paper_id
stringlengths 10
10
⌀ | yes_no
bool 2
classes | paper_index
int64 0
519
⌀ | evidence
stringlengths 0
37.7k
| question
stringlengths 4
11.7k
| answer
stringlengths 1
26k
|
|---|---|---|---|---|---|
null | false
| null |
David Allan Farrish (born August 1, 1956) is a Canadian former professional ice hockey defenceman who played 430 games in the National Hockey League (NHL) with the New York Rangers, Quebec Nordiques, and Toronto Maple Leafs between 1976 and 1984. He featured in the 1979 Stanley Cup Finals with the Rangers.
He also played in the American Hockey League and won the Eddie Shore Award as the league's best defenceman during the 1981–82 season. After his playing career Farrish worked as a coach.
|
Given a reference text about David Allan Farrish, tell me how many games he played in the NHL.
|
David Allan Farrish played 430 games in the NHL.
|
null | false
| 85
|
Minimal pre-processing was done by converting text to lower case, removing the hashtags at the end of tweets and separating each punctuation from the connected token (e.g., awesome!! INLINEFORM0 awesome !!) and replacing comma and new-line characters with white space. The text, then, was tokenized using TensorFlow-Keras tokenizer. Top N terms were selected and added to our dictionary where N=100k for higher count emotions joy, sadness, anger, love and N=50k for thankfulness and fear and N=25k for surprise. Seven binary classifiers were trained for the seven emotions with a batch size of 250 and for 20 epochs with binary cross-entropy as the objective function and Adam optimizer. The architecture of the model can be seen in Figure FIGREF6 . For training each classifier, a balanced dataset was created with selecting all tweets from the target set as class 1 and a random sample of the same size from other classes as class 0. For each classifier, 80% of the data was randomly selected as the training set, and 10% for the validation set, and 10% as the test set. As mentioned before we used the two embedding models, ConceptNet Numberbatch and fastText as the two more modern pre-trained word vector spaces to see how changing the embedding layer can affect the performance. The result of comparison among different embeddings can be seen in Table TABREF10 . It can be seen that the best performance was divided between the two embedding models with minor performance variations.
The comparison of our result with Wang et al. can be seen in Table TABREF9 . as shown, the results from our model shows significant improvement from 10% increase in F-measure for a high count emotion joy up to 61.7 point increase in F-measure for a low count emotion surprise. on average we showed 26.8 point increase in F-measure for all categories and more interestingly our result shows very little variance between different emotions compare to results reported by Wang et al.
As mentioned before we used the two embedding models, ConceptNet Numberbatch and fastText as the two more modern pre-trained word vector spaces to see how changing the embedding layer can affect the performance.
|
To understand how changing the embedding layer affects performance, which two embedding models do the authors use?
|
ConceptNet Numberbatch and fastText
|
null | false
| null |
Which of the following animals are primates: Human, Dog, Bonobos, Cat, Bats, Goat, Bears, Orangutans, Wolf, Lion, Squirrel, Koala, Cheetah, Deer, Orca, Gorillas, Camel, Hamsters, Lemurs, Blue whale, Chimpanzee
|
Not all of them are primates! From that list the primates are:
- Human
- Bonobos
- Orangutans
- Gorillas
- Lemurs
- Chimpanzee
|
|
null | false
| null |
Classify each of the following as either a lepton or a hadron: electron, proton, neutron, pi meson, electron neutrino, muon neutrino, tau neutrino.
|
Protons, neutrons and pi mesons are members of the hadron family of particles. Hadrons are particles that interact through the strong force.
Electrons, electron neutrinos, muon neutrinos and tau neutrinos are members of the lepton family. Leptons interact through the weak force, and if charged, the electromagnetic force.
|
|
null | false
| null |
Which is a species of fish? Haddock or Polar
|
Haddock
|
|
null | false
| null |
What is geodesy?
|
Geodesy is the science of measuring and representing the geometry, gravity, and spatial orientation of the Earth in temporally varying 3D. It is called planetary geodesy when studying other astronomical bodies, such as planets or circumplanetary systems.
Geodynamical phenomena, including crustal motion, tides, and polar motion, can be studied by designing global and national control networks, applying space geodesy and terrestrial geodetic techniques, and relying on datums and coordinate systems. The job titles are geodesist and geodetic surveyor.
|
|
null | false
| null |
The female cone has two types of scale: the bract scales, and the seed scales (or ovuliferous scales), one subtended by each bract scale, derived from a highly modified branchlet. On the upper-side base of each seed scale are two ovules that develop into seeds after fertilization by pollen grains. The bract scales develop first, and are conspicuous at the time of pollination; the seed scales develop later to enclose and protect the seeds, with the bract scales often not growing further. The scales open temporarily to receive pollen, then close during fertilization and maturation, and then re-open again at maturity to allow the seed to escape. Maturation takes 6–8 months from pollination in most Pinaceae genera, but 12 months in cedars and 18–24 months (rarely more) in most pines. The cones open either by the seed scales flexing back when they dry out, or (in firs, cedars and golden larch) by the cones disintegrating with the seed scales falling off. The cones are conic, cylindrical or ovoid (egg-shaped), and small to very large, from 2–60 cm long and 1–20 cm broad.
After ripening, the opening of non-serotinous pine cones is associated with their moisture content—cones are open when dry and closed when wet. This assures that the small, windborne seeds will be dispersed during relatively dry weather, and thus, the distance traveled from the parent tree will be enhanced. A pine cone will go through many cycles of opening and closing during its life span, even after seed dispersal is complete. This process occurs with older cones while attached to branches and even after the older cones have fallen to the forest floor. The condition of fallen pine cones is a crude indication of the forest floor's moisture content, which is an important indication of wildfire risk. Closed cones indicate damp conditions while open cones indicate the forest floor is dry.
|
How does a pine cone work?
|
A pine cone consists of scales which protect seeds. The scales can open and close depending on conditions. The scales will open temporarily to receive pollen, then close again until the seeds mature. Once mature, the scales will open and close depending on their moisture content. Once the seeds are mature, the scales can open again to allow seeds to escape. When dry, the cones open allowing the small windborne seeds to disperse as widely as possible. This cycle of opening and closing continues until the seeds are dispersed.
|
null | false
| null |
What is the name of the National Anthem of the Netherlands?
|
The name of the National Anthem of the Netherlands is "Wilhelmus".
|
|
null | false
| null |
The citron-bellied attila (Attila citriniventris) is a species of bird in the family Tyrannidae.
It is found in Brazil, Colombia, Ecuador, Peru, and Venezuela. Its natural habitat is subtropical or tropical moist lowland forests.
|
Where can you find the Citron-bellied attila?
|
The The citron-bellied attila (Attila citriniventris) can be found in the following countries: Brazil, Colombia, Ecuador, Peru and Venezuela.
|
null | false
| null |
What kind of dog is a Bernedoodle?
|
A Bernedoodle is a dog that is a mix of two other types of dogs: a poodle and a bernese mountain dog. These two dogs are cross-bred to create the Bernedoodle, frequently to get the hair of the poodle rather than the coast of a Bernese. This means the dog does not shed and is hypoallergenic which makes for a much tidier house!
|
|
1904.12087
| false
| null |
As evidenced in Section "Related Work" , the focus of most of these studies is the identification of languages and dialects using contemporary data. A few exceptions include the work by trieschnigg2012exploration who applied language identification methods to historical varieties of Dutch and the work by CLIarxiv on languages written in cuneiform script: Sumerian and Akkadian. Cuneiform is an ancient writing system invented by the Sumerians for more than three millennia.
Cuneiform is an ancient writing system invented by the Sumerians for more than three millennia.
|
What is one of the first writing systems in the world?
|
The answers are shown as follows:
* Cuneiform
|
null | false
| null |
Who created the tech company Apple?
|
Steve Jobs was the founder of Apple
|
|
null | false
| 104
|
Semantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and other NLP tasks. SRL is the problem of finding predicate-argument structure in a sentence, as illustrated below:
INLINEFORM0
Here, the predicate WRITE has two arguments: `Mike' as A0 or the writer, and `a book' as A1 or the thing written. The labels A0 and A1 correspond to the PropBank annotations BIBREF0 .
As the need for SRL arises in different domains and languages, the existing manually annotated corpora become insufficient to build supervised systems. This has motivated work on unsupervised SRL BIBREF1 , BIBREF2 , BIBREF3 . Previous work has indicated that unsupervised systems could benefit from the word alignment information in parallel text in two or more languages BIBREF4 , BIBREF5 , BIBREF6 . For example, consider the German translation of sentence INLINEFORM0 :
INLINEFORM0
If sentences INLINEFORM0 and INLINEFORM1 have the word alignments: Mike-Mike, written-geschrieben, and book-Buch, the system might be able to predict A1 for Buch, even if there is insufficient information in the monolingual German data to learn this assignment. Thus, in languages where the resources are sparse or not good enough, or the distributions are not informative, SRL systems could be made more accurate by using parallel data with resource rich or more amenable languages.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages. The model consists of individual Bayesian models for each language BIBREF3 , and crosslingual latent variables to incorporate soft role agreement between aligned constituents. This latent variable approach has been demonstrated to increase the performance in a multilingual unsupervised part-of-speech tagging model based on HMMs BIBREF4 . We investigate the application of this approach to unsupervised SRL, presenting the performance improvements obtained in different settings involving labeled and unlabeled data, and analyzing the annotation effort required to obtain similar gains using labeled data.
We begin by briefly describing the unsupervised SRL pipeline and the monolingual semantic role induction model we use, and then describe our multilingual model.
In this paper, we propose a joint Bayesian model for unsupervised semantic role induction in multiple languages.
|
What is the joint Bayesian model for?
|
unsupervised semantic role induction in multiple languages
|
null | false
| null |
Good Friday is a Christian holiday commemorating the crucifixion of Jesus and his death at Calvary. It is observed during Holy Week as part of the Paschal Triduum. It is also known as Holy Friday, Great Friday, Great and Holy Friday (also Holy and Great Friday), and Black Friday.
Members of many Christian denominations, including the Catholic, Eastern Orthodox, Lutheran, Anglican, Methodist, Oriental Orthodox, United Protestant and some Reformed traditions (including certain Continental Reformed, Presbyterian and Congregationalist churches), observe Good Friday with fasting and church services. In many Catholic, Lutheran, Anglican and Methodist churches, the Service of the Great Three Hours' Agony is held from noon until 3 pm, the time duration that the Bible records as darkness covering the land to Jesus' sacrificial death on the cross. Communicants of the Moravian Church have a Good Friday tradition of cleaning gravestones in Moravian cemeteries.
The date of Good Friday varies from one year to the next in both the Gregorian and Julian calendars. Eastern and Western Christianity disagree over the computation of the date of Easter and therefore of Good Friday. Good Friday is a widely instituted legal holiday around the world, including in most Western countries and 12 U.S. states. Some predominantly Christian countries, such as Germany, have laws prohibiting certain acts such as dancing and horse racing, in remembrance of the somber nature of Good Friday.
|
What is Good Friday?
|
Good Friday is a Christian holiday to remember the crucifixion of Jesus. Members of a lot of christian denominations observe this holiday with fasting and church services. The date of the holiday varies from year to year depending on the computation of the date of Easter.
|
null | false
| 298
|
At a high level, as shown in Figure FIGREF4 , a product search engine works as follows: a customer issues a query, which is passed to a lexical matching engine (typically an inverted index BIBREF0 , BIBREF1 ) to retrieve all products that contain words in the query, producing a match set. The match set passes through stages of ranking, wherein top results from the previous stage are re-ranked before the most relevant items are finally displayed. It is imperative that the match set contain a relevant and diverse set of products that match the customer intent in order for the subsequent rankers to succeed. However, inverted index-based lexical matching falls short in several key aspects:
In this paper, we address the question: Given rich customer behavior data, can we train a deep learning model to retrieve matching products in response to a query? Intuitively, there is reason to believe that customer behavior logs contain semantic information; customers who are intent on purchasing a product circumvent the limitations of lexical matching by query reformulation or by deeper exploration of the search results. The challenge is the sheer magnitude of the data as well as the presence of noise, a challenge that modern deep learning techniques address very effectively.
Product search is different from web search as the queries tend to be shorter and the positive signals (purchases) are sparser than clicks. Models based on conversion rates or click-through-rates may incorrectly favor accessories (like a phone cover) over the main product (like a cell phone). This is further complicated by shoppers maintaining multiple intents during a single search session: a customer may be looking for a specific television model while also looking for accessories for this item at the lowest price and browsing additional products to qualify for free shipping. A product search engine should reduce the effort needed from a customer with a specific mission (narrow queries) while allowing shoppers to explore when they are looking for inspiration (broad queries).
As mentioned, product search typically operates in two stages: matching and ranking. Products that contain words in the query ( INLINEFORM0 ) are the primary candidates. Products that have prior behavioral associations (products bought or clicked after issuing a query INLINEFORM1 ) are also included in the candidate set. The ranking step takes these candidates and orders them using a machine-learned rank function to optimize for customer satisfaction and business metrics.
We present a neural network trained with large amounts of purchase and click signals to complement a lexical search engine in ad hoc product retrieval. Our first contribution is a loss function with a built-in threshold to differentiate between random negative, impressed but not purchased, and purchased items. Our second contribution is the empirical result that recommends average pooling in combination with INLINEFORM0 -grams that capture short-range linguistic patterns instead of more complex architectures. Third, we show the effectiveness of consistent token hashing in Siamese networks for zero-shot learning and handling out of vocabulary tokens.
In Section SECREF2 , we highlight related work. In Section SECREF3 , we describe our model architecture, loss functions, and tokenization techniques including our approach for unseen words. We then introduce the readers to the data and our input representations for queries and products in Section SECREF4 . Section SECREF5 presents the evaluation metrics and our results. We provide implementation details and optimizations to efficiently train the model with large amounts of data in Section SECREF6 . Finally, we conclude in Section SECREF7 with a discussion of future work.
Our second contribution is the empirical result that recommends average pooling in combination with n-grams that capture short-range linguistic patterns instead of more complex architectures.
|
What do they recommend average pooling in combination with?
|
n-grams that capture short-range linguistic patterns.
|
null | false
| 446
|
We introduce a generalized FedSSL framework that empowers existing SSL methods based on Siamese networks to learn from decentralized data under privacy constraints. Figure depicts the end-to-end training pipeline of the framework. It comprises of three key operations: 1) Local Training in clients; 2) Model Aggregation in the server; 3) Model Communication (upload and update) between the server and clients. We implement and analyze four popular SSL methods -SimCLR, MoCo (V1 and V2), SimSiam, and BYOL. Variances in Siamese networks of these methods lead to differences in executions in these three operations 2 .
Local Training Firstly, each client k conducts self-supervised training on unlabeled data D k based on the same global model W o g downloaded from the server. Regardless of SSL methods, clients train with Siamese networks -an online network W o k and a target network W t k for E local epochs using cooresponding loss functions L. We classify these SSL methods with two major differences (Figure in Appendix A): 1) Only SimSiam and BYOL contain a predictor in the online network, so we denote their online network
, where W k is the online encoder and W p k is the predictor; As for SimCLR and MoCo, W o k = W k . 2) SimCLR and SimSiam share identical weights between the online encoder and the target encoder, so W t k = W k . In contrast, MoCo and BYOL update the target encoder with EMA of the online encoder in every mini-batch:
, where W g is the global encoder. Then, the server sent W o g to clients to update their online networks. The training iterates these three operations until it meets the stopping conditions. At the end of the training, we use the parameters of W o g as the generic representation W for evaluation.
At the end of the training, we use the parameters of Wog as the generic representation W for evaluation.
|
The superscript and subscript of W have been defined. But W itself has not been defined in the paper. From the context, I assume, are these the parameters of your model?
|
Yes. W itself stands for the parameters of the model. We have revised it in Section 3.2 (last line) to prevent misunderstandings
|
null | false
| 388
|
The current coverage of the political landscape in the press and in social media has led to an unprecedented situation. Like never before, a statement in an interview, a press release, a blog note, or a tweet can spread almost instantaneously and reach the public in no time. This proliferation speed has left little time for double-checking claims against the facts, which has proven critical in politics, e.g., during the 2016 presidential campaign in the USA, which was arguably impacted by fake news in social media and by false claims.
Investigative journalists and volunteers have been working hard trying to get to the root of a claim and to present solid evidence in favor or against it. Manual fact-checking has proven very time-consuming, and thus automatic methods have been proposed as a way to speed-up the process. For instance, there has been work on checking the factuality/credibility of a claim, of a news article, or of an information source BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. However, less attention has been paid to other steps of the fact-checking pipeline, which is shown in Figure FIGREF1.
The process starts when a document is made public. First, an intrinsic analysis is carried out in which check-worthy text fragments are identified. Then, other documents that might support or rebut a claim in the document are retrieved from various sources. Finally, by comparing a claim against the retrieved evidence, a system can determine whether the claim is likely true or likely false. For instance, BIBREF8 do this on the basis of a knowledge graph derived from Wikipedia. The outcome could then be presented to a human expert for final judgment.
In this paper, we focus on the first step: predicting check-worthiness of claims. Our contributions can be summarized as follows:
New dataset: We build a new dataset of manually-annotated claims, extracted from the 2016 US presidential and vice-presidential debates, which we gathered from nine reputable sources such as CNN, NPR, and PolitiFact, and which we release to the research community.
Modeling the context: We develop a novel approach for automatically predicting which claims should be prioritized for fact-checking, based on a rich input representation. In particular, we model not only the textual content, but also the context: how the target claim relates to the current segment, to neighboring segments and sentences, and to the debate as a whole, and also how the opponents and the public react to it.
State-of-the-art results: We achieve state-of-the-art results, outperforming a strong rivaling system by a margin, while also demonstrating that this improvement is due primarily to our modeling of the context.
We model the problem as a ranking task, and we train both Support Vector Machines (SVM) and Feed-forward Neural Networks (FNN) obtaining state-of-the-art results. We also analyze the relevance of the specific feature groups and we show that modeling the context yields a significant boost in performance. Finally, we also analyze whether we can learn to predict which facts are check-worthy with respect to each of the individual media sources, thus capturing their biases. It is worth noting that while trained on political debates, many features of our model can be potentially applied to other kinds of information sources, e.g., interviews and news.
The rest of the paper is organized as follows: Section SECREF2 discusses related work. Section SECREF3 describes the process of gathering and annotating our political debates dataset. Section SECREF4 presents our supervised approach to predicting fact-checking worthiness, including the explanation of the model and the information sources we use. Section SECREF5 presents the evaluation setup and discusses the results. Section SECREF6 provides further analysis. Finally, Section SECREF7 presents the conclusions and outlines some possible directions for future research.
We model the problem as a ranking task, and we train both Support Vector Machines (SVM) and Feed-forward Neural Networks (FNN) obtaining state-of-the-art results.
|
What do the authors train in the experiments?
|
Support Vector Machines (SVM) and Feed-forward Neural Networks (FNN).
|
null | false
| null |
What is the most popular female dog name
|
bella
|
|
null | false
| null |
What is the name of Portland, Oregon's light rail system?
|
MAX Light Rail
|
|
null | false
| null |
The first successful password-authenticated key agreement methods were Encrypted Key Exchange methods described by Steven M. Bellovin and Michael Merritt in 1992. Although several of the first methods were flawed, the surviving and enhanced forms of EKE effectively amplify a shared password into a shared key, which can then be used for encryption and/or message authentication. The first provably-secure PAKE protocols were given in work by M. Bellare, D. Pointcheval, and P. Rogaway (Eurocrypt 2000) and V. Boyko, P. MacKenzie, and S. Patel (Eurocrypt 2000). These protocols were proven secure in the so-called random oracle model (or even stronger variants), and the first protocols proven secure under standard assumptions were those of O. Goldreich and Y. Lindell (Crypto 2001) which serves as a plausibility proof but is not efficient, and J. Katz, R. Ostrovsky, and M. Yung (Eurocrypt 2001) which is practical.
The first password-authenticated key retrieval methods were described by Ford and Kaliski in 2000.
A considerable number of alternative, secure PAKE protocols were given in work by M. Bellare, D. Pointcheval, and P. Rogaway, variations, and security proofs have been proposed in this growing class of password-authenticated key agreement methods. Current standards for these methods include IETF RFC 2945, RFC 5054, RFC 5931, RFC 5998, RFC 6124, RFC 6617, RFC 6628 and RFC 6631, IEEE Std 1363.2-2008, ITU-T X.1035 and ISO-IEC 11770-4:2006.
|
Given this paragraph about Password-authenticated key exchange, what year were the first provably-secure protocols created?
|
The first provably-secure protocols for Password-authenticated key exchanges(PAKE) were presented in 2000 at "Eurocrypt 2000" by two groups Mihir Bellare, David Pointcheval and Phillip Rogaway, and Victor Boyko, Philip MacKenzie and Sarvar Patel.
|
null | false
| null |
Where was the first ever India and USA armed forces Tri-Services Exercise Tiger Triumph help?
|
The first ever India and USA armed forces Tri-Services Exercise Tiger Triumph was held in Kikinada Coast, India
|
|
null | false
| 141
|
Automatic summarization, machine translation, question answering, and semantic parsing operations are useful for processing, analyzing, and extracting meaningful information from text. However, when applied to long texts, these tasks usually require some minimal syntactic structure to be identified, such as sentences BIBREF0 , BIBREF1 , BIBREF2 , which always end with a period (“.”) in English BIBREF3 .
However, written Thai does not use an explicit end-of-sentence marker to identify sentence boundaries BIBREF4 . Prior works have adapted traditional machine learning models to predict the beginning position of a sentence. The authors of BIBREF5 , BIBREF6 , BIBREF7 proposed traditional models to determine whether a considered space is a sentence boundary based on the words and their part of speech (POS) near the space. Meanwhile, Zhou N. et al. BIBREF8 considered Thai sentence segmentation as a sequence tagging problem and proposed a CRF-based model with n-gram embedding to predict which word is the sentence boundary. This method achieves the state-of-the-art result for Thai sentence segmentation and achieves greater accuracy than other models by approximately 10% on an Orchid dataset BIBREF9 .
Several deep learning approaches have been applied in various tasks of natural language processing (NLP), including the long short-term memory BIBREF10 , self-attention BIBREF11 , and other models. Huang Z. et al. BIBREF12 proposed a deep learning sequence tagging model called Bi-LSTM-CRF, which integrates a conditional random field (CRF) module to gain the benefit of both deep learning and traditional machine learning approaches. In their experiments, the Bi-LSTM-CRF model achieved an improved level of accuracy in many NLP sequence tagging tasks, such as named entity recognition, POS tagging and chunking.
The CRF module achieved the best result on the Thai sentence segmentation task BIBREF8 ; therefore, we adopt the Bi-LSTM-CRF model as our baseline. This paper makes the following three contributions to improve Bi-LSTM-CRF for sentence segmentation.
First, we propose adding n-gram embedding to Bi-LSTM-CRF due to its success in BIBREF8 and BIBREF12 . By including n-gram embedding, the model can capitalize on both approaches. First, the model gains the ability to extract past and future input features and sentence level tag information from Bi-LSTM-CRF; moreover, with the n-gram addition, it can also extract a local representation from n-gram embedding, which helps in capturing word groups that exist near sentence boundary. Although Jacovi A. et al. BIBREF13 reported that a convolutional neural network (CNN) can be used as an n-gram detector to capture local features, we chose n-gram embedding over a CNN due to its better accuracy, as will be shown in Section SECREF8 .
Second, we propose adding incorporative distant representation into the model via a self-attention mechanism, which can focus on the keywords of dependent clauses that are far from the considered word. Self-attention has been used in many recent state-of-the-art models, most notably the transformer BIBREF11 and BERT BIBREF14 . BERT has outperformed Bi-LSTM on numerous tasks, including question answering and language inference. Therefore, we choose to use self-attention modules to extract distant representations along with local representations to improve model accuracy.
Third, we also apply semi-supervised learning BIBREF15 , allowing us to employ unlimited amounts of unlabeled data, which is particularly important for low-resource languages such as Thai, for which annotation is costly and time-consuming. Many semi-supervised learning approaches have been proposed in the computer vision BIBREF16 , BIBREF17 and natural language processing BIBREF18 , BIBREF19 , BIBREF20 fields. Our choice for semi-supervised learning to enhance model representation is Cross-View Training (CVT) BIBREF20 . Clark K. et al. BIBREF20 claims that CVT can improve the representation layers of the model, which is our goal. However, CVT was not designed to be integrated with self-attention and CRF modules; consequently, we provide a modified version of CVT in this work.
Based on the above three contributions, we pursue two main experiments. The first experiment was conducted on two Thai datasets, Orchid and UGWC BIBREF21 , to evaluate our Thai sentence segmentation model. In this case, our model achieves F1 scores of 92.5% and 88.9% on Orchid and UGWC, respectively, and it outperforms all the baseline models. The second experiment was executed on the IWSLT dataset BIBREF22 and involves an English-language punctuation restoration task. This experiment demonstrates that our model is generalizable to different languages. Our model, which does not require pretrained word vectors, improved the overall F1 score by 0.9% compared to the baselines, including a model that uses pretrained word vectors.
There are five sections in the remainder of this paper. Section SECREF2 reviews the related works on Thai sentence segmentation, English punctuation restoration and introduces the original CVT. Section SECREF3 describes the proposed model architecture and the integration of cross-view training. The datasets, implementation process and evaluation metrics are explained in Section SECREF4 . The results of the experiments are discussed in Section SECREF5 . Finally, Section SECREF6 concludes the paper.
Third, we also apply semi-supervised learning, allowing us to employ unlimited amounts of unlabeled data, which is particularly important for low-resource languages such as Thai, for which annotation is costly and time-consuming.
|
What learning do the authors also apply?
|
Semi-supervised learning.
|
null | false
| 341
|
Recent advances in Machine Learning (ML) and, in particular, in Deep Neural Networks (DNN) have allowed the development of highly accurate predictive systems for numerous applications. Among others, health has received significant attention due to the potential of ML-based diagnostic, monitoring and therapeutic systems, which are fast (when compared to traditional diagnostic processes), easily distributed and cheap to implement (many such systems can be executed in mobile devices). Furthermore, these systems can incorporate biometric data to perform non-invasive diagnostics.
Among other data types, speech has been proposed as a valuable biomarker for the detection of a myriad of diseases, including: neurological conditions, such as Alzheimer’s BIBREF0, Parkinson’s disease (PD) BIBREF1 and Amyotrophic Lateral Sclerosis BIBREF2; mood disorders, such as depression, anxiety BIBREF3 and bipolar disorder BIBREF4; respiratory diseases, such as obstructive sleep apnea (OSA) BIBREF5. However, temporal and financial constraints, lack of awareness in the medical community, ethical issues and patient-privacy laws make the acquisition of medical data one of the greatest obstacles to the development of health-related speech-based classifiers, particularly for deep learning models. For this reason, most systems rely on knowledge-based (KB) features, carefully designed and selected to model disease symptoms, in combination with simple machine learning models (e.g. Linear classifiers, Support Vector Machines). KB features may not encompass subtler symptoms of the disease, nor be general enough to cover varying levels of severity of the disease. To overcome this limitation, some works have instead focused on speaker representation models, such as Gaussian Supervectors and i-vectors. For instance, Garcia et al. BIBREF1 proposed the use of i-vectors for PD classification and Laaridh et al. BIBREF6 applied the i-vector paradigm to the automatic prediction of several dysarthric speech evaluation metrics like intelligibility, severity, and articulation impairment. The intuition behind the use of these representations is the fact that these algorithms model speaker variability, which should include disease symptoms BIBREF1.
Proposed by Snyder et al., x-vectors are discriminative deep neural network-based speaker embeddings, that have outperformed i-vectors in tasks such as speaker and language recognition BIBREF7, BIBREF8, BIBREF9. Even though it may not be evident that discriminative data representations are suitable for disease detection when trained with general datasets (that do not necessarily include diseased patients), recent works have shown otherwise. X-vectors have been successfully applied to paralinguistic tasks such as emotion recognition BIBREF10, age and gender classification BIBREF11, the detection of obstructive sleep apnea BIBREF12 and as a complement to the detection of Alzheimer's Disease BIBREF0. Following this line of research, in this work we study the hypothesis that speaker characteristics embedded in x-vectors extracted from a single network, trained for speaker identification using general data, contain sufficient information to allow the detection of multiple diseases. Moreover, we aim to assess if this information is kept even when language mismatch is present, as has already been shown to be true for speaker recognition BIBREF8. In particular, we use the x-vector model as a feature extractor, to train Support Vector Machines for the detection of two speech-affecting diseases: Parkinson's disease (PD) and obstructive sleep apnea (OSA).
PD is the second most common neurodegenerative disorder of mid-to-late life after Alzheimer’s disease BIBREF13, affecting 1% of people over the age of 65. Common symptoms include bradykinesia (slowness or difficulty to perform movements), muscular rigidity, rest tremor, as well as postural and gait impairment. 89% of PD patients develop also speech disorders, typically hypokinetic dysarthria, which translates into symptoms such as reduced loudness, monoloudness, monopitch, hypotonicity, breathy and hoarse voice quality, and imprecise articulation BIBREF14BIBREF15.
OSA is a sleep-concerned breathing disorder characterized by a complete stop or decrease of the airflow, despite continued or increased inspiratory efforts BIBREF16. This disorder has a prevalence that ranges from 9% to 38% through different populations BIBREF17, with higher incidence in male and elderly groups. OSA causes mood and personality changes, depression, cognitive impairment, excessive daytime sleepiness, thus reducing the patients' quality of life BIBREF18, BIBREF19. It is also associated with diabetes, hypertension and cardiovascular diseases BIBREF16, BIBREF20. Moreover, undiagnosed sleep apnea can have a serious economic impact, having had an estimated cost of $\$150$ billion in the U.S, in 2015 BIBREF21. Considering the prevalence and serious nature of the two diseases described above, speech-based technology that tests for their existence has the potential to become a key tool for early detection, monitoring and prevention of these conditions BIBREF22.
The remainder of this document is organized as follows. Section SECREF2 presents the background concepts on speaker embeddings, and in particular on x-vectors. Section SECREF3 introduces the experimental setup: the corpora, the tasks, the KB features and the speaker embeddings employed. The results are presented and discussed in section SECREF4. Finally, section SECREF5 summarizes the main conclusions and suggests possible directions for future work.
X-vectors have been successfully applied to paralinguistic tasks such as emotion recognition, age and gender classification, the detection of obstructive sleep apnea and as a complement to the detection of Alzheimer's Disease. Following this line of research, in this work we study the hypothesis that speaker characteristics embedded in x-vectors extracted from a single network, trained for speaker identification using general data, contain sufficient information to allow the detection of multiple diseases.
|
What hypothesis do they want to study?
|
The hypothesis that speaker characteristics embedded in x-vectors extracted from a single network, trained for speaker identification using general data, contain sufficient information to allow the detection of multiple diseases.
|
null | false
| null |
How many ways are there to skin a cat?
|
There is are many ways to skin a cat. The most important thing is to make sure no skin is left on the carcass. However this is also a popular proverb that means there are many ways to do something, to achieve a goal.
|
|
null | false
| null |
The CIA has covertly hired Professor Jerry Hathaway at Pacific Technical University to develop the power source for "Crossbow": a laser weapon precise enough to commit illegal political assassinations from outer space. Hathaway uses his position to recruit brilliant students to do the work for him but, aside from his graduate student and toady Kent, does not tell them the reason for their research.
Hathaway's latest prospect is genius high school student Mitch Taylor. Despite his youth and inexperience, Hathaway makes Mitch the lead on the project due to his innovative and original ideas in the field of laser physics. Mitch is roomed with Chris Knight, another member of the team, a legend in the "National Physics Club" and one of Mitch's idols. Mitch's ideal of Chris is shattered, however, when Chris turns out to be more of a goof-off than a hard-working student. Meanwhile, Hathaway hopes Mitch will encourage Chris to straighten up his act and that their two exceptional minds can develop a proper power source.
With the deadline quickly approaching, Mitch feels the pressure to complete the project while Chris continues in his carefree attitude. After inviting Mitch to a pool party to blow off steam, Kent reports this to Hathaway, who lambasts Mitch. Mitch breaks down and tearfully calls his parents, telling them he wants to go home. Kent records the call without Mitch's knowledge and later plays the recording over the school's public address system, humiliating Mitch. As Mitch begins packing to leave, Chris explains about the pressures of school and burdens of being highly intelligent by relating the history of former Pacific Tech student Lazlo Hollyfeld. Lazlo cracked under the pressure and disappeared (actually now living in the university's tunnels beneath Chris and Mitch's closet). Chris, fearing the same could happen to him, learned to lighten up and begin enjoying life. Mitch acquiesces to stay and they exact revenge on Kent by disassembling his car and reassembling it in his dorm room.
Hathaway, angry about the still-incomplete project and Chris's attitude, informs Chris that he intends to fail him in his final course needed for graduation and will give a coveted after-graduation job, originally promised to Chris, to Kent instead, as well as take steps to ensure Chris will never find work in the field of laser physics. Chris is disheartened and Mitch must use Chris's same argument to convince him to stay. The two commit themselves to finishing the 5 megawatt laser and making sure Chris passes Hathaway's final exam.
Kent sabotages their latest, nearly-successful apparatus. Though Chris knows Kent destroyed the laser, he can do nothing about it and is left to brood over the injustice; this inspires him to come up with a whole new system. The new solution works flawlessly, impressing Hathaway; he congratulates Chris and assures him that he will graduate. Chris and Mitch leave and celebrate, but Lazlo arrives to tell them his suspicions regarding the possible uses of such a laser. Realizing he is right, they return to the lab, but all the laser equipment has already been removed by Hathaway.
They surreptitiously implant a radio transmitter in Kent's mouth and use it convince him he is speaking to Jesus. Kent divulges the location of the Air Force base where the equipment has been installed on a B-1 bomber. Chris and Mitch sneak onto the airbase and reprogram the coordinates of the target before going to Hathaway's home to set up a small prism. They gather outside Hathaway's home to watch as another school professor and a Congressman arrive, having been told of the test. Kent arrives and, though he was told by Jesus to remain outside, he goes inside. The laser test begins, with the new target being Hathaway's house. A very large bag of popcorn (Hathaway detests popcorn) is heated by the laser refracted by the prism, filling the house entirely and causing it to burst at the seams, with Kent riding a popcorn wave through the front door. Lazlo arrives in an RV—which he has won using mathematics in a blind sweepstakes contest—to tell them he is leaving. Later, Hathaway arrives to see what has become of his house.
|
Summarize the plot synopsis of the movie Real Genius in 3 sentences.
|
The CIA hires Professor Jerry Hathaway to develop the power source for a laser powerful and precise enough to vaporize human targets from outer space. Hathaway's newly recruited genius Mitch, along with his previous prodigy, Chris, work together to develop the technology and succeed, only to later learn of Hathaway's true intention for their work. The students and their friends sabotage their own invention and teach Hathaway a lesson by having the laser to pop an enormous bag of popcorn in Jerry's house, knowing that he hates popcorn.
|
null | false
| null |
Which is a species of fish? Rock bass or Rock
|
Rock bass
|
|
null | false
| null |
Identify which instrument is string or percussion: Chenda, Suroz
|
Suroz is string, Chenda is percussion.
|
|
null | false
| 34
|
Single-relation factoid questions are the most common form of questions found in search query logs and community question answering websites BIBREF1 , BIBREF2 . A knowledge-base (KB) such as Freebase, DBpedia, or Wikidata can help answer such questions after users reformulate them as queries. For instance, the question Where was Barack Obama born? can be answered by issuing the following KB query: $
\lambda (x).place\_of\_birth(Barack\_Obama, x)
$
However, automatically mapping a natural language question such as Where was Barack Obama born? to its corresponding KB query remains a challenging task.
There are three key issues that make learning this mapping non-trivial. First, there are many paraphrases of the same question. Second, many of the KB entries are unseen during training time; however, we still need to correctly predict them at test time. Third, a KB such as Freebase typically contains millions of entities and thousands of predicates, making it difficult for a system to predict these entities at scale BIBREF1 , BIBREF3 , BIBREF0 . In this paper, we address all three of these issues with a character-level encoder-decoder framework that significantly improves performance over state-of-the-art word-level neural models, while also providing a much more compact model that can be learned from less data.
First, we use a long short-term memory (LSTM) BIBREF4 encoder to embed the question. Second, to make our model robust to unseen KB entries, we extract embeddings for questions, predicates and entities purely from their character-level representations. Character-level modeling has been previously shown to generalize well to new words not seen during training BIBREF5 , BIBREF6 , which makes it ideal for this task. Third, to scale our model to handle the millions of entities and thousands of predicates in the KB, instead of using a large output layer in the decoder to directly predict the entity and predicate, we use a general interaction function between the question embeddings and KB embeddings that measures their semantic relevance to determine the output. The combined use of character-level modeling and a semantic relevance function allows us to successfully produce likelihood scores for the KB entries that are not present in our vocabulary, a challenging task for standard encoder-decoder frameworks.
Our novel, character-level encoder-decoder model is compact, requires significantly less data to train than previous work, and is able to generalize well to unseen entities in test time. In particular, without use of ensembles, we achieve 70.9% accuracy in the Freebase2M setting and 70.3% accuracy in the Freebase5M setting on the SimpleQuestions dataset, outperforming the previous state-of-arts of 62.7% and 63.9% BIBREF0 by 8.2% and 6.4% respectively. Moreover, we only use the training questions provided in SimpleQuestions to train our model, which cover about 24% of words in entity aliases on the test set. This demonstrates the robustness of the character-level model to unseen entities. In contrast, data augmentation is usually necessary to provide more coverage for unseen entities and predicates, as done in previous work BIBREF0 , BIBREF1 .
There are three key issues that make learning this mapping non-trivial. First, there are many paraphrases of the same question. Second, many of the KB entries are unseen during training time; however, we still need to correctly predict them at test time. Third, a KB such as Freebase typically contains mil lions of entities and thousands of predicates, making it difficult for a system to predict these entities at scale.
|
What are the key issues that make learning the mapping non-trivial?
|
First, there are many paraphrases of the same question. Second, many of the KB entries are unseen during training time; however, they still need to correctly predict them at test time. Third, a KB such as Freebase typically contains mil lions of entities and thousands of predicates, making it difficult for a system to predict these entities at scale.
|
null | false
| null |
Describe the benefits of living in a small town.
|
Small town living provides a multitude of good and bad experiences which can be influenced on your personal desires. In a small town rumors and news can travel fast since the social circles are smaller and everyone tends to be more connected. It can be rare to go out and not see someone whom you know or are friends with. With fewer strangers, a small town can feel more safe and comfortable. Often times a small town is more walkable since geographically it is likely to cover a smaller area.
|
|
null | false
| null |
Classify the following as either a makeup product, school supply or both: eye shadow, highlighter, contour stick, eye pencil, binder, marker
|
Eye shadow: makeup product, Highlighter: both, Contour stick: makeup product, Eye pencil: makeup product, binder: school supply, marker: school supply
|
|
null | false
| null |
Tell me whether a human has two or one of each of the following body parts: eye, mouth, nose, leg, ear, neck
|
Eye: Two
Mouth: One
Nose: One
Leg: Two
Ear: Two
Neck: One
|
|
null | false
| null |
When going snowboarding what equipment will you need: Snowboard, boots, umbrella, computer, TV, gloves, bindings
|
snowboard, gloves, boots, bindings
|
|
1702.03274
| false
| null |
We defined the reward as being 1 for successfully completing the task, and 0 otherwise. A discount of $0.95$ was used to incentivize the system to complete dialogs faster rather than slower, yielding return 0 for failed dialogs, and $G = 0.95^{T-1}$ for successful dialogs, where $T$ is the number of system turns in the dialog. Finally, we created a set of 21 labeled dialogs, which will be used for supervised learning.
We defined the reward as being 1 for successfully completing the task, and 0 otherwise. A discount of $0.95$ was used to incentivize the system to complete dialogs faster rather than slower, yielding return 0 for failed dialogs, and $G = 0.95^{T-1}$ for successful dialogs, where $T$ is the number of system turns in the dialog.
0.95^{T-1}
reward 0.95^{T-1}
|
What is the reward model for the reinforcement learning appraoch?
|
reward 1 for successfully completing the task, with a discount by the number of turns, and reward 0 when fail
|
2004.03744
| false
| null |
The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness. Finally, when we run the training loop again, this time doing the model selection on the corrected validation set from SNLI-VE-2.0, we obtain a slightly worse performance of 72.52%, although the difference is not clearly significant.
The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness.
|
How much is performance difference of existing model between original and corrected corpus?
|
The answers are shown as follows:
* 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set
|
null | false
| null |
In RollerCoaster Tycoon 2, players manage amusement parks and attempt to meet various goals to complete assigned scenarios. The game retains the isometric perspective of its predecessor, allowing for high compatibility with amusement parks made in the previous game. Various attractions, including transport rides, gentle rides, thrill rides, roller coasters, water rides, and shops or stalls, can be added to the parks as well as landscaping and decorations. The player satisfies park visitors' needs by building facilities and services such as food stalls, bathrooms, souvenir shops, ATMs, and information kiosks, as well as by hiring staff. The player must simultaneously balance the park's expenses and income.
|
What do you do as a player playing RollerCoaster Tycoon 2?
|
The player satisfies park visitors' needs by building facilities and services such as food stalls, bathrooms, souvenir shops, ATMs, and information kiosks, as well as by hiring staff. The player must simultaneously balance the park's expenses and income.
|
null | false
| 53
|
Medical search engines are an essential component for many online medical applications, such as online diagnosis systems and medical document databases. A typical online diagnosis system, for instance, relies on a medical search engine. The search engine takes as input a user query that describes some symptoms and then outputs clinical concept entries that provide relevant information to assist in diagnosing the problem. One challenge medical search engines face is the segmentation of individual clinical entities. When a user query consists of multiple clinical entities, a search engine would often fail to recognize them as separate entities. For example, the user query “fever joint pain weight loss headache” contains four separate clinical entities: “fever”, “joint pain”, “weight loss”, and “headache”. But when the search engine does not recognize them as separate entities and proceeds to retrieve results for each word in the query, it may find "pain" in body locations other than "joint pain", or it may miss "headache" altogether, for example. Some search engines allow the users to enter a single clinical concept by selecting from an auto-completion pick list. But this could also result in retrieving inaccurate or partial results and lead to poor user experience.
We want to improve the medical search engine so that it can accurately retrieve all the relevant clinical concepts mentioned in a user query, where relevant clinical concepts are defined with respect to the terminologies the search engine uses. The problem of extracting clinical concept mentions from a user query can be seen as a variant of the Concept Extraction (CE) task in the frequently-cited NLP challenges in healthcare, such as 2010 i2b2/VA BIBREF0 and 2013 ShARe/CLEF Task 1 BIBREF1. Both CE tasks in 2010 i2b2/VA and 2013 ShARe/CLEF Task 1 ask the participants to design an algorithm to tag a set of predefined entities of interest in clinical notes. These entity tagging tasks are also known as clinical Named Entity Recognition (NER). For example, the CE task in 2010 i2b2/VA defines three types of entities: “problem”, “treatment”, and “test”. The CE task in 2013 ShARe/CLEF defines various types of disorder such as “injury or poisoning”, "disease or syndrome”, etc. In addition to tagging, the CE task in 2013 ShARe/CLEF has an encoding component which requires selecting one and only one Concept Unique Identifier (CUI) from Systematized Nomenclature Of Medicine Clinical Terms (SNOMED-CT) for each disorder entity tagged. Our problem, similar to the CE task in 2013 ShARe/CLEF, also contains two sub-problems: tagging mentions of entities of interest (entity tagging), and selecting appropriate terms from a glossary to match the mentions (term matching). However, several major differences exist. First, compared to clinical notes, the user queries are much shorter, less technical, and often less coherent. Second, instead of encoding, we are dealing with term matching where we rank a few best terms that match an entity, instead of selecting only one. This is because the users who type the queries may not have a clear idea about what they are looking for, or could be laymen who know little terminology, it may be more helpful to provide a set of likely results and let the users choose. Third, the types of entities are different. Each medical search engine may have its own types of entities to tag. There is also one minor difference in the tagging scheme between our problem and the CE task in 2013 ShARe/CLEF - We limit our scope to dealing with entities of consecutive words and not disjoint entities . We use only Beginning, Inside, Outside (BIO) tags. Given the differences listed above, we need to customize a framework consisting of an entity tagging and term matching component for our CE problem.
We want to improve the medical search engine so that it can accurately retrieve all the relevant clinical concepts mentioned in a user query, where relevant clinical concepts are defined with respect to the terminologies the search engine uses.
|
How does the author hope to improve medical search engines?
|
They want it can accurately retrieve all the relevant clinical concepts mentioned in a user query.
|
null | false
| null |
Why is Spanish spoken in large parts of Latin America?
|
Spanish is spoken in Latin America because one of the original colonizing countries of the Western Hemisphere was Spain
|
|
null | false
| null |
name some famous rock songs in the 1970s
|
Many believe that the 1970s were the golden age of rock bands, and there are hundreds of beautiful songs from this period. Here are just some famous songs that are still very popular on the streaming platforms:
1. Hotel California, Eagles, 1975
2. Stairway to Heaven, Led Zeppelin, 1971
3. Dream On, Aerosmith, 1973
4. Bohemian Rhapsody, Queen, 1975
5. Another brick in the wall: Pink Floyd, 1979
|
|
null | false
| null |
Classify as a technical book vs Non technical book
|
Solution Architecture with AWS - Technical
Quantum Physics made me do it - Technical
The only survivors - Fiction
Camp Zero - Sci - Fi
|
|
null | false
| null |
Bastille Day is the common name given in English-speaking countries to the national day of France, which is celebrated on 14 July each year. In French, it is formally called the Fête nationale française (French: [fɛt nasjɔnal]; "French National Celebration"); legally it is known as le 14 juillet (French: [lə katɔʁz(ə) ʒɥijɛ]; "the 14th of July").
|
What is the Bastille Day?
|
Bastille Day is the national day in France, celebrated on 14 July each year. It refers to a major event in the French Revolution on 4 July 1789 where revolutionary insurgents took control of the Bastille, the symbol of royal authority and monarchy in the center of Paris.
|
null | false
| null |
Of the following list of indoor plants, classify them as either 'low light' or 'indirect bright light', "direct bright light". Snake plant, succulent, Monstera, ficus leaf tree, snake plant, aglaonema, jade plant, pothos, inchplant
|
Snake plant - low light
succulent - direct bright light
Monstera - indirect bright light
ficus leaf tree - indirect bright light
ficus leaf tree - direct bright light
snake plant - low light
snake plant - indirect bright light
aglaonema - low light
jade plant - direct bright light
pothos - indirect bright light
pothos - low light
pothos - direct bright light
inchplant - low light
inchplant - indirect bright light
|
|
null | false
| null |
In deep learning, a convolutional neural network (CNN) is a class of artificial neural network most commonly applied to analyze visual imagery. CNNs use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in image recognition and processing. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series.
CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input.
CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons are usually fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) Developing robust datasets also increases the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set.
|
According to the given paragraph, how do convolutional neural networks avoid overfitting in machine learning?
|
A Convolutional Neural Network (CNN) has a fully connected layer in which each neuron is connected to every neuron in the next layer, this structure helps to avoid overfitting
|
null | false
| null |
Why is Lafayette, California such an appealing place to live?
|
Lafayette, California is a small that is about a 30-minute drive, east of San Francisco in Contra Costa County. To get to Lafayette from San Francisco, you have to drive through a tunnel in the Sibley Volcanic Preserve. Lafayette has a small downtown area with cheery stores, restaurants and coffee shops. It is home to about 25,000 residents and it is very walkable and easy to access public transportation. The weather in Lafayette is considered to be a Mediterranean climate, so it is generally warm and sunny. There is a beautiful reservoir with a walking trail, paddle boats and a well-known lookout tower. The schools are considered to be some of the top schools in California, which makes the area very appealing for families.
|
|
1909.05246
| true
| null |
BLEU: We use the Bilingual Evaluation Understudy (BLEU) BIBREF34 metric which is commonly used in machine translation tasks. The BLEU metric can be used to evaluate dialogue generation models as in BIBREF5, BIBREF35. The BLEU metric is a word-overlap metric which computes the co-occurrence of N-grams in the reference and the generated response and also applies the brevity penalty which tries to penalize far too short responses which are usually not desired in task-oriented chatbots. We compute the BLEU score using all generated responses of our systems.
Per-turn Accuracy: Per-turn accuracy measures the similarity of the system generated response versus the target response. Eric and Manning eric2017copy used this metric to evaluate their systems in which they considered their response to be correct if all tokens in the system generated response matched the corresponding token in the target response. This metric is a little bit harsh, and the results may be low since all the tokens in the generated response have to be exactly in the same position as in the target response.
Per-Dialogue Accuracy: We calculate per-dialogue accuracy as used in BIBREF8, BIBREF5. For this metric, we consider all the system generated responses and compare them to the target responses. A dialogue is considered to be true if all the turns in the system generated responses match the corresponding turns in the target responses. Note that this is a very strict metric in which all the utterances in the dialogue should be the same as the target and in the right order.
F1-Entity Score: Datasets used in task-oriented chores have a set of entities which represent user preferences. For example, in the restaurant domain chatbots common entities are meal, restaurant name, date, time and the number of people (these are usually the required entities which are crucial for making reservations, but there could be optional entities such as location or rating). Each target response has a set of entities which the system asks or informs the user about. Our models have to be able to discern these specific entities and inject them into the generated response. To evaluate our models we could use named-entity recognition evaluation metrics BIBREF36. The F1 score is the most commonly used metric used for the evaluation of named-entity recognition models which is the harmonic average of precision and recall of the model. We calculate this metric by micro-averaging over all the system generated responses.
BLEU: We use the Bilingual Evaluation Understudy (BLEU) BIBREF34 metric which is commonly used in machine translation tasks. The BLEU metric can be used to evaluate dialogue generation models as in BIBREF5, BIBREF35. The BLEU metric is a word-overlap metric which computes the co-occurrence of N-grams in the reference and the generated response and also applies the brevity penalty which tries to penalize far too short responses which are usually not desired in task-oriented chatbots. We compute the BLEU score using all generated responses of our systems.
Per-turn Accuracy: Per-turn accuracy measures the similarity of the system generated response versus the target response. Eric and Manning eric2017copy used this metric to evaluate their systems in which they considered their response to be correct if all tokens in the system generated response matched the corresponding token in the target response. This metric is a little bit harsh, and the results may be low since all the tokens in the generated response have to be exactly in the same position as in the target response.
Per-Dialogue Accuracy: We calculate per-dialogue accuracy as used in BIBREF8, BIBREF5. For this metric, we consider all the system generated responses and compare them to the target responses. A dialogue is considered to be true if all the turns in the system generated responses match the corresponding turns in the target responses. Note that this is a very strict metric in which all the utterances in the dialogue should be the same as the target and in the right order.
F1-Entity Score: Datasets used in task-oriented chores have a set of entities which represent user preferences. For example, in the restaurant domain chatbots common entities are meal, restaurant name, date, time and the number of people (these are usually the required entities which are crucial for making reservations, but there could be optional entities such as location or rating). Each target response has a set of entities which the system asks or informs the user about. Our models have to be able to discern these specific entities and inject them into the generated response. To evaluate our models we could use named-entity recognition evaluation metrics BIBREF36. The F1 score is the most commonly used metric used for the evaluation of named-entity recognition models which is the harmonic average of precision and recall of the model. We calculate this metric by micro-averaging over all the system generated responses.
|
Is human evaluation performed?
|
No.
|
null | false
| null |
In what month did the Russian October revolution take place
|
November
|
|
null | false
| null |
Can you suggest 3 disadvantages of Genetic modification technologies like CRISPR ?
|
1.Uncontrollable mutations
2.Uncontrollable population growth
3.Strong ethical concerns about discrimination
|
|
null | false
| null |
What are some of the reasons people like to visit Brazil?
|
Brazil is a South American country with a diverse landscape and cultural mix. From subtropical grasslands in the South to drier biomes in the northeast to the Amazon rainforest, many people visit the country to experience one of the world's biodiverse hotspots. Several species are endemic to Brazil, particularly in the Amazon region. The country is also known for a culture that blends European, African and Indigenous influences. Many visitors are intrigued by its diverse culture. The nation received many migrants from all over the world in the 19th and 20th centuries, leading to the largest Japanese community outside of Japan, largest Italian population outside of Italy, and the largest population of SubSaharan African descent outside of Nigeria. Many of the cultural expressions of Brazil - capoeira, the large Carnaval parades, Brazilian soccer and jiu-jitsu - have found a devotees outside of the country.
|
|
null | false
| null |
List some of the greatest leadership and business books ever written.
|
1. Why Should Anyone Be Led by You? by Rob Goffee
2. Competing Against Luck: The Story of Innovation and Customer Choice. by Clayton M Christensen, David S. Duncan, Karen Dillon and Taddy Hall
3. Switch: How to Change Things When Change is Hard. by Dan Heath and Chip Heath
4. GOOD STRATEGY/BAD STRATEGY by Richard Rumelt
5. How to Win Friends and Influence People by Dale Carnegie
6. The Advantage: Why Organizational Health Trumps Everything Else in Business by Patrick M. Lencioni
7. Crucial Conversation by Joseph Grenny, Kerry Patterson, Ron McMillan, Al Switzler and Emily Gregory
8. Dare to Lead by Brené Brown (Author)
9. Finish What You Start by Peter Hollins (Author)
10. Radical Candor by Kim Scott (Author)
11. Turn The Ship Around! by L. David Marquet (Author), Stephen R Covey (Foreword)
12. Leaders Eat Last by Simon Sinek (Author)
13. Good To Great by Jim Collins (Author)
14. Decisive by Chip Heath (Author), Dan Heath (Author)
15. Atomic Habits by James Clear
16. Thinking, Fast and Slow by Daniel Kahneman (Author)
17. Our Iceberg is Melting by John Kotter (Author), Holger Rathgeber (Author)
18. Multipliers by Liz Wiseman (Author)
19. Extreme Ownership by Jocko Willink (Author)
20. Trillion Dollar Coach by Eric Schmidt (Author), Jonathan Rosenberg (Author), Alan Eagle (Author)
|
|
null | false
| null |
Which is the albino direwolf in Game of Thrones?
|
The direwolf, Ghost, is the albino pup and is adopted by Jon Snow
|
|
null | false
| 28
|
Our main experiments are on the WikiBio dataset BIBREF0 , which is automatically constructed and contains many divergent references. In § SECREF47 we also present results on the data released as part of the WebNLG challenge.
We developed several models of varying quality for generating text from the tables in WikiBio. This gives us a diverse set of outputs to evaluate the automatic metrics on. Table TABREF32 lists the models along with their hyperparameter settings and their scores from the human evaluation (§ SECREF33 ). Our focus is primarily on neural sequence-to-sequence methods since these are most widely used, but we also include a template-based baseline. All neural models were trained on the WikiBio training set. Training details and sample outputs are included in Appendices SECREF56 & SECREF57 .
We divide these models into two categories and measure correlation separately for both the categories. The first category, WikiBio-Systems, includes one model each from the four families listed in Table TABREF32 . This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs. The second category, WikiBio-Hyperparams, includes 13 different hyperparameter settings of PG-Net BIBREF19 , which was the best performing system overall. 9 of these were obtained by varying the beam size and length normalization penalty of the decoder network BIBREF23 , and the remaining 4 were obtained by re-scoring beams of size 8 with the information extraction model described in § SECREF4 . All the models in this category produce high quality fluent texts, and differ primarily on the quantity and accuracy of the information they express. Here we are testing whether a metric can be used to compare similar systems with a small variation in performance. This is an important use-case as metrics are often used to tune hyperparameters of a model.
The first category, WikiBio-Systems, includes one model each from the four families listed in Table 1. This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs.
|
What is the task of Category WikiBio-Systems?
|
This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs.
|
null | false
| null |
what is the difference between a tale and a tail
|
tale is a noun referencing narration events vs a tail could be rear end of an animal
|
|
null | false
| 284
|
With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
|
Are they the first to experiment with deep learning architectures for the hate speech detection task?
|
Yes.
|
null | false
| null |
Washington played an indispensable role in adopting and ratifying the Constitution of the United States, which replaced the Articles of Confederation in 1789 and remains the world's longest-standing written and codified national constitution to this day. He was then twice elected president by the Electoral College unanimously. As the first U.S. president, Washington implemented a strong, well-financed national government while remaining impartial in a fierce rivalry that emerged between cabinet members Thomas Jefferson and Alexander Hamilton. During the French Revolution, he proclaimed a policy of neutrality while sanctioning the Jay Treaty. He set enduring precedents for the office of president, including use of the title "Mr. President" and taking an Oath of Office with his hand on a Bible. His Farewell Address on September 19, 1796, is widely regarded as a preeminent statement on republicanism.
|
Name two cabinet members mentioned in the following paragraph. Provide these as a bulleted list in order of appearance.
|
* Thomas Jefferson
* Alexander Hamilton
|
null | false
| null |
Currently, only the United States fulfills the criteria to be considered a superpower. However, the United States is no longer the only uncontested foremost superpower and the world's sole hyperpower to dominate in every domain (i.e. military, culture, economy, technology, diplomatic).
Since the 1990s, China, the European Union, India, and Russia have been commonly described as potential superpowers. Japan was formerly considered a potential superpower due to its high economic growth. However, its status as a potential superpower has eroded since the 1990s due to an aging population and economic stagnation.
|
Who is the next superpower country?
|
A nation with the capacity to have great power and influence on the international stage is considered a prospective superpower. These nations have the ability to significantly influence the world due to their combination of economic, political, and military might. While the United States and the Soviet Union were generally considered superpowers during the Cold War, the phrase has now come to refer to other nations as well.
Because of its fast expanding economy and military might, China is sometimes mentioned as a potential superpower. The nation's Belt and Road Initiative is viewed as a major step toward enhancing its worldwide influence. India, on the other hand, is a possible threat because of its enormous population, quickly expanding economy, and potent military.
Due to its combined economic and political might, the European Union is another prospective superpower. Despite having a small military, it has a substantial amount of influence and soft power. Another nation that has been mentioned as a prospective superpower is Russia, which has a sizable military and abundant natural resources. Yet, recent economic troubles could hinder its ability to rise to superpower status.
Brazil's enormous population, wealth of natural resources, and expanding economy are other factors that make it a prospective powerhouse. The nation's participation in international negotiations on climate change as well as its leadership in Latin America have contributed to an increase in its impact on the global scene.
|
null | false
| null |
Lee was born at home on 16 September 1923, the first child to Lee Chin Koon, a Semarang born Singaporean, and Chua Jim Neo, at 92 Kampong Java Road in Singapore, then part of the British Empire. Both of Lee's parents were English-educated third-generation Straits Chinese, with his paternal side being of Hakka descent from Dabu County. He was named 'Kuan Yew', meaning 'light and brightness', alternately meaning 'bringing great glory to one's ancestors'. Lee's paternal grandfather Lee Hoon Leong, who was described as "especially westernised", had worked on British ships as a purser, and hence gave Lee the Western name 'Harry'
As prime minister from 1959 to 1990, Lee presided over many of Singapore's advancements. He oversaw Singapore's transformation from an island nation with a high illiteracy rate and no natural resources into a developed country with a high-income economy within a single generation, commonly termed (from his autobiography) as 'From the third world to the first world'. Singapore's gross national product per capita (GNP) rose from $1,240 in 1959 to $18,437 in 1990. The unemployment rate in Singapore dropped from 13.5% in 1959 to 1.7% in 1990. External trade increased from $7.3 billion in 1959 to $205 billion in 1990. In other areas, the life expectancy at birth for Singaporeans rose from 65 years at 1960 to 74 years in 1990. The population of Singapore increased from 1.6 million in 1959 to 3 million in 1990. The number of public flats in Singapore rose from 22,975 in 1959 (then under the Singapore Improvement Trust) to 667,575 in 1990. The Singaporean literacy rate increased from 52% in 1957 to 90% in 1990. Telephone lines per 100 Singaporeans increased from 3 in 1960 to 38 in 1990. Visitor arrivals to Singapore rose from 100,000 in 1960 to 5.3 million in 1990.
|
Given a reference text about Lee Kuan Yew, tell me when they were born, when they were president, and what their most famous accomplishment was in office.
|
Lee Kuan Yew was born on 16 September 1923.
He served as the inaugural Prime Minister of Singapore from 1959 to 1990.
His most famous accomplishment in office was overseeing Singapore's transformation from an island nation with a high illiteracy rate and no natural resources into a developed country with a high-income economy within a single generation, commonly termed (from his autobiography) as 'From the third world to the first world.
|
null | false
| null |
According to Chapman, the five "love languages" are: words of affirmation (compliments), quality time, receiving gifts, acts of service, and physical touch.
Examples are given from his counseling practice, as well as questions to help determine one's own love languages. According to Chapman's theory, each person has one primary and one secondary love language.
Chapman suggests that to discover another person's love language, one must observe the way they express love to others, and analyze what they complain about most often and what they request from their significant other most often. He theorizes that people tend to naturally give love in the way that they prefer to receive love, and better communication between couples can be accomplished when one can demonstrate caring to the other person in the love language the recipient understands.
An example would be: if a husband's love language is acts of service, he may be confused when he does the laundry and his wife does not perceive that as an act of love, viewing it as simply performing household duties, because the love language she comprehends is words of affirmation (verbal affirmation that he loves her). She may try to use what she values, words of affirmation, to express her love to him, which he would not value as much as she does. If she understands his love language and mows the lawn for him, he perceives it in his love language as an act of expressing her love for him; likewise, if he tells her he loves her, she values that as an act of love.
|
What the five love languages?
|
The five love languages include words of affirmation, quality time, receiving gifts, acts of service, and physical touch.
|
null | false
| null |
Write a review of Brandon Sanderson's book, The Hero of Ages
|
Sanderson neatly ties together loose ends in a gripping end to the trilogy. Overall, the book was largely plot driven. By now Vinn and co are well developed characters, and don't change as much. Sazed has an existential crisis though, which is interesting. Instead, Sanderson focuses on their struggle against a crazy antagonist and slowly dribbles out the last few mysteries of the world they live in.
|
|
null | false
| null |
Bainbridge Island is a city and island in Kitsap County, Washington. It is located in Puget Sound. The population was 23,025 at the 2010 census and an estimated 25,298 in 2019, making Bainbridge Island the second largest city in Kitsap County.
The island is separated from the Kitsap Peninsula by Port Orchard, with Bremerton lying to the southwest. Bainbridge Island is a suburb of Seattle, connected via the Washington State Ferries system and to Poulsbo and the Suquamish Indian Reservation by State Route 305, which uses the Agate Pass Bridge.
|
From the passage provided, extract the population of Bainbridge Island
|
The population was 23,025 at the 2010 census
|
null | false
| null |
As an amateur, Fury represented both England and Ireland. Fury represented Ireland three times at international level. He was based out of the Holy Family Boxing Club in Belfast, Northern Ireland, and later switched to the Smithboro Club in County Monaghan, Ireland. In a double international match against an experienced Polish team in 2007, the Irish team lost 12–6 overall; Fury, however, was victorious in both his fights in Rzeszów and Białystok. In another Irish match against the US, Fury won his bout by knockout. He won bronze at the AIBA Youth World Boxing Championships in 2006.
In England, while representing Jimmy Egan's Boxing Academy in Wythenshawe, Manchester, he participated in the senior national championships in 2006 but was beaten by David Price 22–8. In May 2007, he won the EU Junior Championship, defeating Istvan Bernath in the final. In July 2007 he won silver at the European Junior Championship, losing to Maxim Babanin in the final.
As a junior, Fury was ranked number three in the world behind the Russians Maxim Babanin and Andrey Volkov, but did not get the chance to represent Great Britain at the 2008 Olympics because each country is restricted to one boxer per weight division and David Price was selected. Price came up through the amateur Olympic programme. Fury also unsuccessfully tried to qualify for Ireland. Speaking in 2011, Fury said "I should have gone to the Olympic games in 2008 and won a gold medal for Ireland, but I was denied the chance to go and do it" He was also forced to withdraw from the Irish national championships after officials from the Holy Trinity Boxing Club in West Belfast, the club of the then Irish amateur heavyweight champion, submitted a protest regarding his eligibility as he was not born in Ireland.
Fury won the ABA super-heavyweight title in 2008 by defeating Damien Campbell 19:1. He turned professional later that year. Feeling disillusioned with amateur boxing, he decided not to wait for the 2012 Olympics. He finished with an amateur record of 31–4 (26 KOs).
|
What was Tyson Fury's fight record as an amateur?
|
He finished with an amateur record of 31–4 (26 KOs
|
null | false
| null |
What is Scala?
|
Scala is a strong statically typed high-level general-purpose programming language that supports both object-oriented programming and functional programming.
|
|
null | false
| null |
How can a balance be drawn between capitalism and economic equality?
|
Capitalism, an economic system that promotes the private generation and ownership of wealth and resources, helps promote innovations and economic growth. However, when left unchecked, it can also be a factor in economic inequality, as people are generally unequal in terms of ideas, drive/grit to push through, and access to capital resources. These provide some dis-proportional advantages of the economic wealth and resources to a few. In an ideal world, most players are expected to be considerate and mindful of equitability and balanced economic development. However, this is seldom the case, hence an approach for drawing a balance is by having government policies and laws, that protect the interest of the general citizenry and prevent bad players from taking advantage of the system. This is a very difficult problem, and a lot of countries are still struggling with this dilemma of striking a balance.
|
|
null | false
| null |
What are five different things you could use a ruler to do?
|
You could use a ruler to measure the length of things, to draw a straight line, to check if something is straight, to mark a spot in a book or to hold down a stack of papers
|
|
null | false
| 147
|
Table TABREF14 shows the effectiveness of our model (multi-task transformer) over the baseline transformer BIBREF8 . Our model achieves significant performance gains in the test sets over the baseline for both Italian and Finnish query translation. The overall low MAP for NMT can possibly be improved with larger TC. Moreover, our model validation approach requires access to RC index, and it slows down overall training process. Hence, we could not train our model for a large number of epochs - it may be another cause of the low performance.
We want to show that translation terms generated by our multi-task transformer are roughly equally likely to be seen in the Europarl corpus (TC) or the CLEF corpus (RC). Given a translation term INLINEFORM0 , we compute the ratio of the probability of seeing INLINEFORM1 in TC and RC, INLINEFORM2 . Here, INLINEFORM3 and INLINEFORM4 is calculated similarly. Given a query INLINEFORM5 and its translation INLINEFORM6 provided by model INLINEFORM7 , we calculate the balance of INLINEFORM8 , INLINEFORM9 . If INLINEFORM10 is close to 1, the translation terms are as likely in TC as in RC. Figure FIGREF15 shows the balance values for transformer and our model for a random sample of 20 queries from the validation set of Italian queries, respectively. Figure FIGREF16 shows the balance values for transformer and our model for a random sample of 20 queries from the test set of Italian queries, respectively. It is evident that our model achieves better balance compared to baseline transformer, except for a very few cases.
Given a query INLINEFORM0 , consider INLINEFORM1 as the set of terms from human translation of INLINEFORM2 and INLINEFORM3 as the set of translation terms generated by model INLINEFORM4 . We define INLINEFORM5 and INLINEFORM6 as precision and recall of INLINEFORM7 for model INLINEFORM8 . In Table TABREF19 , we report average precision and recall for both transformer and our model across our train and validation query set over two language pairs. Our model generates precise translation, i.e. it avoids terms that might be useless or even harmful for retrieval. Generally, from our observation, avoided terms are highly likely terms from TC and they are generated because of translation model overfitting. Our model achieves a regularization effect through an auxiliary task. This confirms results from existing multi-tasking literature BIBREF15 .
To explore translation quality, consider pair of sample translations provided by two models. For example, against an Italian query, medaglia oro super vinse medaglia oro super olimpiadi invernali lillehammer, translated term set from our model is {gold, coin, super, free, harmonising, won, winter, olympics}, while transformer output is {olympic, gold, one, coin, super, years, won, parliament, also, two, winter}. Term set from human translation is: {super, gold, medal, won, lillehammer, olypmic, winter, games}. Transformer comes up with terms like parliament, also, two and years that never appears in human translation. We found that these terms are very likely in Europarl and rare in CLEF. Our model also generates terms such as harmonising, free, olympics that not generated by transformer. However, we found that these terms are equally likely in Europarl and CLEF.
To explore translation quality, consider pair of sample translations provided by two models.
|
What should be considered to explore translation quality?
|
Pair of sample translations provided by two models
|
null | false
| null |
Adventfjorden (Advent Bay) is a 7 km long and 4 km wide bay running south-eastwards from the southern side of Isfjorden, on the west coast of Spitsbergen in Svalbard. The name represents a corruption of Adventure Bay - probably named after the Hull-built whaleship Adventure, which operated in Isfjorden in 1656. The fjord was originally[when?] known as Klass Billen Bay. At the head of Adventfjorden is Adventdalen (Advent Valley).
The former mining-camp of Longyearbyen (now Svalbard's main town) lies on the fjord's southwestern shore, while another former mining-camp, Hiorthhamn, is located on its northeastern shore.
|
Given a reference text about Adventfjorden, how wide and long is it?
|
Adventfjorden is 4km wide and 7km long.
|
1911.12569
| false
| null |
We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. The SSEC corpus is an annotation of the SemEval 2016 Task 6 corpus with emotion labels. The re-annotation of the SemEval 2016 Task 6 corpus helps to bridge the gap between the unavailability of a corpus with sentiment and emotion labels. The SemEval 2016 corpus contains tweets which are classified into positive, negative or other. It contains 2,914 training and 1,956 test instances. The SSEC corpus is annotated with anger, anticipation, disgust, fear, joy, sadness, surprise and trust labels. Each tweet could belong to one or more emotion classes and one sentiment class. Table TABREF15 shows the data statistics of SemEval 2016 task 6 and SSEC which are used for sentiment and emotion analysis, respectively.
We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15.
|
What are the datasets used for training?
|
The answers are shown as follows:
* SemEval 2016 Task 6 BIBREF7
* Stance Sentiment Emotion Corpus (SSEC) BIBREF15
|
null | false
| null |
The guitar introduced into the popular market several features that were innovative for electric guitars in the mid-1950s. The distinctive body shape, which has become commonplace among electric guitars, was revolutionary for the time period, and for the first time a mass-market electric guitar did not significantly resemble earlier acoustic models. The double cutaway, elongated horns, and heavily contoured back were all designed for better balance and comfort to play while standing up and slung off the shoulder with a strap. The three-pickup design offered players increased versatility and choice in tone quality over earlier one- and two-pickup electric guitars, and a responsive and simplified vibrato arm integrated into the bridge plate, which marked a significant design improvement over other vibrato systems, such as those manufactured by Bigsby. All of these design elements were popularized and later became an industry standard due to the success of the Stratocaster. The Fender Stratocaster is one of the most iconic electric guitar models of all time.
|
Using the given text, summarize the main features of the Fender Stratocaster
|
The Fender Stratocaster is an electric guiar model introduced in the 1950s. Some of its main design features were part of its revolutionary body shape. For the first time, an electric guitar didn't look like an acoustic counterpart. The double cutaway, long horns, three-pickup and a simplified vibrato arm integrated into the bridge plate are some of the features of the Fender Stratocaster. All of these features, with some variations, became standard in the design of electric guitars.
|
null | false
| 66
|
The acoustic model is a convolutional neural network with gated linear units BIBREF0 , which is fed with the output of the learnable front-end. Following BIBREF11 , the networks uses a growing number of channels, and dropout BIBREF19 for regularization. These acoustic models are trained to predict letters directly with the Auto Segmentation Criterion (ASG) BIBREF20 . The only differences between the WSJ and Librispeech models are their depth, the number of feature maps per layer, the receptive field and the amount of dropout.
These acoustic models are trained to predict letters directly with the Auto Segmentation Criterion (ASG) [22].
|
How do these acoustic models manage to predict letters directly?
|
These acoustic models are trained to predict letters directly with the Auto Segmentation Criterion (ASG) .
|
null | false
| null |
Identify which car manufacturer is German or American: Audi, Buick
|
Audi is German, Buick is American
|
|
null | false
| null |
What are the words of House Ambrose?
|
"Never Resting"
|
|
null | false
| 214
|
The growing popularity of online interactions through social media has been shown to have both positive and negative impacts. While social media improves information sharing, it also facilitates the propagation of online harassment, including hate speech. These negative experiences can have a measurable negative impact on users. Recently, the Pew Research Center BIBREF0 reported that “roughly four-in-ten Americans have personally experienced online harassment, and 63% consider it a major problem.”
To address the growing problem of online hate, an extensive body of work has focused on developing automatic hate speech detection models and datasets BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. However, simply detecting and blocking hate speech or suspicious users often has limited ability to prevent these users from simply turning to other social media platforms to continue to engage in hate speech as can be seen in the large move of individuals blocked from Twitter to Gab BIBREF9. What's more, such a strategy is often at odds with the concept of free speech. As reported by the Pew Research Center BIBREF0, “Despite this broad concern over online harassment, 45% of Americans say it is more important to let people speak their minds freely online; a slightly larger share (53%) feels that it is more important for people to feel welcome and safe online.” The special rapporteurs representing the Office of the United Nations High Commissioner for Human Rights (OHCHR) have recommended that “The strategic response to hate speech is more speech.” BIBREF10 They encourage to change what people think instead of merely changing what they do, so they advocate more speech that educates about cultural differences, diversity, and minorities as a better strategy to counter hate speech.
Therefore, in order to encourage strategies of countering online hate speech, we propose a novel task of generative hate speech intervention and introduce two new datasets for this task. Figure FIGREF5 illustrates the task. Our datasets consist of 5K conversations retrieved from Reddit and 12k conversations retrieved from Gab. Distinct from existing hate speech datasets, our datasets retain their conversational context and introduce human-written intervention responses. The conversational context and intervention responses are critical in order to build generative models to automatically mitigate the spread of these types of conversations.
To summarize, our contributions are three-fold:
We introduce the generative hate speech intervention task and provide two fully-labeled hate speech datasets with human-written intervention responses.
Our data is collected in the form of conversations, providing better context.
The two data sources, Gab and Reddit, are not well studied for hate speech. Our datasets fill this gap.
Due to our data collecting strategy, all the posts in our datasets are manually labeled as hate or non-hate speech by Mechanical Turk workers, so they can also be used for the hate speech detection task. The performance of commonly-used classifiers on our datasets is shown in Section SECREF6.
Our datasets consist of 5K conversations retrieved from Reddit and 12k conversations retrieved from Gab.
|
What do the authors' datasets consist of?
|
The datasets consist of 5K conversations retrieved from Reddit and 12k conversations retrieved from Gab.
|
null | false
| 6
|
Recently, with the emergence of neural seq2seq models, abstractive summarization methods have seen great performance strides BIBREF0, BIBREF1, BIBREF2. However, complex neural summarization models with thousands of parameters usually require a large amount of training data. In fact, much of the neural summarization work has been trained and tested in news domains where numerous large datasets exist. For example, the CNN/DailyMail (CNN/DM) BIBREF3, BIBREF4 and New York Times (NYT) datasets are in the magnitude of 300k and 700k documents, respectively. In contrast, in other domains such as student reflections, summarization datasets are only in the magnitude of tens or hundreds of documents (e.g., BIBREF5). We hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models, and we will indeed later show that this is the case for student reflections.
To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, we combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline.
|
What did the authors do with using the proposed model?
|
The authors explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline.
|
null | false
| null |
Give me some tips for cooking the best bolognese sauce.
|
In order to cook bolognese sauce, it is important to use quality ingredients. You're going to need the following: Italian tomato sauce, onion, celery, carrot, minced meat, extra virgin olive oil, salt, white or red wine and milk. Here are some useful tips:
- Take your time. Cooking a great bolognese sauce takes at least 4 hours. Set the stove level at low.
- Start by cooking the "sofritto". This is a mix of chopped onions, celery and carrots. -
- Add the meat to the "sofritto". Be sure to seal it so it doesn't leak water into your sauce.
- Add some wine and boil it completely.
- When there is no liquid left, add the tomato sauce and some water, and cook it for at 3 to 5 hours. You can add more water during the process if needed.
|
|
null | false
| 65
|
We trained ELMo models for seven languages: Slovenian, Croatian, Finnish, Estonian, Latvian, Lithuanian and Swedish. To obtain high-quality embeddings, we used large monolingual corpora from various sources for each language. Some corpora are available online under permissive licences, others are available only for research purposes or have limited availability. The corpora used in training datasets are a mix of news articles and general web crawl, which we preprocessed and deduplicated. Below we shortly describe the used corpora in alphabetical order of the involved languages. Their names and sizes are summarized in Table TABREF3.
Croatian dataset include hrWaC 2.1 corpus BIBREF9, Riznica BIBREF10, and articles of Croatian branch of Styria media house, made available to us through partnership in a joint project. hrWaC was built by crawling the .hr internet domain in 2011 and 2014. Riznica is composed of Croatian fiction and non-fiction prose, poetry, drama, textbooks, manuals, etc. The Styria dataset consists of 570,219 news articles published on the Croatian 24sata news portal and niche portals related to 24sata.
Estonian dataset contains texts from two sources, CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings BIBREF8, and news articles made available to us by Ekspress Meedia due to partnership in the project. Ekspress Meedia dataset is composed of Estonian news articles between years 2009 and 2019. The CoNLL 2017 corpus is composed of Estonian Wikipedia and webcrawl.
Finnish dataset contains articles by Finnish news agency STT, Finnish part of the CoNLL 2017 dataset, and Ylilauta downloadable version BIBREF11. STT news articles were published between years 1992 and 2018. Ylilauta is a Finnish online discussion board; the corpus contains parts of the discussions from 2012 to 2014.
Latvian dataset consists only of the Latvian portion of the ConLL 2017 corpus.
Lithuanian dataset is composed of Lithuanian Wikipedia articles from 2018, DGT-UD corpus, and LtTenTen. DGT-UD is a parallel corpus of 23 official languages of the EU, composed of JRC DGT translation memory of European law, automatically annotated with UD-Pipe 1.2. LtTenTen is Lithuanian web corpus made up of texts collected from the internet in April 2014 BIBREF12.
Slovene dataset is formed from the Gigafida 2.0 corpus BIBREF13. It is a general language corpus composed of various sources, mostly newspapers, internet pages, and magazines, but also fiction and non-fiction prose, textbooks, etc.
Swedish dataset is composed of STT Swedish articles and Swedish part of CoNLL 2017. The Finnish news agency STT publishes some of its articles in Swedish language. They were made available to us through partnership in a joint project. The corpus contains those articles from 1992 to 2017.
LtTenTen is Lithuanian web corpus made up of texts collected from the internet in April 2014 (Jakubicek et al., 2013).
|
Does the data used for training only contain formal language?
|
No.
|
null | false
| null |
The marathon is a long-distance foot race with a distance of 42.195 km (26 mi 385 yd), usually run as a road race, but the distance can be covered on trail routes. The marathon can be completed by running or with a run/walk strategy. There are also wheelchair divisions. More than 800 marathons are held throughout the world each year, with the vast majority of competitors being recreational athletes, as larger marathons can have tens of thousands of participants.
|
Given this paragraph about marathon, what is the distance covered in kilometres and what are the most common ways to complete one?
|
The distance covered in a marathon is 42.195 kilometers and it can be completed by running and walking combined, though there are also possibilities for wheelchair participants.
|
null | false
| null |
Which of the following are characters from Harry Potter: Hermione Granger, Harry Potter, Ron Weasley, Dobby, Draco Malfoy, Severus Snape, Professor Dumbledore, Luna Lovegood, Indiana Jones, Darth Vader, Jack Sparrow, Terminator, Batman, Superman, Joker, Gollum.
|
Hermione Granger, Harry Potter, Ron Weasley, Dobby, Draco Malfoy, Severus Snape, Professor Dumbledore, Luna Lovegood are from Harry Potter.
|
|
null | false
| null |
Who are the best five living male comedians in the US?
|
1. Dave Chapelle
2. Adam Sandler
3. Chris Rock
4. Kevin Hart
5. Aziz Ansari
|
|
null | false
| null |
Are you going for a walk today?
|
Today I'm definitely going for a walk, the weather is great! These last rainy days haven't been the best, but now I'm feeling refreshed, so staying home would be a waste.
|
|
null | false
| null |
Hawaii (/həˈwaɪi/ (listen) hə-WY-ee; Hawaiian: Hawaiʻi [həˈvɐjʔi] or [həˈwɐjʔi]) is a state in the Western United States, about 2,000 miles (3,200 km) from the U.S. mainland in the Pacific Ocean. It is the only U.S. state outside North America, the only state that is an archipelago, and the only state in the tropics.
|
Where is Hawaii?
|
Hawaii is located in the Pacific Ocean, about two thousand miles from the west coast of North America
|
null | false
| 123
|
Using GANs, we can train generative models in a two-player game setting between a discriminator and a generator, where the discriminator (a binary classifier) learns to distinguish between real and fake data samples and the generator tries to fool the discriminator by generating authentic and high quality output BIBREF16. GANs have shown to be successful in image generation tasks BIBREF17 and recently, some progress has been observed in text generation BIBREF13, BIBREF12, BIBREF15. Our generator is a language model trained using backpropagation through time BIBREF18. During the pre-training phase we optimize for MLE and during the GAN training phase, we optimize on the creativity reward from the discriminator. The discriminator's encoder has the same architecture as the generator encoder module with the addition of a pooled decoder layer. The decoder contains 3 $[Dense Batch Normalization,ReLU]$ blocks and an addtional $Sigmoid$ layer. The discriminator decoder takes the hidden state at the last time step of a sequence concatenated with both the max-pooled and mean-pooled representation of the hidden states BIBREF19 and outputs a number in the range $[0,1]$. The difficulty of using GANs in text generation comes from the discrete nature of text, making the model non-differentiable hence, we update parameters for the generator model with policy gradients as described in Yu BIBREF15.
We utilize AWD-LSTM BIBREF20 and TransformerXL BIBREF21 based language models. For model hyperparameters please to refer to Supplementary Section Table TABREF5. We use Adam optimizer BIBREF22 with $\beta 1= 0.7$ and $\beta 2= 0.8$ similar to BIBREF19 and use a batch size of 50. Other practices for LM training were the same as BIBREF21 and BIBREF20 for Transformer-XL and AWD-LSTM respectively. We refer to our proposed GAN as Creative-GAN and compare it to a baseline (a language model equivalent to our pre-trained generator) and a GumbelGAN model BIBREF14 across all proposed datasets. We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres. The mix of linguistic styles within this corpus offers the potential for interesting variation during the generation phase. We use the same pre-processing as in earlier work BIBREF19, BIBREF23. We reserve 10% of our data for test set and another 10% for our validation set.
We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective. The discriminator's encoder is initialized to the same weights as our fine-tuned language model. Once we have our fine-tuned encoders for each target dataset, we train in an adversarial manner. The discriminator objective here is to score the quality of the creative text. The discriminator is trained for 3 iterations for every iteration of the generator, a practice seen in previous work BIBREF25. Creative-GAN relies on using the reward from the discriminator BIBREF12, BIBREF15 for backpropagation. We follow a similar training procedure for GumbelGAN. Outputs are generated through sampling over a multinomial distribution for all methods, instead of $argmax$ on the log-likelihood probabilities, as sampling has shown to produce better output quality BIBREF4. Please refer to Supplementary Section Table TABREF6 for training parameters of each dataset and Table TABREF5 for hyperparameters of each encoder. We pick these values after experimentation with our validation set. Training and output generation code can be found online.
We first pre-train our generator on the Gutenberg dataset [25] for 20 epochs and then fine-tune [20] them to our target datasets with a language modeling objective.
|
How many epochs does pretraining set to？
|
20 epochs.
|
2003.09971
| false
| null |
Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\hat{c}}_1 \ldots {\hat{c}}_K$, ${\hat{c}}_k \sim p_{\theta }(c|I)$,
The baseline for each sampled caption is defined as the average reward of the rest samples. That is, for caption $\hat{c}_k$, its baseline is
Following BIBREF21, we sample $K$ captions for each image when applying REINFORCE: ${\hat{c}}_1 \ldots {\hat{c}}_K$, ${\hat{c}}_k \sim p_{\theta }(c|I)$,
The baseline for each sampled caption is defined as the average reward of the rest samples.
|
What baseline function is used in REINFORCE algorithm?
|
The answers are shown as follows:
* baseline for each sampled caption is defined as the average reward of the rest samples
|
null | false
| null |
Siliceous ooze is a type of biogenic pelagic sediment located on the deep ocean floor. Siliceous oozes are the least common of the deep sea sediments, and make up approximately 15% of the ocean floor. Oozes are defined as sediments which contain at least 30% skeletal remains of pelagic microorganisms. Siliceous oozes are largely composed of the silica based skeletons of microscopic marine organisms such as diatoms and radiolarians. Other components of siliceous oozes near continental margins may include terrestrially derived silica particles and sponge spicules. Siliceous oozes are composed of skeletons made from opal silica Si(O2), as opposed to calcareous oozes, which are made from skeletons of calcium carbonate organisms (i.e. coccolithophores). Silica (Si) is a bioessential element and is efficiently recycled in the marine environment through the silica cycle. Distance from land masses, water depth and ocean fertility are all factors that affect the opal silica content in seawater and the presence of siliceous oozes.
|
Given the following paragraph on siliceous ooze, how are oozes defined?
|
Oozes are defined as sediments which contain at least 30 percent skeletal remains of pelagic microorganisms.
|
null | false
| null |
Provide a list of signs that you have won at life
|
1. Your house is paid off
2. Your student loans are paid off
3. You are emotionally and physically healthy
4. You have meaningful relationships
5. You respect and love yourself
6. Your kids and spouse are content
7. You are comfortably retired
|
|
null | false
| null |
The Phoenix Suns are an American professional basketball team based in Phoenix, Arizona. They compete in the National Basketball Association (NBA), as a member of the league's Western Conference Pacific Division. The Suns are the only team in their division not to be based in California, and play their home games at the Footprint Center. The Suns are one of four major league sports teams based in the Phoenix area, but are the only one to bill themselves as representing the city (the other teams - the Cardinals, Coyotes, and Diamondbacks - all bill themselves as representing the state of Arizona).
|
Give me the history and background of the Phoenix Suns basketball franchise
|
The Phoenix Suns are an American professional basketball team based in Phoenix, Arizona. They compete in the National Basketball Association (NBA), as a member of the league's Western Conference Pacific Division. The Suns are the only team in their division not to be based in California, and play their home games at the Footprint Center. The Suns are one of four major league sports teams based in the Phoenix area, but are the only one to bill themselves as representing the city (the other teams - the Cardinals, Coyotes, and Diamondbacks - all bill themselves as representing the state of Arizona).
The franchise began play in 1968 as an expansion team, and their early years were mired in mediocrity, but their fortunes changed in the 1970s after partnering Dick Van Arsdale and Alvan Adams with Paul Westphal; the team reached the 1976 NBA Finals, in what is considered to be one of the biggest upsets in NBA history. However, after failing to capture a championship, the Suns would rebuild around Walter Davis for a majority of the 1980s, until the acquisition of Kevin Johnson in 1988.
Under Johnson, and after trading for perennial NBA All-Star Charles Barkley, and combined with the output of Tom Chambers and Dan Majerle, the Suns reached the playoffs for a franchise-record thirteen consecutive appearances and remained a regular title contender throughout the 1990s, and reached the 1993 NBA Finals. However, the team would again fail to win a championship, and entered into another period of mediocrity until the early part of the 2000s.
In 2004, the Suns signed free agent Steve Nash (who had blossomed for the Dallas Mavericks after being traded from Phoenix six years earlier), and returned into playoff contention. With Nash, Shawn Marion, and Amar'e Stoudemire, and under head coach Mike D'Antoni, the Suns became renowned worldwide for their quick, dynamic offense, which led them to tie a franchise record in wins in the 2004–05 season. Two more top two Conference placements followed, but the Suns again failed to attain an NBA championship, and were forced into another rebuild. After ten consecutive seasons without a playoff berth, the Suns reached the 2021 NBA Finals after acquiring Chris Paul, forming a quartet with their young core of Devin Booker, Deandre Ayton and Mikal Bridges. Following a disappointing playoff exit in 2022, the Suns acquired 13-time All-Star and two-time NBA champion Kevin Durant.
The Suns own the NBA's fifth-best all-time winning percentage, and have the second highest winning percentage of any team to have never won an NBA championship. 11 Hall of Famers have played for Phoenix, while two — Barkley and Nash — won NBA Most Valuable Player (MVP) while playing for the team. Additionally, their Hall of Fame radio announcer Al McCoy is the longest-tenured broadcaster in NBA history.
|
null | false
| null |
Give me a list of the 10 best European cities to visit
|
1. London
2. Paris
3. Madrid
4. Vienna
5. Berlin
6. Zurich
7. Prague
8. Zagreb
9. Budapest
10. Rome
|
|
null | false
| 395
|
Natural language generation and document classification have been widely conducted using neural sequence models based on the encoder–decoder architecture. The underlying technique relies on the production of a context vector as the document representation, to estimate both tokens in natural language generation and labels in classification tasks. By combining recurrent neural networks with attention BIBREF0, the model is able to learn contextualized representations of words at the sentence level. However, higher-level concepts, such as discourse structure beyond the sentence, are hard for an RNN to learn, especially for longer documents. We hypothesize that NLP tasks such as summarization and document classification can be improved through the incorporation of discourse information.
In this paper, we propose to incorporate latent representations of discourse units into neural training. A discourse parser can provide information about the document structure as well as the relationships between discourse units. In a summarization scenario, for example, this information may help to remove redundant information or discourse disfluencies. In the case of document classification, the structure of the text can provide valuable hints about the document category. For instance, a scientific paper follows a particular discourse narrative pattern, different from a short story. Similarly, we may be able to predict the societal influence of a document such as a petition document, in part, from its discourse structure and coherence.
Specifically, discourse analysis aims to identify the organization of a text by segmenting sentences into units with relations. One popular representation is Rhetorical Structure Theory (RST) proposed by mann1988rhet, where the document is parsed into a hierarchical tree, where leaf nodes are the segmented units, known as Entity Discourse Units (EDUs), and non-terminal nodes define the relations.
As an example, in Figure FIGREF1 the two-sentence text has been annotated with discourse structure based on RST, in the form of 4 EDUs connected with discourse labels attr and elab. Arrows in the tree capture the nuclearity of relations, wherein a “satellite” points to its “nucleus”. The Nucleus unit is considered more prominent than the Satellite, indicating that the Satellite is a supporting sentence for the Nucleus. Nuclearity relationships between two EDUs can take the following three forms: Nucleus–Satellite, Satellite–Nucleus, and Nucleus–Nucleus. In this work, we use our reimplementation of the state of the art neural RST parser of BIBREF1, which is based on eighteen relations: purp, cont, attr, evid, comp, list, back, same, topic, mann, summ, cond, temp, eval, text, cause, prob, elab.
This research investigates the impact of discourse representations obtained from an RST parser on natural language generation and document classification. We primarily experiment with an abstractive summarization model in the form of a pointer–generator network BIBREF2, focusing on two factors: (1) whether summarization benefits from discourse parsing; and (2) how a pointer–generator network guides the summarization model when discourse information is provided. For document classification, we investigate the content-based popularity prediction of online petitions with a deep regression model BIBREF3. We argue that document structure is a key predictor of the societal influence (as measured by signatures to the petition) of a document such as a petition.
Our primary contributions are as follows: (1) we are the first to incorporate a neural discourse parser in sequence training; (2) we empirically demonstrate that a latent representation of discourse structure enhances the summaries generated by an abstractive summarizer; and (3) we show that discourse structure is an essential factor in modelling the popularity of online petitions.
We primarily experiment with an abstractive summarization model in the form of a pointer–generator network, focusing on two factors: (1) whether summarization benefits from discourse parsing; and (2) how a pointer–generator network guides the summarization model when discourse information is provided.
|
What are the two factors that their experiment focuses on?
|
(1) whether summarization benefits from discourse parsing; and (2) how a pointer–generator network guides the summarization model when discourse information is provided.
|
1909.00088
| true
| null |
FLOAT SELECTED: Figure 4: Graph of average results by MRT/RRT
FLOAT SELECTED: Table 9: Average results by MRT/RRT
FLOAT SELECTED: Figure 4: Graph of average results by MRT/RRT
FLOAT SELECTED: Table 9: Average results by MRT/RRT
|
Does the model proposed beat the baseline models for all the values of the masking parameter tested?
|
No.
|
null | false
| null |
Tell if these are grown in trees or bushes: apples, strawberries, bananas, pineapples, coconut, raspberries, blueberries.
|
Apples: trees, strawberries: bushes, bananas: trees, pineapples: bushes, coconuts: trees, raspberries: bushes, blueberries: bushes.
|
|
null | false
| 20
|
This research addresses the problem of representing the semantics of text documents in multi-lingual comparable corpora. We present a new approach to this problem, based on neural embeddings, and test it on the task of clustering texts into meaningful classes depending on their topics. The setting is unsupervised, meaning that one either does not have enough annotated data to train a supervised classifier or does not want to be limited with a pre-defined set of classes. There is a lot of sufficiently good approaches to this problem in the case of mono-lingual text collections, but the presence of multiple languages introduces complications.
When a text collection contains documents in several languages, it becomes impractical to simply represent the documents as vectors of words occurring in them ("bag-of-words"), as the words surface forms are different, even in closely-related languages. Thus, one has to invent means to cross the inter-lingual gap and bring all documents to some sort of shared representation, without losing information about their topics or categories.
Of course, one obvious way to solve this problem is to translate all documents into one language, and then apply any clustering algorithm. However, this requires either buying human/machine translation services (which can be expensive if you deal with large text collection) or training own statistical machine translation model (which as a rule requires big parallel corpus). This is the reason to search for other solutions.
In this paper, a novel way of reducing the problem of cross-lingual document representation to a monolingual setting is proposed. Essentially, we train Continuous Bag-of-Words models BIBREF0 on large comparable monolingual corpora for two languages our dataset consists of. This provides us with vector representations of words, allowing to measure their semantic similarity. Then, a linear transformation matrix from vectors of language A to vectors of language B is learned, using a small bilingual dictionary as training data. This matrix is then employed to `project' word and document representations from semantic space of language A to semantic space of language B. It allows not only quite accurate `translation' of words, but also of document `semantic fingerprints' (dense representations of document semantics, calculated as an average of the trained distributional vectors for all the words in document).
This approach is evaluated in a setting, where the input is a collection of documents in several languages and some number of topics to which these documents belong (we also have large monolingual corpora to train distributional models on). For each document, we are given its language, but not its topic. The task is to cluster this collection so that documents belonging to one topic were clustered together, independent of their language. Note that we are interested in clustering the collection as a whole, not each language separately (which is trivial).
Our evaluation data consists of comparable corpora of Russian and Ukrainian academic texts. On this material, we show that the `translated semantic fingerprints' method represents documents in different languages precisely enough to allow almost exact clustering according to document topics, with only 5% of incorrect assignments. It significantly outperforms both naive bag-of-words baseline and the not-so-naive method of `orthographic translation' based on Damerau-Levenshtein distance, even enriched with dictionary mappings. At the same time, it does not require large parallel corpora or a ready-made statistical machine translation model.
The rest of the paper is structured as follows. In Section "Related Work" we describe the foundations of our approach and the related work. Section "Academic texts as Comparable Corpora" introduces the employed corpora and the story behind them. Section "Learning to Translate: Ukrainian-to-Russian transformations" is dedicated to learning the transformation matrix, and Section "Experiment Design and Evaluation" describes our experimental setting and evaluation results. We discuss the findings in Section "Discussion" and conclude in Section "Conclusion and Future Work" , also suggesting directions for future work.
Our evaluation data consists of comparable corpora of Russian and Ukrainian academic texts.
|
What do authors' evaluation data consist of?
|
Comparable corpora of Russian and Ukrainian academic texts.
|
null | false
| null |
Malard (Persian: ملارد, also Romanized as Malārd and Melārd; also known as Malār) is a city in the Central District of Malard County, Tehran province, Iran, and serves as capital of the county.
At the 2006 census, its population was 228,673 in 61,302 households. The following census in 2011 counted 290,817 people in 85,445 households. The latest census in 2016 showed a population of 281,027 people in 86,830 households.
|
Where is Malard
|
Malard is a city in the Central District of Malard County, Tehran province, Iran, and serves as capital of the county.
At the 2006 census, its population was 228,673 in 61,302 households. The following census in 2011 counted 290,817 people in 85,445 households. The latest census in 2016 showed a population of 281,027 people in 86,830 households.
|
null | false
| null |
classify the following as outfits vs appliances: dress, shoes, toaster, jackets, fridge, air fryer, instant pot, vest, gloves, pants, jeans
|
outfits: dress, shoes, jackets, vest, gloves, pants, jeans
appliances: toaster, fridge, air fryer, instant pot
|
|
1607.00410
| true
| null |
In the latter part of this paper, we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics. In the datasets, the source and target have different word distributions, and thus adaptation of output parameters is important. We augment the output parameters to facilitate adaptation. Although we use captioning models in the experiments, our method can be applied to any neural networks trained with a cross-entropy loss.
we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics.
|
Did they only experiment with captioning task?
|
Yes.
|
null | false
| 99
|
In this work we formulate our classification problem as follows: given two classes of news articles, respectively $D$ (disinformation) and $M$ (mainstream), a set of news articles $A_i$ and associated class labels $C_i \in \lbrace D,M\rbrace $, and a set of tweets $\Pi _i=\lbrace T_i^1, T_i^2, ...\rbrace $ each of which contains an Uniform Resource Locator (URL) pointing explicitly to article $A_i$, predict the class $C_i$ of each article $A_i$. There is huge debate and controversy on a proper taxonomy of malicious and deceptive information BIBREF1BIBREF2BIBREF15BIBREF16BIBREF17BIBREF3BIBREF11. In this work we prefer the term disinformation to the more specific fake news to refer to a variety of misleading and harmful information. Therefore, we follow a source-based approach, a consolidated strategy also adopted by BIBREF6BIBREF16BIBREF2BIBREF1, in order to obtain relevant data for our analysis. We collected:
Disinformation articles, published by websites which are well-known for producing low-credibility content, false and misleading news reports as well as extreme propaganda and hoaxes and flagged as such by reputable journalists and fact-checkers;
Mainstream news, referring to traditional news outlets which deliver factual and credible information.
We believe that this is currently the most reliable classification approach, but it entails obvious limitations, as disinformation outlets may also publish true stories and likewise misinformation is sometimes reported on mainstream media. Also, given the choice of news sources, we cannot test whether our methodology is able to classify disinformation vs factual but not mainstream news which are published on niche, non-disinformation outlets.
We collected tweets associated to a dozen US mainstream news websites, i.e. most trusted sources described in BIBREF18, with the Streaming API, and we referred to Hoaxy API BIBREF16 for what concerns tweets containing links to 100+ US disinformation outlets. We filtered out articles associated to less than 50 tweets. The resulting dataset contains overall $\sim $1.7 million tweets for mainstream news, collected in a period of three weeks (February 25th, 2019-March 18th, 2019), which are associated to 6,978 news articles, and $\sim $1.6 million tweets for disinformation, collected in a period of three months (January 1st, 2019-March 18th, 2019) for sake of balance of the two classes, which hold 5,775 distinct articles. Diffusion censoring effects BIBREF14 were correctly taken into account in both collection procedures. We provide in Figure FIGREF4 the distribution of articles by source and political bias for both news domains.
As it is reported that conservatives and liberals exhibit different behaviors on online social platforms BIBREF19BIBREF20BIBREF21, we further assigned a political bias label to different US outlets (and therefore news articles) following the procedure described in BIBREF2. In order to assess the robustness of our method, we performed classification experiments by training only on left-biased (or right-biased) outlets of both disinformation and mainstream domains and testing on the entire set of sources, as well as excluding particular sources that outweigh the others in terms of samples to avoid over-fitting.
For what concerns the Italian scenario we first collected tweets with the Streaming API in a 3-week period (April 19th, 2019-May 5th, 2019), filtering those containing URLs pointing to Italian official newspapers websites as described in BIBREF22; these correspond to the list provided by the association for the verification of newspaper circulation in Italy (Accertamenti Diffusione Stampa). We instead referred to the dataset provided by BIBREF23 to obtain a set of tweets, collected continuously since January 2019 using the same Twitter endpoint, which contain URLs to 60+ Italian disinformation websites. In order to get balanced classes (April 5th, 2019-May 5th, 2019), we retained data collected in a longer period w.r.t to mainstream news. In both cases we filtered out articles with less than 50 tweets; overall this dataset contains $\sim $160k mainstream tweets, corresponding to 227 news articles, and $\sim $100k disinformation tweets, corresponding to 237 news articles. We provide in Figure FIGREF5 the distribution of articles according to distinct sources for both news domains. As in the US dataset, we took into account censoring effects BIBREF14 by excluding tweets published before (left-censoring) or after two weeks (right-censoring) from the beginning of the collection process.
The different volumes of news shared on Twitter in the two countries are due both to the different population size of US and Italy (320 vs 60 millions) but also to the different usage of Twitter platform (and social media in general) for news consumption BIBREF24. Both datasets analyzed in this work are available from the authors on request.
A crucial aspect in our approach is the capability to fully capturing sharing cascades on Twitter associated to news articles. It has been reported BIBREF25 that the Twitter streaming endpoint filters out tweets matching a given query if they exceed 1% of the global daily volume of shared tweets, which nowadays is approximately $5\cdot 10^8$; however, as we always collected less than $10^6$ tweets per day, we did not incur in this issue and we thus gathered 100% of tweets matching our query.
We built Twitter diffusion networks following an approach widely adopted in the literature BIBREF6BIBREF17BIBREF2. We remark that there is an unavoidable limitation in Twitter Streaming API, which does not allow to retrieve true re-tweeting cascades because re-tweets always point to the original source and not to intermediate re-tweeting users BIBREF8BIBREF14; thus we adopt the only viable approach based on Twitter's public availability of data. Besides, by disentangling different interactions with multiple layers we potentially reduce the impact of this limitation on the global network properties compared to the single-layer approach used in our baseline.
Using the notation described in BIBREF26. we employ a multi-layer representation for Twitter diffusion networks. Sociologists have indeed recognized decades ago that it is crucial to study social systems by constructing multiple social networks where different types of ties among same individuals are used BIBREF27. Therefore, for each news article we built a multi-layer diffusion network composed of four different layers, one for each type of social interaction on Twitter platform, namely retweet (RT), reply (R), quote (Q) and mention (M), as shown in Figure FIGREF11. These networks are not necessarily node-aligned, i.e. users might be missing in some layers. We do not insert "dummy" nodes to represent all users as it would have severe impact on the global network properties (e.g. number of weakly connected components). Alternatively one may look at each multi-layer diffusion network as an ensemble of individual graphs BIBREF26; since global network properties are computed separately for each layer, they are not affected by the presence of any inter-layer edges.
In our multi-layer representation, each layer is a directed graph where we add edges and nodes for each tweet of the layer type, e.g. for the RT layer: whenever user $a$ retweets account $b$ we first add nodes $a$ and $b$ if not already present in the RT layer, then we build an edge that goes from $b$ to $a$ if it does not exists or we increment the weight by 1. Similarly for the other layers: for the R layer edges go from user $a$ (who replies) to user $b$, for the Q layer edges go from user $b$ (who is quoted by) to user $a$ and for the M layer edges go from user $a$ (who mentions) to user $b$.
Note that, by construction, our layers do not include isolated nodes; they correspond to "pure tweets", i.e. tweets which have not originated any interactions with other users. However, they are present in our dataset, and their number is exploited for classification, as described below.
We used a set of global network indicators which allow us to encode each network layer by a tuple of features. Then we simply concatenated tuples as to represent each multi-layer network with a single feature vector. We used the following global network properties:
Number of Strongly Connected Components (SCC): a Strongly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $u,v$ there is a path in each direction ($u\rightarrow v$, $v\rightarrow u$).
Size of the Largest Strongly Connected Component (LSCC): the number of nodes in the largest strongly connected component of a given graph.
Number of Weakly Connected Components (WCC): a Weakly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $(u, v)$ there is a path $u \leftrightarrow v$ ignoring edge directions.
Size of the Largest Weakly Connected Component (LWCC): the number of nodes in the largest weakly connected component of a given graph.
Diameter of the Largest Weakly Connected Component (DWCC): the largest distance (length of the shortest path) between two nodes in the (undirected version of) largest weakly connected component of a graph.
Average Clustering Coefficient (CC): the average of the local clustering coefficients of all nodes in a graph; the local clustering coefficient of a node quantifies how close its neighbours are to being a complete graph (or a clique). It is computed according to BIBREF28.
Main K-core Number (KC): a K-core BIBREF13 of a graph is a maximal sub-graph that contains nodes of internal degree $k$ or more; the main K-core number is the highest value of $k$ (in directed graphs the total degree is considered).
Density (d): the density for directed graphs is $d=\frac{|E|}{|V||V-1|}$, where $|E|$ is the number of edges and $|N|$ is the number of vertices in the graph; the density equals 0 for a graph without edges and 1 for a complete graph.
Structural virality of the largest weakly connected component (SV): this measure is defined in BIBREF14 as the average distance between all pairs of nodes in a cascade tree or, equivalently, as the average depth of nodes, averaged over all nodes in turn acting as a root; for $|V| > 1$ vertices, $SV=\frac{1}{|V||V-1|}\sum _i\sum _j d_{ij}$ where $d_{ij}$ denotes the length of the shortest path between nodes $i$ and $j$. This is equivalent to compute the Wiener's index BIBREF29 of the graph and multiply it by a factor $\frac{1}{|V||V-1|}$. In our case we computed it for the undirected equivalent graph of the largest weakly connected component, setting it to 0 whenever $V=1$.
We used networkx Python package BIBREF30 to compute all features. Whenever a layer is empty. we simply set to 0 all its features. In addition to computing the above nine features for each layer, we added two indicators for encoding information about pure tweets, namely the number T of pure tweets (containing URLs to a given news article) and the number U of unique users authoring those tweets. Therefore, a single diffusion network is represented by a vector with $9\cdot 4+2=38$ entries.
Aforementioned network properties can be qualitatively explained in terms of social footprints as follows: SCC correlates with the size of the diffusion network, as the propagation of news occurs in a broadcast manner most of the time, i.e. re-tweets dominate on other interactions, while LSCC allows to distinguish cases where such mono-directionality is somehow broken. WCC equals (approximately) the number of distinct diffusion cascades pertaining to each news article, with exceptions corresponding to those cases where some cascades merge together via Twitter interactions such as mentions, quotes and replies, and accordingly LWCC and DWCC equals the size and the depth of the largest cascade. CC corresponds to the level of connectedness of neighboring users in a given diffusion network whereas KC identifies the set of most influential users in a network and describes the efficiency of information spreading BIBREF17. Finally, d describes the proportions of potential connections between users which are actually activated and SV indicates whether a news item has gained popularity with a single and large broadcast or in a more viral fashion through multiple generations.
For what concerns different Twitter actions, users primarily interact with each other using retweets and mentions BIBREF20.
The former are the main engagement activity and act as a form of endorsement, allowing users to rebroadcast content generated by other users BIBREF31. Besides, when node B retweets node A we have an implicit confirmation that information from A appeared in B's Twitter feed BIBREF12. Quotes are simply a special case of retweets with comments.
Mentions usually include personal conversations as they allow someone to address a specific user or to refer to an individual in the third person; in the first case they are located at the beginning of a tweet and they are known as replies, otherwise they are put in the body of a tweet BIBREF20. The network of mentions is usually seen as a stronger version of interactions between Twitter users, compared to the traditional graph of follower/following relationships BIBREF32.
For what concerns the Italian scenario we first collected tweets with the Streaming API in a 3-week period (April 19th, 2019-May 5th, 2019), filtering those containing URLs pointing to Italian official newspapers websites as described in (Vicario et al. 2019);
|
How do the authors collect tweets for the Italian scenario?
|
The authors first collected tweets with the Streaming API in a 3-week period (April 19th, 2019-May 5th, 2019), filtering those containing URLs pointing to Italian official newspapers websites..
|
1911.09845
| false
| null |
In our work, we focus on comparing various methods that model $p(\mathbf {y}|\mathbf {x})$ differently. We compare our proposed discrete CVAE (DCVAE) with the two-stage sampling approach to three categories of response generation models:
Baselines: Seq2seq, the basic encoder-decoder model with soft attention mechanism BIBREF30 used in decoding and beam search used in testing; MMI-bidi BIBREF5, which uses the MMI to re-rank results from beam search.
CVAE BIBREF14: We adjust the original work which is for multi-round conversation for our single-round setting. For a fair comparison, we utilize the same keywords used in our network pre-training as the knowledge-guided features in this model.
Other enhanced encoder-decoder models: Hierarchical Gated Fusion Unit (HGFU) BIBREF12, which incorporates a cue word extracted using pointwise mutual information (PMI) into the decoder to generate meaningful responses; Mechanism-Aware Neural Machine (MANM) BIBREF13, which introduces latent embeddings to allow for multiple diverse response generation.
We compare our proposed discrete CVAE (DCVAE) with the two-stage sampling approach to three categories of response generation models:
Baselines: Seq2seq, the basic encoder-decoder model with soft attention mechanism BIBREF30 used in decoding and beam search used in testing; MMI-bidi BIBREF5, which uses the MMI to re-rank results from beam search.
CVAE BIBREF14: We adjust the original work which is for multi-round conversation for our single-round setting. For a fair comparison, we utilize the same keywords used in our network pre-training as the knowledge-guided features in this model.
Other enhanced encoder-decoder models: Hierarchical Gated Fusion Unit (HGFU) BIBREF12, which incorporates a cue word extracted using pointwise mutual information (PMI) into the decoder to generate meaningful responses; Mechanism-Aware Neural Machine (MANM) BIBREF13, which introduces latent embeddings to allow for multiple diverse response generation.
|
What other kinds of generation models are used in experiments?
|
The answers are shown as follows:
* Seq2seq
* CVAE
* Hierarchical Gated Fusion Unit (HGFU)
* Mechanism-Aware Neural Machine (MANM)
|
null | false
| 305
|
Human learning occurs through interaction BIBREF0 and multimodal experience BIBREF1 , BIBREF2 . Prior work has argued that machine learning may also benefit from interactive, multimodal learning BIBREF3 , BIBREF4 , BIBREF5 , termed virtual embodiment BIBREF6 . Driven by breakthroughs in static, unimodal tasks such as image classification BIBREF7 and language processing BIBREF8 , machine learning has moved in this direction. Recent tasks such as visual question answering BIBREF9 , image captioning BIBREF10 , and audio-video classification BIBREF11 make steps towards learning from multiple modalities but lack the dynamic, responsive signal from exploratory learning. Modern, challenging tasks incorporating interaction, such as Atari BIBREF12 and Go BIBREF13 , push agents to learn complex strategies through trial-and-error but miss information-rich connections across vision, language, sounds, and actions. To remedy these shortcomings, subsequent work introduces tasks that are both multimodal and interactive, successfully training virtually embodied agents that, for example, ground language in actions and visual percepts in 3D worlds BIBREF3 , BIBREF4 , BIBREF14 .
For virtual embodiment to reach its full potential, though, agents should be immersed in a rich, lifelike context as humans are. Agents may then learn to ground concepts not only in various modalities but also in relationships to other concepts, i.e. that forks are often in kitchens, which are near living rooms, which contain sofas, etc. Humans learn by concept-to-concept association, as shown in child learning psychology BIBREF1 , BIBREF2 , cognitive science BIBREF15 , neuroscience BIBREF16 , and linguistics BIBREF17 . Even in machine learning, contextual information has given rise to effective word representations BIBREF8 , improvements in recommendation systems BIBREF18 , and increased reward quality in robotics BIBREF19 . Importantly, scale in data has proven key in algorithms learning from context BIBREF8 and in general BIBREF20 , BIBREF21 , BIBREF22 .
To this end, we present HoME: the Household Multimodal Environment (Figure 1 ). HoME is a large-scale platform for agents to navigate and interact within over 45,000 hand-designed houses from the SUNCG dataset BIBREF23 . Specifically, HoME provides:
HoME is a general platform extensible to many specific tasks, from reinforcement learning to language grounding to blind navigation, in a real-world context. HoME is also the first major interactive platform to support high fidelity audio, allowing researchers to better experiment across modalities and develop new tasks. While HoME is not the first platform to provide realistic context, we show in following sections that HoME provides a more large-scale and multimodal testbed than existing environments, making it more conducive to virtually embodied learning in many scenarios.
To this end, we present HoME: the Household Multimodal Environment (Figure 1 ). HoME is a large-scale platform for agents to navigate and interact within over 45,000 hand-designed houses from the SUNCG dataset.
|
What is the HoME: the Household Multimodal Environment？
|
It is a large-scale platform for agents to navigate and interact within over 45,000 hand-designed houses from the SUNCG dataset.
|
null | false
| null |
What is the best way to improve your tennis skills?
|
There are many ways that you can improve your tennis skills. The most important point is to practice as tennis requires eyes and muscle coordination. Like many other activities, it requires dedications and practice to improve your skills.
One way to practice is to use a tennis wall. In your tennis club or in some parks, find a wall and use that wall as opponent. This way you can focus on hitting the ball consistently and improve your style.
While practicing with a wall is convenient, you will need to play with different people to improve your skills and respond different balls. There are many clubs that organize tournaments which can be a great opportunity to play with other tennis players.
The most important thing is enjoy your time while playing tennis and keeping to practice consistently.
|
|
null | false
| 323
|
Major progress has been made in different tasks in Natural Language Processing, yet our models are still not able to describe why they make their decisions when summarizing an article, translating a sentence, or answering a question. Lack of meta information (e.g. general world knowledge regarding the task) is one important obstacle in the construction of language understanding models capable of reasoning about their considerations when making decisions (predictions).
Wikipedia is a great resource of world knowledge for human beings, but lacks the proper structure to be useful for the models. To address this issue and make a more structured knowledge-base, we are trying to structure Wikipedia. The final goal is to have, for each Wikipedia article, known entities and sets of attributes, with each attribute linking to other entities wherever possible. The initial step towards this goal is to classify the entities into predefined categories and verify the results using human annotators.
Throughout the past years, many have tried classifying Wikipedia articles into different category sets the majority of which range between 3 to 15 class types BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4. Although useful, such categorization type sets are not much helpful when the classified articles are being used as the training data for Question-Answering systems, since the extracted knowledge-base does not provide detailed enough information to the model.
On the other hand, much larger categorization type sets such as Cyc-Taxonomy BIBREF5, Yago-Taxonomy BIBREF6, or Wikipedia's own taxonomy of categories BIBREF7 are not suitable for our task since the tags are not verifiable for annotators. In addition, taxonomies are not designed in a tree format, so some categories might have multiple super-categories and this would make the verification process much harder for the cases that the article is about multiple different topics.
Considering the mentioned problem requirements, we believe Extended Named Entities Hierarchy BIBREF8, containing 200 fine-grained categories tailored for Wikipedia articles, is the best fitting tag set.
Higashinaka et al. higashinaka2012 were the first to use this extended tag set as the categorization output of the dumped Wikipedia pages while using a hand-extracted feature set for converting the pages into their model input vectors. Following their work, Suzuki et al. suzuki2016 modelled the links between different Wikipedia pages as an augmentation to the extracted input features to the classifier. They also proposed a more complex model for learning the mapping between the converted articles and the labels.
Although providing useful insights, none of the works above have considered the multi-lingual nature of many Wikipedia articles. Hence, we decided to hire annotators and educate them on the Extended Named Entities (ENE) tag set to annotate each article with up to 6 different ENE classes, and exploit the Wikipedia language links in the annotated articles to create our multi-lingual Wikipedia classification dataset. Section 2 details our dataset creation process.
We then used the two models mentioned above, which are to the best of our knowledge the only works close enough to our task in hand, to benchmark our dataset. Section 3 provides more details about our feature selection method and the models. Section 4 presents our experimental setup and the classification results.
We then used the two models mentioned above, which are to the best of our knowledge the only works close enough to our task in hand, to benchmark our dataset.
|
What did they use the two models mentioned above to do?
|
To benchmark their dataset.
|
null | false
| null |
Give me a list of top five budget Android phone
|
1. Samsung Galaxy A54 5G
2. Google Pixel 6a
3. OnePlus Nord N20 5G
4. Moto G Stylus 5G
5. Xiaomi 12 5G
|
|
null | false
| null |
What are some good ice cream toppings that are gross when mixed together?
|
Some ice cream topping pairings that would probably not go well together are cinnamon candy and hot fudge, gummy bears and peanut butter cups, and cookie crumbs and pineapple.
|
|
null | false
| null |
Why is working out good for you?
|
Working out provides many benefits. Studies have shown that working out is good for you long term health. It helps build muscle strength, which is shown to decrease in volume as you age. It also has benefits for your cardiovascular system. Working out also provides mental benefits, such as reduced anxiety and stress.
|
|
null | false
| 13
|
From the point of view of dialog systems providing general information from a knowledge base, the most limiting factor is that a large portion of the questions is understood poorly.
Current approaches BIBREF3 , BIBREF4 can only achieve around 50% accuracy on some question answering datasets. Therefore, we think that there is a room for improvements which can be achieved by interactively asking for additional information in conversational dialogs with users. This extra information can be used for improving policies of dialog systems. We call this approach the interactive learning from dialogs.
We can improve dialog systems in several aspects through interactive learning in a direct interaction with users. First, the most straightforward way obviously is getting the correct answer for questions that the system does not know. We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. We hypothesize that users are willing to give such explanations because it could help them to find answers for their own questions. The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording. This area is extensively studied for the purposes of information retrieval BIBREF5 , BIBREF6 .
The main purpose of the collected dataset is to enable interactive learning using the steps proposed above and potentially to evaluate how different systems perform on this task.
We can improve dialog systems in several aspects through interactive learning in a direct interaction with users. First, the most straightforward way obviously is getting the correct answer for questions that the system does not know. We can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. We hypothesize that users are willing to give such explanations because it could help them to find answers for their own questions. The last source of information that we consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording.
|
In what ways does the author propose to improve the dialogue system?
|
First,they can try to ask users for answers on questions that the system encountered in a conversation with a different user and did not understand it. Second, the system can ask the user for a broader explanation of a question. This explanation could help the system to understand the question and provide the correct answer. In addition, the system can learn correct policy for the question which allows providing answers without asking any extra information for similar questions next time. The last source of information that they consider for interactive learning is rephrasing, which could help when the system does know the concept but does not know the correct wording.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.