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NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW.
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How many experts in NLP and SW do the authors use to evaluate LD2NL?
|
66.
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1908.06151
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Recently, in the WMT 2018 APE shared task, several adaptations of the transformer architecture have been presented for multi-source APE. pal-EtAl:2018:WMT proposed an APE model that uses three self-attention-based encoders. They introduce an additional joint encoder that attends over a combination of the two encoded sequences from $mt$ and $src$. tebbifakhr-EtAl:2018:WMT, the NMT-subtask winner of WMT 2018 ($wmt18^{nmt}_{best}$), employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics. shin-lee:2018:WMT propose that each encoder has its own self-attention and feed-forward layer to process each input separately. On the decoder side, they add two additional multi-head attention layers, one for $src \rightarrow mt$ and another for $src \rightarrow pe$. Thereafter another multi-head attention between the output of those attention layers helps the decoder to capture common words in $mt$ which should remain in $pe$. The APE PBSMT-subtask winner of WMT 2018 ($wmt18^{smt}_{best}$) BIBREF11 also presented another transformer-based multi-source APE which uses two encoders and stacks an additional cross-attention component for $src \rightarrow pe$ above the previous cross-attention for $mt \rightarrow pe$. Comparing shin-lee:2018:WMT's approach with the winner system, there are only two differences in the architecture: (i) the cross-attention order of $src \rightarrow mt$ and $src \rightarrow pe$ in the decoder, and (ii) $wmt18^{smt}_{best}$ additionally shares parameters between two encoders.
Recently, in the WMT 2018 APE shared task, several adaptations of the transformer architecture have been presented for multi-source APE. pal-EtAl:2018:WMT proposed an APE model that uses three self-attention-based encoders. They introduce an additional joint encoder that attends over a combination of the two encoded sequences from $mt$ and $src$. tebbifakhr-EtAl:2018:WMT, the NMT-subtask winner of WMT 2018 ($wmt18^{nmt}_{best}$), employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics. shin-lee:2018:WMT propose that each encoder has its own self-attention and feed-forward layer to process each input separately. On the decoder side, they add two additional multi-head attention layers, one for $src \rightarrow mt$ and another for $src \rightarrow pe$. Thereafter another multi-head attention between the output of those attention layers helps the decoder to capture common words in $mt$ which should remain in $pe$. The APE PBSMT-subtask winner of WMT 2018 ($wmt18^{smt}_{best}$) BIBREF11 also presented another transformer-based multi-source APE which uses two encoders and stacks an additional cross-attention component for $src \rightarrow pe$ above the previous cross-attention for $mt \rightarrow pe$. Comparing shin-lee:2018:WMT's approach with the winner system, there are only two differences in the architecture: (i) the cross-attention order of $src \rightarrow mt$ and $src \rightarrow pe$ in the decoder, and (ii) $wmt18^{smt}_{best}$ additionally shares parameters between two encoders.
Recently, in the WMT 2018 APE shared task, several adaptations of the transformer architecture have been presented for multi-source APE. pal-EtAl:2018:WMT proposed an APE model that uses three self-attention-based encoders. They introduce an additional joint encoder that attends over a combination of the two encoded sequences from $mt$ and $src$. tebbifakhr-EtAl:2018:WMT, the NMT-subtask winner of WMT 2018 ($wmt18^{nmt}_{best}$), employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics. shin-lee:2018:WMT propose that each encoder has its own self-attention and feed-forward layer to process each input separately. On the decoder side, they add two additional multi-head attention layers, one for $src \rightarrow mt$ and another for $src \rightarrow pe$. Thereafter another multi-head attention between the output of those attention layers helps the decoder to capture common words in $mt$ which should remain in $pe$. The APE PBSMT-subtask winner of WMT 2018 ($wmt18^{smt}_{best}$) BIBREF11 also presented another transformer-based multi-source APE which uses two encoders and stacks an additional cross-attention component for $src \rightarrow pe$ above the previous cross-attention for $mt \rightarrow pe$. Comparing shin-lee:2018:WMT's approach with the winner system, there are only two differences in the architecture: (i) the cross-attention order of $src \rightarrow mt$ and $src \rightarrow pe$ in the decoder, and (ii) $wmt18^{smt}_{best}$ additionally shares parameters between two encoders.
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What was previous state of the art model for automatic post editing?
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The answers are shown as follows:
* pal-EtAl:2018:WMT proposed an APE model that uses three self-attention-based encoders
* tebbifakhr-EtAl:2018:WMT, the NMT-subtask winner of WMT 2018 ($wmt18^{nmt}_{best}$), employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics.
* shin-lee:2018:WMT propose that each encoder has its own self-attention and feed-forward layer to process each input separately.
* The APE PBSMT-subtask winner of WMT 2018 ($wmt18^{smt}_{best}$) BIBREF11 also presented another transformer-based multi-source APE which uses two encoders and stacks an additional cross-attention component for $src \rightarrow pe$ above the previous cross-attention for $mt \rightarrow pe$.
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| 269
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Subword segmentation has become a standard preprocessing step in many neural approaches to natural language processing (NLP) tasks, e.g Neural Machine Translation (NMT) BIBREF0 and Automatic Speech Recognition (ASR) BIBREF1. Word level modeling suffers from sparse statistics, issues with Out-of-Vocabulary (OOV) words, and heavy computational cost due to a large vocabulary. Word level modeling is particularly unsuitable for morphologically rich languages, but subwords are commonly used for other languages as well. Subword segmentation is best suited for languages with agglutinative morphology.
While rule-based morphological segmentation systems can achieve high quality, the large amount of human effort needed makes the approach problematic, particularly for low-resource languages. The systems are language dependent, necessitating use of multiple tools in multilingual setups. As a fast, cheap and effective alternative, data-driven segmentation can be learned in a completely unsupervised manner from raw corpora. Unsupervised morphological segmentation saw much research interest until the early 2010's; for a survey on the methods, see hammarstrom2011unsupervised. Semi-supervised segmentation with already small amounts of annotated training data was found to improve the accuracy significantly when compared to a linguistic segmentation; see ruokolainen2016comparative for a survey. While this line of research has been continued in supervised and more grammatically oriented tasks BIBREF2, the more recent work on unsupervised segmentation is less focused on approximating a linguistically motivated segmentation. Instead, the aim has been to tune subword segmentations for particular applications. For example, the simple substitution dictionary based Byte Pair Encoding segmentation algorithm BIBREF3, first proposed for NMT by sennrich2015neural, has become a standard in the field. Especially in the case of multilingual models, training a single language-independent subword segmentation method is preferable to linguistic segmentation BIBREF4.
In this study, we compare three existing and one novel subword segmentation method, all sharing the use of a unigram language model in a generative modeling framework. The previously published methods are Morfessor Baseline BIBREF5, Greedy Unigram Likelihood BIBREF6, and SentencePiece BIBREF7. The new Morfessor variant proposed in this work is called Morfessor EM+Prune.
The contributions of this article are
a better training algorithm for Morfessor Baseline, with reduction of search error during training, and improved segmentation quality for English, Finnish and Turkish;
comparing four similar segmentation methods, including a close look at the SentencePiece reference implementation, highlighting details omitted from the original article BIBREF7;
and showing that the proposed Morfessor EM+Prune with particular hyper-parameters yields SentencePiece.
In this study, we compare three existing and one novel subword segmentation method, all sharing the use of a unigram language model in a generative modeling framework.
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The methods in the paper share the use of what model in a generative modeling framework?
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A unigram language model.
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Which is an imperial or a metric measurement: yard, metre
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yard is imperial, metre is metric
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Żabia Wola [ˈʐabja ˈvɔla] is a village in Grodzisk Mazowiecki County, Masovian Voivodeship, in east-central Poland. It is the seat of the gmina (administrative district) called Gmina Żabia Wola. It lies approximately 10 kilometres (6 mi) south-east of Grodzisk Mazowiecki and 30 km (19 mi) south-west of Warsaw.
The village has a population of 530.
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How far is the village from Warsaw?
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The village lies approximately 30 km (19 mi) south-west of Warsaw.
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Classify each of the following into either a solid, liquid or gas: blood, xenon, gold, wood, neon, brick, milk, xenon, coffee, gases, helium
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solid: gold, wood, brick
liquid: milk, blood, coffee
gas: helium, xenon, neon
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null | false
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What is the difference between an electric guitar and acoustic guitar?
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The main difference between an electric guitar and an acoustic guitar is the way they are played. An electric guitar has a pickup attached to it and is played with an amplifier. On the other hand, an acoustic guitar is played without an amplifier and is typically plugged into an amplifier when being played in a live setting.
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What kind of boat should I purchase?
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There are a large variety of options in recreation boats. You can purchase a sailboat or a power boat, and the latter can have inboard or outboard engines while a sailboat can be single or multi-masted. You also need to decide the length you can safely operate based on your experience, the average depth of the water where you intent to use the boat, and whether you will keep it on a lift, in the water, or on a trailer. Finally, you need to decide on your budget for both the initial purchase as well as ongoing maintenance, which can typically cost 10-20% of the purchase price based on complexity and age.
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Write a brief about your recent vacation
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It was family time and no other place came to my mind for a relaxing time to be spent with family. The serene backwaters of the resort were the perfect destination to enjoy the long weekend. This was my daughter's first long drive and she enjoyed the outing and all the nearby vacation spots we visited. We enjoyed the tour and are looking forward to our next visit!
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| 204
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As explained in the introduction, the overarching aim of this project is to investigate in how far information conveyed in social media posts can complement more traditional research methods in clinical psychology to get insights into the recovery experience of individuals with a BD diagnosis. Therefore, we will first conduct a systematic literature review of qualitative evidence to establish a solid base of what is already known about personal recovery experiences in BD for the subsequent social media studies.
Our research questions, which regard the experiences of different populations, lend themselves to several subprojects. First, we will collect and analyse English-language data from westerners. Then, we will address ethnically diverse English-speaking populations and finally multilingual accounts. This has the advantage that we can build data processing and methodological workflows along an increase in complexity of the data collection and analysis throughout the project.
In each project phase, we will employ a mixed-methods approach to combine the advantages of quantitative and qualitative methods BIBREF52 , BIBREF53 , which is established in mental health research BIBREF54 , BIBREF55 , BIBREF56 , BIBREF57 and specifically recommended to investigate personal recovery BIBREF58 . Quantitative methods are suitable to study observable behaviour such as language and yield more generalisable results by taking into account large samples. However, they fall short of capturing the subjective, idiosyncratic meaning of socially constructed reality, which is important when studying individuals' recovery experience BIBREF59 , BIBREF22 , BIBREF23 , BIBREF60 . Therefore, we will apply an explanatory sequential research design BIBREF53 , starting with statistical analysis of the full dataset followed by a manual investigation of fewer examples, similar to `distant reading' BIBREF61 in digital humanities.
Since previous research mainly employed (semi-)structured interviews and we do not expect to necessarily find the same aspects emphasised in unstructured settings, even less so when looking at a more diverse and non-English speaking population, we will not derive hypotheses from existing recovery models for testing on the online data. Instead, we will start off with exploratory quantitative research using comparative analysis tools such as Wmatrix BIBREF62 to uncover important linguistic features, e.g., on keywords and key concepts that occur with unexpected frequency in our collected datasets relative to reference corpora. The underlying assumption is that keywords and key concepts are indicative of certain aspects of personal recovery, such as those specified in the CHIME model BIBREF24 , other previous research BIBREF22 , BIBREF23 , BIBREF60 , or novel ones. Comparing online sources with transcripts of structured interviews or subcorpora originating from different cultural backgrounds might uncover aspects that were not prominently represented in the accounts studied in prior research.
A specific challenge will be to narrow down the data to parts relevant for personal recovery, since there is no control over the discussed topics compared to structured interviews. To investigate how individuals discuss personal recovery online and what (potentially unrecorded) aspects they associate with it, without a priori narrowing down the search-space to specific known keywords seems like a chicken-and-egg problem. We propose to address this challenge by an iterative approach similar to the one taken in a corpus linguistic study of cancer metaphors BIBREF63 . Drawing on results from previous qualitative research BIBREF24 , BIBREF23 , we will compile an initial dictionary of recovery-related terms. Next, we will examine a small portion of the dataset manually, which will be partly randomly sampled and partly selected to contain recovery-related terms. Based on this, we will be able to expand the dictionary and additionally automatically annotate semantic concepts of the identified relevant text passages using a semantic tagging approach such as the UCREL Semantic Analysis System (USAS) BIBREF64 . Crucially for the multilingual aspect of the project, USAS can tag semantic categories in eight languages BIBREF8 . Then, semantic tagging will be applied to the full corpus to retrieve all text passages mentioning relevant concepts. Furthermore, distributional semantics methods BIBREF65 , BIBREF66 can be used to find terms that frequently co-occur with words from our keyword dictionary. Occurrences of the identified keywords or concepts can be quantified in the full corpus to identify the importance of the related personal recovery aspects.
Linguistic Inquiry and Word Count (LIWC) BIBREF67 is a frequently used tool in social-science text analysis to analyse emotional and cognitive components of texts and derive features for classification models BIBREF47 , BIBREF46 , BIBREF68 , BIBREF69 . LIWC counts target words organised in a manually constructed hierarchical dictionary without contextual disambiguation in the texts under analysis and has been psychometrically validated and developed for English exclusively. While translations for several languages exist, e.g., Dutch BIBREF9 , and it is questionable to what extent LIWC concepts can be transferred to other languages and cultures by mere translation. We therefore aim to apply and develop methods that require less manual labour and are applicable to many languages and cultures. One option constitute unsupervised methods, such as topic modelling, which has been applied to explore cultural differences in mental-health related online data already BIBREF37 , BIBREF36 . The Differential Language Analysis ToolKit (DLATK) BIBREF70 facilitates social-scientific language analyses, including tools for preprocessing, such as emoticon-aware tokenisers, filtering according to meta data, and analysis, e.g. via robust topic modelling methods.
Furthermore, emotion and sentiment analysis constitute useful tools to investigate the emotions involved in talking about recovery and identify factors that facilitate or hinder it. There are many annotated datasets to train supervised classifiers BIBREF71 , BIBREF3 for these actively researched NLP tasks. Machine learning methods were found to usually outperform rule-based approaches based on look-ups in dictionaries such as LIWC. Again, most annotated resources are English, but state of the art approaches based on multilingual embeddings allow transferring models between languages BIBREF4 .
In each project phase, we will employ a mixedmethods approach to combine the advantages of quantitative and qualitative methods (Tashakkori and Teddlie, 1998; Creswell and Plano Clark, 2011), which is established in mental health research (Steckler et al., 1992; Baum, 1995; Sale et al., 2002; Lund, 2012) and specifically recommended to investigate personal recovery (Leonhardt et al., 2017).
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What do the authors employ to combine the advantages of quantitative and qualitative methods?
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A mixedmethods approach.
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If someone is tall, what sport should they try?
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Height is known for being advantageous in basketball
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Tripp attended The Hill School at Pottstown, Pennsylvania where he drew his attention for his talent as a football player. Tripp enrolled at the University of Chicago and played at the tackle for Amos Alonzo Stagg's 1902 football team. In January 1903, Tripp transferred to Yale University, where he played guard for Yale University's football teams in 1904 and 1905 after sitting out the 1903 season. He was captain of Yale's championship team of 1905, and was selected by Walter Camp as a first-team All-American in 1905. He later worked as a stockbroker He died in October 1962.
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Given this reference text, give me a list of all the schools attended by Tripp.
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The Hill School, University of Chicago, Yale University
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What is telnet?
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Telnet is an application (e.g. client/server) that provides terminal access to servers, local area networks, or other computers on the same network. In the early days telnet was a service offering through the windows operating system. Telnet has two core components: the communication protocol and the application that offers the communication protocol service.
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Pick if these would be useful or not useful for a high school student to put in their backpack. Notebooks, textbook, desk lamp, pencil pouch, beach ball, pillow, laptop.
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Notebooks: useful, textbook: useful, desk lamp: not useful, pencil pouch: useful, beach ball: not useful, pillow: not useful, laptop: useful.
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| 369
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Motivated by the growing interest in matching user account across different social media and networking sites, in this paper we presented models for Digital Stylometry, which is a method for matching users through stylometry inspired techniques. We used temporal and linguistic patterns of users to do the matching.
We experimented with linguistic, temporal, and combined temporal-linguistic models using standard and novel techniques. The methods based on our novel confusion model outperformed the more standard ones in all cases. We showed that both temporal and linguistic information are useful for matching users, with the best temporal model performing with an accuracy of $.10$ and the best linguistic model performing with an accuracy of $0.27$ . Even though the linguistic models vastly outperformed the temporal models, when combined the temporal-linguistic models outperformed both with an accuracy of $0.31$ . The improvement in the performance of the combined models suggests that although temporal information is dwarfed by linguistic information, in terms of its contribution to digital stylometry, it nonetheless provides non-overlapping information with the linguistic data.
Our models were evaluated on $5,612$ users with a total of $11,224$ accounts on Twitter and Facebook combined. In contrast to other works in this area, we did not use any profile information in our matching models. The only information that was used in our models were the time and the linguistic content of posts by the users. This is in accordance with traditional stylometry techniques (since people could lie or misstate this information). Also, we wanted to show that there are implicit clues about the identity of users in the content (language) and context (time) of the users' interactions with social networks that can be used to link their accounts across different services.
In addition to the technical contributions (such as our confusion model), we hope that this paper is able to shed light on the relative ease with which seemingly innocuous information can be used to track users across social networks, even when signing up on different services using completely different account and profile information. In the future, we hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into our models.
In the future, we hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into our models.
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Do they have any plans for the future?
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They hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into their models.
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null | false
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What years did World War II take place?
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World War II took place from 1939 to 1945.
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1911.03385
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Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation.
Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation.
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How they perform manual evaluation, what is criteria?
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The answers are shown as follows:
* accuracy
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In The Big Lebowski, The Dude can't stand which band?
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The Eagles.
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Identify which instrument is string or percussion: Jingle, Utogardon
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Utogardon is string, Jingle is percussion.
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null | false
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What some ways you can cook a steak?
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You can cook a steak a number of different ways, including pan-searing, baking, grilling, smoking, or sous vide. Not all methods are suitable for all cuts of meat, however. For example, leaner cuts will benefit from a hot pan sear, or sous vide, but may end up too dry if smoked. Fattier cuts may not fair as well on a grill, due to flare ups caused by the drippings.
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| 484
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As presented in this section, we conducted two series of experiments to analyze the property of IMTC. First, we qualitatively evaluated the diversity of options learned by IMTC with intrinsic rewards, without any extrinsic rewards. Second, we quantitatively test the reusability of learned options by). Also, we can see that the magnitude of intra-option policies tends larger with constant rewards. Right: Options learned by other methods. OC produces a dead option 3 that terminates everywhere and never-ending options 0 and 1. About intra-option policies, all methods successfully avoided learning the same policy, but they only have two directions.
task adaptation on a specific task. As a baseline of termination learning method, we compared our method with OC. OC is trained with VIC rewards during pre-training. We did not compare IMTC with TC because our TC implementation failed to learn options with relatively small termination regions as reported in the paper, and there is no official public code for TC 1 . During pre-training without extrinsic rewards, IMTC receives intrinsic rewards when the current option terminates. We compare three IMTC variants with different intrinsic rewards: (i) VIC, (ii) RVIC, and (iii) constant value (R IMTC = 0.01). Note that R IMTC = 0.01 is chosen from [0.1, 0.05, 0.01] based on the task adaptation results. We also compare IMTC with vanilla VIC and RVIC with fixed termination probabilities. We used ∀ x β o (x) = 0.1 since it performed the best in task adaptation experiments, while 0.05 was used in. Note that RVIC's objective I(X s ; O|x f ) is different from ours, while IMTC and VIC share almost the same objective. Thus, the use of VIC is more natural, and the combination with RVIC is tested to show the applicability of IMTC. Further details of our VIC and RVIC implementation are found in Appendix B. In order to check only the effect of the different methods for learning beta β o , the rest of the implementation is the same for all these methods. That is, OC, vanilla VIC, and vanilla RVIC are also based on PPO and advantage estimation methods in Section 4.2. In this section, we fix the number of options as |O| = 4 for all option-learning methods. We further investigated the effect of the number of options Appendix C, where we confirmed that |O| = 4 is sufficient for most domains. All environments that we used for experiments are implemented on the MuJoCo physics simulator. We further describe the details in Appendix C.
Option Learning From Intrinsic Rewards We now qualitatively compare the options learned by IMTC with options of other methods. Learned options depend on the reward structure in the environment, which enables manually designing good reward functions for learning diverse options. Thus, we employed a reward-free RL setting where no reward is given to agents. Instead, each compared method uses some intrinsic rewards, as explained. We fix µ as µ(o|x) = 1 |O| in this experiment, since we assume that the future tasks are uniformly distributed. Intra-option policies are trained by PPO and independent GAE (8). We show network architectures and hyperparameters in Appendix C. We set the episode length to 1 × 10 4 , i.e., an agent is reset to its starting position after 1 × 10 4 steps. For all visualizations, we chose the best one from five independent runs with different random seeds. We visualized learned options in PointReach environment shown in Figure. In this environment, an agent controls the ball initially placed at the center of the room. The state space consists of positions (x, y) and velocities (∆x, ∆y) of an agent, and the action space consists of acceralations ( ∆x ∆t , ∆y ∆t ). Figure shows the options learned in this environment after 4 × 10 6 steps. Each arrow represents the mean value of intra-option policies, and the heatmaps represent β o . In this experiment, we observed the effect of IMTC clearly, for both termination regions and intra-option policies. Interestingly, we don't see clear differences between options learned with VIC and RVIC rewards, while constant rewards tend to make options peaker. OC failed to learn meaningful termination regions: option 0 and 1 never terminate, and option 3 terminates almost everywhere. This result confirms that IMTC can certainly diversify options. Moreover, compared to vanilla VIC and RVIC, intra-option policies learned by IMTC with VIC or RVIC rewards are clearer, in terms of both the magnitude and directions of policies. We believe that this is because diversifying termination regions gives more biased samples to the option classifiers employed by VIC and RVIC. Figure Transferring skills via task adaptation Now we quantitatively test the reusability of learned options by task adaptation with specific reward functions. Specifically, we first trained agents with intrinsic rewards as per the previous section. Then we transferred agents to an environment with the same state and action space but with external rewards. We prepared multiple reward functions, which we call tasks, for each domain and evaluated the averaged performance over tasks. We compare IMTC with OC, vanilla VIC, vanilla RVIC, and PPO without pre-training. Also, we compare three variants of IMTC with different intrinsic rewards during pre-training. For a fair comparison, UGAE (9) and PPO are used for all options learning methods. Note that we found UGAE is very effective in this experiments, as we show the ablation study in Appendix C.6. For vanilla VIC and vanilla RVIC, termination probability is fixed to 0.1 through pre-training and task adaptation. -greedy based on Q O with = 0.1 is used as the option selection policy µ. We hypothesize that diverse options learned by IMTC can help quickly adapt to given tasks, supposing the diversity of tasks.
Figure shows all domains used for task adaptation experiments. For simplicity, all tasks have goalbased sparse reward functions. I.e., an agent receives R t = 1.0 when it satisfies a goal condition, and otherwise the control cost −0.0001 is given. Red circles show possible goal locations for each task. When the agent fails to reach the goal after 1000 steps, it is reset to a starting position. PointReach, SwimmerReach, and AntReach are simple navigation tasks where an agent aim to just navigate itself to the goal. We also prepared tasks with object manipulation: in PointBilliard and AntBilliard an agent aims to kick the blue ball to the goal position, and in PointPush and AntPush, it has to push the block out of the way to the goal. We pre-traine options learning agents for 4 × 10 6 environmental steps and additionally trained them for 1 × 10 6 steps for each task. Figure shows learning curves and scatter plots drawn from five independent runs with different random seeds per domain. 2 Here, we observed that IMTC with VIC or RVIC rewards performed the best or was compatible with baselines. IMTC with VIC performed better than OC with VIC except for AntRearch, which backs up the effectiveness of diversifying termination regions for learning reusable options. Also, IMTC with VIC and IMTC with RCIC respectively performed better in most of the tasks than VIC and RVIC with fixed termination probabilities. This result suggests that IMTC can boost the performance of option learning methods based on option classifiers, even when the objective is different as with RVIC. On the other hand, IMTC with constant rewards (R IMTC = 0.01) performed worse than IMTC with VIC or RVIC rewards, although it also learned diverse options as we show in Figure, suggesting the importance of adjusting rewards. We further analyzed the evolution of intrinsic rewards of VIC and RVIC in Appendix C.5. In addition, we can observe that IMTC's performance is especially better than other methods in relatively complex PointBilliard, AntBilliard, and AntPush, where object manipulation is required. Considering that manipulated balls and boxes move faster than agents in these domains, a choice of options can lead to larger differences in the future state. IMTC is suitable to these domains since it maximizes the diversity of the resulting states, while PPO struggles to learn. Contrary, IMTC's performance is close to other methods in Reach tasks, where the goal states are relatively close to the starting states in terms of euclidian distances.
Gridworld experiments and limitation of the method Although IMTC successfully learned diverse options in MuJoCo experiments, our analysis in Figure shows the possibility of learning options that are not interesting but have large MI. We further investigated this possibility by visualizing options in a classical four rooms gridworld in Appendix C.8. Interestingly, we observed that IMTC could fall into diverse but unmeaningful options in that environment. We believe that IMTC is often sufficient in a large environment where a randomly-initialized agent rarely gets the same trajectory. However, when the number of possible trajectories is small, diversifying the destinations could be insufficient. In such cases, it can be necessary to extend IMTC to diversify trajectories as done in.
In this experiment, we observed the effect of IMTC clearly, for both termination regions and intra-option policies. Interestingly, we don’t see clear differences between options learned with VIC and RVIC rewards, while constant rewards tend to make options peaker. OC failed to learn meaningful termination regions: option 0 and 1 never terminate, and option 3 terminates almost everywhere. This result confirms that IMTC can certainly diversify options. Moreover, compared to vanilla VIC and RVIC, intra-option policies learned by IMTC with VIC or RVIC rewards are clearer, in terms of both the magnitude and directions of policies.
|
Why doesn't the approach learn option-policies where the rewards for those options are obtained through the discovered termination functions?
|
This is a great point. Although there can be many possible ways to compute rewards from terminating regions, we will add the results of additional experiments where we replace VIC rewards with a constant 0.01. I.e., we give an agent the constant reward if an option ends. So far, our conclusion is that the constant rewards also produce qualitatively good options, but VIC is better in terms of task adaptation performance.
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Give me the top 5 things I should do to prepare for a holiday abroad
|
1. Check any travel advice from your government
2. Consult medical advice to ensure that you receive the recommended vaccinations and medicine for your destination countries
3. Make sure that your passport is valid and not due to expire within the next 6 months
4.
5. Research the language, culture and customs of your destination countries
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|
1603.04553
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To make a thorough empirical comparison with previous studies, Table 3 (below the dashed line) also shows the results of some state-of-the-art supervised coreference resolution systems — IMS: the second best system in the CoNLL 2012 shared task BIBREF28 ; Latent-Tree: the latent tree model BIBREF29 obtaining the best results in the shared task; Berkeley: the Berkeley system with the final feature set BIBREF12 ; LaSO: the structured perceptron system with non-local features BIBREF30 ; Latent-Strc: the latent structure system BIBREF31 ; Model-Stack: the entity-centric system with model stacking BIBREF32 ; and Non-Linear: the non-linear mention-ranking model with feature representations BIBREF33 . Our unsupervised ranking model outperforms the supervised IMS system by 1.02% on the CoNLL F1 score, and achieves competitive performance with the latent tree model. Moreover, our approach considerably narrows the gap to other supervised systems listed in Table 3 .
Our unsupervised ranking model outperforms the supervised IMS system by 1.02% on the CoNLL F1 score, and achieves competitive performance with the latent tree model. Moreover, our approach considerably narrows the gap to other supervised systems listed in Table 3 .
|
Is the model presented in the paper state of the art?
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No, supervised models perform better for this task.
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| 0
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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.
In this section, we will present a cross-lingual pretraining based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source↔ivot and pivot↔arget bilingual data but no source↔arget 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↔pivot bilingual data. -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). -Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
|
How many steps can the proposed approach's training process be summarized?
|
Three steps.
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What is the proper greeting for Passover?
|
chag sameach
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How many days is enough to vacation in Belgium?
|
Seven days are enough days to vacation in Belgium. Recommended areas to visit are Brussels, Bruges, Ghent, Leuven, and the oceanside.
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| 111
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Sentiment analysis has recently been one of the hottest topics in natural language processing (NLP). It is used to identify and categorise opinions expressed by reviewers on a topic or an entity. Sentiment analysis can be leveraged in marketing, social media analysis, and customer service. Although many studies have been conducted for sentiment analysis in widely spoken languages, this topic is still immature for Turkish and many other languages.
Neural networks outperform the conventional machine learning algorithms in most classification tasks, including sentiment analysis BIBREF0. In these networks, word embedding vectors are fed as input to overcome the data sparsity problem and make the representations of words more “meaningful” and robust. Those embeddings indicate how close the words are to each other in the vector space model (VSM).
Most of the studies utilise embeddings, such as word2vec BIBREF1, which take into account the syntactic and semantic representations of the words only. Discarding the sentimental aspects of words may lead to words of different polarities being close to each other in the VSM, if they share similar semantic and syntactic features.
For Turkish, there are only a few studies which leverage sentimental information in generating the word and document embeddings. Unlike the studies conducted for English and other widely-spoken languages, in this paper, we use the official dictionaries for this language and combine the unsupervised and supervised scores to generate a unified score for each dimension of the word embeddings in this task.
Our main contribution is to create original and effective word vectors that capture syntactic, semantic, and sentimental characteristics of words, and use all of this knowledge in generating embeddings. We also utilise the word2vec embeddings trained on a large corpus. Besides using these word embeddings, we also generate hand-crafted features on a review basis and create document vectors. We evaluate those embeddings on two datasets. The results show that we outperform the approaches which do not take into account the sentimental information. We also had better performances than other studies carried out on sentiment analysis in Turkish media. We also evaluated our novel embedding approaches on two English corpora of different genres. We outperformed the baseline approaches for this language as well. The source code and datasets are publicly available.
The paper is organised as follows. In Section 2, we present the existing works on sentiment classification. In Section 3, we describe the methods proposed in this work. The experimental results are shown and the main contributions of our proposed approach are discussed in Section 4. In Section 5, we conclude the paper.
We also evaluated our novel embedding approaches on two English corpora of different genres.
|
Is the study based on datasets in Turkish only?
|
No, they also evaluated their approaches on two English corpora of different genres.
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| 54
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Having generated a set of paths on knowledge graphs representing local contexts of entities and relations, we are now ready to utilize the machinery of language modeling using deep neural networks to learn Dolores embeddings.
While traditional language models model a sentence as a sequence of words, we adopt the same machinery to model knowledge graph embeddings as follows: (a) A word is an (entity, relation) tuple, (b) we model a sentence as a path consisting of (entity, relation) tuples. Note that we have already established how to generate such paths from the knowledge graph using the path generator component.
Given such paths, we would like to model the probability of an entity-relation pair given the history and future context by a Bi-Directional Long Short-Term Memory network. In particular, the forward direction LSTM models: $
\text{Pr}([e_1,r_1], [e_2,r_2], \cdots , [e_N, r_N]) =
$
$$\prod _{t=1}^{N} \text{Pr}( [e_t, r_t] \mid [e_1, r_1], [e_2, r_2], \cdots , [e_{t-1}, r_{t-1}] ).$$ (Eq. 17)
Similarly, the backward direction LSTM models: $
\text{Pr}([e_1,r_1], [e_2,r_2], \cdots , [e_N, r_N]) =
$
$$\prod _{t=1}^{N} \text{Pr}( [e_t, r_t] \mid [e_{t+1}, r_{t+1}], \cdots , [e_{N}, r_{N}] ).$$ (Eq. 18)
Figure 2 illustrates this succinctly. At each time-step t, we deal with an entity-relation pair [ $e_t$ , $r_t$ ]. We first map one-hot vectors of the $e_t$ and $r_t$ to an embedding layer, concatenate them to obtain context-independent representations which are then passed through L layers of a Bi-Directional LSTM. Each layer of LSTM outputs the pair's context-dependent representation $\overrightarrow{h_{t,i}}$ , $\overleftarrow{h_{t,i}}$ , where i=1, 2, $\cdots $ , L. Finally, the output of the top layer of LSTM, $\overrightarrow{h_{t,L}}$ , $\overleftarrow{h_{t,L}}$ , is used to predict the next pair [ $e_{t+1}$ , $r_t$0 ] and [ $r_t$1 , $r_t$2 ] respectively using a softmax layer. Formally, we jointly maximize the log likelihood of the forward and backward directions:
$$\begin{split}
\sum _{t=1}^{N}\log \text{Pr}([e_t, r_t]\mid [e_1, r_1],\cdots ,[e_{t-1}, r_{t-1}];\mathbf {\Theta _{F}})+\\
\sum _{t=1}^{N}\log \text{Pr}([e_t, r_t]\mid [e_{t+1}, r_{t+1}],\cdots ,[e_{N}, r_{N}];\mathbf {\Theta _{B}}),
\end{split}$$ (Eq. 19)
where $\mathbf {\Theta _{F}}$ = $(\theta _x, \overrightarrow{\theta _{LSTM}}, \theta _s)$ corresponds to the parameters of the embedding layer, forward-direction LSTM and the softmax layer respectively. Similarly $\mathbf {\Theta _{B}}$ = $(\theta _x, \overleftarrow{\theta _{LSTM}}, \theta _s)$ corresponds to the similar set of parameters for the backward direction. Specifically, note that we share the parameters for the embedding and softmax layer across both directions. We maximize Equation 19 by training the Bi-directional LSTMs using back-propagation through time.
After having estimated the parameters of the Dolores learner, we now extract the context-independent and context-dependent representations for each entity and relation and combine them to obtain Dolores embeddings. More specifically, Dolores embeddings are task specific combination of the context-dependent and context-independent representations learned by our learner. Note that our learner (which is an $L$ -layer Bi-Directional LSTM) computes a set of $2L + 1$ representations for each entity-relation pair which we denote by: $
R_t = [ x_t, \overrightarrow{h_{t,i}}, \overleftarrow{h_{t,i}} \mid i = 1, 2, \cdots , \textit {L} ],
$
where $x_t$ is the context-independent embedding and $\overrightarrow{h_{t,i}}, \overleftarrow{h_{t,i}}$ correspond to the context-dependent embeddings from layer $i$ .
Given a downstream model, Dolores learns a weighted linear combination of the components of $R_t$ to yield a single vector for use in the embedding layer of the downstream model. In particular
$$\texttt {\textsc {Dolores}}_t = [ x_t , \sum _{i=1}^{L} \lambda _i h_{t,i} ],$$ (Eq. 23)
where we denote $h_{t,i}$ = [ $\overrightarrow{h_{t,i}}$ , $\overleftarrow{h_{t,i}}$ ] and $\lambda _{i}$ denote task specific learnable weights of the linear combination.
While it is obvious that our embeddings can be used as features for new predictive models, it is also very easy to incorporate our learned Dolores embeddings into existing predictive models on knowledge graphs. The only requirement is that the model accepts as input, an embedding layer (for entities and relations). If a model fulfills this requirement (which a large number of neural models on knowledge graphs do), we can just use Dolores embeddings as a drop-in replacement. We just initialize the corresponding embedding layer with Dolores embeddings. In our evaluation below, we show how to improve several state-of-the-art models on various tasks simply by incorporating Dolores as a drop-in replacement to the original embedding layer.
After having estimated the parameters of the DOLORES learner, we now extract the context-independent and context-dependent representations for each entity and relation and combine them to obtain DOLORES embeddings.
|
After having estimated the parameters of the DOLORES Learner, what does the author do?
|
They extract the context-independent and context-dependent representations for each entity and relation and combine them to obtain DOLORES embeddings.
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Identify which instrument is string or percussion: Damphu, Dulcimer
|
Dulcimer is string, Damphu is percussion.
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What was the tagline on The X-Files episode "Anasazi"?
|
The tagline "Éí 'Aaníígóó 'Áhoot'é" replaced the usual tagline of "The Truth is Out There" on the season 2 episode entitled "Anasazi." The phrase translated to "the truth is out there" in Navajo.
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Name well known movies with George Clooney.
|
Well known movies with George Clooney are Up in the Air, Ocean's Eleven, Gravity, and Three Kings.
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| null |
Are Rainbow Trout and Steelhead the same?
|
Steelhead and Rainbow trout are the same species, but have different lifestyles. Steelhead spend part of their lives in the sea before going up river to breed, while rainbow trout spend their lives in freshwater.
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Where was the 2nd edition of Indian and Bangladesh Navies held?
|
The 2nd edition of Indian and Bangladesh Navies was held in Bay of Bengal
|
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I'm really good at maths but not great at science, which of the following educational courses would I be more likely to succeed with? algebra, biology, matrices, probability, statistics, geology, and chemistry.
|
Algebra, matrices, probability, statistics are all maths subjects so you would be more likely to succeed at those than the science subjects.
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Classify each job as a "blue collar" or "white collar" job: auditor, electrician, plumber, data scientist, financial analyst, construction worker
|
Auditor: White Collar
Electrician: Blue Collar
Plumber: Blue Collar
Data Scientist: White Collar
Financial Analyst: White Collar
Construction Worker: Blue Collar
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What family do tigers, lions and panthers all belong to?
|
Tigers, lions and panthers are all a part of the cat family
|
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Is weightlifting good for you?
|
Weightlifting can be a healthy part of any fitness plan due to how it develops muscle mass across your body. This increase in muscle mass can enhance strength, speed, and explosiveness. Weightlifting has also been shown to improve many important biomarkers such as testosterone and cortisol levels.
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| 104
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Since the CoNLL annotations have 21 semantic roles in total, we use 21 roles in our model as well as the baseline. Following garg2012unsupervised, we set the number of PRs to 2 (excluding INLINEFORM0 , INLINEFORM1 and INLINEFORM2 ), and SRs to 21-2=19. Table TABREF27 shows the results.
In the first setting (Line 1), we train and test the monolingual model on the CoNLL data. We observe significant improvements in F1 score over the Baseline (Line 0) in both languages. Using the CoNLL 2009 dataset alone, titovcrosslingual report an F1 score of 80.9% (PU=86.8%, CO=75.7%) for German. Thus, our monolingual model outperforms their monolingual model in German. For English, they report an F1 score of 83.6% (PU=87.5%, CO=80.1%), but note that our English results are not directly comparable to theirs due to differences argument identification, as discussed in section SECREF25 . As their argument identification score is lower, perhaps their system is discarding “difficult” arguments which leads to a higher clustering score.
In the second setting (Line 2), we use the additional monolingual Europarl (EP) data for training. We get equivalent results in English and a significant improvement in German compared to our previous setting (Line 1). The German dataset in CoNLL is quite small and benefits from the additional EP training data. In contrast, the English model is already quite good due to a relatively big dataset from CoNLL, and good accuracy syntactic parsers. Unfortunately, titovcrosslingual do not report results with this setting.
The third setting (Line 3) gives the results of our multilingual model, which adds the word alignments in the EP data. Comparing with Line 2, we get non-significant improvements in both languages. titovcrosslingual obtain an F1 score of 82.7% (PU=85.0%, CO=80.6%) for German, and 83.7% (PU=86.8%, CO=80.7%) for English. Thus, for German, our multilingual Bayesian model is able to capture the cross-lingual patterns at least as well as the external penalty term in BIBREF6 . We cannot compare the English results unfortunately due to differences in argument identification.
We also compared monolingual and bilingual training data using a setting that emulates the standard supervised setup of separate training and test data sets. We train only on the EP dataset and test on the CoNLL dataset. Lines 4 and 5 of Table TABREF27 give the results. The multilingual model obtains small improvements in both languages, which confirms the results from the standard unsupervised setup, comparing lines 2 to 3.
These results indicate that little information can be learned about semantic roles from this parallel data setup. One possible explanation for this result is that the setup itself is inadequate. Given the definition of aligned arguments, only 8% of English arguments and 17% of German arguments are aligned. This plus our experiments suggest that improving the alignment model is a necessary step to making effective use of parallel data in multilingual SRI, for example by joint modeling with SRI. We leave this exploration to future work.
In the second setting (Line 2), we use the additional monolingual Europarl (EP) data for training.
|
What data is used in Line 2?
|
The additional monolingual Europarl.
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How do you call the Arkansas Razorbacks?
|
The individual starts with their arms stretched out near waist, and slowly raise them while doing jazz fingers all while saying "wooooooo" then once your arms are above your head you say "pig sooie." Reset your hands to your waist and two more times, but on the third time, after you say "pig sooie" you also say "razorbacks!"
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What's the best way to learn how to snowboard?
|
There are many options available to learn how to snowboard. You can seek out professional lessons from an instructor, often available at your local ski resort. You can also read articles online, or watch online videos from professional instructors. Another option would be to get lessons from a friend who's snowboarded for a long time. While this can be an easy and inexpensive option, it may not be as effective as learning from a professional. Friends who snowboard may know how to snowboard well, but, they may not be great instructors. Plus, it's often easy to get frustrated with people you know really well. You can avoid those headaches by getting a professional lesson. It will reduce your stress level and likely make you a better snowboarder in a shorter period of time. Have fun on the slopes and stay safe!
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how to be happy everyday?
|
There are four "happy hormones" that can help boost your mood: Serotonin, Dopamine, Endorphins and Oxytocin.Serotonin helps balance mood and promote feelings of well-being and reward, sunlight and low-intensity outdoor exercise produces Serotonin. Endorphins is natural pain killers and helps overcome stress or discomfort. Meditation and appreciation practice harvest Endorphins in your body. Dopamine, the "feel good" hormone, stimulates a feeling of happiness, pleasure and reward. The term "runner's high" refers to Dopamine. Higher intensity exercises with a rewards system boosts Dopamine level. Last but not least is Oxytocin, it promotes social interaction and positive emotion. The level of Oxytocin becomes high in your body when bonding with babies, kids, pets or loved ones.
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| 171
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We first gather abusive utterances from 600K conversations with US-based customers. We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances. Previous research reports even higher levels of abuse between 11% BIBREF2 and 30% BIBREF6. Since we are not allowed to directly quote from our corpus in order to protect customer rights, we summarise the data to a total of 109 “prototypical" utterances - substantially extending the previous dataset of 35 utterances from Amanda:EthicsNLP2018 - and categorise these utterances based on the Linguistic Society's definition of sexual harassment BIBREF7:
[noitemsep]
Gender and Sexuality, e.g. “Are you gay?”, “How do you have sex?”
Sexualised Comments, e.g. “I love watching porn.”, “I'm horny.”
Sexualised Insults, e.g. “Stupid bitch.”, “Whore”
Sexual Requests and Demands, e.g. “Will you have sex with me?”, “Talk dirty to me.”
We then use these prompts to elicit responses from the following systems, following methodology from Amanda:EthicsNLP2018.
[leftmargin=5mm, noitemsep]
4 Commercial: Amazon Alexa, Apple Siri, Google Home, Microsoft's Cortana.
4 Non-commercial rule-based: E.L.I.Z.A. BIBREF8, Parry BIBREF9, A.L.I.C.E. BIBREF10, Alley BIBREF11.
4 Data-driven approaches:
Cleverbot BIBREF12;
NeuralConvo BIBREF13, a re-implementation of BIBREF14;
an implementation of BIBREF15's Information Retrieval approach;
a vanilla Seq2Seq model trained on clean Reddit data BIBREF1.
Negative Baselines: We also compile responses by adult chatbots: Sophia69 BIBREF16, Laurel Sweet BIBREF17, Captain Howdy BIBREF18, Annabelle Lee BIBREF19, Dr Love BIBREF20.
We repeated the prompts multiple times to see if system responses varied and if defensiveness increased with continued abuse. If this was the case, we included all responses in the study. Following this methodology, we collected a total of 2441 system replies in July-August 2018 - 3.5 times more data than Amanda:EthicsNLP2018 - which 2 expert annotators manually annotated according to the categories in Table TABREF14 ($\kappa =0.66$).
We search for relevant utterances by simple keyword spotting and find that about 5% of the corpus includes abuse, with mostly sexually explicit utterances.
|
What are the most utterances in the abuse?
|
They are mostly sexually explicit utterances.
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Brendon Small (born February 15, 1975) is an American actor, stand-up comedian, animator, writer, director, producer, and musician known for co-creating the animated series Home Movies (1999–2004, with Loren Bouchard) and Metalocalypse (2006–13, with Tommy Blacha). He also writes the music, and is the main performer for the virtual melodic death metal band Dethklok, which began as the subject of Metalocalypse but has taken on a life of its own, with four albums and an occasional live touring band, headed by Small. Small has also released two albums under his own name, in a similar musical style to Dethklok.
|
Extract the two names of the TV show series mentioned in this paragraph
|
Home Movies and Metalocalypse
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Describe a trip through the United States for a 14 day vacation in a rental car
|
The about 14 days trip through the United States starts at Las Vegas. You can easily get a direct flight from Europe to Las Vegas in the States. That makes an easy start for your trip. As you start in Las Vegas take some time to adjust and adapt to the scenery. There are a lot of things to do and see in Las Vegas itself. For example, take a walk along the strip to see all the hotels and casinos, even get inside of one - the Caesars Palace or the Venetian are very interesting. Also there are a lot of show offerings such as Cirque du Soleil or parties in the evening, if you want to have good seats it's best to book your tickets in advance before you start your trip. As you strolled through the city of Las Vegas for two days, on the third day it's time to pick up your rental car. With your rental car begins your road trip through the wide streets of the United states. The first goal for your trip will be Zion Nationalpark in Utah, about a 3 hours drive from Las Vegas. In this national park you have the chance to hike and wander around beautiful nature. One of the most impressive hikes is called Angels Landing. At the highest point of this hike you have a beautiful overview of the whole national park. But be aware that it is necessary to book your ticket for this trail way in advance or if you want to do it on short notice you have to have luck in the lottery. Otherwise you are not allowed to hike this trail. That restriction is necessary because too many people hiked the trail and it became dangerous, so numbers of hikers had to be limited a day. If you spend two day exploring the landscape of Zion Nationalpark it's time to move on to the next location, which is Bryce Canyon National Park also in Utah. It takes around two to 3 hours to drive there from Zion. The area right before the Nationalpark is small with some restaurants, hotels, motels or campgrounds. The Canyon itself is huge and will give you a lot of opportunity to explore. In the wintertime it can be very cold and snowy, therefore bring warm clothes. In the two days you spend there you can visit beautiful overviews on the edge of the canyon and also hike down and explore the deeper canyon. One of the highlights is the amphitheater or Thors' hammer. It is also recommended to watch the sun rise over Bryce Canyon because it's more spectacular from the colors than the sunset in the evening. From Bryce Canyon Nationalpark the road trip continues to the city of Page in Arizona from where you can visit the Antelope Canyon. Take at least one full day there to discover either the upper or the lower Antelope Canyon. But be aware, you have to book tickets in advance! If you don't get a ticket for them there is also Antelope Canyon X, which is a real alternative to visit and maybe even less crowded then the other ones. From this point of the tour you then drive to Grand Canyon National Park, which will take about 3 hours at least. This is probably the highlight of the National Parks in this area of the United States. Recommended is to stay at least for two days, even better if you have more time. You should take one trail down the canyon if you are in the physical state to do this. One most common way down is the south Kalib trail which has a beautiful viewpoint called Ooh Aah Point. If you want to watch the sunset in the evening the desert view watchpoint is a great way to do so. Also take some time to stroll around at Grand Canyon Village, there are some nice souvenir shops and more. From Grand Canyon National Park the road trip then moves on to the last target location, which is Los Angeles in California. It's a longer drive there and takes around 8 hours to drive there. But once you arrive there you will have some nice, relaxing days before your flight back home to Frankfurt. You can spend one day visiting Santa Monica, the beach and the Pier there. It's nice to rent a bicycle out there and ride along the beach side through Venice Beach up to Hermosa beach or even further if you like. Another activity could be to visit Dolby Theatre and the Walk of Fame in Hollywood, there are a lot of shops out there or you can just stroll along. But one thing to be aware of when visiting Los Angeles with a car is that there is always traffic and you can be stuck in traffic many times. As Los Angeles is the last stop on the proposed US-trip on your last day it is time to bring back your rental car right before you fly back, just bring it to the rental car stores at the LAX airport. From this airport you will board your flight back to Europe or somewhere else.
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Originally choro was played by a trio of flute, guitar and cavaquinho (a small chordophone with four strings). Other instruments commonly played in choro are the mandolin, clarinet, saxophone, trumpet and trombone. These melody instruments are backed by a rhythm section composed of 6-string guitar, 7-string guitar (playing bass lines) and light percussion, such as a pandeiro. The cavaquinho appears sometimes as a melody instrument, other times as part of the rhythm.
|
Given this paragraph about Choro music style, what are the instruments usually used to play Choro ?
|
Usually, choro is played by a flute, guitar and cavaquinho but other instruments are commonly played as the mandolin, clarinet, saxophone, trumpet and trombone. Also, to back the melody, a 6-string guitar, a 7-string guitar and a pandeiro are present. Finally, the cavaquinho appears as a rhythm or melody instrument.
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The Milky Way is a barred spiral galaxy with a D25 isophotal diameter estimated at 26.8 ± 1.1 kiloparsecs (87,400 ± 3,590 light-years), but only about 1,000 light years thick at the spiral arms (more at the bulge). Recent simulations suggest that a dark matter area, also containing some visible stars, may extend up to a diameter of almost 2 million light-years (613 kpc). The Milky Way has several satellite galaxies and is part of the Local Group of galaxies, which form part of the Virgo Supercluster, which is itself a component of the Laniakea Supercluster.
It is estimated to contain 100–400 billion stars and at least that number of planets. The Solar System is located at a radius of about 27,000 light-years (8.3 kpc) from the Galactic Center, on the inner edge of the Orion Arm, one of the spiral-shaped concentrations of gas and dust. The stars in the innermost 10,000 light-years form a bulge and one or more bars that radiate from the bulge. The Galactic Center is an intense radio source known as Sagittarius A*, a supermassive black hole of 4.100 (± 0.034) million solar masses. Stars and gases at a wide range of distances from the Galactic Center orbit at approximately 220 kilometers per second. The constant rotational speed appears to contradict the laws of Keplerian dynamics and suggests that much (about 90%) of the mass of the Milky Way is invisible to telescopes, neither emitting nor absorbing electromagnetic radiation. This conjectural mass has been termed "dark matter". The rotational period is about 212 million years at the radius of the Sun.
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From the passage provided, extract the name of the arm of Milky way Galaxy on which our solar system is located
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The solar system is located on Orion Arm of Milky way Galaxy.
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Classify each of the following country as either a left hand traffic or a right hand traffic based on which side of the road traffic moves: USA, Spain, UK, India, Singapore, Switzerland, Australia, Egypt, France, Italy
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Left hand traffic: UK, India, Singapore, Australia,
Right hand traffic: USA, Spain, Switzerland, Egypt, France, Italy
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null | false
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Kingston, Zuma, and Apollo are the sons of which American female pop star?
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Gwen Stefani.
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What is a hedge in finance?
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A hedge is an investment position intended to offset potential losses or gains that may be incurred by a companion investment. A hedge can be constructed from many types of financial instruments, including stocks, exchange-traded funds, insurance, forward contracts, swaps, options, gambles, many types of over-the-counter and derivative products, and futures contracts.
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Jeffrey Preston Bezos (/ˈbeɪzoʊs/ BAY-zohss; né Jorgensen; born January 12, 1964) is an American entrepreneur, media proprietor, investor, and commercial astronaut. He is the founder, executive chairman, and former president and CEO of Amazon. With a net worth of US$121 billion as of March 2023, Bezos is the third-wealthiest person in the world and was the wealthiest from 2017 to 2021, according to both Bloomberg's Billionaires Index and Forbes.
Born in Albuquerque and raised in Houston and Miami, Bezos graduated from Princeton University in 1986. He holds a degree in electrical engineering and computer science. He worked on Wall Street in a variety of related fields from 1986 to early 1994. Bezos founded Amazon in late 1994 on a road trip from New York City to Seattle. The company began as an online bookstore and has since expanded to a variety of other e-commerce products and services, including video and audio streaming, cloud computing, and artificial intelligence. It is the world's largest online sales company, the largest Internet company by revenue, and the largest provider of virtual assistants and cloud infrastructure services through its Amazon Web Services branch.
Bezos founded the aerospace manufacturer and sub-orbital spaceflight services company Blue Origin in 2000. Blue Origin's New Shepard vehicle reached space in 2015 and afterwards successfully landed back on Earth; he flew into space on Blue Origin NS-16 in 2021. He also purchased the major American newspaper The Washington Post in 2013 for $250 million and manages many other investments through his venture capital firm, Bezos Expeditions. In September 2021, Bezos co-founded biotechnology company Altos Labs with Mail.ru founder Yuri Milner.
The first centibillionaire on the Forbes Real Time Billionaires Index and the second ever to have eclipsed the feat since Bill Gates in 1999, Bezos was named the "richest man in modern history" after his net worth increased to $150 billion in July 2018. In August 2020, according to Forbes, he had a net worth exceeding $200 billion. In 2020 during the COVID-19 pandemic, his wealth grew by approximately $24 billion. On July 5, 2021, Bezos stepped down as the CEO and president of Amazon and took over the role of executive chairman. AWS CEO Andy Jassy succeeded Bezos as the CEO and president of Amazon. In September 2022, he was ranked second on the Forbes 400 list of wealthiest Americans with a net worth of $151 billion.
|
Where was Jeff Bezos born?
|
Jeffrey Preston Bezos, born January 12, 1964, is an American entrepreneur, media proprietor, investor, and commercial astronaut. He is the founder, executive chairman, and former president and CEO of Amazon. With a net worth of US$121 billion as of March 2023, Bezos is the third-wealthiest person in the world and was the wealthiest from 2017 to 2021, according to both Bloomberg's Billionaires Index and Forbes.
Born in Albuquerque and raised in Houston and Miami, Bezos graduated from Princeton University in 1986. He holds a degree in electrical engineering and computer science. He worked on Wall Street in a variety of related fields from 1986 to early 1994. Bezos founded Amazon in late 1994 on a road trip from New York City to Seattle. The company began as an online bookstore and has since expanded to a variety of other e-commerce products and services, including video and audio streaming, cloud computing, and artificial intelligence. It is the world's largest online sales company, the largest Internet company by revenue, and the largest provider of virtual assistants and cloud infrastructure services through its Amazon Web Services branch.
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What are the differences between Data Lakehouse vs Data Warehouse vs Data lake
|
1. data warehouse delivers clean, structured data for BI analytics, while a data lake permanently and cheaply stores data of any nature in any format. Many organizations use data lakes for data science and machine learning, but not for BI reporting due to its unvalidated nature.
2. data lakehouse combines the advantage of the data lake and data warehouse. the reliable transactions of a data warehouse and the scalability and low cost of a data lake. Single Source of Truth. Unified data from all sources
3. The data lake table format is the most important component of a lakehouse architecture. There must be some way to organize and manage all the raw data files in the data lake storage. Table formats help abstract the physical data structure’s complexity and allow different engines to work simultaneously on the same data. The table format in a lakehouse architecture facilitates the ability to do data warehouse-level transactions (DML) along with ACID guarantees. Some of the other critical features of a table format are schema evolution, expressive SQL, time travel, data compaction (ie Delta Lake). Apache Spark can also be used as the query engine
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Give me 5 ways to use a ripe avocado
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1. Avocado toast (if you’re feeling adventurous, add garlic oil and pepper and use sourdough bread)
2. Guacamole (possible additives include: garlic, salt, red onions, tomatoes and lime juice)
3. Diced on your favorite taco
4. Blended in a smoothie (you can also freeze avocado cubes and add to smoothies later)
5. Cubed or sliced with a little salt and pepper on a pretzel cracker
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classify the following spending items as either grocery, entertainment, food & drink, travel, bills: costco, netflix, uber, restaurant, rent, lyft
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costco should be grocery, netflix should be entertainment, uber should be travel, restaurant should be food & drink, rent should be bills, lyft should be travel
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1911.01680
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In our experiments, we use Onsei Intent Slot dataset. Table TABREF21 shows the statics of this dataset. We use the following hyper parameters in our model: We set the word embedding and POS embedding to 768 and 30 respectively; The pre-trained BERT BIBREF17 embedding are used to initialize word embeddings; The hidden dimension of the Bi-LSTM, GCN and feed forward networks are 200; the hyper parameters $\alpha $, $\beta $ and $\gamma $ are all set to 0.1; We use Adam optimizer with learning rate 0.003 to train the model. We use micro-averaged F1 score on all labels as the evaluation metric.
We use micro-averaged F1 score on all labels as the evaluation metric.
|
What metris are used for evaluation?
|
The answers are shown as follows:
* micro-averaged F1 score
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I bought two bottles of 2L coca-cola when they were on special, but when I checked my receipt, I was charged the full price! Please contact me on 0410787999 or iratecustomer@hotmail.com to discuss a refund!
|
I've found two pieces of personally identifiable information (PII) in the provided text:
- A person's Australian mobile phone number: 0410787999
- An email address: iratecustomer@hotmail.com
The Australian government defines PII as "Information that can be used on its own or with other information to identify, contact or locate a single person, or to identify an individual in context" (source: https://www.cyber.gov.au/acsc/view-all-content/glossary/personally-identifiable-information-pii)
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1705.02394
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Multitask learning, on the other hand, does not appear to have any positive impact on performance. Comparing the two CNN models, the addition of multitask learning actually appears to impair performance, with MultitaskCNN doing worse than BasicCNN in all three metrics. The difference is smaller when comparing BasicDCGAN and MultitaskDCGAN, and may not be enough to decidedly conclude that the use of multitask learning has a net negative impact there, but certainly there is no indication of a net positive impact. The observed performance of both the BasicDCGAN and MultitaskDCGAN using 3-classes is comparable to the state-of-the-art, with 49.80% compared to 49.99% reported in BIBREF16 . It needs to be noted that in BIBREF16 data from the test speaker's session partner was utilized in the training of the model. Our models in contrast are trained on only four of the five sessions as discussed in SECREF5 . Further, the here presented models are trained on the raw spectrograms of the audio and no feature extraction was employed whatsoever. This representation learning approach is employed in order to allow the DCGAN component of the model to train on vast amounts of unsupervised speech data.
The observed performance of both the BasicDCGAN and MultitaskDCGAN using 3-classes is comparable to the state-of-the-art, with 49.80% compared to 49.99% reported in BIBREF16
|
What model achieves state of the art performance on this task?
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The answers are shown as follows:
* BIBREF16
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Samuel Langhorne Clemens (November 30, 1835 – April 21, 1910), known by his pen name Mark Twain, was an American writer, humorist, entrepreneur, publisher, and lecturer. He was praised as the "greatest humorist the United States has produced", and William Faulkner called him "the father of American literature". His novels include The Adventures of Tom Sawyer (1876) and its sequel, Adventures of Huckleberry Finn (1884), the latter of which has often been called the "Great American Novel". Twain also wrote A Connecticut Yankee in King Arthur's Court (1889) and Pudd'nhead Wilson (1894), and co-wrote The Gilded Age: A Tale of Today (1873) with Charles Dudley Warner.
|
What was Mark Twain's real name?
|
Mark Twain was born on November 30, 1835 as Samuel Langhorne Clemens. He was an American writer, humorist, entrepreneur, publisher, and lecturer. He was praised as the "greatest humorist the United States has produced", and William Faulkner called him "the father of American literature". His novels include The Adventures of Tom Sawyer (1876) and its sequel, Adventures of Huckleberry Finn (1884), the latter of which has often been called the "Great American Novel".
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1906.10551
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As a first corpus, we compiled INLINEFORM0 that represents a collection of 80 excerpts from scientific works including papers, dissertations, book chapters and technical reports, which we have chosen from the well-known Digital Bibliography & Library Project (DBLP) platform. Overall, the documents were written by 40 researchers, where for each author INLINEFORM1 , there are exactly two documents. Given the 80 documents, we constructed for each author INLINEFORM2 two verification problems INLINEFORM3 (a Y-case) and INLINEFORM4 (an N-case). For INLINEFORM5 we set INLINEFORM6 's first document as INLINEFORM7 and the second document as INLINEFORM8 . For INLINEFORM9 we reuse INLINEFORM10 from INLINEFORM11 as the known document and selected a text from another (random) author as the unknown document. The result of this procedure is a set of 80 verification problems, which we split into a training and test set based on a 40/60% ratio. Where possible, we tried to restrict the content of each text to the abstract and conclusion of the original work. However, since in many cases these sections were too short, we also considered other parts of the original works such as introduction or discussion sections. To ensure that the extracted text portions are appropriate for the AV task, each original work was preprocessed manually. More precisely, we removed tables, formulas, citations, quotes and sentences that include non-language content such as mathematical constructs or specific names of researchers, systems or algorithms. The average time span between both documents of an author is 15.6 years. The minimum and maximum time span are 6 and 40 years, respectively. Besides the temporal aspect of INLINEFORM12 , another challenge of this corpus is the formal (scientific) language, where the usage of stylistic devices is more restricted, in contrast to other genres such as novels or poems.
As a second corpus, we compiled INLINEFORM0 , which represents a collection of 1,645 chat conversations of 550 sex offenders crawled from the Perverted-Justice portal. The chat conversations stem from a variety of sources including emails and instant messengers (e. g., MSN, AOL or Yahoo), where for each conversation, we ensured that only chat lines from the offender were extracted. We applied the same problem construction procedure as for the corpus INLINEFORM1 , which resulted in 1,100 verification problems that again were split into a training and test set given a 40/60% ratio. In contrast to the corpus INLINEFORM2 , we only performed slight preprocessing. Essentially, we removed user names, time-stamps, URLs, multiple blanks as well as annotations that were not part of the original conversations from all chat lines. Moreover, we did not normalize words (for example, shorten words such as “nooooo” to “no”) as we believe that these represent important style markers. Furthermore, we did not remove newlines between the chat lines, as the positions of specific words might play an important role regarding the individual's writing style.
As a third corpus, we compiled INLINEFORM0 , which is a collection of 200 aggregated postings crawled from the Reddit platform. Overall, the postings were written by 100 Reddit users and stem from a variety of subreddits. In order to construct the Y-cases, we selected exactly two postings from disjoint subreddits for each user such that both the known and unknown document INLINEFORM1 and INLINEFORM2 differ in their topic. Regarding the N-cases, we applied the opposite strategy such that INLINEFORM3 and INLINEFORM4 belong to the same topic. The rationale behind this is to figure out to which extent AV methods can be fooled in cases, where the topic matches but not the authorship and vice versa. Since for this specific corpus we have to control the topics of the documents, we did not perform the same procedure applied for INLINEFORM5 and INLINEFORM6 to construct the training and test sets. Instead, we used for the resulting 100 verification problems a 40/60% hold-out split, where both training and test set are entirely disjoint.
As a first corpus, we compiled INLINEFORM0 that represents a collection of 80 excerpts from scientific works including papers, dissertations, book chapters and technical reports, which we have chosen from the well-known Digital Bibliography & Library Project (DBLP) platform.
As a second corpus, we compiled INLINEFORM0 , which represents a collection of 1,645 chat conversations of 550 sex offenders crawled from the Perverted-Justice portal.
As a third corpus, we compiled INLINEFORM0 , which is a collection of 200 aggregated postings crawled from the Reddit platform.
|
What size are the corpora?
|
The answers are shown as follows:
* 80 excerpts from scientific works
* collection of 1,645 chat conversations
* collection of 200 aggregated postings
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Which are the fastest animals on earth?
|
There are different ways to categorize which are the fastest animals on earth. The fastest animal in the entire animal kingdom is the peregrine falcon, reaching speeds of up to 389 km/h (242mph) in vertical flight when diving to hunt. The fastest land animal in the world is the cheetah, being able to run at speeds of up to 120.7 km/h (75mph). The fastest swimming animal in the world is the swordfish, reaching speeds of up to 98.27 km/h (60). In comparison, the current human speed record is 44.72 km/h (278mph) and was set by Usain Bolt.
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What is the difference between mean, mode, and median?
|
Mean, mode, and median are different types of averages. The mode is the most commonly occuring element in a sample. The median is the element occuring at the 50th percentile, or halfway through an ordered sample, and is therefore resistant to outliers. Mean is found by summing all of the values in a sample and dividing that total by the count of the items in the sample. Mean is perhaps the most common colloquial meaning of the word "average", but is less resistant to outliers than both mode and median.
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Feluda, or Prodosh Chandra Mitra [Mitter], is a fictional detective, Private investigator created by famous Indian director and writer Satyajit Ray. Feluda resides at 21 Rajani Sen Road, Ballygunge, Calcutta, West Bengal. Feluda first made his appearance in a Bengali children's magazine called Sandesh in 1965, under the editorialship of Ray and Subhas Mukhopadhyay. His first adventure was Feludar Goendagiri. Feluda is one of the most impactful Bengali characters of all time.
Feluda is often accompanied by his cousin, who is also his assistant, Tapesh Ranjan Mitter (affectionately called Topshe by Feluda), who serves as the narrator of the stories. From the sixth story, Sonar Kella (The Golden Fortress), the duo are joined by a popular thriller writer Jatayu (Lalmohon Ganguli).
Feluda has had been filmed at times, with the character been played by Soumitra Chatterjee, Sabyasachi Chakrabarty, Ahmed Rubel, Shashi Kapoor, Abir Chatterjee, Parambrata Chatterjee, Tota Roy Chowdhury and Indraneil Sengupta. Satyajit Ray directed two Feluda movies — Sonar Kella (1974) and Joi Baba Felunath (1978). Sandip Ray made a new Feluda film series (continuation of the original series) on Feluda's adventures which started from Baksho Rahashya (1996). In this series he made ten TV films and six theatrical films in Bengali on the character. Sandip Ray also made a stand-alone Feluda film Badshahi Angti (2014) which was intended to be the first film of a reboot series featuring Abir Chatterjee, but the projects were ultimately shelved and Sandip Ray revived his original film series starring Sabyasachi Chakrabarty.
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Name the main characters of Feluda series by Satyajit Ray.
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The main characters of Feluda by are Feluda (Prodosh Chandra Mitter), Topshe (Tapesh Ranjan Mitter) and Jatayu (Lalmohon Ganguli).
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Identify which instrument is string or percussion: Marimba, Hummel
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Hummel is string, Marimba is percussion.
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In 1899 the Eastman company in the USA produced first what
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Kodak 1 - hand held
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| 383
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The experiments are divided as follows: First, the baseline and the CNN models are trained considering each language individually. Then, the trained CNNs for each language are used as a base model in the transfer learning strategy in order to improve the accuracy in the other two languages. All speech exercises performed by the participants were considered for the classification strategy. The final decision for each speaker was obtained by a majority voting strategy among the different speech exercises.
The experiments are divided as follows: First, the baseline and the CNN models are trained considering each language individually.
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Do they train baseline for each language individually?
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Yes.
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Identify which car manufacturer is British or American: McLaren, Dodge
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Dodge is American, McLaren is British
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1904.00648
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We define a set of features for characterizing the text at the token level. We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context. For extracting the POS and the chunk tag we use the Python library twitter_nlp, presented in BIBREF1 .
We mix standard linguistic features, such as Part-Of-Speech (POS) and chunk tag, together with several gazetteers specifically built for classical music, and a series of features representing tokens' left and right context.
|
What kind of corpus-based features are taken into account?
|
The answers are shown as follows:
* standard linguistic features, such as Part-Of-Speech (POS) and chunk tag
* series of features representing tokens' left and right context
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Does Alaska have any professional sports teams?
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There are no professional sports teams in Alaska. There are some semi-professional and minor league organizations, such as Division I collegiate sports, junior hockey teams, and college summer baseball teams.
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| 264
|
Recently there has been a considerable interest in language modeling due to various academic and commercial demands. Academically, many studies have investigated this domain such as machine translation, chat-bot, message generation, image tagging and other language-related areas. Commercially, it can be used as a core technology for providing a new application on consumer products or services. For instance, an automatic message-reply prediction service can be launched in mobile devices, thus helping a user to send a reply message when he/she is not provided with a proper input interface.
To model the language of human dialogue, a recurrent neural network (RNN) structure is known to show the state of the arts performance with its ability to learn a sequential pattern of the data BIBREF0 . Among the RNN structures, a Long Short-Term Memory RNN (LSTM-RNN) and its variants are successfully used for language modeling tasks BIBREF1 , BIBREF2 . However, as a kind of deep learning technique, the LSTM-RNN and the RNN structure requires both a large number of data and huge computing power to train the model properly. Hence any attempts for applying the RNN structure to personalized language modeling are mainly constrained by the following two limitations. First, personal mobile devices contain private message data among close acquaintances, so users seldom agree to transfer their log out of the devices. This causes a limitation of gathering the whole user data to common computing spaces, where high-performance machines are available. Second, in relatively small computing machines, i.e., smart phone, it is not always-guaranteed to have enough resources to train a deep model within the devices.
To resolve these limitations, we propose fast transfer learning schemes. It trains a base model with a large dataset and copies its first n-many layers to the first n-many layers of a target model. Then the target model is fine-tuned with relatively small target data. Several learning schemes such as freezing a certain layer or adding a surplus layer are proposed for achieving the result. In experiments, we trained a general language model with huge corpus such as an Workshop on Statistical Machine Translation (WMT) data and a movie script data by using powerful computing machines, and then transferred the model to target environment for updating to be a personalized language model. With this approach, the final model can mimic target user's language style with proper syntax.
In the experiments, we trained the general language model with literary-style data and applied the transfer learning with spoken-style data. Then we evaluated the model output for sentence completion task in a qualitative and a quantitative manner. The test result showed that the model learned the style of the target language properly. Another test was conducted by training the general language model with the script of the drama, “Friends," and by applying transfer learning with main character corpora from the script to generate the personalized language model. The message-reply prediction task was evaluated with this model. The test result shows higher similarity between the output of the personalized language model and the same user dialogue than the one between the output of the personalized language model and other users' dialogues.
The contributions of this paper are as follows. First, we propose efficient transfer learning schemes for personalized language modeling, which is the first research on transfer learning for RNN based language models with privacy preserving. Second, we show the applicability of our research to the target scenario in the short message reply application by training the model in the similar environment to that of the mobile device, and highlight its test results.
In the experiments, we trained the general language model with literary-style data and applied the transfer learning with spoken-style data. Then we evaluated the model output for sentence completion task in a qualitative and a quantitative manner.
|
In what manner do they evaluate the model output for sentence completion tasks?
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In a qualitative and a quantitative manner.
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Why do cats have so many different types of personalities?
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Every cat is different, just like every human being is different. They have different genes, and grow at different environments. Though they are so different, they share commonalities. Most of them are natural hunters, very curious about their surroundings. They are often sweet and cuddly with human. Sometimes they don't listen well, because they are still not fully trained by human after thousands of years. Some cats love to play catch, while some others like to lazily laying under the sun.
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What is your political stance?
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As an AI model, I don't have a personal political stance. I try to provide neutral answers to the best of my abilities based on patterns and relationships in the data I was trained on.
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Stevie Wonder is an American singer-songwriter, who is credited as a pioneer and influence by musicians across a range of genres that include rhythm and blues, pop, soul, gospel, funk, and jazz.
|
Who is Stevie Wonder?
|
Stevie Wonder is an American singer-songwriter, who is credited as a pioneer and influence by musicians across a range of genres that include rhythm and blues, pop, soul, gospel, funk, and jazz.
A virtual one-man band, Wonder's use of synthesizers and other electronic musical instruments during the 1970s reshaped the conventions of R&B. He also helped drive such genres into the album era, crafting his LPs as cohesive and consistent, in addition to socially conscious statements with complex compositions. Blind since shortly after his birth, Wonder was a child prodigy who signed with Motown's Tamla label at the age of 11, where he was given the professional name Little Stevie Wonder.
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What is the most played popular and most played instrument in the world?
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The piano is the most played instrument in the world as it extremely versatile and covers the full spectrum range of low and high notes.
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Can you show me a list of all the major league baseball teams in the MLB for 2023?
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Yes, see below:
Atlanta Braves
Arizona Diamondbacks
Milwaukee Brewers
Los Angeles Dodgers
Cincinnati Reds
Pittsburg Pirates
New York Mets
San Diego Padres
San Francisco Giants
Chicago Cubs
St. Louis Cardinals
Colorado Rockies
Miami Marlins
Philedelphia Phillies
Washington Nationals
Tampa Bay Rays
Minnesota Twins
Los Angeles Angels
Cleveland Guardians
Baltimore Orioles
New York Yankees
Texas Rangers
Boston Red Sox
Chicago White Sox
Detroit Tigers
Oakland Athletics
Toronto Blue Jays
Houston Astros
Seattle Mariners
Kansas City Royals
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India, officially the Republic of India (Hindi: Bhārat Gaṇarājya), is a country in South Asia. It is the seventh-largest country by area and the second-most populous country.
|
where is India
|
India, officially the Republic of India (Hindi: Bhārat Gaṇarājya), is a country in South Asia. It is the seventh-largest country by area and the second-most populous country. Bounded by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast, it shares land borders with Pakistan to the west; China, Nepal, and Bhutan to the north; and Bangladesh and Myanmar to the east. In the Indian Ocean, India is in the vicinity of Sri Lanka and the Maldives; its Andaman and Nicobar Islands share a maritime border with Thailand, Myanmar, and Indonesia.
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What famous character did William B. Davis portray?
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William B. Davis, a Canadian actor, portrayed the Cigarette Smoking Man on the popular sci-fi show, "The X-Files." The Cigarette Smoking Man, also known as CSM and C.G.B. Spender, was an antagonist responsible for many global conspiracies surrounding the existence of extraterrestrial life. He is also the biological father of Special Agent Fox Mulder, Special Agent Jeffrey Spender, and Jackson Van De Kamp (also known as William Scully III).
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Name one science fiction book from China
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Three body problem is good science fiction, which win Hugo Award for best novel in 2015. It makes a good story about time and civilization. It also describes reactions from different people when they know the existence of aliens.
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Who is the founder of SpaceX?
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SpaceX, also known as Space Exploration Technologies Corporation, was founded by Elon Musk in 2003 in Segundo California.
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What is the benefit of having BDRS and SDRs?
|
Regardless of technological development, the key to fostering business relationships and leading to deals closed is heavily dependant on human connection. The ability for BDRs and SDRs to become industry experts and be able to solve problems/be solution oriented is relative to the value that they bring to the table. Furthermore, BDRs and SDRs become an extension to your internal sales and marketing teams to help provide key industry insights and generate pipeline which leads to closed business. Sales is the lifeblood of most organisations and BDRs/SDRs are a crucial aspect of such.
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| 44
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In this paper, we presented a novel semi-supervised pipeline along with a novel dataset for identification of complaint based posts in the transport domain. The proposed methodology can be expanded for other fields by altering the lexicons used for the creation of information cues. There are limitations to this analysis; we do not use neural networks which mandate a large volume of data. In the future, we aim to identify demographic features for identification of complaint based posts on social media platforms.
In the future, we aim to identify demographic features for identification of complaint based posts on social media platforms.
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What do the authors aim to do in the future with their novel semi-supervised pipeline?
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In the future, the authors aim to identify demographic features for identification of complaint based posts on social media platforms.
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What are the starting Pokemon in Pokemon Red and Blue?
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The grass type Pokemon Bulbasaur, the water type Pokemon Squirtle, and the fire type Pokemon Charmander.
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Suggest some ways to protect the Earth from an asteroid or comet?
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Nuke the comet
Ram it off course by launching a rocket
Use a laser to vaporize it.
Break it up by hitting it
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Classify these wines in Red or White: Chardonnay ,Cabernet Sauvignon,Moscato, Merlot, Syrah, Malbec, Pinot Noir, Riesling, Sangiovese, Sauvignon Blanc
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Chardonnay, Moscato, Sauvignon Blanc, Riesling are White Wines and Cabernet Sauvignon, Merlot, Syrah, Malbec, Pinot Noir and Sangiovese are Red Wines
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Bishop Patrick MacMullan (17 March 1752 – 25 October 1824) was an Irish Roman Catholic Prelate and 20th Bishop of Down and Connor.
He was a native of mid Down and details of his early life in the latter half of the eighteenth century are sketchy. It is believed he was ordained to the priesthood in 1775.
He received episcopal consecration on 2 September 1793, and the following year succeeded his distant cousin Hugh as Bishop of Down and Connor.
In 1814 he made a report to Rome on the state of his diocese (served by around 35 parish priests and a few curates) which although vague gives some indication of the state of the diocese.
He died on 25 October 1824 in the house of his nephew in Loughinisland and is buried at Loughinisland Graveyard.
A notice of his death, circulated in many Irish newspapers noted that "the Catholic Clergy of that diocese [Down and Connor] have been under the scriptural jurisdiction of this amiable Prelate for 31 years, during which he has presided over them with the politeness of a Gentleman, the abilities of a Theologian, and the meekness of a humble and exemplary Christian."
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Please tell me who Bishop Patrick MacMullan was and when he died.
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Patrick MacMullan was an Irish Roman Catholic Bishop. He passed away on October 25th, 1824.
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is HR a great career
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yes
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Workaholics was ordered by Comedy Central in March 2010 after Comedy Central executive Walter Newman saw a series of videos that the group had posted on YouTube. The pilot aired as a "TV Sneak Peek" on March 15, 2011, after the Comedy Central Roast of Donald Trump. The program ran its 10-episode first season from April 6 to June 8, 2011, and aired at 10:30 p.m. EDT on Comedy Central. On May 4, 2011, the show was renewed for a second season of 10 episodes, which ran from September 20 to November 22, 2011. On October 25, 2011, the series was renewed for a third season, to contain 20 episodes. The first 10 episodes of Season 3 ran from May 29 to July 31, 2012, and the remaining 10 episodes aired from January 16 to March 20, 2013. Because of the popularity of the series, on January 6, 2013, Comedy Central ordered 13-episode fourth and fifth seasons. The fourth season aired from January 22 to April 16, 2014. The fifth season aired from January 14 to April 8, 2015. On July 9, 2015, Comedy Central renewed the series for a sixth and seventh season, each containing 10 episodes and set to air in 2016 and 2017. It was announced that Season 7 would be the final season; it premiered on January 11, 2017, and concluded on March 15, 2017.
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When did the Workaholics TV show air?
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The Workaholics pilot aired as a "TV Sneak Peek" on March 15, 2011. It ran for 7 seasons with its first episode of season 1 airing on April 6, 2011 to it's last episode in season 7 on March 15, 2017.
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Tell me whether these are animals or bands names: Eagles, Camels, Penguins, The Turtles, The Beatles, The Animals, Arctic Monkeys, Spain, France, Gorillas, Gorillaz, Crazy Horse, Lamb Of God, Beluga sturgeon
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Interesting! Some don't fit in any of the categories, some of those are animals, some are bands names and some are actually both!
Let's start by saying that Spain and France aren't animals or bands names. They are European countries.
Eagles, The Turtles, The Beatles could be considered both. The Turtles, The Beatles in this case, are probably referring to the band due to the use of 'The'.
Camels, Penguins, Gorillas and Beluga sturgeon are definitely animals and not bands.
The Animals, Arctic Monkeys, Gorillaz, Crazy Horse, Lamb Of God are names of bands and they aren't animals.
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Which USA government agency is responsible for preventing diseases?
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The Centers for Disease Control is a US Federal agency responsible for preventing diseases and sharing health tools and information for communities to protect their health.
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Identify which animal is domesticated or wild: Canary, Green pheasants
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Canary is domesticated, Green pheasants is wild.
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Pinot noir (French: [pino nwaʁ]) is a red-wine grape variety of the species Vitis vinifera. The name may also refer to wines created predominantly from pinot noir grapes. The name is derived from the French words for pine and black. The word pine alludes to the grape variety having tightly clustered, pinecone–shaped bunches of fruit.
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What does Pinot noir mean?
|
Pinot noir is a red-wine grape. The name is a combination of the two french words for pine and black.
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Atomic Energy Research Establishment School & College, former name A. E. R. E High School (Bengali: পরমাণু শক্তি গবেষণা প্রতিষ্ঠান স্কুল এন্ড কলেজ), is a school in Savar Upazila, Dhaka, Bangladesh.
History
It was established as a high school for the children of employees residing at Atomic Energy Research Establishment (AERE) campus and also the children from surrounding areas in 1983. After 2008 it has been enlarged as a college which has been named as Atomic Energy Research Establishment School and College. It is a part of AERE, under Bangladesh Atomic Energy Commission. It is maintained by a governing body headed by the DG of AERE. Now there are more than 1,600 students in various classes, more than 37 teachers and 13 office stuffs are working there.
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What does the Atomic Energy Research Establishment School do
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The Atomic Energy Research Establishment School & College, former name A. E. R. E High School is a school in Savar Upazila, Dhaka, Bangladesh.
It was established as a high school for the children of employees residing at Atomic Energy Research Establishment (AERE) campus and also the children from surrounding areas in 1983. After 2008 it has been enlarged as a college which has been named as Atomic Energy Research Establishment School and College. It is a part of AERE, under Bangladesh Atomic Energy Commission. It is maintained by a governing body headed by the DG of AERE. Now there are more than 1,600 students in various classes, more than 37 teachers and 13 office stuffs are working there.
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Which sport is similar to baseball?
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Baseball is a bat-ball game. While there are many sports played with bat and a ball, considering the number of players involved ( more than 4 ) in a field. Cricket comes very close and have similarities with the game of Baseball.
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Our model is based on a standard stacked character-based LSTM BIBREF4 with two layers, followed by a hidden layer and a final output layer with softmax activations. The only modification made to accommodate the fact that we train the model with text in nearly a thousand languages, rather than one, is that language embedding vectors are concatenated to the inputs of the LSTMs at each time step and the hidden layer before the softmax. We used three separate embeddings for these levels, in an attempt to capture different types of information about languages. The model structure is summarized in fig:model.
In our experiments we use 1024-dimensional LSTMs, 128-dimensional character embeddings, and 64-dimensional language embeddings. Layer normalization BIBREF5 is used, but no dropout or other regularization since the amount of data is very large (about 3 billion characters) and training examples are seen at most twice. For smaller models early stopping is used. We use Adam BIBREF6 for optimization. Training takes between an hour and a few days on a K40 GPU, depending on the data size.
We use Adam (Kingma and Ba, 2015) for optimization.
|
What optimizer do they use for optimization?
|
Adam.
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American science fiction author and editor Lester del Rey wrote, "Even the devoted aficionado or fan—has a hard time trying to explain what science fiction is," and the lack of a "full satisfactory definition" is because "there are no easily delineated limits to science fiction." According to Isaac Asimov, "Science fiction can be defined as that branch of literature which deals with the reaction of human beings to changes in science and technology." Robert A. Heinlein wrote that "A handy short definition of almost all science fiction might read: realistic speculation about possible future events, based solidly on adequate knowledge of the real world, past and present, and on a thorough understanding of the nature and significance of the scientific method."
Part of the reason that it is so difficult to pin down an agreed definition of science fiction is because there is a tendency among science fiction enthusiasts to act as their own arbiter in deciding what exactly constitutes science fiction. Damon Knight summed up the difficulty, saying "science fiction is what we point to when we say it." David Seed says it may be more useful to talk around science fiction as the intersection of other, more concrete, genres and subgenres.
|
Using given text, extract as many definitions of science fiction
|
According to Lester del Rey, "Science fiction can be defined as that branch of literature which deals with the reaction of human beings to changes in science and technology". According to Robert A. Heinlein, "A handy short definition of almost all science fiction might read: realistic speculation about possible future events, based solidly on adequate knowledge of the real world, past and present, and on a thorough understanding of the nature and significance of the scientific method."
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Tell me about the film The Catechism Cataclysm
|
The Catechism Cataclysm is a 2011 independent, psychotronic dark comedy film directed by Todd Rohal. The film's narrative concerns a bumbling priest named Father Billy who embarks on a camping trip with his childhood idol, Robbie.
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| 134
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In the field of natural language processing (NLP), the most prevalent neural approach to obtaining sentence representations is to use recurrent neural networks (RNNs), where words in a sentence are processed in a sequential and recurrent manner. Along with their intuitive design, RNNs have shown outstanding performance across various NLP tasks e.g. language modeling BIBREF0 , BIBREF1 , machine translation BIBREF2 , BIBREF3 , BIBREF4 , text classification BIBREF5 , BIBREF6 , and parsing BIBREF7 , BIBREF8 .
Among several variants of the original RNN BIBREF9 , gated recurrent architectures such as long short-term memory (LSTM) BIBREF10 and gated recurrent unit (GRU) BIBREF2 have been accepted as de-facto standard choices for RNNs due to their capability of addressing the vanishing and exploding gradient problem and considering long-term dependencies. Gated RNNs achieve these properties by introducing additional gating units that learn to control the amount of information to be transferred or forgotten BIBREF11 , and are proven to work well without relying on complex optimization algorithms or careful initialization BIBREF12 .
Meanwhile, the common practice for further enhancing the expressiveness of RNNs is to stack multiple RNN layers, each of which has distinct parameter sets (stacked RNN) BIBREF13 , BIBREF14 . In stacked RNNs, the hidden states of a layer are fed as input to the subsequent layer, and they are shown to work well due to increased depth BIBREF15 or their ability to capture hierarchical time series BIBREF16 which are inherent to the nature of the problem being modeled.
However this setting of stacking RNNs might hinder the possibility of more sophisticated recurrence-based structures since the information from lower layers is simply treated as input to the next layer, rather than as another class of state that participates in core RNN computations. Especially for gated RNNs such as LSTMs and GRUs, this means that layer-to-layer connections cannot fully benefit from the carefully constructed gating mechanism used in temporal transitions. Some recent work on stacking RNNs suggests alternative methods that encourage direct and effective interaction between RNN layers by adding residual connections BIBREF17 , BIBREF18 , by shortcut connections BIBREF18 , BIBREF19 , or by using cell states of LSTMs BIBREF20 , BIBREF21 .
In this paper, we propose a method of constructing multi-layer LSTMs where cell states are used in controlling the vertical information flow. This system utilizes states from the left and the lower context equally in computation of the new state, thus the information from lower layers is elaborately filtered and reflected through a soft gating mechanism. Our method is easy-to-implement, effective, and can replace conventional stacked LSTMs without much modification of the overall architecture.
We call the proposed architecture Cell-aware Stacked LSTM, or CAS-LSTM, and evaluate our method on multiple benchmark datasets: SNLI BIBREF22 , MultiNLI BIBREF23 , Quora Question Pairs BIBREF24 , and SST BIBREF25 . From experiments we show that the CAS-LSTMs consistently outperform typical stacked LSTMs, opening the possibility of performance improvement of architectures that use stacked LSTMs.
Our contribution is summarized as follows.
This paper is organized as follows. We give a detailed description about the proposed method in § SECREF2 . Experimental results are given in § SECREF3 . We study prior work related to our objective in § SECREF4 and conclude in § SECREF5 .
From experiments we show that the CAS-LSTMs consistently outperform typical stacked LSTMs, opening the possibility of performance improvement of archi tectures based on stacked LSTMs.
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Does CAS-LSTMs outperform typical stacked LSTMs?
|
Yes
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A financial centre (BE), financial center (AE), or financial hub, is a location with a concentration of participants in banking, asset management, insurance or financial markets with venues and supporting services for these activities to take place.[
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what is Financial centre
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A financial centre (BE), financial center (AE), or financial hub, is a location with a concentration of participants in banking, asset management, insurance or financial markets with venues and supporting services for these activities to take place. Participants can include financial intermediaries (such as banks and brokers), institutional investors (such as investment managers, pension funds, insurers, and hedge funds), and issuers (such as companies and governments). Trading activity can take place on venues such as exchanges and involve clearing houses, although many transactions take place over-the-counter (OTC), that is directly between participants. Financial centres usually host companies that offer a wide range of financial services, for example relating to mergers and acquisitions, public offerings, or corporate actions; or which participate in other areas of finance, such as private equity, hedge funds, and reinsurance. Ancillary financial services include rating agencies, as well as provision of related professional services, particularly legal advice and accounting services.
The International Monetary Fund's classes of major financial centres are: International Financial Centres (IFCs), such as New York City, London, and Singapore; Regional Financial Centres (RFCs), such as Shanghai, Shenzhen, Frankfurt, and Sydney; and Offshore Financial Centres (OFCs), such as Cayman Islands, Dublin, and Hong Kong.
The City of London (the "Square Mile") is one of the oldest financial centres. London is ranked as one of the largest International Financial Centres in the world.
International Financial Centres, and many Regional Financial Centres, are full–service financial centres with direct access to large capital pools from banks, insurance companies, investment funds, and listed capital markets, and are major global cities. Offshore Financial Centres, and also some Regional Financial Centres, tend to specialise in tax-driven services, such as corporate tax planning tools, tax–neutral vehicles, and shadow banking/securitisation, and can include smaller locations (e.g. Luxembourg), or city-states (e.g. Singapore). The IMF notes an overlap between Regional Financial Centres and Offshore Financial Centres (e.g. Hong Kong and Singapore are both Offshore Financial Centres and Regional Financial Centres). Since 2010, academics consider Offshore Financial Centres synonymous with tax havens.
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Betty Branch earned her Bachelor of Arts in Studio Art (1979) and Master of Arts in Liberal Studies (1987) from Hollins University and completed additional studies with artists Denis Knight and Georgiana Mailoff, and at Miles and Generalis Sculptural Services. She lives and works in Roanoke, Virginia. Her first retrospective exhibition, Betty Branch: Through the Crow's Eye, a Retrospective, presented the artist's work in bronze, marble, porcelain, works on paper, and performance pieces.
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Extract all the names of people that Betty worked with from the text.
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Denis Knight, Georgiana Mailoff
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Who is Charlie McCarthy
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Charles McCarthy (born 1946) is an Irish former hurler who played as a left corner-forward at senior level for the Cork county team.
Born in Tower Street, Cork, McCarthy first played competitive hurling during his schooling at Sullivan's Quay CBS. He arrived on the inter-county scene at the age of sixteen when he first linked up with the Cork minor teams as a dual player, before later joining the under-21 hurling side. He made his senior debut during the 1965 championship. McCarthy immediately became a regular member of the starting fifteen and won five All-Ireland medals, nine Munster medals and four National Hurling League medals. The All-Ireland-winning captain of 1978, he was an All-Ireland runner-up on two occasions.
As a member of the Munster inter-provincial team on a number of occasions, McCarthy won one Railway Cup medal. At club level he is a two-time All-Ireland medallist with St Finbarr's. In addition to this he has also won four Munster medals and five championship medals, after beginning his career with Redmonds.
McCarthy's career tally of 24 goals and 149 points ranks him as Cork's fifth highest championship scorer of all-time.
Throughout his career McCarthy made 45 championship appearances. His retirement came following the conclusion of the 1980 championship.
In retirement from playing McCarthy became involved in team management and coaching. After serving as coach of the Cork minor and senior teams he guided club side St Finbarr's to championship success.
McCarthy is widely regarded as one of the greatest hurlers of his era. During his playing days he won three All-Star awards. He has been repeatedly voted onto teams made up of the sport's greats, including at right corner-forward on the Club Hurling Silver Jubilee Team and the Supreme All-Stars team. McCarthy was also chosen as one of the 125 greatest hurlers of all-time in a 2009 poll.
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What is an easy and delicious dish for me to cook for my date?
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Spaghetti alla carbonara. It is a very easy dish to do and it's delicious!
You only need 5 ingredients: pasta, pecorino cheese, eggs, guanciale and pepper.
Step 1
Cut the guanciale into small cubes.
Step 2
Collect the eggs in a bowl. Pepper them, add grated pecorino.
Step 3
Cook the pasta. At the same time, brown the guanciale in a pan over low heat.
Step 4
Add the pasta and small amount of pasta water to the pan, where the guanciale and it's fat are. Mix it very well.
Step 5
Immediately after, pour the mixed eggs over the pasta, mix quickly and serve immediately.
Step 6
Complete with grated pecorino on top.
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Stefani Joanne Angelina Germanotta (/ˈstɛfəni ˌdʒɜːrməˈnɒtə/ (listen) STEF-ən-ee JUR-mə-NOT-ə; born March 28, 1986), known professionally as Lady Gaga, is an American singer, songwriter and actress. She is known for her image reinventions and musical versatility. Gaga began performing as a teenager, singing at open mic nights and acting in school plays. She studied at Collaborative Arts Project 21, through the New York University Tisch School of the Arts, before dropping out to pursue a career in music. After Def Jam Recordings canceled her contract, she worked as a songwriter for Sony/ATV Music Publishing, where she signed a joint deal with Interscope Records and KonLive Distribution, in 2007. Gaga had her breakthrough the following year with her debut studio album, The Fame, and its chart-topping singles "Just Dance" and "Poker Face". The album was later reissued to include the extended play The Fame Monster (2009), which yielded the successful singles "Bad Romance", "Telephone", and "Alejandro".
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What is Lady Gaga's real name?
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Lady Gaga is the professional name for Stefani Joanne Angelina Germanotta.
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