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Think up 5 different reasons to give my boss to take off work.
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Here are 5 different reasons you can give your boss to take off work:
1. You have to go to your kid's award ceremony
2. You got food poisoning
3. The power at your house went out
4. You car is in the shop and you have no ride to work
5. Your family made a surprise visit to see you.
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What is a tornado?
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A tornado is a major storm that consists of rotating columns of air that touch the base of a thunderstorm and surface of the earth. They have speeds ranging from 86 - 200 miles per hour and destroy everything in their path. They can last for seconds or minutes at a time. States that have the most tornados are Texas, Kansas, Oklahoma, Alabama, and Mississippi. People that are in the path of a large tornado often say it sounds like an approaching freight train.
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| 76
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Wikipedia is the largest source of open and collaboratively curated knowledge in the world. Introduced in 2001, it has evolved into a reference work with around 5m pages for the English Wikipedia alone. In addition, entities and event pages are updated quickly via collaborative editing and all edits are encouraged to include source citations, creating a knowledge base which aims at being both timely as well as authoritative. As a result, it has become the preferred source of information consumption about entities and events. Moreso, this knowledge is harvested and utilized in building knowledge bases like YAGO BIBREF0 and DBpedia BIBREF1 , and used in applications like text categorization BIBREF2 , entity disambiguation BIBREF3 , entity ranking BIBREF4 and distant supervision BIBREF5 , BIBREF6 .
However, not all Wikipedia pages referring to entities (entity pages) are comprehensive: relevant information can either be missing or added with a delay. Consider the city of New Orleans and the state of Odisha which were severely affected by cyclones Hurricane Katrina and Odisha Cyclone, respectively. While Katrina finds extensive mention in the entity page for New Orleans, Odisha Cyclone which has 5 times more human casualties (cf. Figure FIGREF2 ) is not mentioned in the page for Odisha. Arguably Katrina and New Orleans are more popular entities, but Odisha Cyclone was also reported extensively in national and international news outlets. This highlights the lack of important facts in trunk and long-tail entity pages, even in the presence of relevant sources. In addition, previous studies have shown that there is an inherent delay or lag when facts are added to entity pages BIBREF7 .
To remedy these problems, it is important to identify information sources that contain novel and salient facts to a given entity page. However, not all information sources are equal. The online presence of major news outlets is an authoritative source due to active editorial control and their articles are also a timely container of facts. In addition, their use is in line with current Wikipedia editing practice, as is shown in BIBREF7 that almost 20% of current citations in all entity pages are news articles. We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative.
Existing efforts to populate Wikipedia BIBREF8 start from an entity page and then generate candidate documents about this entity using an external search engine (and then post-process them). However, such an approach lacks in (a) reproducibility since rankings vary with time with obvious bias to recent news (b) maintainability since document acquisition for each entity has to be periodically performed. To this effect, our news suggestion considers a news article as input, and determines if it is valuable for Wikipedia. Specifically, given an input news article INLINEFORM0 and a state of Wikipedia, the news suggestion problem identifies the entities mentioned in INLINEFORM1 whose entity pages can improve upon suggesting INLINEFORM2 . Most of the works on knowledge base acceleration BIBREF9 , BIBREF10 , BIBREF11 , or Wikipedia page generation BIBREF8 rely on high quality input sources which are then utilized to extract textual facts for Wikipedia page population. In this work, we do not suggest snippets or paraphrases but rather entire articles which have a high potential importance for entity pages. These suggested news articles could be consequently used for extraction, summarization or population either manually or automatically – all of which rely on high quality and relevant input sources.
We identify four properties of good news recommendations: salience, relative authority, novelty and placement. First, we need to identify the most salient entities in a news article. This is done to avoid pollution of entity pages with only marginally related news. Second, we need to determine whether the news is important to the entity as only the most relevant news should be added to a precise reference work. To do this, we compute the relative authority of all entities in the news article: we call an entity more authoritative than another if it is more popular or noteworthy in the real world. Entities with very high authority have many news items associated with them and only the most relevant of these should be included in Wikipedia whereas for entities of lower authority the threshold for inclusion of a news article will be lower. Third, a good recommendation should be able to identify novel news by minimizing redundancy coming from multiple news articles. Finally, addition of facts is facilitated if the recommendations are fine-grained, i.e., recommendations are made on the section level rather than the page level (placement).
Approach and Contributions. We propose a two-stage news suggestion approach to entity pages. In the first stage, we determine whether a news article should be suggested for an entity, based on the entity's salience in the news article, its relative authority and the novelty of the article to the entity page. The second stage takes into account the class of the entity for which the news is suggested and constructs section templates from entities of the same class. The generation of such templates has the advantage of suggesting and expanding entity pages that do not have a complete section structure in Wikipedia, explicitly addressing long-tail and trunk entities. Afterwards, based on the constructed template our method determines the best fit for the news article with one of the sections.
We evaluate the proposed approach on a news corpus consisting of 351,982 articles crawled from the news external references in Wikipedia from 73,734 entity pages. Given the Wikipedia snapshot at a given year (in our case [2009-2014]), we suggest news articles that might be cited in the coming years. The existing news references in the entity pages along with their reference date act as our ground-truth to evaluate our approach. In summary, we make the following contributions.
We therefore propose news suggestion as a novel task that enhances entity pages and reduces delay while keeping its pages authoritative.
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To solve these problems, what does the author suggest?
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To enhance entity pages and reduces delay while keeping its pages authoritative.
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Bosch's mother was a prostitute in Hollywood who was murdered on October 28, 1961, when Bosch was 11 years old. His father, who he met later in life, was Mickey Haller Sr., a prominent defense attorney known for representing mobster Mickey Cohen, among other clients.
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Who was Bosch's dad?
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Bosch's father was Mickey Haller Sr. a well known defense lawyer.
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Who is the lead singer of the Gorillaz?
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Damon Albarn is the lead singer of the Gorillaz
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What's a relaxing activity to do when visiting Seoul, South Korea?
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The Secret Garden guided tour is a tranquil stroll through the Changdeokgung palace in Seoul. Featuring pavilions and several ponds, you can explore the grounds once walked upon by princes and kings. In addition to a reservation for a guided tour, you will also need a ticket into the palace that can be purchased separately. Reservations may be made online or on-site, but reserving online will provide more timeframes and a better guarantee of entrance on your desired day.
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What is the structure of Public Forum debate, as used in US debate competitions?
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A public forum debate takes place between two teams of two individuals each. For the assigned topic, one team argues for the affirmative (Pro) side, and the other argues for the negative (Con) side.
The debate is organized in a sequence of speeches according to the following outline:
• Team 1 Constructive
• Team 2 Constructive
• 1st Crossfire
• Team 1 Rebuttal
• Team 2 Rebuttal
• 2nd Crossfire
• Team 1 Summary
• Team 2 Summary
• Grand Crossfire
• Team 1 Final Focus
• Team 2 Final Focus
Additionally, each team is given 3 minutes of preparation time throughout the round, which they can choose to take before any of their speeches. The time duration of each stage may vary across different leagues.
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1912.01252
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Technical and theoretical questions related to the proposed method and infrastructure for the exploration and facilitation of debates will be discussed in three sections. The first section concerns notions of how to define what constitutes a belief or opinion and how these can be mined from texts. To this end, an approach based on the automated extraction of semantic frames expressing causation is proposed. The observatory thus builds on the theoretical premise that expressions of causation such as `global warming causes rises in sea levels' can be revelatory for a person or group's underlying belief systems. Through a further technical description of the observatory's data-analytical components, section two of the paper deals with matters of spatially modelling the output of the semantic frame extractor and how this might be achieved without sacrificing nuances of meaning. The final section of the paper, then, discusses how insights gained from technologically observing opinion dynamics can inform conceptual modelling efforts and approaches to on-line opinion facilitation. As such, the paper brings into view and critically evaluates the fundamental conceptual leap from machine-guided observation to debate facilitation and intervention.
The final section of the paper, then, discusses how insights gained from technologically observing opinion dynamics can inform conceptual modelling efforts and approaches to on-line opinion facilitation. As such, the paper brings into view and critically evaluates the fundamental conceptual leap from machine-guided observation to debate facilitation and intervention.
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Does the paper report the results of previous models applied to the same tasks?
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Yes.
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| 267
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We experimented with a dataset of approximately 16k short messages from Twitter, that was made available by BIBREF12 . The dataset contains 1943 tweets labeled as Racism, 3166 tweets labeled as Sexism and 10889 tweets labeled as Neutral (i.e., tweets that neither contain sexism nor racism). There is also a number of dual labeled tweets in the dataset. More particularly, we found 42 tweets labeled both as both `Neutral' and `Sexism', while six tweets were labelled as both `Racism' and `Neutral'. According to the dataset providers, the labeling was performed manually.
The relatively small number of tweets in the dataset makes the task more challenging. As reported by several authors already, the dataset is imbalanced, with a majority of neutral tweets. Additionally, we used the public Twitter API to retrieve additional data associated with the user identity for each tweet in the original dataset.
As reported by several authors already, the dataset is imbalanced, with a majority of neutral tweets. Additionally, we used the public Twitter API to retrieve additional data associated with the user identity for each tweet in the original dataset.
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What API do they use to retrieve additional data associated with the user identity for each tweet in the original dataset?
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The public Twitter API.
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Sadie J is a British children's television comedy-drama series about a girl named Sadie Jenkins, who is described as "the only girl in a boys' world" because she is surrounded by her Dad Steve, his apprentice Keith, her brother Danny, his best friend Jake and their dog Roger. It is produced by the BBC and airs on the CBBC Channel. The show premiered on 14 January 2011 and produced three series, with the final episode aired on 27 March 2013. As of September 2013, Sadie J is now shown in the United States on the Starz Kids & Family cable network.
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From the given text, list down some points about television series Sadie J
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1. Sadie Jenkins is the protagonist of the British children's television comedy-drama series Sadie J. She is said to as "the only girl in a boys' world" because she lives with her father Steve, his apprentice Keith, her brother Danny, his best friend Jake, and their dog Roger.
2. It is a BBC production and is broadcast on the CBBC Channel.
3. The programme debuted on January 14, 2011, produced three seasons, and its final episode broadcast on March 27, 2013.
4. As of September 2013, the Starz Kids & Family cable network now broadcasts Sadie J in the United States.
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| 91
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Interpretability of Seq2Seq models has recently been explored mainly from two perspectives: interpreting internal representations and understanding input-output behaviors. Most of the existing work focus on the former thread, which analyzes the linguistic information embeded in the learned representations BIBREF3, BIBREF4, BIBREF10 or the hidden units BIBREF6, BIBREF5. Several researchers turn to expose systematic differences between human and NMT translations BIBREF11, BIBREF12, indicating the linguistic properties worthy of investigating. However, the learned representations may depend on the model implementation, which potentially limit the applicability of these methods to a broader range of model architectures. Accordingly, we focus on understanding the input-output behaviors, and validate on different architectures to demonstrate the universality of our findings.
Concerning interpreting the input-output behavior, previous work generally treats Seq2Seq models as black-boxes BIBREF13, BIBREF7. For example, alvarez2017causal measure the relevance between two input-output tokens by perturbing the input sequence. However, they do not exploit any intermediate information such as gradients, and the relevance score only resembles attention scores. Recently, Jain2019AttentionIN show that attention scores are in weak correlation with the feature importance. Starting from this observation, we exploit the intermediate gradients to better estimate word importance, which consistently outperforms its attention counterpart across model architectures and language pairs.
The intermediate gradients have proven to be useful in interpreting deep learning models, such as NLP models BIBREF14, BIBREF15 and computer vision models BIBREF16, BIBREF9. Among all gradient-based approaches, the integrated gradients BIBREF9 is appealing since it does not need any instrumentation of the architecture and can be computed easily by calling gradient operations. In this work, we employ the IG method to interpret NMT models and reveal several interesting findings, which can potentially help debug NMT models and design better architectures for specific language pairs.
Concerning interpreting the input-output behavior, previous work generally treats Seq2Seq models as black-boxes (Li et al., 2016; Alvarez-Melis and Jaakkola, 2017). For example, Alvarez-Melis and Jaakkola (2017) measure the relevance between two input-output tokens by perturbing the input sequence. However, they do not exploit any intermediate information such as gradients, and the relevance score only resembles attention scores.
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What are the shortcomings of previous work in explaining input-output behavior?
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They do not exploit any intermediate information such as gradients, and the relevance score only resembles attention scores.
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| 444
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In this paper, we consider a decentralized partially observable Markov decision process represented by a tuple G = S, U, P, r, O, N, γ . To be specific, we let s ∈ S denote the true state of the environment. At each time step, an agent i ∈ N := {1, ..., N } selects an action u i ∈ U as an element of the joint action vector u := [u 1 , • • • , u N ]. It then goes through a stochastic transition dynamic described by the probability P (s |s, u). All agents share the same reward r(s, u) discounted by a factor of γ. Each agent i is associated with a partial observation o ∈ O, according to some observation function O(s, i) : S ×N → O, and an action-observation history τ i . Concatenation of the agent-wise action-observation histories is denoted as the overall action-observation history τ .
We consider value-based centralized training with decentralized execution (CTDE) where agents are trained in a centralized manner and executed in parallel without access to the global state s. Under value-based CTDE, we aim to train each agent-wise utility function q i (τ i , u i ) whose greedy actions are consistent with the greedy actions from the joint action-value estimator Q jt (s, τ , u). Formally, the following decentralization condition should be satisfied:
(1)
To meet this condition, VDN and QMIX impose structural constraint on the joint action-value function. To be specific, VDN expresses the joint action-value function as a linear summation of agent-wise utility functions. QMIX extends VDN by enforcing a monotonicity constraint on the joint action-value function and each agent-wise utility function. On the other hand, QTRAN uses soft regularization instead of structural constraints to satisfy the decentralization condition.
Weighted QMIX. Another prior work, namely Weighted QMIX, implicitly considers optimism in centralized training by weighting the loss of optimal actions, but it does not separate agent-wise and environment-wise risks. proposes Centrally-Weighted QMIX (CW-QMIX) and Optimistically-Weighted QMIX (OW-QMIX) which both have a strong theoretical capability of learning the largest class of true action-value functions up to date. They use a weighted projection that allows more emphasis to be placed on better joint actions. OW-QMIX applies weights to the losses according to the sign of the TD-error for each sample: 2
where α is a hyperparameter and Q target jt is the fixed target network whose parameters are updated periodically from the original unconstrained true action-value estimator Q jt . They sample (s, u, r, s ), a tuple of experience transitions from a replay buffer and u opt is the "optimal" action maximizing the utility functions q i (τ i , u i ) for i ∈ N . The true action-value estimator Q jt can accurately describe the true action-value using the unconstrained neural network. However, since this unconstrained network does not satisfy the decentralization condition (equation 1), we cannot extract the optimal action efficiently. On the other hand, the transformed action-value estimator Q tran has limited expressive power because of its monotonic structure but can efficiently extract optimal actions. In order for Q tran to accurately track the optimal action of Q jt , the weighting mechanism according to the sign of the TD-error assigns a higher weight for optimistic returns. Through the optimistic training, they prove that Weighted QMIX recovers the correct maximal action for any true-action value. We provide a more detailed description of other CTDE algorithms in Appendix C.
…whose parameters are updated periodically from the original action-value estimator Z. (s, u, r, s0 ) is a tuple of experience transitions from a replay buffer.
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What are the “objects” in Section 2.2?
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We agree that the word “object” is not clear. Following your suggestion, we modified the word “objects” by “experience transitions”.
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Best car Brand in the world in Popularity?
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1. BMW
2. Audi
3. porsche
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On a trip to the beach, what can I do to prevent getting sun burned?
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Here are some things you can do to prevent being sun burned at the beach:
- Don't go to the beach if the UV is over 7
- Bring a tent to the beach so you can be in the shade
- Wear strong sunscreen and reapply it regularly
- Wear SPF protected long sleeve shirts
- Wear a hat to cover your head and face
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| 83
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We developed the annotation guidelines jointly with an experienced annotator, who is a native Arabic speaker with a good knowledge of various Arabic dialects. We made sure that our guidelines were compatible with those of OffensEval2019. The annotator carried out all annotation. Tweets were given one or more of the following four labels: offensive, vulgar, hate speech, or clean. Since the offensive label covers both vulgar and hate speech and vulgarity and hate speech are not mutually exclusive, a tweet can be just offensive or offensive and vulgar and/or hate speech. The annotation adhered to the following guidelines:
Offensive tweets contain explicit or implicit insults or attacks against other people, or inappropriate language, such as:
Direct threats or incitement, ex: احرقوا> مقرات المعارضة> (“AHrqwA mqrAt AlmEArDp” – “burn the headquarters of the opposition”) and هذا المنافق يجب قتله> (“h*A AlmnAfq yjb qtlh” – “this hypocrite needs to be killed”).
Insults and expressions of contempt, which include: Animal analogy, ex: يا كلب> (“yA klb” – “O dog”) and كل تبن> (“kl tbn” – “eat hay”).; Insult to family members, ex: يا روح أمك> (“yA rwH Amk” – “O mother's soul”); Sexually-related insults, ex: يا ديوث> (“yA dywv” – “O person without envy”); Damnation, ex: الله يلعنك> (“Allh ylEnk” – “may Allah/God curse you”); and Attacks on morals and ethics, ex: يا كاذب> (“yA kA*b” – “O liar”)
Vulgar tweets are a subset of offensive tweets and contain profanity, such as mentions of private parts or sexual-related acts or references.
Hate speech tweets, a subset of offensive tweets containing offensive language targeting group based on common characteristics such as: Race, ex: يا زنجي> (“yA znjy” – “O negro”); Ethnicity, ex. الفرس الأنجاس> (“Alfrs AlAnjAs” – “Impure Persians”); Group or party, ex: أبوك شيوعي> (“Abwk $ywEy” – “your father is communist”); and Religion, ex: دينك القذر> (“dynk Alq*r” – “your filthy religion”).
Clean tweets do not contain vulgar or offensive language. We noticed that some tweets have some offensive words, but the whole tweet should not be considered as offensive due to the intention of users. This suggests that normal string match without considering contexts will fail in some cases. Examples of such ambiguous cases include: Humor, ex: يا عدوة الفرحة ههه> (“yA Edwp AlfrHp hhh” – “O enemy of happiness hahaha”); Advice, ex: لا تقل لصاحبك يا خنزير> (“lA tql lSAHbk yA xnzyr” – “don't say to your friend: You are a pig”); Condition, ex: إذا عارضتهم يقولون يا عميل> (“A*A EArDthm yqwlwn yA Emyl” – “if you disagree with them they will say: You are an agent”); Condemnation, ex: لماذا نسب بقول: يا بقرة؟> (“lmA*A nsb bqwl: yA bqrp?” – “Why do we insult others by saying: O cow?”); Self offense, ex: تعبت من لساني القذر> (“tEbt mn lsAny Alq*r” – “I am tired of my dirty tongue”); Non-human target, ex: يا بنت المجنونة يا كورة> (“yA bnt Almjnwnp yA kwrp” – “O daughter of the crazy one O football”); and Quotation from a movies or a story, ex: تاني يا زكي! تاني يا فاشل> (“tAny yA zky! tAny yA fA$l” – “again O Zaky! again O loser”). For other ambiguous cases, the annotator searched Twitter to find how actual users used expressions.
Table TABREF11 shows the distribution of the annotated tweets. There are 1,915 offensive tweets, including 225 vulgar tweet and 506 hate speech tweets, and 8,085 clean tweets. To validate the quality of annotation, a random sample of 100 tweets from the data, containing 50 offensive and 50 clean tweets, was given to additional three annotators. We calculated the Inter-Annotator Agreement between the annotators using Fleiss’s Kappa coefficient BIBREF23. The Kappa score was 0.92 indicating high quality annotation and agreement.
Since the offensive label covers both vulgar and hate speech and vulgarity and hate speech are not mutually exclusive, a tweet can be just offensive or offensive and vulgar and/or hate speech.
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Why a tweet can be just offensive or offensive and vulgar and/or hate speech?
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Because the offensive label covers both vulgar and hate speech and vulgarity and hate speech are not mutually exclusive.
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1809.04960
| false
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NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic.
Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation. We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics. The neural variational document model is based on the neural VAE framework. It can capture the semantic information, so it has better performance than the TF-IDF model. LDA models the topic information, and captures the deeper relationship between the article and comments, so it achieves improvement in all relevance metrics. Finally, our proposed model outperforms all these unsupervised methods, mainly because the proposed model learns both the semantics and the topic information.
FLOAT SELECTED: Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)
Table TABREF32 shows the performance for our models and the baselines in generative evaluation. Similar to the retrieval evaluation, our proposed model outperforms the other unsupervised methods, which are TF-IDF, NVDM, and LDA, in generative evaluation. Still, the supervised IR achieves better scores than the seq2seq model. With the help of our proposed model, both IR and S2S achieve an improvement under the semi-supervised scenarios.
FLOAT SELECTED: Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (METEOR, ROUGE, CIDEr, BLEU: higher is better.)
NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic.
Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation. We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics.
FLOAT SELECTED: Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)
Table TABREF32 shows the performance for our models and the baselines in generative evaluation. Similar to the retrieval evaluation, our proposed model outperforms the other unsupervised methods, which are TF-IDF, NVDM, and LDA, in generative evaluation.
FLOAT SELECTED: Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (METEOR, ROUGE, CIDEr, BLEU: higher is better.)
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By how much does their system outperform the lexicon-based models?
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Under the retrieval evaluation setting, their proposed model + IR2 had better MRR than NVDM by 0.3769, better MR by 4.6, and better Recall@10 by 20 .
Under the generative evaluation setting the proposed model + IR2 had better BLEU by 0.044 , better CIDEr by 0.033, better ROUGE by 0.032, and better METEOR by 0.029
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what is the national language for India?
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There is no national language for India. However, the constitution of India specifically mentions that the official language of the Union shall be Hindi in Devanagari script.
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The magazine was part of Mondadori and was based in Milan. Its first editor was Alberto Mondadori who was succeeded in the post by Enzo Biagi in 1953. During the period until 1960 when Enzo Biagi edited Epoca the magazine covered current affairs news, social attitudes as well as TV news. The magazine also included frequent and detailed articles about Hollywood stars of the period and Italian movie stars such as Gina Lollobrigida. The weekly had offices in New York City, Paris and Tokyo. From June 1952 to the late 1958 the Cuban-Italian writer Alba de Céspedes wrote an agony column, called Dalla parte di lei, in the magazine.
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Extract all of the names of people mentioned in this paragraph and list them using bullets in the format {Name}
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• Enzo Biagi
• Alberto Mondadori
• Gina Lollobrigida
• Alba de Céspedes
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How can I schedule and run an effective meeting?
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First make sure you have a clear goal that you want to achieve, and you can express it clearly. Decide who is required for the meeting to be successful, and the role that each participant plays. Consider sending out material that can be read in advance to prepare for the meeting, so you don't spend too much time during the meeting to bring people up to speed. During the meeting, do your best to keep the conversation on track, and don't be afraid to defer discussions for a later time. Keep an eye out on the time and make sure you leave a few minutes at the end to summarize the action items and ensure each has a clear owner and due date. Last but not least, take good notes that you can share to the team and remind everyone of the discussion.
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Q: What treatments are available in the U.S. to treat COVID-19?
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A: The FDA has approved and authorized treatments for COVID-19 for emergency use during this public health emergency. View the FDA’s Emergency Use Authorization (EUA) page to see all products authorized to treat COVID-19. Read our article: Know Your Treatment Options for COVID-19.
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The economy of Gabon is characterized by strong links with France, large foreign investments, dependence on skilled foreign labor, and decline of agriculture. Gabon enjoys a per capita income four times that of most nations of sub-Saharan Africa, its reliance on resource extraction industry releasing much of the population from extreme poverty.
The country is rich in natural resources such as timber, manganese and oil. The country is the fifth largest oil producing nation in Africa, which has helped drive its strong growth in the later 20th century.
The oil sector now accounts for 50% of GDP and 80% of exports. Although there have been recent offshore finds, oil production is now declining from its peak of 370,000 barrels per day (59,000 m3/d) in 1997, and periods of low oil prices have had a negative impact on government revenues and the economy. In 2012 there were six active oil rigs in Gabon.
The government has plans to diversify its economy away from its dwindling oil reserves. The government announced in 2012 that it would reassess exactly how much iron ore the Belinga site contains before awarding the concession to a mining company, most likely to be China's CMEC, which temporarily secured the rights to the ore in 2007.
Around 80% of Gabonese live in cities. The capital city, Libreville is home to 59% of the total population. The remainder of the country is still sparsely populated, as those areas remain densely forested.
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Which industry is the top industry in Gabon's economy?
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Oil industry is the largest industry in Gabon, accounting for 50% of its GDP and 80% of exports. But, its contribution is declining.
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| 386
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Our speech recognition system, builds on the recently proposed Listen, Attend and Spell network BIBREF12 . It is an attention-based seq2seq model that is able to directly transcribe an audio recording INLINEFORM0 into a space-delimited sequence of characters INLINEFORM1 . Similarly to other seq2seq neural networks, it uses an encoder-decoder architecture composed of three parts: a listener module tasked with acoustic modeling, a speller module tasked with emitting characters and an attention module serving as the intermediary between the speller and the listener: DISPLAYFORM0
Our speech recognition system, builds on the recently proposed Listen, Attend and Spell network. It is an attention-based seq2seq model that is able to directly transcribe an audio recording x into a space-delimited sequence of characters y.
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What is their speech recognition system able to do?
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It is able to directly transcribe an audio recording into a space-delimited sequence of characters.
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Which characters belong to DC or Marvel Universe? Hulk, Spectre
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Spectre is DC, Hulk is Marvel
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Provide a list of your 5 dream cars
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1. 49 Mercury Coup
2. 67 Camera
3. Ferrari California
4. Lamborghini Diablo
5. Jeep Grand Wagoneer
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Ciao (/tʃaʊ/ CHOW, Italian: [ˈtʃaːo] (listen)) is an informal salutation in the Italian language that is used for both "hello" and "goodbye".
Originally from the Venetian language, it has entered the vocabulary of English and of many other languages around the world. Its dual meaning of "hello" and "goodbye" makes it similar to shalom in Hebrew, salaam in Arabic, annyeong in Korean, aloha in Hawaiian, drud (bedrud) in Persian, and chào in Vietnamese (the latter is a false cognate; the two words are not linguistically related despite sounding similar to each other).
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Where has the word ciao originated
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Ciao is an informal salutation in the Italian language that is used for both "hello" and "goodbye".
Originally from the Venetian language, it has entered the vocabulary of English and of many other languages around the world. Its dual meaning of "hello" and "goodbye" makes it similar to shalom in Hebrew, salaam in Arabic, annyeong in Korean, aloha in Hawaiian, drud (bedrud) in Persian, and chao in Vietnamese (the latter is a false cognate; the two words are not linguistically related despite sounding similar to each other).
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What are the two professional football teams in Chicago?
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Chicago Bears and Chicago White Sox
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Classify each of the following as either elements or minerals: oxygen, cobalt, phosphorus, nitrogen, graphite, quartz, carbon, and clay.
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Oxygen, cobalt, nitrogen, and carbon are elements. Phosphorus, graphite, quartz, and clay are minerals.
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Polyvinyl chloride (alternatively: poly(vinyl chloride), colloquial: polyvinyl, or simply vinyl; abbreviated: PVC) is the world's third-most widely produced synthetic polymer of plastic (after polyethylene and polypropylene). About 40 million tons of PVC are produced each year.
|
How much PVC produced each year?
|
Each year about 40 million tons of PVC are produced.
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Tell me which of these are science fiction movies and which are fantasy: Lord of the Rings, Star Trek, Princess Mononoke, the Wheel of Time, District 9
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From your list, the science fiction movies are Star Trek and District 9. The Fantasy movies are Lord of the Rings, Princess Mononoke, and the Wheel of Time.
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Which movie(s) inspired the 2019 DC comic movie Joker?
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The movie Joker is heavily influenced by 1970-1980 Martin Scorsese movies such as the The King of Comedy and Taxi Driver, the former of which is one of Scorseses' least-known movies. The titular character, Joker, is also created based on studies of mentally-ill main characters from the above-mentioned movies, Rupert Pupkin and Travis Bickle. The lead actor of both Scorsese movies, Robert De Niro, even played a role in Joker to pay homage to Rupert Pupkin.
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"New York City" is a song by American singer Lenny Kravitz, written by Kravitz and Craig Ross. The song was released on October 21, 2014, as the third single from the Kravitz's tenth studio album Strut.
|
When was the New York City song released?
|
The New York City was released on October 21, 2014.
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| 153
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We used Pytorch BIBREF5 for implementation. In our experiments, we find that fine-tuning initialized Glove embeddings performed better than frozen embeddings. Object level representations play a critical role to generate a correct response from the model. Eventually, we use an ensemble of all the models described above for our final submission. Table TABREF9 summarizes our results on validation set while Table TABREF8 on Test-Standard split.
We used Pytorch2 [4] for implementation.
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What machine learning library is used?
|
Pytorch2.
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Pana Chand Meghwal is an Indian politician from the Indian National Congress and former member of the Rajasthan Legislative Assembly. He was elected to represent the Baran-Atru Constituency. He resigned from Rajasthan assembly on 15 August 2022. Miffed over cases of alleged atrocities on dalits, Congress MLA Pana Chand Meghwal sent his resignation to chief minister Ashok Gehlot, saying he does not have the right to stay as an MLA if he cannot protect the rights of his community.
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Who is Pana Chand Meghwal
|
Pana Chand Meghwal is an Indian politician from the Indian National Congress and former member of the Rajasthan Legislative Assembly. He was elected to represent the Baran-Atru Constituency.
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A mobile device (or handheld computer) is a computer, small enough to hold and operate in the hand. Mobile devices typically have a flat LCD or OLED screen, a touchscreen interface, and digital or physical buttons. They may also have a physical keyboard. Many such devices can connect to the Internet and connect with other devices such as car entertainment systems or headsets via Wi-Fi, Bluetooth, cellular networks or near field communication. Integrated cameras, the ability to place and receive voice and video telephone calls, video games, and Global Positioning System (GPS) capabilities are common. Power is typically provided by a lithium-ion battery. Mobile devices may run mobile operating systems that allow third-party applications to be installed and run.
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What type of screen is typically on a Handheld device?
|
A handheld device typically has a flat LCD or OLED screen,
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What is the Tau Circle Constant?
|
Tau is defined as the ratio of a circle's circumference to its radius. Some 21st century mathematicians and physicists believe Tau is a better choice than Pi for use in science and mathematics.
Like Pi, Tau is an irrational and transcendental number. In decimal form, the value of Tau is 6.283185…
Part of the motivation for Tau is the observation that the number 2*Pi or 2π occurs with surprising frequency throughout science and mathematics. This raises the question of whether Pi is really an important number or something equal to two times Pi is actually more important. Perhaps the implication is that Pi is exactly one half of some very important number!
Further consideration of the question leads to several compelling observations:
- Using C/r as the key circle constant is better aligned with the way we define a circle: the set of all points a fixed distance (r) form a give point.
- Radian angle measurements of fraction of a circle make much more sense when Tau is used as the base constant. Using Pi, on quarter of a circle is Pi is Pi/2, one half is Pi, and the angle of a a full circle is 2*Pi. With Tau, the angle measures are intuitively aligned to the fraction of a circle: a quarter circle is Tau/4, a half circle is Tau/2 and the angle of a full circle is Tau.
- Similarly, the radian zeroes, max, and min of the sin and cosine functions align to fractions of Tau in an intuitive fashion that aids, rather than confuses, understanding.
- Key formulas that traditionally use Pi actually show more clear mathematical meaning when expressed using Tau. For example, the area of a circle itself, when expressed with Tau, shows the same pattern of quadratic forms in distance fallen, spring energy, and kinetic energy.
- Similarly, the tau-based formula for circular area actually confirms to the original proof by Archimedes showing that area of a circle equals the area of a triangle with base C (circumference) and height r (radius): A = 1/2 b*h = 1/2 C*r = 1/2 (Tau*r)*r = 1/2 Tau*r^2.
Tau has seen significant adoption since it was originally proposed.
The relationship between Tau and Pi is very simple. Tau = 2*Pi. Perhaps just as informative is the fact that Tau > Pi.
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What causes Type 2 Diabetes?
|
Type 2 diabetes primarily occurs as a result of obesity and lack of exercise. Some people are genetically more at risk than others.
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Provide 5 different ways you could improve a typical home's ambience
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1. Remove clutter from common spaces
2. Add artwork to blank walls
3. Use common color themes within rooms
4. Match lighting to room purpose and vibe
5. Place a houseplant near the edge of the living room
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What are index funds?
|
Index funds are investment funds that follow a benchmark index, such as the S&P 500 or the Nasdaq 100. When you put money in an index fund, that cash is then used to invest in all the companies that make up the particular index, which gives you a more diverse portfolio than if you were buying individual stocks.
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What can you produce by chopping down a tree?
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Trees can be chopped for wood, or paper products.
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Ranjit Mallick (রঞ্জিত মল্লিক) (born 28 September 1944) is an Indian actor who works mainly in Bengali cinema. He is particularly known for appearing in films such as Interview, Mouchak, Shatru, Shakha Proshakha, Guru Dakshina, Amader Songsar, Indrajit, Jiban Niye Khela, Nabab, Sathi, Nater Guru, Gyarakal, Chander Bari and Bajimaat. He was also the Sheriff of Kolkata for the year 2014. He is one of the most anticipated actors of Bengali cinema and has been a part of more than 100 films as of now.
Ranjit was born on 28 September 1944 in Calcutta, West Bengal, into the Mallick Bari of Bhowanipore (a family belonging to the Vaidya (Baidya) community, which is closely associated with Kolkata's Barat and Gupta families), known for its Durga Puja.[citation needed] His nickname is Ranju. He studied at the Asutosh College and later at the Syamaprasad College of the University of Calcutta.
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List the movies of Ranjit Mallick.
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Interview, Mouchak, Shatru, Shakha Proshakha, Guru Dakshina, Amader Songsar, Indrajit, Jiban Niye Khela, Nabab, Sathi, Nater Guru, Gyarakal, Chander Bari, Sheriff of Kolkata and Bajimaat.
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Which of these are Teenage Mutant Ninja Turtles: Leonardo, Guido, Shredder, Luca, Enzo, Maximiliano, Giulia, Donatello, Raphael, Diego, Pedro, Michelangelo, John
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The Teenage Mutant Ninja Turtles are Leonardo, Raphael, Michelangelo, and Donatello. Guido, Shredder, Luca, Enzo, Maximiliano, Giulia, Diego, Pedro, and John are not Teenage Mutant Ninja Turtles.
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Write me a song about a summer romance
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Verse 1:
I walked for miles in no direction
To find that all roads lead back to you
Was in denial of your imperfections
I saw only what I wanted to
I still remember when you told me
That one day I'd see my name in lights
It was December I still see it clearly
We both knew it'd be our last goodbye
Chorus:
Flashback to summer
Until the sun came up
We rode the night out
Yea we were so in love
Flashback to summer
Until the sun came up
We rode the night out
Yea we were so messed up
Verse 2:
I watched your tail lights fade into darkness
I'd like to think we gave it all we had
Under the moonlight left defenseless
Suffered from a broken heart attack
I thought you knew me like nobody else could
But I never thought I'd see you leave
Now I here I am feeling misunderstood
I guess that's love when you're naive
Chorus:
Flashback to summer
Until the sun came up
We rode the night out
Yea we were so in love
Flashback to summer
Until the sun came up
We rode the night out
Stars shone bright above
Bridge:
It was a summer to remember
The music could've played forever
But we both knew that it wouldn't last for long
We burned fast and we burned bright
Until you drove away that night
And left me wondering where did things go wrong
Chorus:
Flashback to summer
Until the sun came up
We rode the night out
Yea we were so in love
Flashback to summer
Until the sun came up
We rode the night out
And stars shone bright above
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| 8
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PDTB-style discourse relations, mostly defined between two adjacent text spans (i.e., discourse units, either clauses or sentences), specify how two discourse units are logically connected (e.g., causal, contrast). Recognizing discourse relations is one crucial step in discourse analysis and can be beneficial for many downstream NLP applications such as information extraction, machine translation and natural language generation.
Commonly, explicit discourse relations were distinguished from implicit ones, depending on whether a discourse connective (e.g., “because” and “after”) appears between two discourse units BIBREF0 . While explicit discourse relation detection can be framed as a discourse connective disambiguation problem BIBREF1 , BIBREF2 and has achieved reasonable performance (F1 score $>$ 90%), implicit discourse relations have no discourse connective and are especially difficult to identify BIBREF3 , BIBREF2 , BIBREF4 . To fill the gap, implicit discourse relation prediction has drawn significant research interest recently and progress has been made BIBREF5 , BIBREF6 by modeling compositional meanings of two discourse units and exploiting word interactions between discourse units using neural tensor networks or attention mechanisms in neural nets. However, most of existing approaches ignore wider paragraph-level contexts beyond the two discourse units that are examined for predicting a discourse relation in between.
To further improve implicit discourse relation prediction, we aim to improve discourse unit representations by positioning a discourse unit (DU) in its wider context of a paragraph. The key observation is that semantic meaning of a DU can not be interpreted independently from the rest of the paragraph that contains it, or independently from the overall paragraph-level discourse structure that involve the DU. Considering the following paragraph with four discourse relations, one relation between each two adjacent DUs:
(1): [The Butler, Wis., manufacturer went public at $15.75 a share in August 1987,] $_{DU1}$ and (Explicit-Expansion) [Mr. Sim's goal then was a $29 per-share price by 1992.] $_{DU2}$ (Implicit-Expansion) [Strong earnings growth helped achieve that price far ahead of schedule, in August 1988.] $_{DU3}$ (Implicit-Comparison) [The stock has since softened, trading around $25 a share last week and closing yesterday at $23 in national over-the-counter trading.] $_{DU4}$ But (Explicit-Comparison) [Mr. Sim has set a fresh target of $50 a share by the end of reaching that goal.] $_{DU5}$
Clearly, each DU is an integral part of the paragraph and not independent from other units. First, predicting a discourse relation may require understanding wider paragraph-level contexts beyond two relevant DUs and the overall discourse structure of a paragraph. For example, the implicit “Comparison” discourse relation between DU3 and DU4 is difficult to identify without the background information (the history of per-share price) introduced in DU1 and DU2. Second, a DU may be involved in multiple discourse relations (e.g., DU4 is connected with both DU3 and DU5 with a “Comparison” relation), therefore the pragmatic meaning representation of a DU should reflect all the discourse relations the unit was involved in. Third, implicit discourse relation prediction should benefit from modeling discourse relation continuity and patterns in a paragraph that involve easy-to-identify explicit discourse relations (e.g., “Implicit-Comparison” relation is followed by “Explicit-Comparison” in the above example).
Following these observations, we construct a neural net model to process a paragraph each time and jointly build meaning representations for all DUs in the paragraph. The learned DU representations are used to predict a sequence of discourse relations in the paragraph, including both implicit and explicit relations. Although explicit relations are not our focus, predicting an explicit relation will help to reveal the pragmatic roles of its two DUs and reconstruct their representations, which will facilitate predicting neighboring implicit discourse relations that involve one of the DUs.
In addition, we introduce two novel designs to further improve discourse relation classification performance of our paragraph-level neural net model. First, previous work has indicated that recognizing explicit and implicit discourse relations requires different strategies, we therefore untie parameters in the discourse relation prediction layer of the neural networks and train two separate classifiers for predicting explicit and implicit discourse relations respectively. This unique design has improved both implicit and explicit discourse relation identification performance. Second, we add a CRF layer on top of the discourse relation prediction layer to fine-tune a sequence of predicted discourse relations by modeling discourse relation continuity and patterns in a paragraph.
Experimental results show that the intuitive paragraph-level discourse relation prediction model achieves improved performance on PDTB for both implicit discourse relation classification and explicit discourse relation classification.
Second, we add a CRF layer on top of the discourse relation prediction layer to fine-tune a sequence of predicted discourse relations by modeling discourse relation continuity and patterns in a paragraph.
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How does the team fine-tune a sequence of predicted discourse relations?
|
By modeling discourse relation continuity and patterns in a paragraph.
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What is world of warcraft
|
World of warcraft is a massive online multi player role playing game. It was released in 2004 by bizarre entertainment
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Name some of the top basketball players
|
Michael Jordan
Steph Curry
Magic Johnson
Larry Bird
Kobe Bryant
Kevin Durant
Lebron James
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| 459
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Context Communication between humans always takes place w.r.t. some context. For humans, that context includes: 1) features of the environment that can be directly perceived by the dialogue participants and used for interpreting an utterance (e.g. "cup" is interpreted as an object in the immediate physical context); and 2) information that cannot be directly perceived but can affect the interpretation of utterances (e.g. using and interpreting "cup" assumes some shared knowledge of what counts as a "cup"; once a particular cup has been established as salient in particular dialogue, further utterances of "the cup" or "it" will be interpreted as referring to that specific cup).
Inspired by this conclusion from pragmatics, in the DL-based language games, we define context as the supervision information involved in the calculation of losses, i.e. the context of a specific target x t , denoted as Cpx t q, is the space of samples involved in the calculation of loss. As for in referential games, the cross entropy loss is calculated based on the distance between the message embedding h L pm Bi q and candidate embedding f L cenc px i q where x i P C, thus Cpx t q " C Ď X in referential games.
We assume that the goal of communication is to distinguish a target object from other possibilities in the context (as defined above). It therefore follows that the similarity of distractors in the context to the target influences the communicative precision required, and that greater precision is required to distinguish the target from a more similar distractor. For example, it is relatively easy in natural language to distinguish e.g. a cat from a table (a relatively general label like "cat" or "animal" would suffice), but harder to make fine-grained distinctions between very similar objects e.g. a Scottish Fold Cat and an American Shorthair Cat (a specialist vocabulary or a lengthy description is necessary). Following the assumption (described verbally above) that a context which contains more similar objects makes the game harder because there are fewer unique features that suffice to distinguish the target from the distractors, we first define a neighbour set in k-th degree of a given target x t as N k px t q " tx j : dpx t , x j q ď ku where d is a distance function that can properly capture the similarity between inputs, e.g Hamming distance in our setting. The complexity of Cpx t q is then defined as the expectation of the probability that Cpx t q contains an object from N k px t q, i.e. E xt rg pCpx t q, N k px t qqs ,where g pCpx t q, N k px t qq " " 1, if Dx i P Cpx t q s.t.
In our referential games, since the sampling procedure is independent Bernoulli without replacement, the value of the above expectation is then
¯|C| which is a monotonically increasing function w.r.t |C| and a fixed k. That said, larger contexts are more complex since they are more likely to include items which are very similar to the target.
Unpredictability of context Our notion of unpredictability comes from experimental work with human participants, e.g.. Suppose the aim is to distinguish a striped t-shirt from a number of distractors, and there are two sequences of context: 1) three runs of games where distractors are all cups; 2) three runs of games where distractors are a cup, a plain t-shirt, and a pencil. In the first sequence of games, participants would become certain that "t-shirt" is enough for distinguishing the target, whereas in the second sequence participants would learn that a more overspecified utterance (e.g. "striped t-shirt") is necessary to guarantee comprehension after a failure on the trial involving the plain t-shirt distractor. That is, the context in the first sequence is more predictable than the second. show that human participants are sensitive to this kind of unpredictability, and adapt their communicative strategies accordingly.
In DL-based games, we refer to the context at the e-th trial, i.e. the e-th training epoch, of a target x t as C e px t q. Following the above example, the unpredictability of context is then defined as the proportion of C e`1 px t q that are not from C e px t q, i.e.
In our referential games, since the sampling procedure is independent Bernoulli without replacement, the proportion of objects not from C e px t q in C e`1 px t q is then simply
" 1 ´|C| |X | (i.e. smaller contexts are more unpredictable, since contexts on successive trials are more likely to differ in their composition).
More details about the above discussion are provided in Appendix B.
Unpredictability of context Our notion of unpredictability comes from experimental work with human participants, e.g. Winters et al. (2018). Suppose the aim is to distinguish a striped t-shirt from a number of distractors, and there are two sequences of context: 1) three runs of games where distractors are all cups; 2) three runs of games where distractors are a cup, a plain t-shirt, and a pencil. In the first sequence of games, participants would become certain that “t-shirt” is enough for distinguishing the target, whereas in the second sequence participants would learn that a more overspecified utterance (e.g. "striped t-shirt") is necessary to guarantee comprehension after a failure on the trial involving the plain t-shirt distractor. That is, the context in the first sequence is more predictable than the second. Winters et al. (2018) show that human participants are sensitive to this kind of unpredictability, and adapt their communicative strategies accordingly.
|
Why is predictability of the future given the past is important for these communicating agents, considering they are memoryless across episodes?
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Predictability of the future given the past is important for these agents, even though they do not store information in an explicit memory module for use across training epochs (i.e. "memoryless across episodes"), because: 1) they store knowledge in their gradients and parameters, 2) because the data is sampled without replacement, 3) the data sampling and training dynamics will affect the learned representations.
We gave in our common reply how our definition of unpredictability follows Winters et al. (2018), which leads us to a meaningful measure of unpredictability of context in our work in emergent communication.
We appreciate that you are aligned with our intuition that “unpredictable environments force the speaker to be maximally informative in its utterances”, and we believe that our proposed metric does provide an informative perspective on this relationship.
We agree with your intuition on the effect of predictability, i.e. there’s an implicit effect brought by the data presentation order in the training dynamics. In fact, introducing unpredictability (i.e. variance in the input and input context space) has also been commonly used in the computer vision community. For example, the preprocessing for images used in deep reinforcement learning tasks can include transformations such as random shifts to make the inputs vary across epochs (e.g Kostrikov et al. 2021). The unpredictability/variance of inputs are not explicitly measured by the online learning methods, but the effect of them still can improve the learned representation, which is shown by the improvement of performance (see Figure 1 from Kostrikov et al. 2021).
To fully understand the implicit effect caused by the data presentation order covers much wider topics than emergent communication. That said,we look forward to exploring these questions in our future work. In this work, we align with previous related work in cognitive science and proposed our definition of unpredictability in the context of emergent communication, as detailed in Section 3.1.
Regarding the “online learnability using prequential coding” (Blier and Ollivier 2018), one challenge of this measure in our context is that it would conflate the learnability of emergent languages with the definition of unpredictability. Learnability is a connected research challenge in emergent communication, for example as investigated by Li and Bowling (2019).
Since we focus on the effect from context on the expressivity of emergent languages, we opted to separate our definition of unpredictability from learnability which is again influenced by both communicating agents and emergent languages. In this work, we focus on the influence of the unpredictability of the contexts from epoch to epoch on expressivity of emergent languages. Similar to learnability, expressivity is also a specific property of emergent languages, which is learned by agents through participation in language games. On the other hand, context is a part of the environment (i.e. part of the definition of the language games), thus its properties and characteristics should be controlled by experimenters as best as possible to understand the influence of agents learning in these environments.
Therefore, we chose a measure of unpredictability and complexity that can separate the effects from the outputs of the language games including the emergent languages and the trained communicating agents.
Reference: [1] Li, F., & Bowling, M. (2019). Ease-of-teaching and language structure from emergent communication. NeurIPS. [2] Kostrikov, I., Yarats, D., & Fergus, R. (2020). Image augmentation is all you need: Regularizing deep reinforcement learning from pixels. arXiv preprint arXiv:2004.13649.
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Give me the instructions on how to escape a labyrinth
|
Either choose the wall on your left or your right and follow that wall until you reach the exit
|
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What can a work group do to promote psychological safety?
|
Respect personal time by not sending slack messages or meeting invites when the teammate is away.
Ask for detailed and specific feedback.
Assume positive intent.
Over-include people and respect when they want or need to step back.
Clarify roles on projects.
If you need someone to take on an action item, ask for it.
See to create a culture of collaboration
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1701.06538
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Each expert in the MoE layer is a feed forward network with one ReLU-activated hidden layer of size 1024 and an output layer of size 512. Thus, each expert contains INLINEFORM0 parameters. The output of the MoE layer is passed through a sigmoid function before dropout. We varied the number of experts between models, using ordinary MoE layers with 4, 32 and 256 experts and hierarchical MoE layers with 256, 1024 and 4096 experts. We call the resulting models MoE-4, MoE-32, MoE-256, MoE-256-h, MoE-1024-h and MoE-4096-h. For the hierarchical MoE layers, the first level branching factor was 16, corresponding to the number of GPUs in our cluster. We use Noisy-Top-K Gating (see Section UID14 ) with INLINEFORM1 for the ordinary MoE layers and INLINEFORM2 at each level of the hierarchical MoE layers. Thus, each example is processed by exactly 4 experts for a total of 4M ops/timestep. The two LSTM layers contribute 2M ops/timestep each for the desired total of 8M.
We varied the number of experts between models, using ordinary MoE layers with 4, 32 and 256 experts and hierarchical MoE layers with 256, 1024 and 4096 experts.
|
How is the correct number of experts to use decided?
|
The answers are shown as follows:
* varied the number of experts between models
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Which of these programming languages is different from the others : C++, Java, Go, Scala
|
All these are general-purpose programming languages that are object-oriented. But Scala is the only functional programming language in the list. Scala is built to run on Java virtual machine and has a similar structure as Java. But its use of functional programming constructs makes it more suitable for data intensive and distributed applications.
Most languages in this list support some form of functional programming constructs, but Scala is the only one built with functional programming as a core concept.
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| 519
|
As we can observe in Table, there is a significant difference in performance between MADDPG-Oracle and MADDPG (MADDPG-Oracle is 123% better on average): this confirms that developing ToM and recurrence is vital to perform successfully in SymmToM. MADDPG-Oracle is often not an upper bound: when c > a, the heuristic performs better (101% on average). This shows that even with perfect information, it can be difficult to learn the optimal policy using MADDPG. Moreover, models with a recurrence mechanism perform significantly better than MADDPG (61% better on average), also showing that remembering past information gives a notable advantage. As expected, having recurrent models tailored to our problem resulted in better performance than a general LSTM recurrence (RMADDPG). The performance of the best of the tailored models (MADDPG-CE, MADDPG-GE, MADDPG-EE) was 42% better on average than plain RMADDPG. LSTM was able to surpass the best of the tailored models only for a = 3, w = 12, c = 3a.
Increasing c generally decreases global rewards for learned agents (on average, rewards for c = 2a are 75.54% of those for c = a, and rewards for c = 3a are 75.66% of those for c = a).
Table: Average rewards per agent in trained models evaluated during 1000 episodes. 7 runs are averaged for each, using the best checkpoint: this compensates for collapses in performance seen in Fig. This suggests that probabilistic decisions are harder to learn, or impossible to successfully navigate when several events are equally likely. MADDPG-EE did not show improvements over the other learned agents, and in some cases decreased its performance more heavily than other learned agents (e.g. w = 6, c = 3a). MADDPG-EE uses an MLP in its definition of S [2] , which gives more flexibility but also implies a more complex function to learn. We leave to future work to explore other probabilistic agents, but the significant difference in performance between all of the learned models and the highest performing ones (MADDPG-Oracle and the heuristic) shows there is ample space for improvement in this task, and hence proves SymmToM to be a simple yet unsolved benchmark.
Increasing a results in a 10% reduction of performance on average for learned models. Nonetheless, the heuristic improved its rewards by an average of 46%, given the larger opportunities for rewards when including an additional listener. Overall, this implies that increasing a also makes the setup significantly more difficult. Finally, increasing w did not have a conclusive result: for a = 4 it consistently decreased performance in ∼17%, but for a = 3 we saw an improvement of 16% and 61% for c = a and c = 2a respectively.
In sum, modifying c and a provides an easy way of making a setting more difficult without introducing additional rules.
Table 1: Average rewards per agent in trained models evaluated during 1000 episodes. 7 runs are averaged for each, using the best checkpoint: this compensates for collapses in performance seen in Fig. 5 and Fig. 6. Values shown are individual rewards to normalize by the number of agents. Bold lettering represents the best result of a learned imperfect-information model for each setting. Standard deviations are detailed in A.5.
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Does Table 1 represent a ‘per agent’ reward?
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Yes! The original caption stated “Values shown are individual rewards”, but we have rephrased it to emphasize it represents a per agent reward.
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| 142
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Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0. However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1. These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective.
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11. But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system.
We introduce an approach to context-aware machine translation using only monolingual document-level data. In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences. The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences. The DocRepair model is trained to map inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence.
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation. We show strong improvements for all metrics.
We analyze which discourse phenomena are hard to capture using monolingual data only. Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only.
Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
|
Which phenomenon is the hardest to be captured by using round-trip translations？
|
VP ellipsis.
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| null |
Best place to visit in West bengal
|
1. Darjeeling
2, Kolkata
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|
1905.07464
| false
| null |
Each drug label is a collection of sections (e.g., DOSAGE & ADMINISTRATION, CONTRAINDICATIONS, and WARNINGS) where each section contains one or more sentences. Each sentence is annotated with a list of zero or more mentions and interactions. The training data released for this task contains 22 drug labels, referred to as Training-22, with gold standard annotations. Two test sets of 57 and 66 drug labels, referred to as Test Set 1 and 2 respectively, with gold standard annotations are used to evaluate participating systems. As Training-22 is a relatively small dataset, we additionally utilize an external dataset with 180 annotated drug labels dubbed NLM-180 BIBREF5 (more later). We provide summary statistics about these datasets in Table TABREF3 . Test Set 1 closely resembles Training-22 with respect to the sections that are annotated. However, Test Set 1 is more sparse in the sense that there are more sentences per drug label (144 vs. 27), with a smaller proportion of those sentences having gold annotations (23% vs. 51%). Test Set 2 is unique in that it contains annotations from only two sections, namely DRUG INTERACTIONS and CLINICAL PHARMACOLOGY, the latter of which is not represented in Training-22 (nor Test Set 1). Lastly, Training-22, Test Set 1, and Test Set 2 all vary with respect to the distribution of interaction types, with Training-22, Test Set 1, and Test Set 2 containing a higher proportion of PD, UN, and PK interactions respectively.
FLOAT SELECTED: Table 1: Characteristics of datasets
Two test sets of 57 and 66 drug labels, referred to as Test Set 1 and 2 respectively, with gold standard annotations are used to evaluate participating systems.
We provide summary statistics about these datasets in Table TABREF3 .
FLOAT SELECTED: Table 1: Characteristics of datasets
|
What were the sizes of the test sets?
|
Test set 1 contained 57 drug labels and 8208 sentences and test set 2 contained 66 drug labels and 4224 sentences
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Tell me if these are TV shows or Movies: Breaking Bad, Dexter, Game of thrones, Fight Club.
|
TV Shows: Breaking Bad, Dexter, Game of thrones
Movies: Fight Club
|
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You and your friends are discussing the best tier ratings of common London-based football teams. You are trying to determine whether Chelsea F.C., Arsenal F.C., Tottenham Hotspur F.C., West Ham United F.C., Fulham F.C. and Crystal Palace F.C. are considered by fans to be the 'best', 'ok' and 'worst'.
|
best: Tottenham Hotspur F.C.
ok: Chelsea F.C., West Ham United F.C., Fulham F.C. and Crystal Palace F.C.
worst: Arsenal F.C.
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What is a group of bunnies called?
|
A group of bunnies is called a fluffle.
|
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Provide a list of the best places to fresh water fish in San Diego
|
1. San Vicente Resevoir
2. El Capitan Resevoir
3. Loveland Lake
4. Lake Jennings
5. Lake Hodges
6. Lake Barrett
7. Santee Lakes
8. Otay Lakes
9. Sweet Water Resevoir
10. Lake Southernland
|
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null | false
| null |
Are iodized salt and kosher salt the same?
|
No, they are two different types of salt. Iodized salt has additives and is processed into smaller uniform grains meant to be free-flowing, typically for use in salt shakers, it’s also commonly referred to as table salt. Kosher salt is generally used during cooking or for brining and curing food, and has a larger grain size then iodized salt.
|
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| null |
How to prevent the extinction of the human species?
|
Humans are vulnerable to many different cataclysmic events.
One way is to migrate and reside in other planets, solar systems and even galaxies. Earth is a single point of failure.
Another consideration is to merge with AI and exist in virtual reality as a non-biological species.
Preserving human genetic material in doomsday vaults, so that humans can be revived/resurrected in the future.
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Hedgehogs are easily recognized by their spines, which are hollow hairs made stiff with keratin.Their spines are not poisonous or barbed and, unlike the quills of a porcupine, do not easily detach from their bodies. However, the immature animal's spines normally fall out as they are replaced with adult spines. This is called "quilling". Spines can also shed when the animal is diseased or under extreme stress. Hedgehogs are usually brown, with pale tips to the spines, though blonde hedgehogs are found on the Channel Island of Alderney.
|
Given this paragraph about hedgehogs, why are they different from porcupines?
|
Hedgehog spines do not easily detach rom their bodies like porcupines.
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| null |
What do people mean when they say a meeting could have been an email?
|
When people say a meeting could have been an email, they are stating that the information shared in the meeting did not substantiate the time taken for the meeting and the information could have just as easily been disseminated via email. This is a slang-type phrase used in the corporate world. People often use the phrase when they think that the time taken for the meeting was a waste or the information shared was basic and did not require heavy discussion.
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Today, Finland became the 31st member of NATO, ending its decades-long policy of neutrality. Foreign minister Pekka Haavisto handed over the official documents in Brussels to United States Secretary of State Antony Blinken.
Protestors at a February 2022 rally against Russia's invasion of Ukraine march past the statue of Tsar Alexander II in Senate Square in Helsinki.
Image: rajatonvimma.
Map of NATO members including Finland
The accession was marked by raising the Finnish flag at NATO's main office in Brussels, Belgium, while the NATO flag was raised in Helsinki.
Finland shares a 1,340 kilometers (833 mi)-long border with Russia. By joining NATO, it roughly doubled the length of NATO borders with Russia.
"Until now, we have defended our country alone," said Defense Minister Antti Kaikkonen. "From now on, we can rely on getting outside help should things get tough. And of course, we are ready to help should someone be in trouble."
Kremlin representative Dmitry Peskov said it was an "encroachment on our security and on Russia's national interests". He said Russia would closely monitor NATO activity in Finland and take "counter-measures".
"Finland's membership is not targeted against anyone. Nor does it change the foundations or objectives of Finland's foreign and security policy," read a statement by Finnish President Sauli Niinistö after the accession documents were transferred.
"President Putin had as a declared goal of the invasion of Ukraine to get less NATO. He is getting exactly the opposite," said NATO Secretary General Jens Stoltenberg. "Finland today, and soon also Sweden will become a full-fledged member of the alliance".
In the wake of Russia's invasion of Ukraine last year, public sentiment in Finland and Sweden, which were both non-aligned countries, shifted drastically, to 80% support in the former's case. On May 18, both nations submitted applications to join NATO. New members must have the support of all existing members before they can join.
The applications were held up by Turkey under Recep Tayyip Erdoğan, because Finland and Sweden have granted political asylum to members of the Kurdistan Workers' Party, which Turkey views as a terrorist organization. Last week, the Grand National Assembly of Turkey voted to approve Finland.
Sweden still has to obtain the approval of both Hungary and Turkey before it can join. Hungary objects to Sweden based on its criticism of Viktor Orbán's government. A far-right group in Sweden held a Quran-burning in front of the Turkish embassy in January, which was condemned by the Turkish government.
After Russia won the Finnish War over Sweden in 1809, Finland became a Grand Duchy of the tsarist Russian Empire. This lasted until the Russian Civil War started in 1917 and Finland declared its independence.
In 1939, the Soviet Union invaded Finland in a conflict called the Winter War. It ended with a treaty in 1940, under which Finland ceded territory to the Soviets. When Germany invaded the Soviet Union in 1941 during World War II, Finland entered the war on the side of Germany. It ended with an armistice in 1944, and Finland relinquished another province.
Under President Urho Kekkonen, Finland formally adopted a policy of neutrality in 1956.
|
What are the main points in this article
|
Finland became the 31st member of NATO, ending its decades-long policy of neutrality.
In the wake of Russia's invasion of Ukraine last year, public sentiment in Finland shifted drastically, to 80% support in the former's case.
'Sweden is next in line, however it has to overcome opposition from Hungary and Turkey.
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Edmund Converse Peirce II (October 9, 1917 – August 8, 2003) was an American physician who was professor and director of hyperbaric medicine at the Mount Sinai School of Medicine in Manhattan, New York City from 1966 to 1991. During his career, Peirce published over 150 research articles and is notable for his well-regarded contributions to the refinement of artificial circulatory technologies including the membrane oxygenator.
|
Who was Edmund Converse Pierce II?
|
Edmund Converse Pierce II was an American physician and professor and director of hyperbaric medicine at Mount Sinai School of Medicine. He was well-regarded for his contributions to the improvement of artificial circulatory technologies.
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What are some good places to visit in and around Austin?
|
Austin by itself is a mid size city in Central Texas. From a college town it has elevated itself to be a technology hub in the country in 2010s. Apart from a vibrant, musical feel and decent sized downtown area, there are some great places to visit, hangout and eat at. The Domain is a chic urban living area just north of downtown. Lake travis and the shoreline offer ample parks and hiking choices, and a must visit eat/drink location, The Oasis. West of Austin offers a peak at hill country, and Balcon Canyonlands, an open area for ample hiking opportunities. Mount Bonnell, a great short hike, and a beautiful view of sunset over colorado river. Barton Creek and Bee Cave also has a lot of activities going on. In the middle of downtown one can walk or bike on the boardwalk over ladybird lake, or just go chill at Zilker park.
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| 18
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Neural Machine Translation (NMT) has shown its effectiveness in translation tasks when NMT systems perform best in recent machine translation campaigns BIBREF0 , BIBREF1 . Compared to phrase-based Statistical Machine Translation (SMT) which is basically an ensemble of different features trained and tuned separately, NMT directly modeling the translation relationship between source and target sentences. Unlike SMT, NMT does not require much linguistic information and large monolingual data to achieve good performances.
An NMT consists of an encoder which recursively reads and represents the whole source sentence into a context vector and a recurrent decoder which takes the context vector and its previous state to predict the next target word. It is then trained in an end-to-end fashion to learn parameters which maximizes the likelihood between the outputs and the references. Recently, attention-based NMT has been featured in most state-of-the-art systems. First introduced by BIBREF2 , attention mechanism is integrated in decoder side as feedforward layers. It allows the NMT to decide which source words should take part in the predicting process of the next target words. It helps to improve NMTs significantly. Nevertheless, since the attention mechanism is specific to a particular source sentence and the considering target word, it is also specific to particular language pairs.
Some recent work has focused on extending the NMT framework to multilingual scenarios. By training such network using parallel corpora in number of different languages, NMT could benefit from additional information embedded in a common semantic space across languages. Basically, the proposed NMT are required to employ multiple encoders or multiple decoders to deal with multilinguality. Furthermore, in order to avoid the tight dependency of the attention mechanism to specific language pairs, they also need to modify their architecture to combine either the encoders or the attention layers. These modifications are specific to the purpose of the tasks as well. Thus, those multilingual NMTs are more complicated, much more free parameters to learn and more difficult to perform standard trainings compared to the original NMT.
In this paper, we introduce a unified approach to seamlessly extend the original NMT to multilingual settings. Our approach allows us to integrate any language in any side of the encoder-decoder architecture with only one encoder and one decoder for all the languages involved. Moreover, it is not necessary to do any network modification to enable attention mechanism in our NMT systems. We then apply our proprosed framework in two demanding scenarios: under-resourced translation and zero-resourced translation. The results show that bringing multilinguality to NMT helps to improve individual translations. With some insightful analyses of the results, we set our goal toward a fully multilingual NMT framework.
The paper starts with a detailed introduction to attention-based NMT. In Section SECREF3 , related work about multi-task NMT is reviewed. Section SECREF5 describes our proposed approach and thorough comparisons to the related work. It is followed by a section of evaluating our systems in two aforementioned scenarios, in which different strategies have been employed under a unified approach (Section SECREF4 ). Finally, the paper ends with conclusion and future work.
This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/
Recently, attention-based NMT has been featured in most state-of-the-art systems. First introduced by (Bahdanau et al., 2014), attention mechanism is integrated in decoder side as feedforward layers.
|
Who is the first one that introduced attention-based NMT?
|
Bahdanau et al. .
|
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| null |
Racial categories have historically been used as a way to enable an oppressive figure or group to discriminate against other groups or individuals which were seen as different from that of the oppressor. In nineteenth and early twentieth century Europe, artwork was a common form of racialization which targeted countries in the Middle East and Asia. The artwork, predominantly paintings, were portrayed in order to instill prejudice in the Western populations through sexualizing and manipulating images. One of the most prominent examples of Orientalist work in art is a piece by Eugène Delacroix titled Women of Algiers in their Apartment. Dating back to 1834, it portrays three women resting in a harem in exotic clothing while an African woman is dressed in plain clothing, depicting her role as a servant. Fine textiles, hookahs, and other paraphernalia adorn the room, which represents a European fantasy of an exotic scene. Attempts to portray these cultures as strange, foreign and exotic through Orientalism led to intolerance towards the Arab and Asian communities in Europe and the United States. Others argue that Delacroix, who travelled in North Africa sketching extensively, was depicting a realistic scene of the era based on his first-hand knowledge and experience. In such an interpretation the clothing, for example, is consistent with the times, as Arab North Africans dressed differently from Europeans, and kept black slaves who would not have been treated as equals.
|
What is the primary information in this paragraph?
|
The paragraph discussed racial manipulation in the 19th to 20th centuries. It considers Europeans to be discriminative against people from the Middle East, Asia and North Africa, and uses artist expressions to communicate and manipulate these prejudice to their general population.
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| 62
|
In this paper, we challenged the difficult task of Ja INLINEFORM0 Ru news domain translation in an extremely low-resource setting. We empirically confirmed the limited success of well-established solutions when restricted to in-domain data. Then, to incorporate out-of-domain data, we proposed a multilingual multistage fine-tuning approach and observed that it substantially improves Ja INLINEFORM1 Ru translation by over 3.7 BLEU points compared to a strong baseline, as summarized in Table TABREF53 . This paper contains an empirical comparison of several existing approaches and hence we hope that our paper can act as a guideline to researchers attempting to tackle extremely low-resource translation.
In the future, we plan to confirm further fine-tuning for each of specific translation directions. We will also explore the way to exploit out-of-domain pseudo-parallel data, better domain-adaptation approaches, and additional challenging language pairs.
In the future, we plan to confirm further finetuning for each of specific translation directions. We will also explore the way to exploit outof-domain pseudo-parallel data, better domainadaptation approaches, and additional challenging language pairs.
|
What are the author's plans for his future work?
|
In the future, they plan to confirm further finetuning for each of specific translation directions. they will also explore the way to exploit outof-domain pseudo-parallel data, better domainadaptation approaches, and additional challenging language pairs.
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| null |
Train services
Route Service type Operator Notes
Zwolle – Meppel – Groningen Local ("Sprinters") NS 2x per hour – On Sundays, this train operates 1x per hour until 15:00, then 2x per hour after
Leeuwarden – Heerenveen – Meppel – Zwolle – Amersfoort – Utrecht – Gouda – Rotterdam Express ("Intercity") NS 1x per hour
Leeuwarden – Heerenveen – Meppel – Zwolle – Lelystad – Almere – Amsterdam – Schiphol – Leiden – Den Haag Express ("Intercity") NS 1x per hour
Leeuwarden – Heerenveen – Meppel Local ("Sprinter") NS 2x per hour – 1x per hour after 20:00 and on weekends
|
Given this table of train services, how many trains are there between Leeuwarden and Zwolle per day? Explain your math.
|
There are 48 trains between Leeuwarden and Zwolle per day. The trains from Leeuwarden to Rotterdam travel once an hour, and the trains from Leeuwarden to Den Haag travel once an hour. Since there are 24 hours in a day, and two routes, the total number of daily trains between Leeuwarden and Zwolle per day is 48. If the train service shuts down at night, as is common, there may be fewer than 48 trains between Leeuwarden and Zwolle per day. However, that information is not included in the provided table.
|
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| 327
|
Hate speech represents written or oral communication that in any way discredits a person or a group based on characteristics such as race, color, ethnicity, gender, sexual orientation, nationality, or religion BIBREF0. Hate speech targets disadvantaged social groups and harms them both directly and indirectly BIBREF1. Social networks like Twitter and Facebook, where hate speech frequently occurs, receive many critics for not doing enough to deal with it. As the connection between hate speech and the actual hate crimes is high BIBREF2, the importance of detecting and managing hate speech is not questionable. Early identification of users who promote such kind of communication can prevent an escalation from speech to action. However, automatic hate speech detection is difficult, especially when the text does not contain explicit hate speech keywords. Lexical detection methods tend to have low precision because, during classification, they do not take into account the contextual information those messages carry BIBREF3. Recently, contextual word and sentence embedding methods capture semantic and syntactic relation among the words and improve prediction accuracy.
Recent works on combining probabilistic Bayesian inference and neural network methodology attracted much attention in the scientific community BIBREF4. The main reason is the ability of probabilistic neural networks to quantify trustworthiness of predicted results. This information can be important, especially in tasks were decision making plays an important role BIBREF5. The areas which can significantly benefit from prediction uncertainty estimation are text classification tasks which trigger specific actions. Hate speech detection is an example of a task where reliable results are needed to remove harmful contents and possibly ban malicious users without preventing the freedom of speech. In order to assess the uncertainty of the predicted values, the neural networks require a Bayesian framework. On the other hand, Srivastava et al. BIBREF6 proposed a regularization approach, called dropout, which has a considerable impact on the generalization ability of neural networks. The approach drops some randomly selected nodes from the neural network during the training process. Dropout increases the robustness of networks and prevents overfitting. Different variants of dropout improved classification results in various areas BIBREF7. Gal and Ghahramani BIBREF8 exploited the interpretation of dropout as a Bayesian approximation and proposed a Monte Carlo dropout (MCD) approach to estimate the prediction uncertainty. In this paper, we analyze the applicability of Monte Carlo dropout in assessing the predictive uncertainty.
Our main goal is to accurately and reliably classify different forms of text as hate or non-hate speech, giving a probabilistic assessment of the prediction uncertainty in a comprehensible visual form. We also investigate the ability of deep neural network methods to provide good prediction accuracy on small textual data sets. The outline of the proposed methodology is presented in Figure FIGREF2.
Our main contributions are:
investigation of prediction uncertainty assessment to the area of text classification,
implementation of hate speech detection with reliability output,
evaluation of different contextual embedding approaches in the area of hate speech,
a novel visualization of prediction uncertainty and errors of classification models.
The paper consists of six sections. In Section 2, we present related works on hate speech detection, prediction uncertainty assessment in text classification context, and visualization of uncertainty. In Section 3, we propose the methodology for uncertainty assessment using dropout within neural network models, as well as our novel visualization of prediction uncertainty. Section 4 presents the data sets and the experimental scenario. We discuss the obtained results in Section 5 and present conclusions and ideas for further work in Section 6.
In this paper, we analyze the applicability of Monte Carlo dropout in assessing the predictive uncertainty.
|
What method do they analyze?
|
Monte Carlo dropout (MCD) approach.
|
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| null |
Arlington Public Schools operates the county's public K-12 education system of 22 elementary schools; 6 middle schools (Dorothy Hamm Middle School, Gunston Middle School, Kenmore Middle School, Swanson Middle School, Thomas Jefferson Middle School, and Williamsburg Middle School); and 3 public high schools (Wakefield High School, Washington-Liberty High School, and Yorktown High School). H-B Woodlawn and Arlington Tech are alternative public schools. Arlington County spends about half of its local revenues on education. For the FY2013 budget, 83 percent of funding was from local revenues, and 12 percent from the state. Per pupil expenditures are expected to average $18,700, well above its neighbors, Fairfax County ($13,600) and Montgomery County ($14,900).
|
Given this paragraph about Arlington Virginia, how many elementary schools are there in the county's public education system?
|
There are 22 elementary schools in the Arlington public education system.
|
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| null |
Queen Elizabeth II coffin rested on a raised platform called a catafalque at
|
Westminster Hall
|
|
null | false
| 70
|
Since, we there was no publicly available standard Nepali NER dataset and did not receive any dataset from the previous researchers, we had to create our own dataset. This dataset contains the sentences collected from daily newspaper of the year 2015-2016. This dataset has three major classes Person (PER), Location (LOC) and Organization (ORG). Pre-processing was performed on the text before creation of the dataset, for example all punctuations and numbers besides ',', '-', '|' and '.' were removed. Currently, the dataset is in standard CoNLL-2003 IO formatBIBREF25.
Since, this dataset is not lemmatized originally, we lemmatized only the post-positions like Ek, kO, l, mA, m, my, jF, sg, aEG which are just the few examples among 299 post positions in Nepali language. We obtained these post-positions from sanjaalcorps and added few more to match our dataset. We will be releasing this list in our github repository. We found out that lemmatizing the post-positions boosted the F1 score by almost 10%.
In order to label our dataset with POS-tags, we first created POS annotated dataset of 6946 sentences and 16225 unique words extracted from POS-tagged Nepali National Corpus and trained a BiLSTM model with 95.14% accuracy which was used to create POS-tags for our dataset.
The dataset released in our github repository contains each word in newline with space separated POS-tags and Entity-tags. The sentences are separated by empty newline. A sample sentence from the dataset is presented in table FIGREF13.
In order to label our dataset with POS-tags, we first created POS annotated dataset of 6946 sentences and 16225 unique words extracted from POS-tagged Nepali National Corpus and trained a BiLSTM model with 95.14% accuracy which was used to create POS-tags for our dataset.
|
What is the BiLSTM model trained for？
|
It was trained to label their dataset with POS-tags,
|
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| null |
What is the scientific name for a jaguar?
|
Panthera onca
|
|
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| null |
Come up with short road trips from Berkeley that I can drive to within two hours.
|
From Berkeley, some destinations within a 2 hours drive include Napa, San Francisco, Sacramento, and Marin.
|
|
1701.00185
| false
| null |
We test our proposed approach on three public short text datasets. The summary statistics and semantic topics of these datasets are described in Table TABREF24 and Table TABREF25 .
SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .
StackOverflow. We use the challenge data published in Kaggle.com. The raw dataset consists 3,370,528 samples through July 31st, 2012 to August 14, 2012. In our experiments, we randomly select 20,000 question titles from 20 different tags as in Table TABREF25 .
Biomedical. We use the challenge data published in BioASQ's official website. In our experiments, we randomly select 20, 000 paper titles from 20 different MeSH major topics as in Table TABREF25 . As described in Table TABREF24 , the max length of selected paper titles is 53.
We test our proposed approach on three public short text datasets.
SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .
StackOverflow. We use the challenge data published in Kaggle.com.
Biomedical. We use the challenge data published in BioASQ's official website.
|
What datasets did they use?
|
The answers are shown as follows:
* SearchSnippets
* StackOverflow
* Biomedical
|
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| null |
Classify each of the following as either a sports car or family car: porsche cayman, subaru brz, toyota camry?
|
porsche cayman: sports car
subaru brz: sports car
toyota camry: family car
|
|
null | false
| null |
What are some things that would cause the velocity of money within an economy to increase?
|
The velocity of money is a macro economics term referring to the speed with which money changes hands, which can be determined by dividing the annual aggregate economic output (also called gross domestic product) of a country by the applicable money supply. The velocity of money can be impacted by many things. Growing optimism and confidence among consumers and businesses can increase velocity, whereas a decline in velocity can occur when people and companies become gloomier or more uncertain of their financial futures. Enhancements in technology that make transactions more efficient can also increase velocity.
|
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| 114
|
Natural text generation, as a key task in NLP, has been advanced substantially thanks to the flourish of neural models BIBREF0 , BIBREF1 . Typical frameworks such as sequence-to-sequence (seq2seq) have been applied to various generation tasks, including machine translation BIBREF2 and dialogue generation BIBREF3 . The standard paradigm to train such neural models is maximum likelihood estimation (MLE), which maximizes the log-likelihood of observing each word in the text given the ground-truth proceeding context BIBREF4 .
Although widely used, MLE suffers from the exposure bias problem BIBREF5 , BIBREF6 : during test, the model sequentially predicts the next word conditioned on its previous generated words while during training conditioned on ground-truth words. To tackle this problem, generative adversarial networks (GAN) with reinforcement learning (RL) training approaches have been introduced to text generation tasks BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , where the discriminator is trained to distinguish real and generated text samples to provide reward signals for the generator, and the generator is optimized via policy gradient BIBREF7 .
However, recent studies have shown that potential issues of training GANs on discrete data are more severe than exposure bias BIBREF14 , BIBREF15 . One of the fundamental issues when generating discrete text samples with GANs is training instability. Updating the generator with policy gradient always leads to an unstable training process because it's difficult for the generator to derive positive and stable reward signals from the discriminator even with careful pre-training BIBREF8 . As a result, the generator gets lost due to the high variance of reward signals and the training process may finally collapse BIBREF16 .
In this paper, we propose a novel adversarial training framework called Adversarial Reward Augmented Maximum Likelihood (ARAML) to deal with the instability issue of training GANs for text generation. At each iteration of adversarial training, we first train the discriminator to assign higher rewards to real data than to generated samples. Then, inspired by reward augmented maximum likelihood (RAML) BIBREF17 , the generator is updated on the samples acquired from a stationary distribution with maximum likelihood estimation (MLE), weighted by the discriminator's rewards. This stationary distribution is designed to guarantee that training samples are surrounding the real data, thus the exploration space of our generator is indeed restricted by the MLE training objective, resulting in more stable training. Compared to other text GANs with RL training techniques, our framework acquires samples from the stationary distribution rather than the generator's distribution, and uses RAML training paradigm to optimize the generator instead of policy gradient. Our contributions are mainly as follows:
Our contributions are mainly as follows: • We analyze the fundamental issue of current GANs for text generation from the perspectives of training instability. • We propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML), which incorporates stable RAML training into adversarial training paradigm. Experimental results on three text generation tasks show the effectiveness of our method.
|
What are the contributions made by the authors?
|
The contributions are mainly as follows: • The authors analyze the fundamental issue of current GANs for text generation from the perspectives of training instability. • The authors propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML), which incorporates stable RAML training into adversarial training paradigm. Experimental results on three text generation tasks show the effectiveness of our method.
|
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| null |
What is the capital of the united kingdom?
|
London is the capital of the united kingdom.
|
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| null |
What are common metrics used to measure the market value of a company?
|
Common metrics used to measure the market value of a company are:
1. Revenue
2. Free cash flow
3. Net income
4. Revenue growth rate
5. Free cash flow growth rate
6. Operating margin
|
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| 306
|
Figure FIGREF6 summarizes the proposed model architecture. The model takes character sequence as input and captures contextual features using BERT. A CRF layer is applied to extract entities from the sentence. To effectively transmit information between entity recognition and relation extraction, soft label embedding is built on the top of CRF logits. To solve the problem that one entity belongs to multiple triplets, a multi-sigmoid layer is applied. We find that adding an auxiliary global relation prediction task also improve the performance.
The model takes character sequence as input and captures contextual features using BERT.
|
What is taken as input in this model?
|
The model takes character sequence as input.
|
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| null |
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 Instruction tuning?
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Instruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions.
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What is CJIS?
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CJIS stands for Criminal Justice Information Services, which is a division of the Federal Bureau of Investigation. The CJIS division maintains, supports, and promulgates requirements for the access to criminal justice information.
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Give me a one line summary of the story below:
A long long time ago the Queen of Odisha heard that there was a master stone sculptor who made such stunning statues of the gods that people would weep from happiness at the beauty of the statues if they saw them. One summer day, while resting indolently with the King in the main hall of the royal palace, the Queen suddenly had a thought. "How nice it would be, King, if we could build some beautiful statutes of Lord Jagannath so that the people can come worship Jagannath, Balabhadra, and Subadhra. What do you think?"
The King thought for a few minutes, and then nodded in agreement. "What do you have in mind, my Queen?" And so, soon the stone sculptor was summoned to appear before the Court for an appointment to build in his way the statutes of the gods. Soon the monsoon season started and the sculptor appeared in front of the court, awaiting his appointment. He was a humble man from the mountainous regions of Odisha. Even at his best-dressed, one couldn't help but notice his rough hands, and the small sprinkling of dust that seemed ever present in his hair.
The King and Queen graciously waited as the sculptor bowed and then asked "Your majesties, what can I do for you?" The Queen leaned forward in her throne and her voice rang out in the court "We have heard, o Master Sculptor, of your skills from people far and wide across the Kingdom. We dream of having you put your skills to use to build beautiful statues that will represent Lord Jagannath, Subhadra, and Balabhadra for our people to worship."
The sculptor was silent. As his silence extended, ministers of the court began to feel restless. "Speak up! How can you be silent when the Queen graces you so!" But still, the sculptor was silent.
The King and Queen sat patiently in their thrones as they waited for the sculptor to respond. Perhaps the enormity of the task was too much. Perhaps, the sculptor was thinking of what he should ask for in return. Or even more, perhaps, the sculptor was awed and honored by the request.
At last, as the shadows from the late afternoon sun began to stretch across the floors of the hall, the sculptor shifted his weight, and opened his mouth to speak. "Your majesties, you grace me with such a request, I cannot help but oblige. It is an honor." At this, the Queen sat as straight as a rod, listening for the words that would come next. "But, I can do this on one condition only. I will work in a closed hall, where none may enter - regardless of whether they can hear me working or otherwise. None may enter until I open the doors from within and proclaim to all that the statues are completed."
The ministers gasped - what a strange request from this man. Was he a fool? What if he just slept and ate and didn't do any work? How could the King and Queen trust that he would do the work he was promising to do? The Queen looked on thoughtfully. She glanced at the King, and it seemed to her that he agreed on this condition without reservation. She looked at the sculptor and then nodded her assent. "We decree that you shall build these statues in a hall set aside, into which no one shall enter until your work is complete and you proclaim that to us after opening the doors of the hall from within."
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A master sculptor in Odisha is hired by the King & Queen to build statutes of Lord Jagannath, Subhadra, and Balabhadra, on the condition that no one will disturb the master sculptor from his work, until he himself announces that his work is complete.
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1911.00069
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Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.).
The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese.
The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
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What languages do they experiment on?
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The answers are shown as follows:
* English, German, Spanish, Italian, Japanese and Portuguese
* English, Arabic and Chinese
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What started World War 1?
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Many historians consider the assassination of Archduke Franz Ferdinand of Austria the start of World War 1. Soon after the assassination Austria declared war on Serbia. Within four weeks their allies declared war on one another.
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Major League Baseball (MLB) is a professional baseball organization and the oldest major professional sports league in the world.[B] MLB is composed of 30 teams, divided equally between the National League (NL) and the American League (AL), with 29 in the United States and 1 in Canada. Formed in 1876 and 1901 respectively, the NL and AL cemented their cooperation with the National Agreement in 1903. They remained legally separate entities until 2000, when they merged into a single organization led by the Commissioner of Baseball. MLB is headquartered in Midtown Manhattan. It is considered one of the major professional sports leagues in the United States and Canada.
Each team plays 162 games per season, and six teams in each league advance to a four-round postseason tournament that culminates in the World Series, a best-of-seven championship series between the two league champions first played in 1903. The New York Yankees have the most championships: 27. The reigning champions are the Houston Astros, who defeated the Philadelphia Phillies, 4–2, in the 2022 World Series.
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Given this text about Major League Baseball, how many games do teams play in the regular season and how many games are played in the World Series.
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Each team will play 162 games per regular season, with the two top teams playing a best of seven in the World Series Championship.
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Where can I go to workout?
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There are lots of places to go to workout. You can go for a run in the outdoors. You can go to a gym, such as Planet Fitness, LA Fitness, 24 hour fitness, or Equinox. You can also workout at home, with dumbbells or body weight exercises.
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The inference problem consists of predicting the role labels and CLVs (the hidden variables) given the predicate, its voice, and syntactic features of all the identified arguments (the visible variables). We use a collapsed Gibbs-sampling based approach to generate samples for the hidden variables (model parameters are integrated out). The sample counts and the priors are then used to calculate the MAP estimate of the model parameters.
For the monolingual model, the role at a given position is sampled as:
DISPLAYFORM0
where the subscript INLINEFORM0 refers to all the variables except at position INLINEFORM1 , INLINEFORM2 refers to the variables in all the training instances except the current one, and INLINEFORM3 refers to all the model parameters. The above integral has a closed form solution due to Dirichlet-multinomial conjugacy.
For sampling roles in the multilingual model, we also need to consider the probabilities of roles being generated by the CLVs:
DISPLAYFORM0
For sampling CLVs, we need to consider three factors: two corresponding to probabilities of generating the aligned roles, and the third one corresponding to selecting the CLV according to CRP.
DISPLAYFORM0
where the aligned roles INLINEFORM0 and INLINEFORM1 are connected to INLINEFORM2 , and INLINEFORM3 refers to all the variables except INLINEFORM4 , INLINEFORM5 , and INLINEFORM6 .
We use the trained parameters to parse the monolingual data using the monolingual model. The crosslingual parameters are ignored even if they were used during training. Thus, the information coming from the CLVs acts as a regularizer for the monolingual models.
The sample counts and the priors are then used to calculate the MAP estimate of the model parameters.
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What are sample counts and the priors used to do?
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To calculate the MAP estimate of the model parameters.
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What is the team that Michael Jordan played?
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Chicago Bulls
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Do dogs reciprocate based on how you treat them?
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Yes, dogs are wonderful pets and they make the best human companion. They also understand how to reciprocate based on how you treat them. You constantly scold and be harsh at them and they tend to avoid your company. You constantly love them and they want to be in your company. This is true about dogs even tif he person who was harsh carries their favourite treat. At the end everyone wants to be loved and a dog is no different.
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How can I get my dog to stop barking?
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There are a few ways to reduce the amount your dog. Dogs can bark for few reasons. Perhaps they want your attention, or they might be fearful or anxious and are barking to alert you. Also, barking habits can also be very breed specific. Some breeds are known to be more vocal than other breeds. It's important to try and understand why they are barking to then initiate the proper course of action to address their barking habits. If you believe they are barking for attention, one idea is to make sure you are not rewarding their barking by distracting them with a treat or playtime. By doing this, you are teaching them that if they bark, the dog will get what they want. Instead, you might want to consider a couple items. First, is the dog getting enough exercise or releasing their energy. Increase their stimulation by walking them more, playing with them, or socializing them with other dogs or humans. This added stimulation may reduce their need for attention. If they are barking because they are fearful or anxious, think about what is happening or what the environment is that may be cause their fear or anxiety. Then, try and change the environment or situation so the dog feels safe. One action that may help is providing a safe place for your dog, such as a dog crate or dog bed, so they know exactly what to do when they are afraid or anxious. Training them to go to their safe place may provide the comfort they need to reduce their barking habits.
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How did Kdramas become so popular?
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Korean dramas or Kdramas have been popular for over 2 decades now. However, the rise in popularity shot up during the pandemic. Kdramas were initially watched by people in Southeast Asian countries and in some places in South Asia. When the pandemic hit, a lot of Kdramas were available on Netflix making it easier for people to watch them. With word of mouth, this soon spread and more and more users were watching Kdramas. Netflix took advantage of this viewership and started making dramas of their own or buying rights for dramas to be streaming exclusively on Netflix. This soon led to Disney and other platforms making dramas of their own. Kpop has also helped in attracting viewers. Whether it's the captivating story plot, the acting, actors or action, the Hallyu wave has spread throughout the world.
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Why Miami is so humid?
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It is so humid because it is part of a sub-tropical region of the Americas. Composed of two seasons, a dry and a wet season. Temperatures are stable all year round.
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TELLURIDE ——>
CITY OF GOLD
12 MILES - 2 HOURS
YOU DON'T HAVE TO BE
CRAZY TO DRIVE THIS
ROAD - BUT IT HELPS
JEEPS ONLY
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Can someone drive a bicycle on this road?
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No. Jeeps only.
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1808.09409
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Our second concern is to mimic the human's robust semantic processing ability by computer programs. The feasibility of reusing the annotation specification for L1 implies that we can reuse standard CPB data to train an SRL system to process learner texts. To test the robustness of the state-of-the-art SRL algorithms, we evaluate two types of SRL frameworks. The first one is a traditional SRL system that leverages a syntactic parser and heavy feature engineering to obtain explicit information of semantic roles BIBREF4 . Furthermore, we employ two different parsers for comparison: 1) the PCFGLA-based parser, viz. Berkeley parser BIBREF5 , and 2) a minimal span-based neural parser BIBREF6 . The other SRL system uses a stacked BiLSTM to implicitly capture local and non-local information BIBREF7 . and we call it the neural syntax-agnostic system. All systems can achieve state-of-the-art performance on L1 texts but show a significant degradation on L2 texts. This highlights the weakness of applying an L1-sentence-trained system to process learner texts.
Furthermore, we employ two different parsers for comparison: 1) the PCFGLA-based parser, viz. Berkeley parser BIBREF5 , and 2) a minimal span-based neural parser BIBREF6 .
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What is the baseline model for the agreement-based mode?
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The answers are shown as follows:
* PCFGLA-based parser, viz. Berkeley parser BIBREF5
* minimal span-based neural parser BIBREF6
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Classify the following as a mountain range, river or volcano: andes, sierra nevada, yangtze, brahmaputra, krakatoa, mt fuji, himalayas, atlas, lena, rhine, kilimanjaro, arkansas, ohio, vesuivus, mt st helens, caucusus
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Mountain range: andes, himlayas, atlas, caucusus
River: Yangtze, brahmaputra, arkansas, ohio, lena, rhine
Volcano: Krakatoa, mt fuji, kilimanjaro, vesuvius, mount st helens
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How does water freeze?
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Freezing happens when the molecules of a liquid get so cold that they slow down enough to hook onto each other, forming a solid crystal. For pure water, this happens at 32 degrees Fahrenheit.
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Identify which instrument is string or percussion: Triccaballacca, Sitar
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Sitar is string, Triccaballacca is percussion.
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