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In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
Train a pivot→target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy is sue (Howard and Ruder 2018).
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Why do authors freeze several layers of the pre-trained universal encoder during the training process?
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To avoid the degeneracy is sue.
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Tell me whether these foods are high or low in carbs: rice, spaghetti, chicken, pork, quinoa, salami
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Rice: High in Carbs
Spaghetti: High in Carbs
Chicken: Low in Carbs
Pork: Low in Carbs
Quinoa: High in Carbs
Salami: Low in Carbs
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1908.09137
| false
| null |
As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute).
As shown in Table , the proposed PS-rnn-elmo shows a significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute).
|
How much better performance of proposed model compared to answer-selection models?
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The answers are shown as follows:
* significant MAP performance improvement compared to the previous best model, CompClip-LM (0.696 to 0.734 absolute)
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null | false
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Which is a species of fish? Nurseryfish or Nursery
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Nurseryfish
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null | false
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How many times Max Verstappen won the F1 Championship ?
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2 times
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null | false
| 362
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The proliferation of the online social media has lately resulted in the democratization of online content sharing. Among other media, Twitter is very popular for research and application purposes due to its scale, representativeness and ease of public access to its content. However, tweets, that are short messages of up to 140 characters, pose several challenges to traditional Natural Language Processing (NLP) systems due to the creative use of characters and punctuation symbols, abbreviations ans slung language.
Named Entity Recognition (NER) is a fundamental step for most of the information extraction pipelines. Importantly, the terse and difficult text style of tweets presents serious challenges to NER systems, which are usually trained using more formal text sources such as newswire articles or Wikipedia entries that follow particular morpho-syntactic rules. As a result, off-the-self tools trained on such data perform poorly BIBREF0 . The problem becomes more intense as the number of entities to be identified increases, moving from the traditional setting of very few entities (persons, organization, time, location) to problems with more. Furthermore, most of the resources (e.g., software tools) and benchmarks for NER are for text written in English. As the multilingual content online increases, and English may not be anymore the lingua franca of the Web. Therefore, having resources and benchmarks in other languages is crucial for enabling information access worldwide.
In this paper, we propose a new benchmark for the problem of NER for tweets written in French. The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities. The annotators were native speakers of French and had previous experience in the task of NER. Overall, the generated datasets consists of INLINEFORM0 tweets, split in training and test parts.
The paper is organized in two parts. In the first, we discuss the data preparation steps (collection, annotation) and we describe the proposed dataset. The dataset was first released in the framework of the CAp 2017 challenge, where 8 systems participated. Following, the second part of the paper presents an overview of baseline systems and the approaches employed by the systems that participated. We conclude with a discussion of the performance of Twitter NER systems and remarks for future work.
The tweets were collected using the publicly available Twitter API and annotated with 13 types of entities.
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How many types of entities does the dataset have?
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13.
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The word "aurora" is derived from the name of the Roman goddess of the dawn, Aurora, who travelled from east to west announcing the coming of the sun. Ancient Greek poets used the corresponding name Eos metaphorically to refer to dawn, often mentioning its play of colors across the otherwise dark sky (e.g., "rosy-fingered dawn").
|
Where does the word aurora come from?
|
The word "aurora" is derived from the name of the Roman goddess of the dawn,
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null | false
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Which astronomer is name-dropped in "Bohemian Rhapsody?"
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Galileo.
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1705.03487
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By using the probability values that emerge from the activation function in the neural network, rather than just the final classification, we can draw a barycentric Newton diagram, as shown in Figure 4 . The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates. This visualization allows an intuitive interpretation of which country a recipe belongs to. If the probability of Japanese is high, the recipe is mapped near the Japanese. The countries on the Newton diagram are placed by spectral graph drawing BIBREF9 , so that similar countries are placed nearby on the circle. The calculation is as follows. First we define the adjacency matrix $W$ as the similarity between two countries. The similarity between country $i$ and $j$ is calculated by cosine similarity of county $i$ vector and $j$ vector. These vector are defined in next section. $W_{ij} = sim(vec_i, vec_j)$ . The degree matrix $D$ is a diagonal matrix where $D_{ii} = \sum _{j} W_{ij}$ . Next we calculate the eigendecomposition of $D^{-1}W$ . The second and third smallest eingenvalues and corresponded eingevectors are used for placing the countries. Eigenvectors are normalized so as to place the countries on the circle.
The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates. This visualization allows an intuitive interpretation of which country a recipe belongs to. If the probability of Japanese is high, the recipe is mapped near the Japanese. The countries on the Newton diagram are placed by spectral graph drawing BIBREF9 , so that similar countries are placed nearby on the circle.
|
What is barycentric Newton diagram?
|
The answers are shown as follows:
* The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates.
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Think of different types of pies that can be served at dinner.
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Many different types of pies can be served with dinner. Some pies that go well with dinner include apple pie, pecan pie, pumpkin pie, key lime pie and coconut pie. There are many types of pies depending on what you are looking for.
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null | false
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What is SQL?
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SQL is short for Structured Query Language, which is a declarative programming language typically used for querying, exploring, and interacting with structured data.
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null | false
| 117
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We obtained our Quora dataset BIBREF7 through web-based crawls between June 2014 to August 2014. This crawling exercise has resulted in the accumulation of a massive Q&A dataset spanning over a period of over four years starting from January 2010 to May 2014. We initiated crawling with 100 questions randomly selected from different topics so that different genre of questions can be covered. The crawling of the questions follow a BFS pattern through the related question links. We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions. For each question, we separately crawl their revision logs that contain different types of edit information for the question and the activity log of the question asker.
We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions.
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What is the size of the Quora dataset?
|
It contains 822,040 unique questions with a total of 1,833,125 answers.
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null | false
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Give me a bulleted list of hot dog toppings.
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- Mustard
- Ketchup
- Relish
- Chopped onion
- Chopped tomato
- Chili
- Saukraut
- Coleslaw
- Shredded cheese
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1912.13072
| false
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Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models) . For many of the tasks we model, there have not been standard benchmarks for comparisons across models. This makes it difficult to measure progress and identify areas worthy of allocating efforts and budgets. As such, by publishing our toolkit models, we believe model-based comparisons will be one way to relieve this bottleneck. For these reasons, we also package models from our recent works on dialect BIBREF12 and irony BIBREF14 as part of AraNet .
Although we create new models for tasks such as sentiment analysis and gender detection as part of AraNet, our focus is more on putting together the toolkit itself and providing strong baselines that can be compared to. Hence, although we provide some baseline models for some of the tasks, we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models) . For many of the tasks we model, there have not been standard benchmarks for comparisons across models. This makes it difficult to measure progress and identify areas worthy of allocating efforts and budgets.
|
What models did they compare to?
|
The answers are shown as follows:
* we do not explicitly compare to previous research since most existing works either exploit smaller data (and so it will not be a fair comparison), use methods pre-dating BERT (and so will likely be outperformed by our models)
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null | false
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How do you charge a phone
|
This depends on the phone brand. Most modern phones have wireless charging that allows phones to be placed on a charging mat or pad. Some phones require you to plug them in. Plugs for phones vary by manufacturer. Apple for instance has used a proprietary charging system called lightning for years but recently was sued and now will have to comply with an industry standard of using USB-C to charge their phones.
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William Alfred Bayly (15 July 1906 – 20 July 1934) was a New Zealand farmer and convicted murderer. He was born in Auckland, New Zealand, on 15 July 1906.
In October 1928, Bayly's cousin, 17-year-old Elsie Walker found dead in some scrub in a scoria pit in Panmure, Auckland, with a bruise on her head. Bayly was suspected of killing her, but he was never charged.
Bill Bayly and his wife Phyllis had been dairy farming at Ruawaro, near Huntly, since November 1928. Their neighbours were Samuel and Christobel Lakey. Relations between Bill Bayly and the Lakeys were initially friendly, but soured when Mrs Lakey was said to have accused Bayly of having murdered his niece.
On 16 October 1933 neighbours were concerned as to why the Lakey's cows hadn't been milked that morning. Christobel was discovered lying face down in her pond next to the farmhouse. She had been struck a blow to the face then held down underwater until she drowned. Her husband was missing. Some speculated that her husband had killed her and fled, but it soon became evident that both Lakeys had been the victims of foul play. Two days later bloodstains were found on a wheeled frame near the boundary between the Lakey and Bayly farms, bloodstains were discovered on Bill's sledge, Lakey's guns were found buried in Bayly's swamp, and chemical tests revealed charred bone fragments on a shovel taken from his dairy.
In December Bayly, who had been under surveillance by the police, disappeared, leaving a suicide note. He soon surfaced in Auckland, and was arrested for the murder of Christobel Lakey. More evidence was found in Bayly's garden. Bayly had attempted to incinerate all traces of Lakey. On 10 January 1934 Bayly, was charged with Samuel Lakey's murder.
Bayly was hanged in Mount Eden Prison, Auckland, on 20 July 1934. He was survived by his wife and two young sons.
Police finally buried the remains of Samuel Lakey alongside those of Christobel in Huntly cemetery in 2015. His remains had been used for forensic training.
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Who is William Alfred Bayly?
|
William Alfred Bayly was a New Zealand farmer, who killed his neighbor, Christobel Lakey. He was also suspected of killing others, but was not convicted. He was arrested in Auckland, and was hanged.
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null | false
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What are the best camps ideas for kids?
|
Kids camps cover many different activities, depending on the season, and the age of the children attending. Here are some fun camp ideas, that most kids will enjoy:
1. Sports camp - kids play different sports every day
2. Art camp - kids get a chance to use different materials to be creative
3. Sailing camp - if the child loves the water, sailing is a great activity to learn
4. Math & Reading camp - this might sound like school, but reading and math can be fun too, and specialized camps try to gamify these foundational academic skills
5. Adventure camp - give kids some freedom and a taste of the outdoors, adventure camps tend to involve hiking, kayaking or canoeing, exploring, and learning about the natural environment
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null | false
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David Robert Jones (8 January 1947 – 10 January 2016), known professionally as David Bowie (/ˈboʊi/ BOH-ee), was an English singer-songwriter and actor. A leading figure in the music industry, he is regarded as one of the most influential musicians of the 20th century. Bowie was acclaimed by critics and musicians, particularly for his innovative work during the 1970s. His career was marked by reinvention and visual presentation, and his music and stagecraft had a significant impact on popular music.
|
Given this paragraph about David Bowie, in which decade might you describe his music as having peaked?
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1970s
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1704.05572
| true
| null |
Given a multiple-choice question $qa$ with question text $q$ and answer choices A= $\lbrace a_i\rbrace $ , we select the most relevant tuples from $T$ and $S$ as follows.
Selecting from Tuple KB: We use an inverted index to find the 1,000 tuples that have the most overlapping tokens with question tokens $tok(qa).$ . We also filter out any tuples that overlap only with $tok(q)$ as they do not support any answer. We compute the normalized TF-IDF score treating the question, $q$ as a query and each tuple, $t$ as a document: $ &\textit {tf}(x, q)=1\; \textmd {if x} \in q ; \textit {idf}(x) = log(1 + N/n_x) \\ &\textit {tf-idf}(t, q)=\sum _{x \in t\cap q} idf(x) $
Given a multiple-choice question $qa$ with question text $q$ and answer choices A= $\lbrace a_i\rbrace $ , we select the most relevant tuples from $T$ and $S$ as follows.
Selecting from Tuple KB: We use an inverted index to find the 1,000 tuples that have the most overlapping tokens with question tokens $tok(qa).$ .
|
Is an entity linking process used?
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No.
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null | false
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What is the best tv series in the world
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Dexter- The Dexter is so exciting to watch that it should be the best TV series in the world
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1912.03234
| false
| null |
A two-step validation was conducted for English-speaking customers. An initial A/B testing for the LR model in a production setting was performed to compare the labelling strategies. A second offline comparison of the models was conducted on historical data and a selected labelling strategy. One month of data and a subset of the customers was used (approx. eighty thousand). The sampled dataset presents a fraction of positive labels of approximately 0.5 for reuse and 0.2 for one-day return. Importantly, since this evaluation is done on a subset of users, the dataset characteristic's do not necessarily represent real production traffic. The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc). The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set. As a benchmark, we also consider two additional methods: a non-personalized popularity model and one that follows BIBREF16, replacing the transformer joke encoder with a CNN network (the specialized loss and other characteristics of the DL model are kept).
The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set.
|
What evaluation metrics were used?
|
The answers are shown as follows:
* AUC-ROC
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null | false
| null |
What is a landlord?
|
A landlord is a person, business, or other entity that owns some form of real-estate that they rent out for an amount of money paid yearly, monthly, semi-monthly or weekly. The type of real estate owned can be land, commercial real-estate, or home real-estate. A landlord evaluates different tenant applications submitted to rent or use their owned real-estate. Once a match is found an agreement or contract is signed and executed between both parties. The tenant pays the landlord an agreed amount of money during some finite time to be able to use the landlord's owned real estate for a specific purpose outlined in the original signed contract.
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1912.08320
| false
| null |
We drew the main corpus of ML application papers from ArXiV, the oldest and most established “preprint” repositories, originally for researchers to share papers prior to peer review. Today, ArXiV is widely used to share both drafts of papers that have not (yet) passed peer review (“preprints”) and final versions of papers that have passed peer review (often called “postprints”). Users submit to any number of disciplinary categories and subcategories. Subcategory moderators perform a cursory review to catch spam, blatant hoaxes, and miscategorized papers, but do not review papers for soundness or validity. We sampled all papers published in the Computer Science subcategories of Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), Computational Linguistics (cs.CL), Computers and Society (cs.CY), Information Retrieval (cs.IR), and Computer Vision (CS.CV), the Statistics subcategory of Machine Learning (stat.ML), and Social Physics (physics.soc-ph). We filtered for papers in which the title or abstract included at least one of the words “machine learning”, “classif*”, or “supervi*” (case insensitive). We then filtered to papers in which the title or abstract included at least “twitter” or “tweet” (case insensitive), which resulted in 494 papers. We used the same query on Elsevier's Scopus database of peer-reviewed articles, selecting 30 randomly sampled articles, which mostly selected from conference proceedings. One paper from the Scopus sample was corrupted, so only 29 papers were examined.
We sampled all papers published in the Computer Science subcategories of Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), Computational Linguistics (cs.CL), Computers and Society (cs.CY), Information Retrieval (cs.IR), and Computer Vision (CS.CV), the Statistics subcategory of Machine Learning (stat.ML), and Social Physics (physics.soc-ph). We filtered for papers in which the title or abstract included at least one of the words “machine learning”, “classif*”, or “supervi*” (case insensitive). We then filtered to papers in which the title or abstract included at least “twitter” or “tweet” (case insensitive), which resulted in 494 papers. We used the same query on Elsevier's Scopus database of peer-reviewed articles, selecting 30 randomly sampled articles, which mostly selected from conference proceedings. One paper from the Scopus sample was corrupted, so only 29 papers were examined.
|
How were the machine learning papers from ArXiv sampled?
|
The answers are shown as follows:
* sampled all papers published in the Computer Science subcategories of Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), Computational Linguistics (cs.CL), Computers and Society (cs.CY), Information Retrieval (cs.IR), and Computer Vision (CS.CV), the Statistics subcategory of Machine Learning (stat.ML), and Social Physics (physics.soc-ph)
* filtered for papers in which the title or abstract included at least one of the words “machine learning”, “classif*”, or “supervi*” (case insensitive)
* filtered to papers in which the title or abstract included at least “twitter” or “tweet” (case insensitive)
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null | false
| null |
What type of mouse do you use with a mac
|
Magic mouse
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null | false
| null |
Think up of a bunch of different holidays celebrated in the US
|
Some US holidays include 4th of July, Memorial Day, Labor Day, Juneteenth and Thanksgiving
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null | false
| 106
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In the following experiment, we focus on automatic identification of arguments in the discourse. Our approach is based on supervised and semi-supervised machine learning methods on the gold data Toulmin dataset introduced in section SECREF51 .
An argument consists of different components (such as premises, backing, etc.) which are implicitly linked to the claim. In principle one document can contain multiple independent arguments. However, only 4% of the documents in our dataset contain arguments for both sides of the issue. Thus we simplify the task and assume there is only one argument per document.
Given the low inter-annotator agreement on the pathos dimension (Table TABREF77 ), we focus solely on recognizing the logical dimension of argument. The pathos dimension of argument remains an open problem for a proper modeling as well as its later recognition.
Since the smallest annotation unit is a token and the argument components do not overlap, we approach identification of argument components as a sequence labeling problem. We use the BIO encoding, so each token belongs to one of the following 11 classes: O (not a part of any argument component), Backing-B, Backing-I, Claim-B, Claim-I, Premise-B, Premise-I, Rebuttal-B, Rebuttal-I, Refutation-B, Refutation-I. This is the minimal encoding that is able to distinguish two adjacent argument components of the same type. In our data, 48% of all adjacent argument components of the same type are direct neighbors (there are no "O" tokens in between).
We report Macro- INLINEFORM0 score and INLINEFORM1 scores for each of the 11 classes as the main evaluation metric. This evaluation is performed on the token level, and for each token the predicted label must exactly match the gold data label (classification of tokens into 11 classes).
As instances for the sequence labeling model, we chose sentences rather than tokens. During our initial experiments, we observed that building a sequence labeling model for recognizing argument components as sequences of tokens is too fine-grained, as a single token does not convey enough information that could be encoded as features for a machine learner. However, as discussed in section UID73 , the annotations were performed on data pre-segmented to sentences and annotating tokens was necessary only when the sentence segmentation was wrong or one sentence contained multiple argument components. Our corpus consists of 3899 sentences, from which 2214 sentences (57%) contain no argument component. From the remaining ones, only 50 sentences (1%) have more than one argument component. Although in 19 cases (0.5%) the sentence contains a Claim-Premise pair which is an important distinction from the argumentation perspective, given the overall small number of such occurrences, we simplify the task by treating each sentence as if it has either one argument component or none.
The approximation with sentence-level units is explained in the example in Figure FIGREF112 . In order to evaluate the expected performance loss using this approximation, we used an oracle that always predicts the correct label for the unit (sentence) and evaluated it against the true labels (recall that the evaluation against the true gold labels is done always on token level). We lose only about 10% of macro INLINEFORM0 score (0.906) and only about 2% of accuracy (0.984). This performance is still acceptable, while allowing to model sequences where the minimal unit is a sentence.
Table TABREF114 shows the distribution of the classes in the gold data Toulmin, where the labeling was already mapped to the sentences. The little presence of rebuttal and refutation (4 classes account only for 3.4% of the data) makes this dataset very unbalanced.
We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. Each sentence ( INLINEFORM0 ) is represented as a vector of real-valued features.
We defined the following feature sets:
FS0: Baseline lexical features
word uni-, bi-, and tri-grams (binary)
FS1: Structural, morphological, and syntactic features
First and last 3 tokens. Motivation: these tokens may contain discourse markers or other indicators for argument components, such as “therefore” and “since” for premises or “think” and “believe” for claims.
Relative position in paragraph and relative position in document. Motivation: We expect that claims are more likely to appear at the beginning or at the end of the document.
Number of POS 1-3 grams, dependency tree depth, constituency tree production rules, and number of sub-clauses. Based on BIBREF113 .
FS2: Topic and sentiment features
30 features taken from a vector representation of the sentence obtained by using Gibbs sampling on LDA model BIBREF114 , BIBREF115 with 30 topics trained on unlabeled data from the raw corpus. Motivation: Topic representation of a sentence might be valuable for detecting off-topic sentences, namely non-argument components.
Scores for five sentiment categories (from very negative to very positive) obtained from Stanford sentiment analyzer BIBREF116 . Motivation: Claims usually express opinions and carry sentiment.
FS3: Semantic, coreference, and discourse features
Binary features from Clear NLP Semantic Role Labeler BIBREF117 . Namely, we extract agent, predicate + agent, predicate + agent + patient + (optional) negation, argument type + argument value, and discourse marker which are based on PropBank semantic role labels. Motivation: Exploit the semantics of Capturing the semantics of the sentences.
Binary features from Stanford Coreference Chain Resolver BIBREF118 , e.g., presence of the sentence in a chain, transition type (i.e., nominal–pronominal), distance to previous/next sentences in the chain, or number of inter-sentence coreference links. Motivation: Presence of coreference chains indicates links outside the sentence and thus may be informative, for example, for classifying whether the sentence is a part of a larger argument component.
Results of a PTDB-style discourse parser BIBREF119 , namely the type of discourse relation (explicit, implicit), presence of discourse connectives, and attributions. Motivation: It has been claimed that discourse relations play a role in argumentation mining BIBREF120 .
FS4: Embedding features
300 features from word embedding vectors using word embeddings trained on part of the Google News dataset BIBREF121 . In particular, we sum up embedding vectors (dimensionality 300) of each word, resulting into a single vector for the entire sentence. This vector is then directly used as a feature vector. Motivation: Embeddings helped to achieve state-of-the-art results in various NLP tasks BIBREF116 , BIBREF122 .
Except the baseline lexical features, all feature types are extracted not only for the current sentence INLINEFORM0 , but also for INLINEFORM1 preceding and subsequent sentences, namely INLINEFORM2 , INLINEFORM3 , INLINEFORM4 INLINEFORM5 , INLINEFORM6 , where INLINEFORM7 was empirically set to 4. Each feature is then represented with a prefix to determine its relative position to the current sequence unit.
Let us first discuss the upper bounds of the system. Performance of the three human annotators is shown in the first column of Table TABREF139 (results are obtained from a cumulative confusion matrix). The overall Macro- INLINEFORM0 score is 0.602 (accuracy 0.754). If we look closer at the different argument components, we observe that humans are good at predicting claims, premises, backing and non-argumentative text (about 0.60-0.80 INLINEFORM1 ), but on rebuttal and refutation they achieve rather low scores. Without these two components, the overall human Macro- INLINEFORM2 would be 0.707. This trend follows the inter-annotator agreement scores, as discussed in section UID75 .
In our experiments, the feature sets were combined in the bottom-up manner, starting with the simple lexical features (FS0), adding structural and syntactic features (FS1), then adding topic and sentiment features (FS2), then features reflecting the discourse structure (FS3), and finally enriched with completely unsupervised latent vector space representation (FS4). In addition, we were gradually removing the simple features (e.g., without lexical features, without syntactic features, etc.) to test the system with more “abstract” feature sets (feature ablation). The results are shown in Table TABREF139 .
The overall best performance (Macro- INLINEFORM0 0.251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets. Classification of non-argumentative text (the "O" class) yields about 0.7 INLINEFORM1 score even in the baseline setting. The boundaries of claims (Cla-B), premises (Pre-B), and backing (Bac-B) reach in average lower scores then their respective inside tags (Cla-I, Pre-I, Bac-I). It can be interpreted such that the system is able to classify that a certain sentence belongs to a certain argument component, but the distinction whether it is a beginning of the argument component is harder. The very low numbers for rebuttal and refutation have two reasons. First, these two argument components caused many disagreements in the annotations, as discussed in section UID86 , and were hard to recognize for the humans too. Second, these four classes have very few instances in the corpus (about 3.4%, see Table TABREF114 ), so the classifier suffers from the lack of training data.
The results for the in-domain cross validation scenario are shown in Table TABREF140 . Similarly to the cross-validation scenario, the overall best results were achieved using the largest feature set (01234). For mainstreaming and red-shirting, the best results were achieved using only the feature set 4 (embeddings). These two domains contain also fewer documents, compared to other domains (refer to Table TABREF71 ). We suspect that embeddings-based features convey important information when not enough in-domain data are available. This observation will become apparent in the next experiment.
The cross-domain experiments yield rather poor results for most of the feature combinations (Table TABREF141 ). However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario. These results indicate that embedding features generalize well across domains in our task of argument component identification. We leave investigating better performing vector representations, such as paragraph vectors BIBREF123 , for future work.
Error analysis based on the probabilistic confusion matrix BIBREF124 shown in Table TABREF142 reveals further details. About a half of the instances for each class are misclassified as non-argumentative (the "O" prediction).
Backing-B is often confused with Premise-B (12%) and Backing-I with Premise-I (23%). Similarly, Premise-I is misclassified as Backing-I in 9%. This shows that distinguishing between backing and premises is not easy because these two components are similar such that they support the claim, as discussed in section UID86 . We can also see that the misclassification is consistent among *-B and *-I tags.
Rebuttal is often misclassified as Premise (28% for Rebuttal-I and 18% for Rebuttal-B; notice again the consistency in *-B and *-I tags). This is rather surprising, as one would expect that rebuttal would be confused with a claim, because its role is to provide an opposing view.
Refutation-B and Refutation-I is misclassified as Premise-I in 19% and 27%, respectively. This finding confirms the discussion in section UID86 , because the role of refutation is highly context-dependent. In a pragmatic perspective, it is put forward to indirectly support the claim by attacking the rebuttal, thus having a similar function to the premise.
We manually examined miss-classified examples produced the best-performing system to find out which phenomena pose biggest challenges. Properly detecting boundaries of argument components caused problems, as shown in Figure FIGREF146 (a). This goes in line with the granularity annotation difficulties discussed in section UID86 . The next example in Figure FIGREF146 (b) shows that even if boundaries of components were detected precisely, the distinction between premise and backing fails. The example also shows that in some cases, labeling on clause level is required (left-hand side claim and premise) but the approximation in the system cannot cope with this level of detail (as explained in section UID111 ). Confusing non-argumentative text and argument components by the system is sometimes plausible, as is the case of the last rhetorical question in Figure FIGREF146 (c). On the other hand, the last example in Figure FIGREF146 (d) shows that some claims using figurative language were hard to be identified. The complete predictions along with the gold data are publicly available.
SVMhmm offers many hyper-parameters with suggested default values, from which three are of importance. Parameter INLINEFORM0 sets the order of dependencies of transitions in HMM, parameter INLINEFORM1 sets the order of dependencies of emissions in HMM, and parameter INLINEFORM2 represents a trading-off slack versus magnitude of the weight-vector. For all experiments, we set all the hyper-parameters to their default values ( INLINEFORM3 , INLINEFORM4 , INLINEFORM5 ). Using the best performing feature set from Table TABREF139 , we experimented with a grid search over different values ( INLINEFORM6 , INLINEFORM7 , INLINEFORM8 ) but the results did not outperform the system trained with default parameter values.
The INLINEFORM0 scores might seem very low at the first glance. One obvious reason is the actual performance of the system, which gives a plenty of room for improvement in the future. But the main cause of low INLINEFORM2 numbers is the evaluation measure — using 11 classes on the token level is very strict, as it penalizes a mismatch in argument component boundaries the same way as a wrongly predicted argument component type. Therefore we also report two another evaluation metrics that help to put our results into a context.
Krippendorff's INLINEFORM0 — It was also used for evaluating inter-annotator agreement (see section UID75 ).
Boundary similarity BIBREF125 — Using this metric, the problem is treated solely as a segmentation task without recognizing the argument component types.
As shown in Table TABREF157 (the Macro- INLINEFORM0 scores are repeated from Table TABREF139 ), the best-performing system achieves 0.30 score using Krippendorf's INLINEFORM1 , which is in the middle between the baseline and the human performance (0.48) but is considered as poor from the inter-annotator agreement point of view BIBREF54 . The boundary similarity metrics is not directly suitable for evaluating argument component classification, but reveals a sub-task of finding the component boundaries. The best system achieved 0.32 on this measure. Vovk2013MT used this measure to annotate argument spans and his annotators achieved 0.36 boundary similarity score. Human annotators in BIBREF125 reached 0.53 boundary similarity score.
The overall performance of the system is also affected by the accuracy of individual NPL tools used for extracting features. One particular problem is that the preprocessing models we rely on (POS, syntax, semantic roles, coreference, discourse; see section UID115 ) were trained on newswire corpora, so one has to expect performance drop when applied on user-generated content. This is however a well-known issue in NLP BIBREF126 , BIBREF127 , BIBREF128 .
To get an impression of the actual performance of the system on the data, we also provide the complete output of our best performing system in one PDF document together with the gold annotations in the logos dimension side by side in the accompanying software package. We believe this will help the community to see the strengths of our model as well as possible limitations of our current approaches.
We defined the following feature sets: r FS0: Baseline lexical features – word uni-, bi-, and tri-grams (binary)
|
Does the FS0 mean Baseline lexical features in the experiment?
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Yes, it is.
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Bacardi 151 is a discontinued brand of highly alcoholic rum made by Bacardi Limited of Hamilton, Bermuda. It is named for its alcohol concentration level of 151 U.S. proof, that is, 75.5% alcohol by volume. This is much higher than typical rum, which averages around 35%–40% alcohol by volume. Bacardi 151 was sold in the United States and Canada from at least 1963 until 2016, when its production was discontinued.
Safety and lawsuits
Because of its high alcohol content, Bacardi 151 was particularly flammable among liquors. For this reason it was often used as the ignition source for "flaming shots" and even for at-home pyrotechnic stunts. The bottle carried a warning label advising consumers, among other things, "do not use this product for flaming dishes or other drinks." Bottles were also equipped with a stainless steel flame arrester crimped onto the neck of the bottle to prevent the rum inside the bottle from igniting. Nevertheless, Bacardi faced at least two lawsuits from consumers who claimed to have been badly injured by the product's flames. While the company never issued a formal statement regarding the product's discontinuation, it has been speculated that "it's a pretty good guess that Bacardi got sick of being sued."
Mixed drinks
Bacardi 151 was typically used in sweet drinks, such as the Hurricane and Caribou Lou, which combine rum and fruit juices.
|
Why was Bacardi 151 discontinued in the US?
|
Because of its high alcohol content, Bacardi 151 was particularly flammable among liquors. Bacardi faced at least two lawsuits from consumers who claimed to have been badly injured by the product's flames. It has been speculated that "it's a pretty good guess that Bacardi got sick of being sued."
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What is stream processing ?
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Stream processing refers to techniques used to process a continuous stream of data. In batch processing, the amount of data is known up front when the processing starts. However, in stream processing, the computation (or query) runs ‘forever’ and as new data continues to get added to the input the computation updates its response. To handle an infinite stream of data without needing to maintain infinite history stream processing queries often include the idea of windows which limits how much history is needed to be kept.
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1910.07481
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We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment.
|
What evaluation metrics did they use?
|
The answers are shown as follows:
* BLEU and TER scores
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These islands can be divided into three recognizable groups located on the Azores Plateau:
The Eastern Group (Grupo Oriental) of São Miguel, Santa Maria and Formigas Islets
The Central Group (Grupo Central) of Terceira, Graciosa, São Jorge, Pico and Faial
The Western Group (Grupo Ocidental) of Flores and Corvo.
|
Extract the names of the islands located on the Central Group of the Azores plateau from the text. Separate them with a comma.
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Terceira, Graciosa, São Jorge, Pico and Faial
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Modern pizza evolved from similar flatbread dishes in Naples, Italy, in the 18th or early 19th century.
The word pizza was first documented in AD 997 in Gaeta and successively in different parts of Central and Southern Italy. Pizza was mainly eaten in Italy and by emigrants from there. This changed after World War II when Allied troops stationed in Italy came to enjoy pizza along with other Italian foods.
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What is the Pizza history?
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The history of pizza begins in antiquity, as various ancient cultures produced basic flatbreads with several toppings.
A precursor of pizza was probably the focaccia, a flatbread known to the Romans as panis focacius, to which toppings were then added. Modern pizza evolved from similar flatbread dishes in Naples, Italy, in the 18th or early 19th century.
The word pizza was first documented in AD 997 in Gaeta and successively in different parts of Central and Southern Italy. Pizza was mainly eaten in Italy and by emigrants from there. This changed after World War II when Allied troops stationed in Italy came to enjoy pizza along with other Italian foods.
Some commentators have suggested that the origins of modern pizza can be traced to pizzarelle, which were kosher for Passover cookies eaten by Roman Jews after returning from the synagogue on that holiday, though some also trace its origins to other Italian paschal bread. Other examples of flatbreads that survive to this day from the ancient Mediterranean world are focaccia (which may date back as far as the ancient Etruscans); Manakish in the Levant, coca (which has sweet and savory varieties) from Catalonia, Valencia and the Balearic Islands; the Greek Pita; Lepinja in the Balkans; or Piadina in the Romagna part of Emilia-Romagna in Italy
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Kafka stores key-value messages that come from arbitrarily many processes called producers. The data can be partitioned into different "partitions" within different "topics". Within a partition, messages are strictly ordered by their offsets (the position of a message within a partition), and indexed and stored together with a timestamp. Other processes called "consumers" can read messages from partitions. For stream processing, Kafka offers the Streams API that allows writing Java applications that consume data from Kafka and write results back to Kafka. Apache Kafka also works with external stream processing systems such as Apache Apex, Apache Beam, Apache Flink, Apache Spark, Apache Storm, and Apache NiFi.
Kafka runs on a cluster of one or more servers (called brokers), and the partitions of all topics are distributed across the cluster nodes. Additionally, partitions are replicated to multiple brokers. This architecture allows Kafka to deliver massive streams of messages in a fault-tolerant fashion and has allowed it to replace some of the conventional messaging systems like Java Message Service (JMS), Advanced Message Queuing Protocol (AMQP), etc. Since the 0.11.0.0 release, Kafka offers transactional writes, which provide exactly-once stream processing using the Streams API.
Kafka supports two types of topics: Regular and compacted. Regular topics can be configured with a retention time or a space bound. If there are records that are older than the specified retention time or if the space bound is exceeded for a partition, Kafka is allowed to delete old data to free storage space. By default, topics are configured with a retention time of 7 days, but it's also possible to store data indefinitely. For compacted topics, records don't expire based on time or space bounds. Instead, Kafka treats later messages as updates to older message with the same key and guarantees never to delete the latest message per key. Users can delete messages entirely by writing a so-called tombstone message with null-value for a specific key.
There are five major APIs in Kafka:
Producer API – Permits an application to publish streams of records.
Consumer API – Permits an application to subscribe to topics and processes streams of records.
Connector API – Executes the reusable producer and consumer APIs that can link the topics to the existing applications.
Streams API – This API converts the input streams to output and produces the result.
Admin API – Used to manage Kafka topics, brokers, and other Kafka objects.
The consumer and producer APIs are decoupled from the core functionality of Kafka through an underlying messaging protocol. This allows writing compatible API layers in any programming language that are as efficient as the Java APIs bundled with Kafka. The Apache Kafka project maintains a list of such third party APIs.
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From the passage provided, list the major APIs in Kafka. Separate them with a comma.
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Producer API, Consumer API, Connector API, Streams API, Admin API
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| 69
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Text summarization is a task to generate a shorter and concise version of a text while preserving the meaning of the original text. The task can be divided into two subtask based on the approach: extractive and abstractive summarization. Extractive summarization is a task to create summaries by pulling out snippets of text form the original text and combining them to form a summary. Abstractive summarization asks to generate summaries from scratch without the restriction to use the available words from the original text. Due to the limitations of extractive summarization on incoherent texts and unnatural methodology BIBREF0 , the research trend has shifted towards abstractive summarization.
Sequence-to-sequence models BIBREF1 with attention mechanism BIBREF2 have found great success in generating abstractive summaries, both from a single sentence BIBREF3 and from a long document with multiple sentences BIBREF4 . However, when generating summaries, it is necessary to determine the main topic and to sift out unnecessary information that can be omitted. Sequence-to-sequence models have the tendency to include all the information, relevant or not, that are found in the original text. This may result to unconcise summaries that concentrates wrongly on irrelevant topics. The problem is especially severe when summarizing longer texts.
In this paper, we propose to use entities found in the original text to infer the summary topic, mitigating the aforementioned problem. Specifically, we leverage on linked entities extracted by employing a readily available entity linking system. The importance of using linked entities in summarization is intuitive and can be explained by looking at Figure 1 as an example. First (O1 in the Figure), aside from auxiliary words to construct a sentence, a summary is mainly composed of linked entities extracted from the original text. Second (O2), we can depict the main topic of the summary as a probability distribution of relevant entities from the list of entities. Finally (O3), we can leverage on entity commonsense learned from a separate large knowledge base such as Wikipedia.
To this end, we present a method to effectively apply linked entities in sequence-to-sequence models, called Entity2Topic (E2T). E2T is a module that can be easily attached to any sequence-to-sequence based summarization model. The module encodes the entities extracted from the original text by an entity linking system (ELS), constructs a vector representing the topic of the summary to be generated, and informs the decoder about the constructed topic vector. Due to the imperfections of current ELS's, the extracted linked entities may be too ambiguous and coarse to be considered relevant to the summary. We solve this issue by using entity encoders with selective disambiguation and by constructing topic vectors using firm attention.
We experiment on two datasets, Gigaword and CNN, with varying lengths. We show that applying our module to a sequence-to-sequence model with attention mechanism significantly increases its performance on both datasets. Moreover, when compared with the state-of-the-art models for each dataset, the model obtains a comparable performance on the Gigaword dataset where the texts are short, and outperforms all competing models on the CNN dataset where the texts are longer. Furthermore, we provide analysis on how our model effectively uses the extracted linked entities to produce concise and better summaries.
Due to the imperfections of current ELS's, the extracted linked entities may be too ambiguous and coarse to be considered relevant to the summary. We solve this issue by using entity encoders with selective disambiguation and by constructing topic vectors using firm attention.
|
How to solve the issue caused by the imperfections of current ELS's?
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By using entity encoders with selective disambiguation and by constructing topic vectors using firm attention.
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Robert Gordon Orr OC (born March 20, 1948) is a Canadian former professional ice hockey player, widely acknowledged as one of the greatest of all time. Orr used his ice skating speed, scoring, and play-making abilities to revolutionize the position of defenceman. He played in the National Hockey League (NHL) for 12 seasons, the first 10 with the Boston Bruins, followed by two with the Chicago Black Hawks. Orr remains the only defenceman to have won the league scoring title with two Art Ross Trophies. He holds the record for most points and assists in a single season by a defenceman. Orr won a record eight consecutive Norris Trophies as the NHL's best defenceman and three consecutive Hart Trophies as the league's most valuable player (MVP). Orr was inducted into the Hockey Hall of Fame in 1979 at age 31, the youngest to be inducted at that time. In 2017, Orr was named by the National Hockey League as one of the "100 Greatest NHL Players" in history.
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What team did Bobby Orr play for in his 5th season?
|
Bobby Orr played for the Boston Bruins in his 5th season in the National Hockey League (NHL).
He played in the NHL for 12 seasons, the first 10 with the Boston Bruins, followed by two with the Chicago Black Hawks.
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write a short essay on California wild fire
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California is known for its hot, dry summers, which increase the risk of wildfires. Every year, the state experiences dozens of fires, which can be devastating to both the environment and people's homes and businesses.
In recent years, California has seen some of the worst wildfires in its history, with tens of thousands of acres of land destroyed and hundreds of homes lost. These fires are often started by a combination of high temperatures, dry conditions, and strong winds, which can quickly spread flames and make them difficult to control.
The effects of these fires are felt not just by those directly impacted, but by everyone in the state. Smoke and ash from the fires can cause respiratory problems and other health issues, and the destruction of habitats can harm wildlife populations and disrupt entire ecosystems.
Despite the challenges, California has made significant progress in fighting wildfires in recent years. The state has invested in new technologies and equipment to help firefighters respond more quickly and effectively to fires, and has increased efforts to prevent fires from starting in the first place.
While wildfires will likely always be a risk in California, the state's proactive approach to managing them gives hope that we can continue to protect people, wildlife, and the environment from their devastating effects.
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In 1999, Jensen Huang was named Entrepreneur of the Year in High Technology by Ernst & Young.[citation needed] In 2003, Huang received the Dr. Morris Chang Exemplary Leadership Award, which recognizes a leader who has made exceptional contributions to driving the development, innovation, growth, and long-term opportunities of the fabless semiconductor industry, from the Fabless Semiconductor Association. He was also a National Finalist for the EY Entrepreneur of the Year Award in 2003 and was an Award Recipient for the Northern California region in 1999.[citation needed]
Additionally, Huang is a recipient of the Daniel J. Epstein Engineering Management Award from the University of Southern California and was named an Alumni Fellow by Oregon State University. Huang was awarded an honorary doctorate from Oregon State University at the June 13, 2009, commencement ceremony.
In 2018, Huang was listed in the inaugural Edge 50, naming the world's top 50 influencers in edge computing. In October 2019, Harvard Business Review named Jensen Huang best-performing CEO in the world. In November 2020, Jensen Huang was named "Supplier CEO of the year" by Eurostars AutomotiveNewsEurope. Huang was awarded an honorary doctorate from National Taiwan University at the school anniversary in November, 2020.[better source needed] In August 2021, the Semiconductor Industry Association (SIA) announced that Jensen Huang is the 2021 recipient of the industry’s highest honor, the Robert N. Noyce Award. In September 2021, he was included in the Time 100, Time's annual list of the 100 most influential people in the world.
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From the passage provided, extract the awards received by Jensen Huang from 1999-2021.
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Jensen Huang's awards include: 1) Entrepreneur of the Year in High Technology by Ernst & Young, 2) Dr. Morris Chang Exemplary Leadership Award, 3) National Finalist for the EY Entrepreneur of the Year Award, 4) Award Recipient for the Northern California region, 5) Daniel J. Epstein Engineering Management Award, 6) Alumni Fellow of Oregon State University, 7) honorary doctorate from Oregon State University, 8) Edge 50 member 9) best-performing CEO in the world 10) Supplier CEO of the year, 11) honorary doctorate from National Taiwan University, 12) Robert N. Noyce Award, 13) Time 100 member.
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Classify each of the following as either a classical or jazz artist
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Richard Wagner, Miles Davis, Ella Fitzgerald, Frederic Chopin, Amadeus Mozart, Dave Brubeck, Claude Debussy, Thelonious Monk, Robert Schumann, Louis Armstrong
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How did Andy Warhol create the "piss paintings"?
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Andy Warhol invited staffers who worked with him to help him paint by urinating on canvases that were covered by Warhol in copper paint. The urine reacted with the copper paint chemically to create different colours. The urine also created physical impacts on the copper paint on the canvas.
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| 385
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Neural sequence models BIBREF0, BIBREF1 typically generate outputs in an autoregressive left-to-right manner. These models have been successfully applied to a range of task, for example machine translation BIBREF2. They often rely on an encoder that processes the source sequence, and a decoder that generates the output sequence conditioned on the output of the encoder. The decoder will typically generate the target sequence one token at a time, in an autoregressive left-to-right fashion.
Recently, research in insertion-based non- or partially- autoregressive models has spiked BIBREF3, BIBREF4, BIBREF5, BIBREF6. These model are more flexible than their autoregressive counterparts. They can generate sequences in any order, and can benefit from parallel token generation. They can learn complex orderings (e.g., tree orderings) and may be more applicable to task like cloze question answering BIBREF6 and text simplification, where the order of generation is not naturally left to right, and the source sequence might not be fully observed. One recently proposed approach is the Insertion Transformer BIBREF3, where the target sequence is modelled with insertion-edits. As opposed to traditional sequence-to-sequence models, the Insertion Transformer can generate sequences in any arbitrary order, where left-to-right is a special case. Additionally, during inference, the model is endowed with parallel token generation capabilities. The Insertion Transformer can be trained to follow a soft balanced binary tree order, thus allowing the model to generate $n$ tokens in $O(\log _2 n)$ iterations.
In this work we propose to generalize this insertion-based framework, we present a framework which emits both insertions and deletions. Our Insertion-Deletion Transformer consists of an insertion phase and a deletion phase that are executed iteratively. The insertion phase follows the typical insertion-based framework BIBREF3. However, in the deletion phase, we teach the model to do deletions with on-policy training. We sample an input sequence on-policy from the insertion model (with on-policy insertion errors), and teach the deletion model its appropriate deletions.
This insertion-deletion framework allows for flexible sequence generation, parallel token generation and text editing. In a conventional insertion-based model, if the model makes a mistake during generation, this cannot be undone. Introducing the deletion phase makes it possible to undo the mistakes made by the insertion model, since it is trained on the on-policy errors of the insertion phase. The deletion model extension also enables the framework to efficiently handle tasks like text simplification and style transfer by starting the decoding process from the original source sequence.
A concurrent work was recently proposed, called the Levenshtein Transformer (LevT) BIBREF7. The LevT framework also generates sequences with insertion and deletion operations. Our approach has some important distinctions and can be seen as a simplified version, for both the architecture and the training algorithm. The training algorithm used in the LevT framework uses an expert policy. This expert policy requires dynamic programming to minimize Levenshtein distance between the current input and the target. This approach was also explored by BIBREF8, BIBREF9. Their learning algorithm arguably adds more complexity than needed over the simple on-policy method we propose. The LevT framework consists of three stages, first the number of tokens to be inserted is predicted, then the actual tokens are predicted, and finally the deletion actions are emitted. The extra classifier to predict the number of tokens needed to be inserted adds an additional Transformer pass to each generation step. In practice, it is also unclear whether the LevT exhibits speedups over an insertion-based model following a balanced binary tree order. In contrast, our Insertion-Deletion framework only has one insertion phase and one deletion phase, without the need to predict the number of tokens needed to be inserted. This greatly simplifies the model architecture, training procedure and inference runtime.
An alternative approach for text editing is proposed by BIBREF10, which they dub Deliberation Networks. This work also acknowledges the potential benefits from post-editing output sequences and proposes a two-phase decoding framework to facilitate this.
In this paper, we present the insertion-deletion framework as a proof of concept by applying it to two synthetic character-based translation tasks and showing it can significantly increase the BLEU score over the insertion-only framework.
Our Insertion-Deletion Transformer consists of an insertion phase and a deletion phase that are executed iteratively.
|
What does the insertion-deletion framework consist of?
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An insertion phase and a deletion phase that are executed iteratively
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Why is the immigration system broken?
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The immigration system is broken because it is based on outdated policies and procedures, and does not reflect the realities of today's global economy. The current system is too expensive and inefficient, and fails to provide a fair and equitable solution for all migrants.
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The OA class is a solitary steam locomotive built by the Baldwin Locomotive Works for the Wellington and Manawatu Railway (WMR) in New Zealand. Ordered in 1894, it entered service in August of that year as No. 13 and was the first narrow gauge Vauclain compound in the world. In 1908, the WMR and its locomotive fleet were purchased by New Zealand Railways Department (NZR) and incorporated into the national rail network, and, although No. 13 bore a likeness to members of the O class, it was sufficiently different that it warranted separate classification. The designation of OA was created and it was numbered OA 457. It operated for another two decades until it was withdrawn in December 1929 in Auckland. The locomotive was known to WMR staff as "The Lady".
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What class of locomotive was the No. 13?
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The No. 13 locomotive was an OA class.
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| 29
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MLP contains three layers - an input layer, an output layer and some hidden layers. Input layer receives the signal, the output layer gives a decision or prediction about the input and the computation of the MLP is conducted in the hidden layers. In our system, we use 100 layers. For weight optimization, we use Limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization algorithm.
SVM gives an optimal hyper-plane and it maximizes the margin between classes. We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped. For decision function shape, we use the original one-vs-one (ovo) decision function.
NBC is based on Bayes' Theorem which gives probability of an event occurrence based on some conditions related to that event. We use Multinomial Naive Bayes Classifier with smoothing parameter equals to 0.1. A zero probability cancels the effects of all the other probabilities.
Stochastic gradient descent optimizes an objective function with suitable smoothness properties BIBREF27. It selects few examples randomly instead of whole data for each iteration. We use 'L2' regularization for reduction of overfitting.
Gradient Boosting Classifier produces a prediction model consisting of weak prediction models. Gradient boosting uses decision trees. We use 100 boosting stages in this work.
K-NN is a supervised classification and regression algorithm. It uses the neighbours of the given sample to identify its class. K determines the number of neighbours needed to be considered. We set the value of K equals to 13 in this work.
RF is an ensemble learning technique. It constructs large number of decision trees during training and then predicts the majority class. We use 500 decision trees in the forest and "entropy" function to measure the quality of a split.
We use Radial Basis Function (RBF) kernel in our system to make decision boundary curve-shaped.
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How to make a decision boundary curve-shaped in the authors' system?
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The authors use Radial Basis Function (RBF) kernel in their system to make decision boundary curve-shaped.
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What is primary innovation that a piano delivered over a harpsichord?
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Since a harpsichord plucks the strings the same way each time in response to a keystroke, it delivers the same loudness for each note. A piano on the other hand uses padded hammers to strike the strings, and allows the musician to control each strike in such a way that they have control over the dynamics of each note. This control allows pianists to express a broad range of emotions by adjusting the loudness and softness according to the feelings they want to convey to the listener.
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What are the colors of the sky during a sunset?
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The sky is blue, pink and purple during a sunset
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What types of bikes are there?
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There are gravel bikes, road bikes, mountain bikes, BMX bikes, recumbent bikes, unicycles, hybrid bikes, electric bikes, cruiser bikes, trail bikes, CX bikes, enduro bikes, touring bikes, fixed gear bikes, kids bikes, fat bikes, tandem bikes, folding bikes, trikes and low rider bikes.
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1809.01060
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This effect of context on human ratings is very similar to the one reported in BIBREF5 . They find that sentences rated as ill formed out of context are improved when they are presented in their document contexts. However the mean ratings for sentences judged to be highly acceptable out of context declined when assessed in context. BIBREF5 's linear regression chart for the correlation between out-of-context and in-context acceptability judgments looks remarkably like our Fig FIGREF15 . There is, then, a striking parallel in the compression pattern that context appears to exert on human judgments for two entirely different linguistic properties.
This pattern requires an explanation. BIBREF5 suggest that adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence, rather than simply judging syntactic well formedness (measured as naturalness) when a sentence is considered in isolation. On this view, compression of rating results from a pressure to construct a plausible interpretation for any sentence within its context.
This effect of context on human ratings is very similar to the one reported in BIBREF5 . They find that sentences rated as ill formed out of context are improved when they are presented in their document contexts.
BIBREF5 suggest that adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence, rather than simply judging syntactic well formedness (measured as naturalness) when a sentence is considered in isolation.
|
What provisional explanation do the authors give for the impact of document context?
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The answers are shown as follows:
* adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence
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1708.01776
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We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network.
We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network.
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How do they measure correlation between the prediction and explanation quality?
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They look at the performance accuracy of explanation and the prediction performance
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What was Freddie Mercury's real name?
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Farrokh Bulsara
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Tell me which of these are great Irish Bands: The Hot House Flowers, The Waterboys,The Proclaimers, Deacon Blue, U2, Westlife,
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Ireland has produced some of the greatest bands in the world. U2 is probably the most well known Irish band, formed in 1976. The Hot House Flowers are an Irish rock band that combine traditional Irish music with influences from soul, gospel and rock. The Boomtown Rats are another irish rock band famous for their signature song 'I don't like Mondays'. Westlife are an irish pop group who focus on cover versions. While The Waterboys, Proclaimers and Deacon Blue are all influenced by celtic music they are in fact Scottish
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| 457
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In this section, we provide a regret analysis of the NeuralEI algorithm. We first provide necessary background on the neural tangent kernel (NTK) theory, which plays an important role in our analysis. Following a recent line of research, we define the covariance between two data point x, y ∈ R d as follows:
for all i, j ∈ [T K]. Based on the above definition, we impose the following assumption on the contexts generated by the adversary and the corresponding NTK matrix H.
Assumption 1. Let H be defined in Eq(5). There exists λ 0 > 0 such that H ≥ λ 0 I. In addition, for any
Remark 1. Compared to Algorithm 1 for linear bandits, our Algorithm 2 needs an additional Assumption 1 to guarantee the convergence. The assumption that the NTK matrix is positive definite has been considered in prior work on NTK which is a mild condition and also imposed in other related works. The assumption on contexts ensures that f (x i,t ; θ 0 ) = 0 for any
The NTK technique builds a connection between deep neural networks and kernel methods. It enables us to adapt some complexity measures for kernel methods to describe the complexity of the neural network through the notation of the effective dimensions as defined in. The effective dimension d of matrix H with regularization parameter λ is defined as d = log det(I+H/λ) log(1+T K/λ) . Using these notations, we are now ready to present the second main result of the paper. Let a * (t) = argmax i∈[K] E[r i,t ] be the optimal action at round t that maximizes the expected reward, we define the expected cumulative regret after T iterations as
Then, we achieve the following upper regret bound for our Algorithm 2 by combining our EI techniques for LinEI with NTK techniques. A completed proof is provided in Supplementary Material.
Theorem 2. Under Assumption 1, set the parameters in Algorithm 2 as λ = 1 + 1/T ,
and the network width m satisfies m ≥ poly(γ, T, K, L, log(1/δ)), then with probability at least 1 − δ, the regret of Algorithm 2 is bounded as
Remark 2. The regret bound depends on the parameter β. The best choice is β = 2 that tightens the regret. Theorem 2 implies the regret of NeuralEI is on the order of Õ( d√ T ). Similar to previous results, our results require a large value of m. This is rooted in the current deep learning theory based on the neural tangent kernel.
Then, we achieve the following upper regret bound for our Algorithm 2 by combining our EI techniques for LinEI with NTK techniques.
|
LinEI and NeuralEI modify the original EI by using a threshold function, whether introducing this threshold function is necessary. Do regret bounds still hold without this modification? If not, can the authors elaborate more on the technical difficulties as well as how this modification solves the issues?
|
We will explain that a threshold function is necessary and our modifications solve the issues. Due to the problem of typing math formulas in OpenReview please see our explanation in section Remark on pages 20 and 21 for details in Supplementary Material. We apologize due to this inconvenience!
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Berwick is a borough in Columbia County, Pennsylvania, United States. It is part of Northeastern Pennsylvania and is located 28 miles (45 km) southwest of Wilkes-Barre. As of the 2020 census, Berwick had a population of 10,355. It is one of the two principal communities of the Bloomsburg–Berwick metropolitan area, which covers Columbia and Montour counties, and had a combined population of 85,562 at the 2010 census.
|
Given this paragraph about Berwick, what was its population during the most up to date census?
|
10,355
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null | false
| 437
|
Figure 8: The Loads of each expert in 7th MoE-Layer, with 16 experts in total, in the sentence level.
|
Why use FLOPs instead of actual training time as the metric?
|
Because the actual training time could be affected by the system engineering efforts on the implementation details, which are not our focus in this approach. Instead, in our experiments, we prefer to choose the computation complexity for fair comparisons. Detailed analysis (Appendix) shows that the routing pattern of DTS is relative stable, with heavy-loaded experts almost keep fixed. And we can utilize system techniques, e.g., partitioning hot experts among multiple GPUs and merging multiple cold experts onto less GPUs, for load balance between GPUs. Furthermore, we are also glad to explore the system-level optimization about how to deal with dynamic capacity of different experts in our future work.
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null | false
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2021 Nobel Prize was awarded to which all Journalists
|
Maria Ressa of Philippines & Dmitry Muratov of Russia
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Morpheus and his crew are a group of rebels who hack into the Matrix to "unplug" enslaved humans and recruit them; their understanding of the Matrix's simulated nature allows them to bend its physical laws. Morpheus warns Neo that death within the Matrix kills the physical body too and explains that the Agents are sentient programs that eliminate threats to the system, while machines called Sentinels eliminate rebels in the real world. Neo's prowess during virtual training cements Morpheus's belief that Neo is "the One", a human prophesied to free humankind. The group enters the Matrix to visit the Oracle, a prophet-like program who predicted that the One would emerge. She implies to Neo that he is not the One and warns that he will have to choose between Morpheus's life and his own. Before they can leave the Matrix, Agents and police ambush the group, tipped off by Cypher, a disgruntled crew member who has betrayed Morpheus in exchange for a deal to be plugged back into the Matrix to live a comfortable life.
|
What are agents in the matrix and what is their relationship to sentinels?
|
Agents in the Matrix are sentient 'programs' designed to remove threats from the system. They can be thought of as an Anti-Virus solution within the Matrix program.
Sentinels are physical squid-like robots in the real world outside the matrix whose aim is to destroy and capture remaining human settlements.
There is no direct relationship between Agents and sentinels except that they both work for the same entity against the humans.
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The season ended with Super Bowl XIX when the San Francisco 49ers defeated the Miami Dolphins 38–16 at Stanford Stadium in California. This was the first Super Bowl televised by ABC, who entered into the annual championship game rotation with CBS and NBC. This game marked the second shortest distance between the Super Bowl host stadium (Stanford, California) and a Super Bowl team (San Francisco 49ers). The 49ers became the first team in NFL history to win 15 games in a regular season and to win 18 in an entire season (including the postseason). Additionally, two major offensive records were set this season, with quarterback Dan Marino establishing a new single-season passing yards record with 5,084 (later broken by Drew Brees in 2011, 2012, 2013 and 2016, by Tom Brady in 2011, by Peyton Manning in 2013, by Ben Roethlisberger and Patrick Mahomes in 2018 and by Jameis Winston in 2019) and Eric Dickerson establishing a new single-season rushing yards record with 2,105. Another statistical record broken was Mark Gastineau for most sacks in a single season, with 22 (surpassed by Michael Strahan in 2001).
|
Who was the best team in the NFL during the 1984 season?
|
The 1984 Super Bowl featured the San Francisco 49ers, who were the first team to win 15 games in a season, against the Miami Dolphins. The 49ers won the game 38-16, making them the first team in NFL history to win 18 games in a season counting the playoffs.
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Is Ireland a good surf spot?
|
Yes it is. Ireland is seen as one of the best hidden gems for surfing in the world particularly in winter when waves can reach heights of 40ft+. Global professionals such as Kelly Slater, Mick Fanning and Stephanie Gilmore travel to Ireland on a regular basis for the surf. Some recommended areas for surfing are, Sligo Tramore Beach, Bundoran, Dunfanaghy or most West Coast beaches that attract a larger swell. The water is cold 365 days of the year with highest temperatures of 14 degrees Celsius in the Summer so a 5ml thick wetsuit is recommend all year round.
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Which characters belong to DC or Marvel Universe? Banshee, Silver Banshee
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Banshee is Marvel, Silver Banshee is DC
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Tendulkar scored 143 (131) runs in Sharjah against the Australian team in the 1998 Coca-Cola cup, including 5 sixes in an inning against Shane Warne, Damien Fleming and Michael Kasprowicz. The inning is known as the "Desert Storm" because it was interrupted by a sand storm. In 2020, a poll from the ICC declared it Tendulkar's best ODI inning.
In early 1998, while playing in an Australian tour of India, Tendulkar scored three consecutive centuries. The focus of the tour was the clash between Tendulkar and spinner Shane Warne, both at the peak of their careers. In the lead-up to the series, Tendulkar simulated scenarios in the nets with Laxman Sivaramakrishnan. In their tour opener, Australia faced Mumbai at the Brabourne Stadium in a three-day first class match. Tendulkar scored an unbeaten 204 runs as Shane Warne conceded 111 runs in 16 overs and Australia lost the match within three days. He also had a role with the ball in the five-match ODI series in India following the Tests, including a five wicket haul in an ODI in Kochi. Set 310 runs to win, Australia were cruising at 203 for 3 in the 31st over when Tendulkar turned the match for India, taking the wickets of Michael Bevan, Steve Waugh, Darren Lehmann, Tom Moody and Damien Martyn for 32 runs in 10 overs. The Test match success was followed by two consecutive centuries in April 1998 in a Triangular cricket tournament in Sharjah—the first in a must-win game to take India to the finals and then again in the finals, both against Australia. These twin knocks were also known as the Desert Storm innings. Following the series, Warne ruefully joked that he was having nightmares about his Indian nemesis.
Tendulkar's contribution in the ICC 1998 quarterfinal at Dhaka paved the way for India's entry into the semifinals, when he took four Australian wickets after scoring 141 runs in 128 balls.
|
From the passage provided find how many runs did shane warne concede in the three-day first class match at the Brabourne Stadium
|
Shane Warne conceded 111 runs in 16 overs
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Lysergic acid diethylamide, commonly known as LSD (from German Lysergsäure-diethylamid), also known colloquially as acid, is a potent psychedelic drug. Effects typically include intensified thoughts, emotions, and sensory perception. At sufficiently high dosages LSD manifests primarily mental, visual, as well as auditory, hallucinations. Dilated pupils, increased blood pressure, and increased body temperature are typical. Effects typically begin within half an hour and can last for up to 20 hours. LSD is also capable of causing mystical experiences and ego dissolution. It is used mainly as a recreational drug or for spiritual reasons. LSD is both the prototypical psychedelic and one of the "classical" psychedelics, being the psychedelics with the greatest scientific and cultural significance. LSD is typically either swallowed or held under the tongue.
|
What is the colloquial term for LSD?
|
Lysergic acid diethylamide is commonly known as LSD or acid
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Why do people exercise?
|
Exercise provides many benefits to physical and mental health. It increases strength, cardiovascular health, extends life expectancy, and generally improve people mental and emotional well-being.
Often exercise occur during very enjoyable activities, such as sports and outdoor recreation. Those activities attract people to exercising.
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|
1910.12129
| false
| null |
The NLG model we use to establish a baseline for this dataset is a standard Transformer-based BIBREF19 sequence-to-sequence model. For decoding we employ beam search of width 10 ($\alpha = 1.0$). The generated candidates are then reranked according to the heuristically determined slot coverage score. Before training the model on the ViGGO dataset, we confirmed on the E2E dataset that it performed on par with, or even slightly better than, the strong baseline models from the E2E NLG Challenge, namely, TGen BIBREF20 and Slug2Slug BIBREF21.
We evaluate our model's performance on the ViGGO dataset using the following standard NLG metrics: BLEU BIBREF22, METEOR BIBREF23, ROUGE-L BIBREF24, and CIDEr BIBREF25. Additionally, with our heuristic slot error rate (SER) metric we approximate the percentage of failed slot realizations (i.e., missed, incorrect, or hallucinated) across the test set. The results are shown in Table TABREF16.
FLOAT SELECTED: Table 4: Baseline system performance on the ViGGO test set. Despite individual models (Bo3 – best of 3 experiments) often having better overall scores, we consider the Ao3 (average of 3) results the most objective.
The NLG model we use to establish a baseline for this dataset is a standard Transformer-based BIBREF19 sequence-to-sequence model.
The results are shown in Table TABREF16.
FLOAT SELECTED: Table 4: Baseline system performance on the ViGGO test set. Despite individual models (Bo3 – best of 3 experiments) often having better overall scores, we consider the Ao3 (average of 3) results the most objective.
|
Is any data-to-text generation model trained on this new corpus, what are the results?
|
Yes, Transformer based seq2seq is evaluated with average BLEU 0.519, METEOR 0.388, ROUGE 0.631 CIDEr 2.531 and SER 2.55%.
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null | false
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What is the University of Georgia's mascot?
|
The University of Georgia's mascot is a bulldog.
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The British Virgin Islands (BVI), officially the Virgin Islands, are a British Overseas Territory in the Caribbean, to the east of Puerto Rico and the US Virgin Islands and north-west of Anguilla. The islands are geographically part of the Virgin Islands archipelago and are located in the Leeward Islands of the Lesser Antilles and part of the West Indies.
The British Virgin Islands consist of the main islands of Tortola, Virgin Gorda, Anegada and Jost Van Dyke, along with more than 50 other smaller islands and cays. About 16 of the islands are inhabited. The capital, Road Town, is on Tortola, the largest island, which is about 20 km (12 mi) long and 5 km (3 mi) wide. The islands had a population of 28,054 at the 2010 Census, of whom 23,491 lived on Tortola; current estimates put the population at 35,802 (July 2018).
|
Where are the British Virgin Islands (BVI) and which islands are a part of the BVI?
|
The British Virgin Islands (BVI), officially the Virgin Islands, are a British Overseas Territory in the Caribbean, to the east of Puerto Rico and the US Virgin Islands and north-west of Anguilla. The islands are geographically part of the Virgin Islands archipelago and are located in the Leeward Islands of the Lesser Antilles and part of the West Indies.
The British Virgin Islands consist of the main islands of Tortola, Virgin Gorda, Anegada and Jost Van Dyke, along with more than 50 other smaller islands and cays.
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Bull sharks are large and stout, with females being larger than males. The bull shark can be up to 81 cm (2 ft 8 in) in length at birth. Adult female bull sharks average 2.4 m (8 ft) long and typically weigh 130 kg (290 lb), whereas the slightly smaller adult male averages 2.25 m (7 ft) and 95 kg (209 lb). While a maximum size of 3.5 m (11 ft) is commonly reported, a single record exists of a female specimen of exactly 4.0 m (13 ft). A 3.25 m (10.7 ft) long pregnant individual reached 450 kg (990 lb). Bull sharks are wider and heavier than other requiem sharks of comparable length, and are grey on top and white below. The second dorsal fin is smaller than the first. The bull shark's caudal fin is longer and lower than that of the larger sharks, and it has a small snout, and lacks an interdorsal ridge.
|
According to this paragraph about bull sharks, how long are male adult sharks in meters?
|
Male adult bull sharks average 2.25 meters in length.
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James grew up playing basketball for St. Vincent–St. Mary High School in his hometown of Akron, Ohio. He was heavily touted by the national media as a future NBA superstar. A prep-to-pro, he was selected by the Cleveland Cavaliers with the first overall pick of the 2003 NBA draft. Named the 2004 NBA Rookie of the Year, he soon established himself as one of the league's premier players, leading the Cavaliers to their first NBA Finals appearance in 2007 and winning the NBA MVP award in 2009 and 2010. After failing to win a championship with Cleveland, James left in 2010 as a free agent to join the Miami Heat; this was announced in a nationally televised special titled The Decision and is among the most controversial free agency moves in sports history.
|
According to the following paragraph on LeBron James, how did he announce joining the Miami Heat?
|
LeBron James announced his move from the Cleveland Cavaliers to the Miami Heat in a television special called "The Decision" that was televised nationally.
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null | false
| 145
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Language modeling is a probabilistic description of language phenomenon. It provides essential context to distinguish words which sound similar and therefore has one of the most useful applications in Natural Language Processing (NLP) especially in downstreaming tasks like Automatic Speech Recognition (ASR). Recurrent Neural Networks (RNN) especially Long Short Term Memory (LSTM) networks BIBREF0 have been the typical solution to language modeling which do achieve strong results. In spite of these results, their fundamental sequential computation constraint has restricted their use in the modeling of long-term dependencies in sequential data. To address these issues Transformer architecture was introduced. Transformers relies completely on an attention mechanism to form global dependencies between input and output. It also offers more parallelization and has achieved SOTA results in language modeling outperforming LSTM models BIBREF1.
In recent years,we have seen a lot of development based on this standard transformer models particularly on unsupervised pre-training(BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7 which have set state-of-the art results on multiple NLP benchmarks. One such model architecture has been the Bidirectional Encoder Representations from Transformers (BERT) model which uses a deep bidirectional transformer architecture.
Another architecture of interest would be the Transformer-XL, which introduces the notion of recurrence in a self-attention model.
The primary research focus though has been mostly on English language for which abundant data is present. It is interesting to see the performance of these models for an agglutinative language like Finnish, which is morphologically richer than English.
In this project, we explore the implementation of Transformer-based models (BERT and Transformer-XL) in language modeling for Finnish. We will use the same training data as in BIBREF8 so that we can do fair comparisons with the performance of the LSTM models. Also, as the BERT model is a bi-directional transformer, we will have to approximate the conditional probabilities given a sequence of words. We also experiment with using sub-word units with Transformer-XL to cope with the large vocabulary problems associated with the Finnish Language. With smaller units, the modeled sequences are longer, and we hope that the recursive XL architecture can allow us to still model long term effects. To the best of our knowledge this is the first work with the Finnish language to use the following:
Approximation of perplexity using a BERT architecture
Using Transformer-XL architecture with sub-word units.
Comparison of Transformer and LSTM models as language models in the same comparable settings with an agglutinative language.
We will use the same training data as in [9] so that we can do fair comparisons with the performance of the LSTM models.
|
Why do the authors use the same training data as in [9]?
|
In order to do fair comparisons with the performance of the LSTM models.
|
1907.06458
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In this work we consider directed graphs. Let INLINEFORM0 represent a graph comprised of a set of vertices INLINEFORM1 and a set of edges ( INLINEFORM2 ), which are ordered pairs. Further, each edge can have a real-valued weight assigned. Let INLINEFORM3 represent a document comprised of tokens INLINEFORM4 . The order in which tokens in text appear is known, thus INLINEFORM5 is a totally ordered set. A potential way of constructing a graph from a document is by simply observing word co-occurrences. When two words co-occur, they are used as an edge. However, such approaches do not take into account the sequence nature of the words, meaning that the order is lost. We attempt to take this aspect into account as follows. The given corpus is traversed, and for each element INLINEFORM6 , its successor INLINEFORM7 , together with a given element, forms a directed edge INLINEFORM8 . Finally, such edges are weighted according to the number of times they appear in a given corpus. Thus the graph, constructed after traversing a given corpus, consists of all local neighborhoods (order one), merged into a single joint structure. Global contextual information is potentially kept intact (via weights), even though it needs to be detected via network analysis as proposed next.
In this work we consider directed graphs. Let INLINEFORM0 represent a graph comprised of a set of vertices INLINEFORM1 and a set of edges ( INLINEFORM2 ), which are ordered pairs. Further, each edge can have a real-valued weight assigned. Let INLINEFORM3 represent a document comprised of tokens INLINEFORM4 . The order in which tokens in text appear is known, thus INLINEFORM5 is a totally ordered set. A potential way of constructing a graph from a document is by simply observing word co-occurrences. When two words co-occur, they are used as an edge. However, such approaches do not take into account the sequence nature of the words, meaning that the order is lost. We attempt to take this aspect into account as follows. The given corpus is traversed, and for each element INLINEFORM6 , its successor INLINEFORM7 , together with a given element, forms a directed edge INLINEFORM8 . Finally, such edges are weighted according to the number of times they appear in a given corpus. Thus the graph, constructed after traversing a given corpus, consists of all local neighborhoods (order one), merged into a single joint structure. Global contextual information is potentially kept intact (via weights), even though it needs to be detected via network analysis as proposed next.
|
How are graphs derived from a given text?
|
The answers are shown as follows:
* The given corpus is traversed, and for each element INLINEFORM6 , its successor INLINEFORM7 , together with a given element, forms a directed edge INLINEFORM8 . Finally, such edges are weighted according to the number of times they appear in a given corpus. Thus the graph, constructed after traversing a given corpus, consists of all local neighborhoods (order one), merged into a single joint structure. Global contextual information is potentially kept intact (via weights), even though it needs to be detected via network analysis
|
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The ICC Men's T20 World Cup (earlier known as ICC World Twenty20) is the international championship of Twenty20. Organised by cricket's governing body, the International Cricket Council (ICC), the tournament consists of 20 teams, with top 12 teams from the previous tournament getting automatic qualifications. while the 8 other teams chosen through the Regional Qualifiers
The event has generally been held every two years. In May 2016, the ICC put forward the idea of having a tournament in 2018, with South Africa being the possible host, but the ICC later dropped the idea of a 2018 edition at the conclusion of the 2017 ICC Champions Trophy. The 2020 edition of the tournament was scheduled to take place in 2020 in Australia but due to the COVID-19 pandemic, the tournament was postponed until 2021, with the intended host changed to India. The tournament was later relocated to the United Arab Emirates (UAE) and Oman due to problems relating to the COVID-19 pandemic in India, taking place 5 years after the previous (2016) iteration.
|
What is ICC Men's T20 World Cup?
|
The ICC Men's T20 World Cup is the international championship of Twenty20 cricket. In Twenty20 format cricket each match will consist of 2 teams playing maximum of 20 overs each. ICC Men's T20 World Cup tournament generally played with 20 teams, with top 12 teams from the previous tournament getting automatic qualifications, while the remaining 8 teams are chosen through the Regional Qualifiers. Recent tournament was held in 2022 and England was the winner. The next tournament will be hosted in 2024 and hosted by USA and West Indies
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Competition
There are 20 clubs in the Premier League. During the course of a season (from August to May) each club plays the others twice (a double round-robin system), once at their home stadium and once at that of their opponents, for 38 games. Teams receive three points for a win and one point for a draw. No points are awarded for a loss. Teams are ranked by total points, then goal difference, and then goals scored. If still equal, teams are deemed to occupy the same position. If there is a tie for the championship, for relegation, or for qualification to other competitions, the head-to-head record between the tied teams is taken into consideration (points scored in the matches between the teams, followed by away goals in those matches.) If two teams are still tied, a play-off match at a neutral venue decides rank.
Promotion and relegation
A system of promotion and relegation exists between the Premier League and the EFL Championship. The three lowest placed teams in the Premier League are relegated to the Championship, and the top two teams from the Championship promoted to the Premier League, with an additional team promoted after a series of play-offs involving the third, fourth, fifth and sixth placed clubs. The number of clubs was reduced from 22 to 20 in 1995, when four teams were relegated from the league and only two teams promoted. The top flight had only been expanded to 22 teams at the start of the 1991–92 season – the year prior to the formation of the Premier League.
On 8 June 2006, FIFA requested that all major European leagues, including Italy's Serie A and Spain's La Liga, be reduced to 18 teams by the start of the 2007–08 season. The Premier League responded by announcing their intention to resist such a reduction. Ultimately, the 2007–08 season kicked off again with 20 teams.
|
Give me an overview of how promotion and relegation works in the English Premier League
|
The bottom 3 teams in the Premier League are relegated to the EFL Championship. The top 2 teams and the winner of a tournament between the 3rd through 6th ranked teams in the EFL Championship are promoted to the Premiere League.
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I want to get in better shape. I work at a desk all day, and I've never really been in good shape. Growing up, I didn't play sports or spend a lot of time outdoors. I know I need to improve my physical health, but I really don't know how to get started. Can you recommend a workout routine for me?
|
Sure! I can recommend a plan to help you get in shape. To get started, consistency is key, so you do not want to overdo it and injure yourself. I recommend a simple routine. For the first week, walk 20 minutes a day at a moderate pace. For the second week, walk 30 minutes a day at a moderate pace. For the third week, walk 30 minutes and start to do 3 sets of 5 wall push-ups each day. Wall push-ups are push-ups against a wall, and you can make them easier or harder depending on how far from the wall you stand. For the fourth week and beyond, increase your daily walk by 5 min, until you can consistently walk 60 min a day. You should also increase the number of sets and repetitions of your wall push-ups until you can do 5 sets of 10 repetitions. When you are comfortable with this routine, you are ready to go to a gym, start jogging, or use weights to continue improving your physical health.
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How can I join two pieces of wood together for making furniture or drawers?
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There are a number of joints which can be used to join wood. Miter joints involve cutting bevels in the wood and joining those bevels together using wood glue, brad nails, or splines. A mortise and tenon joint is made by cutting a hole, mortise, in one piece of wood and an identical sized peg, tenon, in the opposite piece and mating those together with glue or dowels to provide strength.
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Classify the sentences below based on the sentiment - neutral, positive, negative, or angry
1. My shirt is red in color
2. I am so mad about losing my Airpods.
3. I am tired of being stuck in traffic every day.
4. I am so stressed and overwhelmed at work.
5. Work is so energizing
6. I love sunsets at the beach.
7. My best vacation place is Rome
8. They sing Fado in Lisbon
9. Somebody's going to get hurt so bad - watch it !
10. I have a meeting at 3pm
|
While the same statement can be neutral or negative/positive for different people, I think a generic categorization of these sentences would be :
Angry sentiment : Words like mad or intention to hurt shows underlying anger. They are also inherently negative emotions.
2. I am so mad about losing my Airpods.
9. Somebody's going to get hurt so bad - watch it !
Negative sentiment : Tired and stressed indicate negative emotions
3. I am tired of being stuck in traffic every day.
4. I am so stressed and overwhelmed at work.
Positive sentiment : These sentences describe something personal and positive.
5. Work is so energizing
6. I love sunsets at the beach.
7. My best vacation place is Rome
Neutral sentiment : These are usually statements, facts, or trivia.
1. My shirt is red in color
8. They sing Fado in Lisbon
10. I have a meeting at 3pm
Added punctuations, emojis, or way a sentence is said can also reveal added layers of sentiment.
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null | false
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Give a list of domestic animals can be turned to pets
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dogs cats rabbits parrots pegions etc
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World War II changed the political alignment and social structure of the globe and set the foundation for the international order of the world's nations during the Cold War and into present day. The United Nations was established to foster international co-operation and prevent future conflicts, with the victorious great powers—China, France, the Soviet Union, the United Kingdom, and the United States—becoming the permanent members of its Security Council. The Soviet Union and the United States emerged as rival superpowers, setting the stage for the nearly half-century-long Cold War. In the wake of European devastation, the influence of its great powers waned, triggering the decolonization of Africa and Asia. Most countries whose industries had been damaged moved towards economic recovery and expansion. Political and economic integration, especially in Europe, began as an effort to forestall future hostilities, end pre-war enmities, and forge a sense of common identity.
|
Which two of the world's "super power" nations engaged in a cold war, following World War II?
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The Soviet Union and the United States.
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null | false
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Identify which car manufacturer is German or American: Bitter, Cord
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Cord is American, Bitter is German
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|
1905.10044
| false
| null |
We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions. We split these questions into a 3.2k dev set, 3.2k test set, and 9.4k train set, ensuring questions from NQ are always in the train set. “Yes” answers are slightly more common (62.31% in the train set). The queries are typically short (average length 8.9 tokens) with longer passages (average length 108 tokens).
We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions.
|
what is the size of BoolQ dataset?
|
The answers are shown as follows:
* 16k questions
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What outfit should I wear when traveling to San Francisco in May?
|
The weather in San Francisco tends to vacillate between cool and warm temperatures. In order to dress appropriately and be comfortable all day, you should dress in layers. Start with a good pair of jeans, a t-shirt and a lightweight sweater. For your outerwear, choose a puffer jacket to add some insulation in case the wind picks up. Finally, San Francisco is best explored on foot, so wear a comfortable pair of sneakers.
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What did batteries get invented?
|
Volta invented the first battery around 1800. It worked by storing and releasing a charge of energy caused by a chemical reaction. Since this time batteries have only improved with time. Around the end of the 19th century batteries were a primary source of electricity for electric grids. With continuous future improvements to batteries they were being used in most consumer electronics like phones, computers and televisions.
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What should I do if I visit Sweden?
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Sweden is a very interesting country to visit and things to do depend on the season. During winter, Sweden offers some of the best skiing in the World. In the North, you shouldn't miss the Ice Hotel in Jukkasjärvi.
The spring in Sweden is magical as nature comes back to life. You'll find many locals walking around Kungsträdgården enjoying the cherry blossom on the trees.
During Summer there are many interesting things to do. Go to Dalarna and enjoy a traditional Mid Summer celebration. In the South, Österlen is a great place to visit with incredible sandy beaches. The island of Gotland is frequently visited by Swedes wanting to enjoy some sun. Both Stockholm and Gothenburg have great archipelagos offering endless possibilities to fish and enjoy nature.
The fall offers dramatic changes in nature and great scenery. For many Swedes, the fall is hunting season with elk hunt being a popular hobby.
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Halloween or Hallowe'en (less commonly known as Allhalloween, All Hallows' Eve, or All Saints' Eve) is a celebration observed in many countries on 31 October, the eve of the Western Christian feast of All Saints' Day. It begins the observance of Allhallowtide, the time in the liturgical year dedicated to remembering the dead, including saints (hallows), martyrs, and all the faithful departed.
One theory holds that many Halloween traditions were influenced by Celtic harvest festivals, particularly the Gaelic festival Samhain, which are believed to have pagan roots. Some go further and suggest that Samhain may have been Christianized as All Hallow's Day, along with its eve, by the early Church. Other academics believe Halloween began solely as a Christian holiday, being the vigil of All Hallow's Day. Celebrated in Ireland and Scotland for centuries, Irish and Scottish immigrants took many Halloween customs to North America in the 19th century, and then through American influence Halloween had spread to other countries by the late 20th and early 21st century.
Popular Halloween activities include trick-or-treating (or the related guising and souling), attending Halloween costume parties, carving pumpkins or turnips into jack-o'-lanterns, lighting bonfires, apple bobbing, divination games, playing pranks, visiting haunted attractions, telling scary stories, and watching horror or Halloween-themed films. Some people practice the Christian religious observances of All Hallows' Eve, including attending church services and lighting candles on the graves of the dead, although it is a secular celebration for others. Some Christians historically abstained from meat on All Hallows' Eve, a tradition reflected in the eating of certain vegetarian foods on this vigil day, including apples, potato pancakes, and soul cakes.
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Based on the reference text, please provide a short bulleted list of 4 popular Halloween activities.
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Popular Halloween activities include:
1. Trick-or-Treating
2. Going to costume parties
3. Carving pumpkins
4. Watching horror movies
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The earthquake occurred on a Tuesday morning and leveled almost all the buildings in Lone Pine and nearby settlements. Of the estimated 250–300 inhabitants of Lone Pine, 27 are known to have perished and 52 of the 59 houses were destroyed. One report states that the main buildings were thrown down in almost every town in Inyo County. About 130 kilometers (81 mi) south of Lone Pine, at Indian Wells, adobe houses sustained cracks. Property loss has been estimated at $250,000 (equivalent to $5,654,861 in 2021). As in many earthquakes, adobe, stone and masonry structures fared worse than wooden ones which prompted the closing of nearby Camp Independence which was an adobe structure destroyed in the quake.
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How many inhabitants of Lone Pine survived as a result of the earthquake? Tell me how you came to that conclusion.
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It is estimated that between 223 and 273 inhabitants of Lone Pine survived the earthquake.
The passage shows that there was "an estimated 250–300 inhabitants of Lone Pine" and that "27 are known to have perished". If we subtract 27 from 250, you get 223, and if we subtract 27 from 300, we get 273. Therefore, it can be estimated that the number of inhabitants that survived the earthquake is between 223 and 273.
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1911.00523
| false
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Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list):
[itemsep=0pt,leftmargin=*,topsep=0pt]
Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.
Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively.
How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task.
General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing.
Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations:
Although we expect more complicated words (#characters) to be echoed more often, this is not the case on average. We also observe an interesting example of Simpson's paradox in the results for Wordnet depth BIBREF38: shallower words are more likely to be echoed across all words, but deeper words are more likely to be echoed in content words and stopwords.
Our prediction task is thus a straightforward binary classification task at the word level. We develop the following five groups of features to capture properties of how a word is used in the explanandum (see Table TABREF18 for the full list):
[itemsep=0pt,leftmargin=*,topsep=0pt]
Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.
Word usage in an OP or PC (two groups). These features capture how a word is used in an OP or PC. As a result, for each feature, we have two values for the OP and PC respectively.
How a word connects an OP and PC. These features look at the difference between word usage in the OP and PC. We expect this group to be the most important in our task.
General OP/PC properties. These features capture the general properties of a conversation. They can be used to characterize the background distribution of echoing.
Table TABREF18 further shows the intuition for including each feature, and condensed $t$-test results after Bonferroni correction. Specifically, we test whether the words that were echoed in explanations have different feature values from those that were not echoed. In addition to considering all words, we also separately consider stopwords and content words in light of Figure FIGREF8. Here, we highlight a few observations:
[itemsep=0pt,leftmargin=*,topsep=0pt]
Although we expect more complicated words (#characters) to be echoed more often, this is not the case on average. We also observe an interesting example of Simpson's paradox in the results for Wordnet depth BIBREF38: shallower words are more likely to be echoed across all words, but deeper words are more likely to be echoed in content words and stopwords.
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What features are proposed?
|
The answers are shown as follows:
* Non-contextual properties of a word
* Word usage in an OP or PC (two groups)
* How a word connects an OP and PC
* General OP/PC properties
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null | false
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what is a competition?
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A competition is when teams or individuals are in the act of competing against each other. They can be trying to do the same activity the highest number times. Examples of competitions are chess competitions, basketball / volleyball / hockey competitions, or ice skating competitions. Competitions often tell who is the best at what the competition is measuring (e.g. sports). Independent competitions usually are not Team sports and usually include an individual trying to achieve a high score. Their scores are compared against other independent scores to determine who is the best with independent competitions.
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Urodacus novaehollandiae, also known as the coastal burrowing scorpion or black sand scorpion, is a species of scorpion in the Urodacidae family. It is endemic to Australia, and was first described in 1861 by German naturaliat Wilhelm Peters.
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Given this paragraph about a specific breed of scorpion, where can the coastal burrowing scorpion be found?
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The coastal burrowing scorpion, whose scientific name is Urodacus novaehollandiae, can only be found in Australia.
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At one point during her career in activism, after two weeks of sit-ins she was arrested and put into jail. While in jail, the guards separated the white women from the black women. "The only connection we had with the others was the music". However, with these sit-ins, she helped contribute to the abolishing of lunch-counter segregation in Nashville. Candie notes how she was "naive" saying "I was sure we would have just a few sit-ins, point out to the nation that there was something wrong, and the world would change." In 1966, the SNCC voted to remove whites from their membership. Candie says SNCC should get more credit for reducing the fear in Mississippi and prompting many people to join the movement.
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Summarize this paragraph about Candie Carawan's Highlander Center activism.
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Candie was an activist who helped contribute to the abolishment of lunch counter segregation in Nashville by participating in sit-ins. She notes that she was naive and thought a few sit-ins would bring awareness and cause the world to change. She was arrested after two weeks of participation in these sit-ins. The SNCC voted to remove whites from their membership in 1966, and Candie believes they should receive more credit for reducing fear in Mississippi and prompting many people to join the movement.
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null | false
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What is Palmitoyl-CoA?
|
Palmitoyl-CoA is an acyl-CoA thioester. It is an "activated" form of palmitic acid and can be transported into the mitochondrial matrix by the carnitine shuttle system (which transports fatty acyl-CoA molecules into the mitochondria), and once inside can participate in beta-oxidation. Alternatively, palmitoyl-CoA is used as a substrate in the biosynthesis of sphingosine (this biosynthetic pathway does not require transfer into the mitochondria).
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null | false
| null |
Who is Cristiano Ronaldo?
|
Cristiano Ronaldo is a Portuguese professional footballer. Who played for major European clubs such as Sporing Cp, Manchester United, Real Madrid and Juventus. He has won 32 trophies in his career and five Ballon d'Or. He is regarded as one of the greatest of all time aka GOAT. Currently he plays as a forward for Saudi Professional League club Al Nassr.
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Plan a 10-15 day trip from bay area to Arizona, Las Vegas, San Diego, Los Angeles
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If you are starting from the Bay area you can follow the following circuit for a fun road trip:
- Day 1: Drive to LA. Rest for the night
- Day 2: Spend in LA - Visit Hollywood bowl overlook, LA county museum, Universal Studios, Beverly Hill
- Day 3: Drive to San Diego. Spend time at Laguna Beach.
- Day 4: Across days 3 and 4 plan to explore Pt Lomas, La Jolla, downtown San Diego, hotel Coronado, Japanese Garden, and old town (only after 7 pm)
- Day 5: Drive to Joshua Tree.
- Day 6: Spend at Joshua Tree. Do short hikes across the park, and visit the cholla cactus garden, hidden valley loop, skull rock, and arch rock. End day at Keys views for sunset. If it is a new moon night, spend the night time stargazing.
- Day 7: Drive to Arizona. Visit Grand Canyon
- Day 8: Spend the day at Sedona
- Day 9: Drive from Grand Canyon to Page. Stay at Page for a night. Visit Lower Antelope Canyon, lake Powell. Visit Horse Shoe Bend
- Day 10: Drive from Page to Las Vegas. Check out the Vegas Strip and visit all the fancy hotels. End the day with a show such as Cirque De Soleil or any other show going on in Vegas.
- Day 11: Drive to Death Valley. Stop at all the points of interest such as Mesquite Sand dunes, etc. Return back to Las Vegas for the night
- Day 12: Drive back home (bay area, CA)
You can always add Utah after Arizona (Zion national park, Canyonlands, arches national park etc.) to extend the trip further to 15 full days.
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why is twitter messages limited to 140 characters
|
Twitter was originally designed for phone text messages and most phones had a limited amount of characters to send messages.
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null | false
| 140
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We now describe our general model for forecasting future conversational events. Our model integrates two components: (a) a generative dialog model that learns to represent conversational dynamics in an unsupervised fashion; and (b) a supervised component that fine-tunes this representation to forecast future events. Figure FIGREF13 provides an overview of the proposed architecture, henceforth CRAFT (Conversational Recurrent Architecture for ForecasTing).
Terminology. For modeling purposes, we treat a conversation as a sequence of $N$ comments $C = \lbrace c_1,\dots ,c_N\rbrace $. Each comment, in turn, is a sequence of tokens, where the number of tokens may vary from comment to comment. For the $n$-th comment ($1 \le n \le N)$, we let $M_n$ denote the number of tokens. Then, a comment $c_n$ can be represented as a sequence of $M_n$ tokens: $c_n = \lbrace w_1,\dots ,w_{M_n}\rbrace $.
Generative component. For the generative component of our model, we use a hierarchical recurrent encoder-decoder (HRED) architecture BIBREF60, a modified version of the popular sequence-to-sequence (seq2seq) architecture BIBREF61 designed to account for dependencies between consecutive inputs. BIBREF23 showed that HRED can successfully model conversational context by encoding the temporal structure of previously seen comments, making it an ideal fit for our use case. Here, we provide a high-level summary of the HRED architecture, deferring deeper technical discussion to BIBREF60 and BIBREF23.
An HRED dialog model consists of three components: an utterance encoder, a context encoder, and a decoder. The utterance encoder is responsible for generating semantic vector representations of comments. It consists of a recurrent neural network (RNN) that reads a comment token-by-token, and on each token $w_m$ updates a hidden state $h^{\text{enc}}$ based on the current token and the previous hidden state:
where $f^{\text{RNN}}$ is a nonlinear gating function (our implementation uses GRU BIBREF62). The final hidden state $h^{\text{enc}}_M$ can be viewed as a vector encoding of the entire comment.
Running the encoder on each comment $c_n$ results in a sequence of $N$ vector encodings. A second encoder, the context encoder, is then run over this sequence:
Each hidden state $h^{\text{con}}_n$ can then be viewed as an encoding of the full conversational context up to and including the $n$-th comment. To generate a response to comment $n$, the context encoding $h^{\text{con}}_n$ is used to initialize the hidden state $h^{\text{dec}}_{0}$ of a decoder RNN. The decoder produces a response token by token using the following recurrence:
where $f^{\text{out}}$ is some function that outputs a probability distribution over words; we implement this using a simple feedforward layer. In our implementation, we further augment the decoder with attention BIBREF63, BIBREF64 over context encoder states to help capture long-term inter-comment dependencies. This generative component can be pre-trained using unlabeled conversational data.
Prediction component. Given a pre-trained HRED dialog model, we aim to extend the model to predict from the conversational context whether the to-be-forecasted event will occur. Our predictor consists of a multilayer perceptron (MLP) with 3 fully-connected layers, leaky ReLU activations between layers, and sigmoid activation for output. For each comment $c_n$, the predictor takes as input the context encoding $h^{\text{con}}_n$ and forwards it through the MLP layers, resulting in an output score that is interpreted as a probability $p_{\text{event}}(c_{n+1})$ that the to-be-forecasted event will happen (e.g., that the conversation will derail).
Training the predictive component starts by initializing the weights of the encoders to the values learned in pre-training. The main training loop then works as follows: for each positive sample—i.e., a conversation containing an instance of the to-be-forecasted event (e.g., derailment) at comment $c_e$—we feed the context $c_1,\dots ,c_{e-1}$ through the encoder and classifier, and compute cross-entropy loss between the classifier output and expected output of 1. Similarly, for each negative sample—i.e., a conversation where none of the comments exhibit the to-be-forecasted event and that ends with $c_N$—we feed the context $c_1,\dots ,c_{N-1}$ through the model and compute loss against an expected output of 0.
Note that the parameters of the generative component are not held fixed during this process; instead, backpropagation is allowed to go all the way through the encoder layers. This process, known as fine-tuning, reshapes the representation learned during pre-training to be more directly useful to prediction BIBREF55.
We implement the model and training code using PyTorch, and we are publicly releasing our implementation and the trained models together with the data as part of ConvoKit.
We now describe our general model for forecasting future conversational events. Our model integrates two components: (a) a generative dialog model that learns to represent conversational dynamics in an unsupervised fashion; and (b) a supervised component that fine-tunes this representation to forecast future events.
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What components are integrated with their model?
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(a) a generative dialog model that learns to represent conversational dynamics in an unsupervised fashion; and (b) a supervised component that fine-tunes this representation to forecast future events.
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What is the best way to travel to space?
|
For the first half dozen decades of manned missions to space, the only way to participate was to earn a highly-competitive place in the national space programs of one of just a few countries that funded missions beyond the earth's atmosphere. Today, these national space programs are supplemented by a few private ventures that offer space tourism, typically for exorbitant fees.
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null | false
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Give me the list of philosophers of science from the 19th century
|
Here is the list of philosophers of science from the 19th century:
- Auguste Comte
- William Whewell
- George Henry Lewes
- William Stanley Jevons
- Ernst Mach
- Charles Sanders Peirce
- Edmund Husserl
- Frederich Engels
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null | false
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Tell me whether these are cats or dogs: French Bulldogs, Ragdoll, British Shorthair, Labrador, Retrievers, Golden Retrievers, German Shepherds, Maine Coon, Poodles, Bulldogs, Persian, Rottweilers, Devon Rex, Beagles.
|
French Bulldogs are a type of dog.
Ragdoll is a type of cat.
British Shorthair is a type of cat.
Labrador Retrievers are a type of dog.
Golden Retrievers are a type of dog.
German Shepherds are a type of dog.
Maine Coon is a type of cat.
Poodles are a type of dog.
Bulldogs are a type of dog.
Persian is a type of cat.
Rottweilers are a type of dog.
Devon Rex is a type of cat.
Beagles are a type of dog.
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null | false
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What is an aurora?
|
An aurora, also commonly known as the polar lights, is a natural light display in Earth's sky, predominantly seen in high-latitude regions (around the Arctic and Antarctic). Auroras display dynamic patterns of brilliant lights that appear as curtains, rays, spirals, or dynamic flickers covering the entire sky.
Auroras are the result of disturbances in the magnetosphere caused by the solar wind. Major disturbances result from enhancements in the speed of the solar wind from coronal holes and coronal mass ejections. These disturbances alter the trajectories of charged particles in the magnetospheric plasma. These particles, mainly electrons and protons, precipitate into the upper atmosphere (thermosphere/exosphere). The resulting ionisation and excitation of atmospheric constituents emit light of varying colour and complexity. The form of the aurora, occurring within bands around both polar regions, is also dependent on the amount of acceleration imparted to the precipitating particles.
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Identify which animal species is alive or extinct: Puertasaurus, Southern rockhopper penguin
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Southern rockhopper penguin is alive, Puertasaurus is extinct.
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null | false
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Classify these emotions as positive or negative: Love, Joy, Awe, Happiness, Interest, Excitement, Sad, Fear, Distrust, Anxiety, Anger, Disgust, Trust, Apathy, Annoyance, Exuberance, Rage
|
Positive: Love, Joy, Awe, Happiness, Interest, Excitement, Trust, Exuberance
Negative: Sad, Fear, Distrust, Anxiety, Anger, Disgust, Apathy, Annoyance, Rage
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null | false
| 335
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While the demand for physical and manual labor is gradually declining, there is a growing need for a workforce with soft skills. Which soft skill do you think would be the most valuable in your daily life? According to an article in Forbes BIBREF0 , 70% of employed Americans agree that public speaking skills are critical to their success at work. Yet, it is one of the most dreaded acts. Many people rate the fear of public speaking even higher than the fear of death BIBREF1 . To alleviate the situation, several automated systems are now available that can quantify behavioral data for participants to reflect on BIBREF2 . Predicting the viewers' ratings from the speech transcripts would enable these systems to generate feedback on the potential audience behavior.
Predicting human behavior, however, is challenging due to its huge variability and the way the variables interact with each other. Running Randomized Control Trials (RCT) to decouple each variable is not always feasible and also expensive. It is possible to collect a large amount of observational data due to the advent of content sharing platforms such as YouTube, Massive Open Online Courses (MOOC), or ted.com. However, the uncontrolled variables in the observational dataset always keep a possibility of incorporating the effects of the “data bias” into the prediction model. Recently, the problems of using biased datasets are becoming apparent. BIBREF3 showed that the error rates in the commercial face-detectors for the dark-skinned females are 43 times higher than the light-skinned males due to the bias in the training dataset. The unfortunate incident of Google's photo app tagging African-American people as “Gorilla” BIBREF4 also highlights the severity of this issue.
We address the data bias issue as much as possible by carefully analyzing the relationships of different variables in the data generating process. We use a Causal Diagram BIBREF5 , BIBREF6 to analyze and remove the effects of the data bias (e.g., the speakers' reputations, popularity gained by publicity, etc.) in our prediction model. In order to make the prediction model less biased to the speakers' race and gender, we confine our analysis to the transcripts only. Besides, we normalize the ratings to remove the effects of the unwanted variables such as the speakers' reputations, publicity, contemporary hot topics, etc.
For our analysis, we curate an observational dataset of public speech transcripts and other meta-data collected from the ted.com website. This website contains a large collection of high-quality public speeches that are freely available to watch, share, rate, and comment on. Every day, numerous people watch and annotate their perceptions about the talks. Our dataset contains 2231 public speech transcripts and over 5 million ratings from the spontaneous viewers of the talks. The viewers annotate each talk by 14 different labels—Beautiful, Confusing, Courageous, Fascinating, Funny, Informative, Ingenious, Inspiring, Jaw-Dropping, Long-winded, Obnoxious, OK, Persuasive, and Unconvincing.
We use two neural network architectures in the prediction task. In the first architecture, we use LSTM BIBREF7 for a sequential input of the words within the sentences of the transcripts. In the second architecture, we use TreeLSTM BIBREF8 to represent the input sentences in the form of a dependency tree. Our experiments show that the dependency tree-based model can predict the TED talk ratings with slightly higher performance (average F-score 0.77) than the word sequence model (average F-score 0.76). To the best of our knowledge, this is the best performance in the literature on predicting the TED talk ratings. We compare the performances of these two models with a baseline of classical machine learning techniques using hand-engineered features. We find that the neural networks largely outperform the classical methods. We believe this gain in performance is achieved by the networks' ability to capture better the natural relationship of the words (as compared to the hand engineered feature selection approach in the baseline methods) and the correlations among different rating labels.
In the second architecture, we use TreeLSTM (Tai et al., 2015) to represent the input sentences in the form of a dependency tree.
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What do the authors use in the second architecture?
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TreeLSTM.
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null | false
| null |
Why does moon change its shape?
|
Moon does not change its shape, but we see only part of the moon which makes us think that moon changes its shape. Moon does not have light of its own. It reflects light from the sun. We only see the part that reflects light. Moon goes through phases for about 30 days where its shape changes and the cycle repeats. The different phases of the moon are New Moon, Waxing Crescent, first quarter, waxing gibbous, full moon, waning gibbous, third quarter, waning crescent and then new moon again . New moon is when no moon is seen in the sky. Slowing we start seeing part of the moon. When we see crescent shape after new moon it is called waxing crescent. First quarter is when we see moon as a semi circle. We continue to see more part of the moon towards waxing gibbous and then eventually we see complete circle on Full Moon. After this, some part of the moon starts being hidden and unable to see. This result in waning gibbous. Soon we again see semi circle of the moon known as Third quarter. Parts of moon continue to get hidden and we see only a crescent shape known as waning crescent and back to New moon.
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null | false
| null |
Why should I use EKS over ECS?
|
EKS enables a greater degree of portability and reduces lock-in risks, as compared to ECS. Because it is proprietary, ECS has no equivalent in other public clouds. EKS is based on Kubernetes, which is open source and can run in any public cloud or on-premises location. EKS also provides much richer functionality in comparison with ECS.
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|
2003.01472
| true
| null |
We present two use cases on which Seshat was developped: clinical interviews, and daylong child-centered recordings.
We present two use cases on which Seshat was developped: clinical interviews, and daylong child-centered recordings.
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Did they experiment with the tool?
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Yes.
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