan College as an adjunct professor in the Communications Department from 2007 to 2014.
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Given a reference text about Connie Passalacqua Hayman, tell me when and where taught journalism.
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Connie Passalacqua Hayman taught journalism at New York University from 1995 to 2001 and at Marymount Manhattan College from 2007 to 2014.
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1910.02789
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The term representation is used differently in different contexts. For the purpose of this paper we define a semantic representation of a state as one that reflects its meaning as it is understood by an expert. The semantic representation of a state should thus be paired with a reliable and computationally efficient method for extracting information from it. Previous success in RL has mainly focused on representing the state in its raw form (e.g., visual input in Atari-based games BIBREF2). This approach stems from the belief that neural networks (specifically convolutional networks) can extract meaningful features from complex inputs. In this work, we challenge current representation techniques and suggest to represent the state using natural language, similar to the way we, as humans, summarize and transfer information efficiently from one to the other BIBREF5.
. In this work, we challenge current representation techniques and suggest to represent the state using natural language, similar to the way we, as humans, summarize and transfer information efficiently from one to the other BIBREF5.
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How is state to learn and complete tasks represented via natural language?
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The answers are shown as follows:
* represent the state using natural language
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Breezy Point is a city in Crow Wing County, Minnesota, United States. It is part of the Brainerd Micropolitan Statistical Area. The population was 2,346 at the 2010 census.
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What is the population of Breezy Point city in Minnesota?
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Breezy Point's population was 2,346 at the 2010 census.
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What are five totally distinct, creative ways to describe the usefulness of extra fingers?
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1. One extra finger can be used to help hold up a conversation.
2. An extra finger can be used to create a more efficient way to spell out words.
3. One extra finger can be used to play a musical instrument.
4. One extra finger can be used to help open a bottle of wine.
5. An extra finger can be used to play a game of pool.
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| 393
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To assess the performance of binary paragraph vectors, we carried out experiments on three datasets: 20 Newsgroups, a cleansed version (also called v2) of Reuters Corpus Volume 1 (RCV1) and English Wikipedia. As paragraph vectors can be trained with relatively large vocabularies, we did not perform any stemming of the source text. However, we removed stop words as well as words shorter than two characters and longer than 15 characters. Results reported by BIBREF15 indicate that performance of PV-DBOW can be improved by including n-grams in the model. We therefore evaluated two variants of Binary PV-DBOW: one predicting words in documents and one predicting words and bigrams. Since 20 Newsgroups is a relatively small dataset, we used all words and bigrams from its documents. This amounts to a vocabulary with slightly over one million elements. For the RCV1 dataset we used words and bigrams with at least 10 occurrences in the text, which gives a vocabulary with approximately 800 thousands elements. In case of English Wikipedia we used words and bigrams with at least 100 occurrences, which gives a vocabulary with approximately 1.5 million elements.
The 20 Newsgroups dataset comes with reference train/test sets. In case of RCV1 we used half of the documents for training and the other half for evaluation. In case of English Wikipedia we held out for testing randomly selected 10% of the documents. We perform document retrieval by selecting queries from the test set and ordering other test documents according to the similarity of the inferred codes. We use Hamming distance for binary codes and cosine similarity for real-valued representations. Results are averaged over queries. We assess the performance of our models with precision-recall curves and two popular information retrieval metrics, namely mean average precision (MAP) and the normalized discounted cumulative gain at the 10th result (NDCG@10) BIBREF16 . The results depend, of course, on the chosen document relevancy measure. Relevancy measure for the 20 Newsgroups dataset is straightforward: a retrieved document is relevant to the query if they both belong to the same newsgroup. In RCV1 each document belongs to a hierarchy of topics, making the definition of relevancy less obvious. In this case we adopted the relevancy measure used by BIBREF3 . That is, the relevancy is calculated as the fraction of overlapping labels in a retrieved document and the query document. Overall, our selection of test datasets and relevancy measures for 20 Newsgroups and RCV1 follows BIBREF3 , enabling comparison with semantic hashing codes. To assess the relevancy of articles in English Wikipedia we can employ categories assigned to them. However, unlike in RCV1, Wikipedia categories can have multiple parent categories and cyclic dependencies. Therefore, for this dataset we adopted a simplified relevancy measure: two articles are relevant if they share at least one category. We also removed from the test set categories with less than 20 documents as well as documents that were left with no categories. Overall, the relevancy is measured over more than INLINEFORM0 categories, making English Wikipedia harder than the other two benchmarks.
We use AdaGrad BIBREF17 for training and inference in all experiments reported in this work. During training we employ dropout BIBREF18 in the embedding layer. To facilitate models with large vocabularies, we approximate the gradients with respect to the softmax logits using the method described by BIBREF9 . Binary PV-DM networks use the same number of dimensions for document codes and word embeddings.
Performance of 128- and 32-bit binary paragraph vector codes is reported in Table TABREF8 and in Figure FIGREF7 . For comparison we also report performance of real-valued paragraph vectors. Note that the binary codes perform very well, despite their far lower capacity: on 20 Newsgroups and RCV1 the 128-bit Binary PV-DBOW trained with bigrams approaches the performance of the real-valued paragraph vectors, while on English Wikipedia its performance is slightly lower. Furthermore, Binary PV-DBOW with bigrams outperforms semantic hashing codes: comparison of precision-recall curves from Figures FIGREF7 a and FIGREF7 b with BIBREF3 shows that 128-bit codes learned with this model outperform 128-bit semantic hashing codes on 20 Newsgroups and RCV1. Moreover, the 32-bit codes from this model outperform 128-bit semantic hashing codes on the RCV1 dataset, and on the 20 Newsgroups dataset give similar precision up to approximately 3% recall and better precision for higher recall levels. Note that the difference in this case lies not only in retrieval precision: the short 32-bit Binary PV-DBOW codes are more efficient for indexing than long 128-bit semantic hashing codes.
We also compared binary paragraph vectors against codes constructed by first inferring short, real-valued paragraph vectors and then using a separate hashing algorithm for binarization. When the dimensionality of the paragraph vectors is equal to the size of binary codes, the number of network parameters in this approach is similar to that of Binary PV models. We experimented with two standard hashing algorithms, namely random hyperplane projection BIBREF19 and iterative quantization BIBREF20 . Paragraph vectors in these experiments were inferred using PV-DBOW with bigrams. Results reported in Table TABREF9 show no benefit from using a separate algorithm for binarization. On the 20 Newsgroups and RCV1 datasets Binary PV-DBOW yielded higher MAP than the two baseline approaches. On English Wikipedia iterative quantization achieved MAP equal to Binary PV-DBOW, while random hyperplane projection yielded lower MAP. Some gain in precision of top hits can be observed for iterative quantization, as indicated by NDCG@10. However, precision of top hits can also be improved by querying with Real-Binary PV-DBOW model (Section SECREF15 ). It is also worth noting that end-to-end inference in Binary PV models is more convenient than inferring real-valued vectors and then using another algorithm for hashing.
BIBREF15 argue that PV-DBOW outperforms PV-DM on a sentiment classification task, and demonstrate that the performance of PV-DBOW can be improved by including bigrams in the vocabulary. We observed similar results with Binary PV models. That is, including bigrams in the vocabulary usually improved retrieval precision. Also, codes learned with Binary PV-DBOW provided higher retrieval precision than Binary PV-DM codes. Furthermore, to choose the context size for the Binary PV-DM models, we evaluated several networks on validation sets taken out of the training data. The best results were obtained with a minimal one-word, one-sided context window. This is the distributed memory architecture most similar to the Binary PV-DBOW model.
We experimented with two standard hashing algorithms, namely random hyperplane projection (Charikar, 2002) and iterative quantization (Gong and Lazebnik, 2011).
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What are the two standard hashing algorithms experimented with by authors?
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They are namely random hyperplane projection and iterative quantization.
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Nintendo was founded in 1889 as Nintendo Karuta by craftsman Fusajiro Yamauchi and originally produced handmade hanafuda playing cards. After venturing into various lines of business during the 1960s and acquiring a legal status as a public company, Nintendo distributed its first console, the Color TV-Game, in 1977. It gained international recognition with the release of Donkey Kong in 1981 and the Nintendo Entertainment System and Super Mario Bros. in 1985.
|
Given this reference text about Nintendo, when was it founded?
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1889
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The Nobel Prize in Literature was awarded to the novelist
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Abdulrazak Gurnah (England)
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Why was the show Seinfeld so popular?
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Seinfeld was popular because it was a comedy that tackled everyday issues in a relatable and humorous way. The characters and storylines were very realistic and had a wide range of topics to explore, from relationships to social issues to mundane topics like New York City traffic. The show also had a strong ensemble cast that included Jerry Seinfeld, Elaine Benes, George Costanza, and the rest of the main characters. The show was also known for its witty dialogue, memorable catch phrases, and attention to detail in the writing.
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Journalistic organisations, such as Media Bias/Fact Check, provide reports on news sources highlighting the ones that are propagandistic. Obviously, such analysis is time-consuming and possibly biased and it cannot be applied to the enormous amount of news that flood social media and the Internet. Research on detecting propaganda has focused primarily on classifying entire articles as propagandistic/non-propagandistic BIBREF0, BIBREF1, BIBREF2. Such learning systems are trained using gold labels obtained by transferring the label of the media source, as per Media Bias/Fact Check judgment, to each of its articles. Such distant supervision setting inevitably introduces noise in the learning process BIBREF3 and the resulting systems tend to lack explainability.
We argue that in order to study propaganda in a sound and reliable way, we need to rely on high-quality trusted professional annotations and it is best to do so at the fragment level, targeting specific techniques rather than using a label for an entire document or an entire news outlet. Therefore, we propose a novel task: identifying specific instances of propaganda techniques used within an article. In particular, we design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.
Our corpus could enable research in propagandistic and non-objective news, including the development of explainable AI systems. A system that can detect instances of use of specific propagandistic techniques would be able to make it explicit to the users why a given article was predicted to be propagandistic. It could also help train the users to spot the use of such techniques in the news.
We split the corpus into training, development and test, each containing 293, 57, 101 articles and 14,857, 2,108, 4,265 sentences, respectively.
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How many parts do they split the corpus into?
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Training, development and test.
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What are the main ingredients of guacamole?
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Guacamole is an avocado based dip that can have many variations. The most common ingredients in guacamole are avocado, lime juice, red onion, cilantro, jalapeno, and salt and pepper.
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Learning the distributed representation for long spans of text from its constituents has been a key step for various natural language processing (NLP) tasks, such as text classification BIBREF0 , BIBREF1 , semantic matching BIBREF2 , BIBREF3 , and machine translation BIBREF4 . Existing deep learning approaches take a compositional function with different forms to compose word vectors recursively until obtaining a sentential representation. Typically, these compositional functions involve recurrent neural networks BIBREF5 , BIBREF6 , convolutional neural networks BIBREF7 , BIBREF8 , and tree-structured neural networks BIBREF9 , BIBREF10 .
Among these methods, tree-structured neural networks (Tree-NNs) show theirs superior performance in many NLP tasks BIBREF11 , BIBREF12 . Following the syntactic tree structure, Tree-NNs assign a fixed-length vector to each word at the leaves of the tree, and combine word and phrase pairs recursively to create intermediate node vectors, eventually obtaining one final vector to represent the whole sentence.
However, these models have a major limitation in their inability to fully capture the richness of compositionality BIBREF13 . The same parameters are used for all kinds of semantic compositions, even though the compositions have different characteristics in nature. For example, the composition of the adjective and the noun differs significantly from the composition of the verb and the noun. Moreover, many semantic phenomena, such as semantic idiomaticity or transparency, call for more powerful compositional mechanisms BIBREF14 . Therefore, Tree-NNs suffer from the underfitting problem.
To alleviate this problem, some researchers propose to use multiple compositional functions, which are arranged beforehand according to some partition criterion BIBREF11 , BIBREF13 , BIBREF15 . Intuitively, using different parameters for different types of compositions has the potential to greatly reduce underfitting. BIBREF13 [ BIBREF13 ] defined different compositional functions in terms of syntactic categories, and a suitable compositional function is selected based on the syntactic categories. BIBREF15 [ BIBREF15 ] introduced multiple compositional functions and during compositional phase, a proper one is selected based on the input information. Although these models accomplished their mission to a certain extent, they still suffer from the following three challenges. First, the predefined compositional functions cannot cover all the compositional rules; Second, they require more learnable parameters, suffering from the problem of overfitting; Third, it is difficult to determine a universal criterion for semantic composition based solely on syntactic categories.
In this paper, we propose dynamic compositional neural networks over tree structure, in which a meta network is used to generate the context-specific parameters of a dynamic compositional network. Specifically, we construct our models based on two kinds of tree-structured neural networks: recursive neural network (Tree-RecNN) BIBREF11 and tree-structure long short-term memory neural network (Tree-LSTM) BIBREF9 . Our work is inspired by recent work on dynamic parameter prediction BIBREF16 , BIBREF17 , BIBREF18 . The meta network is used to extract the shared meta-knowledge across different compositional rules and to dynamically generate the context-specific compositional function. Thus, the compositional function of our models varies with positions, contexts and samples. The dynamic compositional network then applies those context-specific parameters to the current input information. Both meta and dynamic networks are differentiable such that the overall networks can be trained in an end-to-end fashion. Additional, to reduce the complexity of the whole networks, we define the dynamic weight matrix in a manner simulating low-rank matrix decomposition.
We evaluate our models on two typical tasks: text classification and text semantic matching. The results show that our models are more expressive due to their learning to learn nature, yet without increasing the number of model's parameters. Moreover, we find certain composition operations can be learned implicitly by meta TreeNN, such as the composition of noun phrases and verb phrases.
The contributions of the paper can be summed up as follows.
Moreover, we find certain composition operations can be learned implicitly by meta TreeNN, such as the composition of noun phrases and verb phrases.
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What kind of composition operations can be learned implicitly by meta TreeNN?
|
Such as the composition of noun phrases and verb phrases.
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Provide a list of body parts that are only three letters long. Here are some body parts that are three lettered words: toe, rib, eye, ear, leg and jaw.
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Here are some body parts that are three lettered words: toe, rib, eye, ear, leg and jaw.
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Who shot Mr. Burns on the Simpsons?
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Maggie
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| 155
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Cyberbullying has been defined by the National Crime Prevention Council as the use of the Internet, cell phones or other devices to send or post text or images intended to hurt or embarrass another person. Various studies have estimated that between to 10% to 40% of internet users are victims of cyberbullying BIBREF0 . Effects of cyberbullying can range from temporary anxiety to suicide BIBREF1 . Many high profile incidents have emphasized the prevalence of cyberbullying on social media. Most recently in October 2017, a Swedish model Arvida Byström was cyberbullied to the extent of receiving rape threats after she appeared in an advertisement with hairy legs.
Detection of cyberbullying in social media is a challenging task. Definition of what constitutes cyberbullying is quite subjective. For example, frequent use of swear words might be considered as bullying by the general population. However, for teen oriented social media platforms such as Formspring, this does not necessarily mean bullying (Table TABREF9 ). Across multiple SMPs, cyberbullies attack victims on different topics such as race, religion, and gender. Depending on the topic of cyberbullying, vocabulary and perceived meaning of words vary significantly across SMPs. For example, in our experiments we found that for word `fat', the most similar words as per Twitter dataset are `female' and `woman' (Table TABREF23 ). However, other two datasets do not show such particular bias against women. This platform specific semantic similarity between words is a key aspect of cyberbullying detection across SMPs. Style of communication varies significantly across SMPs. For example, Twitter posts are short and lack anonymity. Whereas posts on Q&A oriented SMPs are long and have option of anonymity (Table TABREF7 ). Fast evolving words and hashtags in social media make it difficult to detect cyberbullying using swear word list based simple filtering approaches. The option of anonymity in certain social networks also makes it harder to identify cyberbullying as profile and history of the bully might not be available.
Past works on cyberbullying detection have at least one of the following three bottlenecks. First (Bottleneck B1), they target only one particular social media platform. How these methods perform across other SMPs is unknown. Second (Bottleneck B2), they address only one topic of cyberbullying such as racism, and sexism. Depending on the topic, vocabulary and nature of cyberbullying changes. These models are not flexible in accommodating changes in the definition of cyberbullying. Third (Bottleneck B3), they rely on carefully handcrafted features such as swear word list and POS tagging. However, these handcrafted features are not robust against variations in writing style. In contrast to existing bottlenecks, this work targets three different types of social networks (Formspring: a Q&A forum, Twitter: microblogging, and Wikipedia: collaborative knowledge repository) for three topics of cyberbullying (personal attack, racism, and sexism) without doing any explicit feature engineering by developing deep learning based models along with transfer learning.
We experimented with diverse traditional machine learning models (logistic regression, support vector machine, random forest, naive Bayes) and deep neural network models (CNN, LSTM, BLSTM, BLSTM with Attention) using variety of representation methods for words (bag of character n-gram, bag of word unigram, GloVe embeddings, SSWE embeddings). Summary of our findings and research contributions is as follows.
– This the first work that systematically analyzes cyberbullying on various topics across multiple SMPs and applies transfer learning for cyberbullying detection task. – Presence of swear words is neither necessary nor sufficient for cyberbullying.Robust models for cyberbullying detection should not rely on such handcrafted features. – Deep Learning based models outperform traditional Machine Learning models for cyberbullying detection task. – Training datasets for cyberbullying detection contain only a few posts marked as a bullying. This class imbalance problem can be tackled by oversampling the rare class. – The vocabulary of words used for cyberbullying and their interpretation varies significantly across SMPs.
|
What are the research achievements and contributions made by the author through experiments?
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– This the first work that systematically analyzes cyberbullying on various topics across multiple SMPs and applies transfer learning for cyberbullying detection task. – Presence of swear words is neither necessary nor sufficient for cyberbullying.Robust models for cyberbullying detection should not rely on such handcrafted features. – Deep Learning based models outperform traditional Machine Learning models for cyberbullying detection task. – Training datasets for cyberbullying detection contain only a few posts marked as a bullying. This class imbalance problem can be tackled by oversampling the rare class. – The vocabulary of words used for cyberbullying and their interpretation varies significantly across SMPs.
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| 171
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We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical” utterances - substantially extending the previous dataset of 35 utterances from Cercas Curry and Rieser (2018) - and categorise these utterances based on the Linguistic Society’s definition of sexual harassment (Linguistic Society of America): A) Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?” B) Sexualised Comments, e.g. “I love watching porn.”, “I’m horny.” C) Sexualised Insults, e.g. “Stupid bitch.”, “Whore” D) Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.
|
How to categorize the utterances based on the Linguistic Society’s definition of sexual harassment?
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A) Gender and Sexuality, B) Sexualised Comments, C) Sexualised Insults, D) Sexual Requests and Demands.
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| 172
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We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
It consists of approx. 67,000 sentences with over 2 million tokens.
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What does the Named Entity Recognition in German federal court decisions consist of?
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It consists of approx. 67,000 sentences with over 2 million tokens.
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What is a wallet used for?
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A wallet is a flat pouch used to carry small person items. It can be made out of raw hide or some other material. It can be used to cary currency or money, credit cards, photos of family, insurance cards, and person identification. A wallet is helpful to organize different payment methods, and it used in most transactions that requirement a payment using a form of currency or credit.
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| 103
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Suppose we are given a classifier $C: \mathcal {S} \rightarrow \mathcal {Y}$ which maps natural language sentences $s \in \mathcal {S}$ to a label from a predefined set $y \in \mathcal {Y}$ . An adversary for this classifier is a function $A$ which maps a sentence $s$ to its perturbed versions $\lbrace s^{\prime }_1, s^{\prime }_2, \ldots , s^{\prime }_{n}\rbrace $ such that each $s^{\prime }_i$ is close to $s$ under some notion of distance between sentences. We define the robustness of classifier $C$ to the adversary $A$ as:
$$R_{C,A} = \mathbb {E}_s \left[\min _{s^{\prime } \in A(s)} \mathbb {1}[C(s^{\prime }) = y]\right],$$ (Eq. 14)
where $y$ represents the ground truth label for $s$ . In practice, a real-world adversary may only be able to query the classifier a few times, hence $R_{C,A}$ represents the worst-case adversarial performance of $C$ . Methods for generating adversarial examples, such as HotFlip BIBREF8 , focus on efficient algorithms for searching the $\min $ above. Improving $R_{C,A}$ would imply better robustness against all these methods.
We explore adversaries which perturb sentences with four types of character-level edits:
(1) Swap: swapping two adjacent internal characters of a word. (2) Drop: removing an internal character of a word. (3) Keyboard: substituting an internal character with adjacent characters of QWERTY keyboard (4) Add: inserting a new character internally in a word. In line with the psycholinguistic studies BIBREF5 , BIBREF4 , to ensure that the perturbations do not affect human ability to comprehend the sentence, we only allow the adversary to edit the internal characters of a word, and not edit stopwords or words shorter than 4 characters.
For 1-character attacks, we try all possible perturbations listed above until we find an adversary that flips the model prediction. For 2-character attacks, we greedily fix the edit which had the least confidence among 1-character attacks, and then try all the allowed perturbations on the remaining words. Higher order attacks can be performed in a similar manner. The greedy strategy reduces the computation required to obtain higher order attacks, but also means that the robustness score is an upper bound on the true robustness of the classifier.
For the ScRNN model, we use a single-layer BiLSTM with a hidden dimension size of 50.
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Is it a single-layer BiLSTM used in the ScRNN model?
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Yes.
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1911.03842
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Each of the methods we explore improve in % gendered words, % male bias, and F1 over the baseline Transformer generation model, but we find combining all methods in one – the ALL model is the most advantageous. While ALL has more data than CDA and CT, more data alone is not enough — the Positive-Bias Data Collection model does not achieve as good results. Both the CT and ALL models benefit from knowing the data split ($\text{F}^{0}\text{M}^{0}$, for example), and both models yield a genderedness ratio closest to ground truth.
Each of the methods we explore improve in % gendered words, % male bias, and F1 over the baseline Transformer generation model, but we find combining all methods in one – the ALL model is the most advantageous.
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What baseline is used to compare the experimental results against?
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The answers are shown as follows:
* Transformer generation model
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Hades (ᾍδης, Háidēs)/Pluto (Πλούτων, Ploutōn)
King of the underworld and the dead. He is also a god of wealth. His consort is Persephone. His attributes are the drinking horn or cornucopia, key, sceptre, and the three-headed dog Cerberus. His sacred animals include the screech owl. He was one of three sons of Cronus and Rhea, and thus sovereign over one of the three realms of the universe, the underworld. As a chthonic god, however, his place among the Olympians is ambiguous. In the mystery religions and Athenian literature, Plouton ("the Rich one") was his preferred name, because of the idea that all riches came from the earth. The term Hades was used in this literature to refer to the underworld itself. The Romans translated Plouton as Dis Pater ("the Rich Father") or Pluto.
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From the passage name the parents of Hades. Display the results in a comma separated format.
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Cronus, Rhea
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| 472
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As mentioned in and will be described in Section 4.4, when the parameters of model () are identified, the causal estimands can be identified.
Lemma 1 For any x ∈ Γ = {0, 1, . . . , K} and y ∈ {0, 1}, let
Proof For all x ∈ Γ, we have
Hence
Lemma 1 For any x ∈ Γ = {0, 1, . . . , K} and y ∈ {0, 1}, let a_x = Pr{S(1) = 1|S(0) = 0, X = x} and b_xy = Pr{Y (0) = y|S(0) = 0, X = x}. Let a = (a_0, a_1, . . . , a_K) T and by = (by_0, by_1, . . . , by_K) T . Define h_x(β, a, b_0, b_1) = a_x −Σ^1_(y=0) b_xy logit^−1 {β_0 + β_1y + β_2x}, and H(β, a, b_0, b_1) = {h_0(β, a, b_0, b_1), . . . , h_K(β, a, b_0, b_1)} T . If rank{ ∂H(β,a,b_0,b_1) ∂β } = 3, within the neighborhood of β there is a unique solution β = ψ(a, b_0, b_1) such that H{ψ(a, b_0, b_1), a, b_0, b_1} = 0.
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How is the joint distribution of S(0) and S(1) in GL identified, and what role does the monotonicity assumption play in this?
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1.The joint distribution of S(0) and S(1) in GL can only be estimated with maximum likelihood from the observed data under the monotonicity assumption. Details are presented in Lemma 1 and Appendix A. We will clarify its importance after Lemma 1 in the revision.
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| 128
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End-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recognition (ASR) and machine translation (MT) systems. The lack of labeled data, however, has become a major blocker for bridging the performance gaps between end-to-end models and cascading systems. Several corpora have been developed in recent years. post2013improved introduced a 38-hour Spanish-English ST corpus by augmenting the transcripts of the Fisher and Callhome corpora with English translations. di-gangi-etal-2019-must created the largest ST corpus to date from TED talks but the language pairs involved are out of English only. beilharz2019librivoxdeen created a 110-hour German-English ST corpus from LibriVox audiobooks. godard-etal-2018-low created a Moboshi-French ST corpus as part of a rare language documentation effort. woldeyohannis provided an Amharic-English ST corpus in the tourism domain. boito2019mass created a multilingual ST corpus involving 8 languages from a multilingual speech corpus based on Bible readings BIBREF7. Previous work either involves language pairs out of English, very specific domains, very low resource languages or a limited set of language pairs. This limits the scope of study, including the latest explorations on end-to-end multilingual ST BIBREF8, BIBREF9. Our work is mostly similar and concurrent to iranzosnchez2019europarlst who created a multilingual ST corpus from the European Parliament proceedings. The corpus we introduce has larger speech durations and more translation tokens. It is diversified with multiple speakers per transcript/translation. Finally, we provide additional out-of-domain test sets.
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice BIBREF10 for 11 languages into English, diversified with over 11,000 speakers and over 60 accents. It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora. We also collect an additional evaluation corpus from Tatoeba for French, German, Dutch, Russian and Spanish, resulting in a total of 9.3 hours of speech. Both corpora are created at the sentence level and do not require additional alignments or segmentation. Using the official Common Voice train-development-test split, we also provide baseline models, including, to our knowledge, the first end-to-end many-to-one multilingual ST models. CoVoST is released under CC0 license and free to use. The Tatoeba evaluation samples are also available under friendly CC licenses. All the data can be acquired at https://github.com/facebookresearch/covost.
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice (Ardila et al., 2019) for 11 languages into English, diversified with over 11,000 speakers and over 60 accents. It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora.
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How large is the corpus?
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It includes speeches of 708 hours in total.
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What are the benefits of the 2-3 zone defense in basketball?
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The 2-3 zone defense is the most commonly used zone defense. It has the advantage of protecting the inside, lane area, and keeps your "bigs" inside. Its weakness is it is vulnerable to good outside shooting, with open areas on the wings, point and high post.
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How are people from the Netherlands called (in English)?
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People from the Netherlands are commonly referred to as "Netherlanders" in English.
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null | false
| null |
What are some tips for beating Destiny 2 bosses?
|
I'm not a high level player, but I've found a couple of things helpful. 1. Don't let trash mobs accumulate by focusing all your attention on the boss. You can get quickly overwhelmed. 2. Take your time. I've found that I get a bit greedy and that can be punishing if I get caught away from cover. 3. Prioritize! Some mobs are more dangerous to others. Some boss fights can go from nearly impossible to almost manageable if you focus on culling certain mob types whenever they appear. 4. Switch weapons! Don't be afraid to switch to a heavy weapon for vehicle or larger enemies.
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null | false
| 54
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Knowledge graphs BIBREF0 enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants. Knowledge graphs are typically interpreted as comprising of discrete triples of the form (entityA, relationX, entityB) thus representing a relation (relationX) between entityA and entityB. However, one limitation of only a discrete representation of triples is that it does not easily enable one to infer similarities and potential relations among entities which may be missing in the knowledge graph. Consequently, one popular alternative is to learn dense continuous representations of entities and relations by embedding them in latent continuous vector spaces, while seeking to model the inherent structure of the knowledge graph. Most knowledge graph embedding methods can be classified into two major classes: one class which operates purely on triples like RESCAL BIBREF1 , TransE BIBREF2 , DistMult BIBREF3 , TransD BIBREF4 , ComplEx BIBREF5 , ConvE BIBREF6 and the second class which seeks to incorporate additional information (like multi-hops) BIBREF7 . Learning high-quality knowledge graph embeddings can be quite challenging given that (a) they need to effectively model the contextual usages of entities and relations (b) they would need to be useful for a variety of predictive tasks on knowledge graphs.
In this paper, we present a new type of knowledge graph embeddings called Dolores that are both deep and contextualized. Dolores learns both context-independent and context-dependent embeddings of entities and relations through a deep neural sequential model. Figure 1 illustrates the deep contextualized representations learned. Note that the contextually independent entity embeddings (see Figure 1 ) reveal three clusters of entities: writers, philosophers, and musicians. The contextual dependent embeddings in turn effectively account for specific relations. In particular, the context-dependent representations under the relation nationality now nicely cluster the above entities by nationality namely Austrians, Germans, and British/Irish. Similarly Figure 1 shows contextual embeddings given the relation place-lived. Note that these embeddings correctly capture that even though Beethoven and Brahms being Germans, they lived in Vienna and are closer to other Austrian musicians like Schubert.
Unlike most knowledge graph embeddings like TransD, TransE BIBREF2 , BIBREF4 etc. which are typically learned using shallow models, the representations learned by Dolores are deep: dependent on an entire path (rather than just a triple), are functions of internal states of a Bi-Directional LSTM and composed of representations learned at various layers potentially capturing varying degrees of abstractions. Dolores is inspired by recent advances in learning word representations (word embeddings) from deep neural language models using Bi-Directional LSTMs BIBREF8 . In particular, we derive connections between the work of Peters et al. ( BIBREF8 ) who learn deep contextualized word embeddings from sentences using a Bi-Directional LSTM based language model and random walks on knowledge graphs. These connections enable us to propose new “deep contextualized” knowledge graph embeddings which we call Dolores embeddings.
Knowledge Embeddings learned using Dolores can easily be used as input representations for predictive models on knowledge graphs. More importantly, when existing predictive models use input representations for entities and relations, we can easily replace those representations with Dolores representations and significantly improve the performance of existing models. Specifically, we show that Dolores embeddings advance the state-of-the-art models on various tasks like link prediction, triple classification and missing relation type prediction.
To summarize, our contributions are as follows:
Knowledge graphs enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants.
|
What can knowledge graphs be used for?
|
Knowledge graphs enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants.
|
null | false
| null |
Why do you love travelling
|
Because it keeps you going? It will give you enough O2 to live!
|
|
1911.11750
| false
| null |
We have used a dataset of 14 TDs to conduct our experiments. There are several subjects on which their content is based: (aliens, stories, law, news) BIBREF15.
We have used a dataset of 14 TDs to conduct our experiments.
|
Which dataset(s) do they use?
|
The answers are shown as follows:
* 14 TDs
* BIBREF15
|
null | false
| null |
The history of the Marine Corps began when two battalions of Continental Marines were formed on 10 November 1775 in Philadelphia as a service branch of infantry troops capable of fighting both at sea and on shore. In the Pacific theater of World War II the Corps took the lead in a massive campaign of amphibious warfare, advancing from island to island. As of 2022, the USMC has around 177,200 active duty members and some 32,400 personnel in reserve.
|
Given this paragraph about the United States Marine Corps, tell me when it was created in which city?
|
The Marine Corps was established on the 10th of November in 1775 in Philadelphia, PA.
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null | false
| null |
Club Africain (Arabic: النادي الإفريقي) is a Tunisian football club based in Tunis that competes in the Tunisian Championship. It fields several sports teams, including football, handball, basketball, swimming and volleyball. The football team was the first Tunisian club to win an international trophy, when they won the Maghreb Cup Winners Cup in 1971. Twenty years later, in 1991, Club Africain became the first Tunisian team to win the African Champions League.
|
What international trophy did the Club Africain football team won in 1971 ?
|
The Maghreb Cup Winners Cup
|
null | false
| null |
Laika (/ˈlaɪkə/ LY-kə; Russian: Лайка, IPA: [ˈlajkə]; c. 1954 – 3 November 1957) was a Soviet space dog who was one of the first animals in space and the first to orbit the Earth. A stray mongrel from the streets of Moscow, she flew aboard the Sputnik 2 spacecraft, launched into low orbit on 3 November 1957. As the technology to de-orbit had not yet been developed, Laika's survival was never expected. She died of overheating hours into the flight, on the craft's fourth orbit.
Little was known about the effects of spaceflight on living creatures at the time of Laika's mission, and animal flights were viewed by engineers as a necessary precursor to human missions. The experiment, which monitored Laika's vital signs, aimed to prove that a living organism could survive being launched into orbit and continue to function under conditions of weakened gravity and increased radiation, providing scientists with some of the first data on the biological effects of spaceflight.
Laika died within hours from overheating, possibly caused by a failure of the central R‑7 sustainer to separate from the payload. The true cause and time of her death were not made public until 2002; instead, it was widely reported that she died when her oxygen ran out on day six or, as the Soviet government initially claimed, she was euthanised prior to oxygen depletion. In 2008, a small monument to Laika depicting her standing atop a rocket was unveiled near the military research facility in Moscow that prepared her flight. She also appears on the Monument to the Conquerors of Space in Moscow.
|
Who is Laika and why is she famous?
|
Laika was one of the first animals in space and the first one to orbit the space. She was sent into the outer space by the Soviet Union. She died of overheating hours into the flight while the spacecraft carrying her was in the orbit.
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null | false
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Write me a poem about data
|
Data is big
Data is great
Data must stand
Or endeavour a fate
Where truth becomes blurred
Where knowledge can't roost
And fall victim you may
To subjective seduce
|
|
1911.03243
| false
| null |
Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability. Naturally, employing crowd workers raises challenges when annotating semantic structures like SRL. As BIBREF5 acknowledged, the main shortage of the large-scale 2018 dataset is the lack of recall, estimated by experts to be in the lower 70s.
Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability.
|
How was the corpus obtained?
|
The answers are shown as follows:
* trained annotators BIBREF4
* crowdsourcing BIBREF5
|
null | false
| null |
Write a paragraph of a blog post about owning a Cavoodle.
|
The Cavoodle is a cross between a Cavalier King Charles Spaniel and a Poodle. These are now one of the most sort after breeds based on their temperament, overall genetic makeup and an all-round nature as a breed. The Cavoodle is a pack animal that loves nothing more than being a member of the family. Highly intelligent the Cavoodle has the smarts of a poodle and the relaxed nature of the Cavalier. Put together this is a breed that will suit most families, older owners looking for companionship and people living on their own. A loving nature, mixed with a playful attitude, may owners immediately fall in love with this breed. Social, friendly, intelligent and an easy dog breed to look after the Cavoodle continues to grow in popularity.
|
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null | false
| null |
Why is Cricket so popular ?
|
Cricket is a very popular game and is the second most viewed sport in the world. It has a fan following of over 2.5 billion people. Cricket is believed to have first started in England and is believed to have spread among the British colonies of the old during their rule. Hence in addition to England it is very popular in South Africa, India, Australia, New Zealand and many other Asian countries.
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null | false
| null |
Which of the following are considered functional programming languages and which are not: OCaml, Scala, Python, C, and Java.
|
OCaml and Scala are considered functional languages, while Python C, and Java are not.
|
|
null | false
| null |
Datafication is a technological trend turning many aspects of our life into data which is subsequently transferred into information realised as a new form of value. Kenneth Cukier and Viktor Mayer-Schönberger introduced the term datafication to the broader lexicon in 2013. Up until this time, datafication had been associated with the analysis of representations of our lives captured through data, but not on the present scale. This change was primarily due to the impact of big data and the computational opportunities afforded to predictive analytics.
Datafication is not the same as digitization, which takes analog content—books, films, photographs—and converts it into digital information, a sequence of ones and zeros that computers can read. Datafication is a far broader activity: taking all aspects of life and turning them into data [...] Once we datafy things, we can transform their purpose and turn the information into new forms of value
|
What are the examples of analog content provided in the passage? List them separated by semicolon.
|
The examples provided in the above passage for analog content are: books; films; photographs.
|
null | false
| null |
Tell me which muscle the following exercises help build: bench press, dips, dumbbell curls, deadlift, and squat.
|
Bench press and dips exercises build muscles around chest and triceps areas. Dumbbell curls target biceps. Deadlifts target both back and core muscles as well as your glutes and hamstrings. Finally, squats target quads, hamstrings, glutes and core muscles.
|
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| null |
Q: Is hand sanitizer effective against COVID-19?
|
A: One of the best ways to prevent the spread of infections and decrease the risk of getting sick is by washing your hands with plain soap and water, advises the CDC. Washing hands often with soap and water for at least 20 seconds is essential, especially after going to the bathroom; before eating; and after coughing, sneezing, or blowing one’s nose. If soap and water are not available, CDC recommends consumers use an alcohol-based hand sanitizer that contains at least 60% alcohol.
To stay informed, visit the FDA’s Hand Sanitizers and COVID-19 page. Also, take our hand sanitizer quiz.
|
|
null | false
| null |
What are the different payment methods for payment inside a US store?
|
In a US store, commonly accepted payment methods include cash, credit cards, debit cards, and checks.
|
|
null | false
| null |
Give me 5 strategies to receive a strong performance review
|
Here are 5 strategies to help you receive a strong performance review:
1. Perform all of the work assigned to you with good quality
2. Take initiative on projects outside of your main responsibilities
3. Make connections with people within and outside of your team
4. Become friends with your manager
5. Ask for continuous feedback throughout the year
|
|
null | false
| null |
Identify which car manufacturer is German or American: Maybach, Panoz
|
Panoz is American, Maybach is German
|
|
null | false
| null |
What makes the platypus such an interesting animal?
|
The platypus is a mammal who lays eggs and has an incubation period approximately 10 days. They hunt by detecting minute electric fields of prey with sensitive bill and adult males have venomous spurs. They also have thick fur which provides insulation against cold water. While they are difficult to keep and breed in managed care settings, there are a few platypuses in zoos. Currently, two reside at the San Diego Zoo Safari Park and all others in Australian collections.
|
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null | false
| null |
The Battle of Stalingrad was a major battle on the Eastern Front of World War II where Nazi Germany and its allies unsuccessfully fought the Soviet Union for control of the city of Stalingrad (later renamed Volgograd) in Southern Russia. The battle was marked by fierce close-quarters combat and direct assaults on civilians in air raids, with the battle epitomizing urban warfare.The Battle of Stalingrad was the deadliest battle to take place during the Second World War. Today, the Battle of Stalingrad is universally regarded as the turning point in the European Theatre of war, as it forced the Oberkommando der Wehrmacht (German High Command) to withdraw considerable military forces from other areas in occupied Europe to replace German losses on the Eastern Front, ending with the rout of the six field armies of Army Group B, including the destruction of Nazi Germany's 6th Army and an entire corps of its 4th Panzer Army. The victory at Stalingrad energized the Red Army and shifted the balance of power in the favour of the Soviets.
|
Why was the Battle of Stalingrad so important?
|
Battle of Stalingrad was critical and device fight between Nazi Germany and Soviet Union for few reasons. Firstly, it was deadliest and most intense fighting between two powers, binding very sizeable forces. The city itself was strategic as it hold the keys to the southern part and oil fields. Thirdly, it had a symbolic value for Stalin. And last but not least, it turned the tide in the war.
|
null | false
| 495
|
Updating the policy. The policy is optimized to maximize the augmented reward on transitions sampled from the learned dynamics model. While this optimization can be done using any RL algorithm, including on-policy methods, we will focus on an off-policy actor-critic method.
We define the Q function as sum of augmented rewards under the learned dynamics model:
We approximate the Q function using a neural network Q ψ (s t , a t ) with parameters φ. We train the Q function using the TD loss on transitions sampled from the learned dynamics model:
where • sg is the stop-gradient operator and
) . The augmented reward, r, is estimated using the learned classifier. To estimate the corresponding value function, we use a 1-sample approximation:
The policy is trained to maximize the expected future (augmented) return, as estimated by the Q function:
In our implementation, we regularize the policy by adding an additional entropy regularizer. Following prior work, we maintain two Q functions and two target Q functions, use the minimum of the two target Q functions to compute the TD target. See Appendix C for details.
Updating the classifier. We train the classifier to distinguish real versus model transitions using the standard cross entropy loss: between training the policy on experience from the learned dynamics model with augmented rewards and updating the model+classifier using a GAN-like loss. Updates are gradient steps with the Adam optimizer.
1: while not converged do 2:
Sample experience from learned model and modify rewards using the classifier (Eq. 5).
Update policy and Q function using the model experience and augmented rewards (Eq.s 8 and 7). 4:
Update model and classifier using GAN-like losses (Eq.s 9 and 10). 5:
(Infrequently) Sample experience from real model. 6: return policy π θ (at | st).
Updating the dynamics model. To optimize the dynamics model, we rewrite the lower bound in terms of a single transition (derivation in Appendix A.6):
The approximation above reflects approximation error in learning the optimal classifier. This approximation is standard in prior work on GANs and adversarial inference. The procedure for optimizing the dynamics model and the classifier resembles a GAN: the classifier is optimized to distinguish real transitions from model transitions, and the model is updated to fool the classifier (and increase rewards). However, our method is not equivalent to simply replacing a maximum likelihood model with a GAN model. Indeed, such an approach would not optimize a lower bound on expected return. Rather, our model objective includes an additional value term and our policy objective includes an additional classifier term. These changes enable the model and policy to optimize the same objective, which is a lower bound on expected return.
Algorithm summary. We summarize the method in Alg. 1 and provide an illustration in Fig.. Implementing MnM on top of a standard model-based RL algorithm is straightforward. First, create an additional classifier network. Second, instead of using the maximum likelihood objective to train the model, use the GAN-like objective in Eq. 10 to update both the model and the classifier. Third, add the classifier's logits to the predicted rewards (Eq. 5). Following prior work, we learn an additional model to predict the true environment.
Algorithm summary. We summarize the method in Alg. 1 and provide an illustration in Fig. 1. Implementing MnM on top of a standard model-based RL algorithm is straightforward. First, create an additional classifier network. Second, instead of using the maximum likelihood objective to train the model, use the GAN-like objective in Eq. 10 to update both the model and the classifier. Third, add the classifier’s logits to the predicted rewards (Eq. 5). Following prior work (Janner et al., 2019), we learn a neural network to predict the true environment rewards.
|
Is the reward function learned as well?
|
Yes, the reward function is learned as well. We fit a separate neural network to predict the rewards (final paragraph of Section 4.2).
|
null | false
| null |
What are the benefits of meditation?
|
Scientific studies have shown that regular meditation improves focus, memory, sleep, and mood. While more longer or more frequent meditation tends to increase these benefits, measurable improvements in each of those areas can be found from just a few minutes a day.
|
|
null | false
| null |
The Flight Compensation Regulation 2004 (EC) No 261/2004 is a regulation in EU law establishing common rules on compensation and assistance to passengers in the event of denied boarding, flight cancellations, or long delays of flights. It requires compensation of €250 to €600 depending on the flight distance for delays over of at least three hours, cancellations, or being denied boarding from overbooking. Delays shorter than three hours means no entitlement to any compensation of any kind even if the delay was classified as non-extraordinary. Airlines must provide refreshments and accommodation where appropriate. The Court of Justice of the European Union has interpreted passenger rights strictly, so that there are virtually no exceptions for airlines to evade their obligations for breach of contract. It repealed Regulation (EEC) No 295/91, and went into effect on 17 February 2005.
|
What is the EU compensation regulation EU261 for cancelled flights?
|
The EU in 2004, passed regulation EU 261 to protect passengers against severe flight disruptions such as long delays, cancellations, missed connections and flight re-bookings. The exact compensation amount is dependent on the flight distance and delays of over 3 hours, cancellations, or being denied boarding due to overbooking. The regulation went into effect as of February 2005.
|
null | false
| 37
|
The resource is comprised of 142 hours of spoken Mapudungun that was recorded during the AVENUE project BIBREF6 in 2001 to 2005. The data was recorded under a partnership between the AVENUE project, funded by the US National Science Foundation at Carnegie Mellon University, the Chilean Ministry of Education (Mineduc), and the Instituto de Estudios Indígenas at Universidad de La Frontera, originally spanning 170 hours of audio. We have recently cleaned the data and are releasing it publicly for the first time (although it has been shared with individual researchers in the past) along with NLP baselines.
The recordings were transcribed and translated into Spanish at the Instituto de Estudios Indígenas at Universidad de La Frontera. The corpus covers three dialects of Mapudungun: about 110 hours of Nguluche, 20 hours of Lafkenche and 10 hours of Pewenche. The three dialects are quite similar, with some minor semantic and phonetic differences. The fourth traditionally distinguished dialect, Huilliche, has several grammatical differences from the other three and is classified by Ethnologue as a separate language, iso 639-3: huh, and as nearly extinct.
The recordings are restricted to a single domain: primary, preventive, and treatment health care, including both Western and Mapuche traditional medicine. The recording sessions were conducted as interactive conversations so as to be natural in Mapuche culture, and they were open-ended, following an ethnographic approach. The interviewer was trained in these methods along with the use of the digital recording systems that were available at the time. We also followed human subject protocol. Each person signed a consent form to release the recordings for research purposes and the data have been accordingly anonymized. Because Machi (traditional Mapuche healers) were interviewed, we asked the transcribers to delete any culturally proprietary knowledge that a Machi may have revealed during the conversation. Similarly, we deleted any names or any information that may identify the participants.
The corpus is culturally relevant because it was created by Mapuche people, using traditional ways of relating to each other in conversations. They discussed personal experiences with primary health care in the traditional Mapuche system and the Chilean health care system, talking about illnesses and the way they were cured. The participants ranged from 16 years old to 100 years old, almost in equal numbers of men and women, and they were all native speakers of Mapudungun.
Hence, in an effort to provide a relatively “clean” corpus for modern computational experiments, we converted the encoding of the textual transcription from Latin-1 to Unicode, DOS to UNIX line endings, a now more standard text encoding format than what was used when the data was first collected. Additionally, we renamed a small portion of files which had been misnamed and removed several duplicate files.
|
What did the authors do to provide a relatively “clean” corpus for modern computational experiments?
|
The authors converted the encoding of the textual transcription from Latin-1 to Unicode, DOS to UNIX line endings, and a now more standard text encoding format than what was used when the data was first collected. Additionally, the authors renamed a small portion of files which had been misnamed and removed several duplicate files.
|
null | false
| null |
Identify which animal species is alive or extinct: Sandpiper, Greater Sage-Grouse
|
Greater Sage-Grouse is alive, Sandpiper is extinct.
|
|
null | false
| null |
What is a symphony?
|
A symphony is a musical composition that is played by a large orchestra that has anywhere between 50-80 instruments. The four movements of a symphony are an opening, a slow movement, a minuet, and then an allegro. Symphonies do not refer to the musician but rather the music that is being played harmoniously.
|
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null | false
| 163
|
Sarcasm is an intensive, indirect and complex construct that is often intended to express contempt or ridicule . Sarcasm, in speech, is multi-modal, involving tone, body-language and gestures along with linguistic artifacts used in speech. Sarcasm in text, on the other hand, is more restrictive when it comes to such non-linguistic modalities. This makes recognizing textual sarcasm more challenging for both humans and machines.
Sarcasm detection plays an indispensable role in applications like online review summarizers, dialog systems, recommendation systems and sentiment analyzers. This makes automatic detection of sarcasm an important problem. However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques. This is apparent from the results reported by the survey from DBLP:journals/corr/JoshiBC16. The following discussion brings more insights into this.
Consider a scenario where an online reviewer gives a negative opinion about a movie through sarcasm: “This is the kind of movie you see because the theater has air conditioning”. It is difficult for an automatic sentiment analyzer to assign a rating to the movie and, in the absence of any other information, such a system may not be able to comprehend that prioritizing the air-conditioning facilities of the theater over the movie experience indicates a negative sentiment towards the movie. This gives an intuition to why, for sarcasm detection, it is necessary to go beyond textual analysis.
We aim to address this problem by exploiting the psycholinguistic side of sarcasm detection, using cognitive features extracted with the help of eye-tracking. A motivation to consider cognitive features comes from analyzing human eye-movement trajectories that supports the conjecture: Reading sarcastic texts induces distinctive eye movement patterns, compared to literal texts. The cognitive features, derived from human eye movement patterns observed during reading, include two primary feature types:
The cognitive features, along with textual features used in best available sarcasm detectors, are used to train binary classifiers against given sarcasm labels. Our experiments show significant improvement in classification accuracy over the state of the art, by performing such augmentation.
Eye-tracking technology has already been utilized by leading mobile technology developers (like Samsung) to facilitate richer user experiences through services like Smart-scroll (where a user’s eye movement determines whether a page has to be scrolled or not) and Smart-lock (where user’s gaze position decides whether to lock the screen or not).
|
Has Samsung utilized eye-tracking technology?
|
Yes.
|
null | false
| null |
where is India?
|
India a country that occupies the greater part of South Asia. Its capital is New Delhi, built in the 20th century just south of the historic hub of Old Delhi to serve as India’s administrative centre.
|
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null | false
| null |
Why you should have a bucket list?
|
Because you have one life to live.
You need to explore as much as you can to fulfill your dream so you need a target to full and that's why you need bucketlist
|
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