[Paper Reading] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
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(1) The document presents an unsupervised method called Sent2Vec to learn sentence embeddings using compositional n-gram features. (2) Sent2Vec extends the continuous bag-of-words model to train sentence embeddings by composing word vectors with n-gram embeddings. (3) Experimental results show Sent2Vec outperforms other unsupervised models on most benchmark tasks, highlighting the robustness of the sentence embeddings produced.
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![1
lUnsupervised training of word representation, such as Word2Vec
[Mikolov et al., 2013] is now routinely trained on very large amounts of
raw text data, and have become ubiquitous building blocks of a majority
of current state-of-the-art NLP applications.
lWhile very useful semantic representations are available for words.
Abstract & Introduction (1)](https://image.slidesharecdn.com/paperreadingnaacl-unsupervisedlearningofsentenceembeddingsusingcompositionaln-gramfeatures-181121025013/85/Paper-Reading-Unsupervised-Learning-of-Sentence-Embeddings-using-Compositional-n-Gram-Features-2-320.jpg)
![4
Approach
Their proposed model (Sent2Vec) can be seen as an extension of the Cl -
BOW [Mikolov et al., 2013] training objecKve to train sentence instead
of word embeddings.
Sent2Vec is a simple unsupervised model allowing to coml -pose
sentence embeddings using word vectors along with n-gram
embeddings, simultaneously training composiKon and the embed-ding
vectors themselves.](https://image.slidesharecdn.com/paperreadingnaacl-unsupervisedlearningofsentenceembeddingsusingcompositionaln-gramfeatures-181121025013/85/Paper-Reading-Unsupervised-Learning-of-Sentence-Embeddings-using-Compositional-n-Gram-Features-5-320.jpg)
![6
Model (2)
lConceptually, Sent2Vec can be interpreted as a natural extension of the
word-contexts from C-BOW [Mikolov et al., 2013] to a larger sentence
context.
!(#): the list of n−grams (including un−igrams)
ι&(') ∈{0, 1}|/|: binary vector encoding !(#) #: sentence
67: source (or context) embed−ding](https://image.slidesharecdn.com/paperreadingnaacl-unsupervisedlearningofsentenceembeddingsusingcompositionaln-gramfeatures-181121025013/85/Paper-Reading-Unsupervised-Learning-of-Sentence-Embeddings-using-Compositional-n-Gram-Features-7-320.jpg)
![8
Model (4)
lSubsampling
lTo select the possible target unigrams (positives), they use subsampling as in
[Joulin et al., 2017; Bojanowski et al., 2017], each word ! being discarded
with probability 1 − $%(!)
()*
:the set of words sampled negaJvely for the word !+ ∈ -
-: sentence
4): source (or context) embed−ding
5): target embed−ding$6 ! ≔
8)
∑):∈; 8):
8):the normalized frequency of ! in the corpus](https://image.slidesharecdn.com/paperreadingnaacl-unsupervisedlearningofsentenceembeddingsusingcompositionaln-gramfeatures-181121025013/85/Paper-Reading-Unsupervised-Learning-of-Sentence-Embeddings-using-Compositional-n-Gram-Features-9-320.jpg)
![15
Conclusion
lIn this paper, they introduce a novel, computationally efficient, unsupervised,
C-BOW-inspired method to train and infer sentence embeddings.
lOn supervised evaluations, their method, on an average, achieves better
performance than all other unsupervised competitors with the exception of
Skip-Thought [Kiros et al., 2015].
lHowever, their model is generalizable, extremely fast to train, simple to
understand and easily interpretable, showing the relevance of simple and
well-grounded representation models in contrast to the models using deep
architectures.
lFuture work could focus on augmenting the model to exploit data with
ordered sentences.](https://image.slidesharecdn.com/paperreadingnaacl-unsupervisedlearningofsentenceembeddingsusingcompositionaln-gramfeatures-181121025013/85/Paper-Reading-Unsupervised-Learning-of-Sentence-Embeddings-using-Compositional-n-Gram-Features-16-320.jpg)
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Domain-Specific Term Extraction for Concept Identification in Ontology Constr...Innovation Quotient Pvt Ltd
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Chunker Based Sentiment Analysis and Tense Classification for Nepali Text
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Efficient Algorithms for Isogeny Computation on Hyperelliptic Curves: Their A...
Efficient Algorithms for Isogeny Computation on Hyperelliptic Curves: Their A...IJCNCJournal We present efficient algorithms for computing isogenies between hyperelliptic curves, leveraging higher genus curves to enhance cryptographic protocols in the post-quantum context. Our algorithms reduce the computational complexity of isogeny computations from O(g4) to O(g3) operations for genus 2 curves, achieving significant efficiency gains over traditional elliptic curve methods. Detailed pseudocode and comprehensive complexity analyses demonstrate these improvements both theoretically and empirically. Additionally, we provide a thorough security analysis, including proofs of resistance to quantum attacks such as Shor's and Grover's algorithms. Our findings establish hyperelliptic isogeny-based cryptography as a promising candidate for secure and efficient post-quantum cryptographic systems.
Structural Response of Reinforced Self-Compacting Concrete Deep Beam Using Fi...
Structural Response of Reinforced Self-Compacting Concrete Deep Beam Using Fi...Journal of Soft Computing in Civil Engineering Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
Compiler Design_Syntax Directed Translation.pptx
Compiler Design_Syntax Directed Translation.pptxRushaliDeshmukh2 When we associate semantic rules with productions, we use two notations:
Syntax-Directed Definitions
Translation Schemes
Syntax-Directed Definitions:
give high-level specifications for translations
hide many implementation details such as order of evaluation of semantic actions.
We associate a production rule with a set of semantic actions, and we do not say when they will be evaluated.
Translation Schemes:
indicate the order of evaluation of semantic actions associated with a production rule.
In other words, translation schemes give a little bit information about implementation details.
Data Structures_Introduction to algorithms.pptx
Data Structures_Introduction to algorithms.pptxRushaliDeshmukh2 Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)
How to use nRF24L01 module with Arduino
How to use nRF24L01 module with ArduinoCircuitDigest Learn how to wirelessly transmit sensor data using nRF24L01 and Arduino Uno. A simple project demonstrating real-time communication with DHT11 and OLED display.
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...Journal of Soft Computing in Civil Engineering Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
MODULE 03 - CLOUD COMPUTING- [BIS 613D] 2022 scheme.pptx![MODULE 03 - CLOUD COMPUTING- [BIS 613D] 2022 scheme.pptx](https://cdn.slidesharecdn.com/ss_thumbnails/module03-cloudcomputing-250506150212-e107fd7e-thumbnail.jpg?width=560&fit=bounds)
MODULE 03 - CLOUD COMPUTING- [BIS 613D] 2022 scheme.pptxAlvas Institute of Engineering and technology, Moodabidri Cloud Platform Architecture over Virtualized Datacenters: Cloud Computing and
Service Models, Data Center Design and Interconnection Networks, Architectural Design of Compute and Storage Clouds, Public Cloud Platforms: GAE, AWS and Azure, Inter-Cloud
Resource Management.
Novel Plug Flow Reactor with Recycle For Growth Control
Novel Plug Flow Reactor with Recycle For Growth ControlChris Harding This is a PFRR process that is highly cost effective and Wolfram Scored to be highly practical and useful for nanometer sized Metal-Organic-Framework
ZJIT: Building a Next Generation Ruby JIT
ZJIT: Building a Next Generation Ruby JITmaximechevalierboisv1 YJIT can make Ruby code run faster, but this is a balancing act, because the JIT compiler itself must consume both memory and CPU cycles to compile and optimize your code while it is running. Furthermore, in large-scale production environments such as those of GitHub, Shopify and Stripe, we end up in a situation where YJIT is compiling the same code over and over again on a very large number of servers, which seems very inefficient.
In this presentation, we will go over the design of ZJIT, a next generation Ruby JIT which aims to save and reuse compiled code between executions. We hope that this will help us eliminate duplicated work while also allowing the compiler to spend more time optimizing code so that we can get better performance.
Process Parameter Optimization for Minimizing Springback in Cold Drawing Proc...
Process Parameter Optimization for Minimizing Springback in Cold Drawing Proc...Journal of Soft Computing in Civil Engineering In tube drawing process, a tube is pulled out through a die and a plug to reduce its diameter and thickness as per the requirement. Dimensional accuracy of cold drawn tubes plays a vital role in the further quality of end products and controlling rejection in manufacturing processes of these end products. Springback phenomenon is the elastic strain recovery after removal of forming loads, causes geometrical inaccuracies in drawn tubes. Further, this leads to difficulty in achieving close dimensional tolerances. In the present work springback of EN 8 D tube material is studied for various cold drawing parameters. The process parameters in this work include die semi-angle, land width and drawing speed. The experimentation is done using Taguchi’s L36 orthogonal array, and then optimization is done in data analysis software Minitab 17. The results of ANOVA shows that 15 degrees die semi-angle,5 mm land width and 6 m/min drawing speed yields least springback. Furthermore, optimization algorithms named Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithm (GA) are applied which shows that 15 degrees die semi-angle, 10 mm land width and 8 m/min drawing speed results in minimal springback with almost 10.5 % improvement. Finally, the results of experimentation are validated with Finite Element Analysis technique using ANSYS.
Structural Response of Reinforced Self-Compacting Concrete Deep Beam Using Fi...
Structural Response of Reinforced Self-Compacting Concrete Deep Beam Using Fi...Journal of Soft Computing in Civil Engineering
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...
Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different ...Journal of Soft Computing in Civil Engineering
MODULE 03 - CLOUD COMPUTING- [BIS 613D] 2022 scheme.pptx
MODULE 03 - CLOUD COMPUTING- [BIS 613D] 2022 scheme.pptxAlvas Institute of Engineering and technology, Moodabidri
Process Parameter Optimization for Minimizing Springback in Cold Drawing Proc...
Process Parameter Optimization for Minimizing Springback in Cold Drawing Proc...Journal of Soft Computing in Civil Engineering
[Paper Reading] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
- 1. Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features 2018/07/12 M1 Hiroki Shimanaka Matteo Pagliardini, Prakhar Gupta and Martin Jaggi NAACL 2018
- 2. 1 lUnsupervised training of word representation, such as Word2Vec [Mikolov et al., 2013] is now routinely trained on very large amounts of raw text data, and have become ubiquitous building blocks of a majority of current state-of-the-art NLP applications. lWhile very useful semantic representations are available for words. Abstract & Introduction (1)
- 3. 2 lA strong trend in deep-learning for NLP leads towards increasingly powerful and com-plex models. ØWhile extremely strong in expressiveness, the increased model complexity makes such models much slower to train on larger datasets. lOn the other hand, simpler “shallow” models such as matrix factorizations (or bilinear models) can benefit from training on much larger sets of data, which can be a key advantage, especially in the unsupervised setting. Abstract & Introduction (2)
- 4. Abstract & Introduction (3) lThe authors present a simple but efficient unsupervised objective to train distributed representations of sentences. lTheir method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings. 3
- 5. 4 Approach Their proposed model (Sent2Vec) can be seen as an extension of the Cl - BOW [Mikolov et al., 2013] training objecKve to train sentence instead of word embeddings. Sent2Vec is a simple unsupervised model allowing to coml -pose sentence embeddings using word vectors along with n-gram embeddings, simultaneously training composiKon and the embed-ding vectors themselves.
- 6. 5 Model (1) lTheir model is inspired by simple matrix factor models (bilinear models) such as recently very successfully used in unsupervised learning of word embeddings. !∈ℝ$×& : target word vectors '∈ℝ&×|)|: learnt source word vectors ): vocabulary ℎ: hidden size ι4 ∈{0, 1}|)| :binary vector encoding : :: sentence
- 7. 6 Model (2) lConceptually, Sent2Vec can be interpreted as a natural extension of the word-contexts from C-BOW [Mikolov et al., 2013] to a larger sentence context. !(#): the list of n−grams (including un−igrams) ι&(') ∈{0, 1}|/|: binary vector encoding !(#) #: sentence 67: source (or context) embed−ding
- 8. 7 Model (3) lNegative sampling !"# :the set of words sampled nega6vely for the word $% ∈ ' ': sentence.": source (or context) embed−ding /": target embedding 0":the normalized frequency of $ in the corpus
- 9. 8 Model (4) lSubsampling lTo select the possible target unigrams (positives), they use subsampling as in [Joulin et al., 2017; Bojanowski et al., 2017], each word ! being discarded with probability 1 − $%(!) ()* :the set of words sampled negaJvely for the word !+ ∈ - -: sentence 4): source (or context) embed−ding 5): target embed−ding$6 ! ≔ 8) ∑):∈; 8): 8):the normalized frequency of ! in the corpus
- 10. 9 Experimental Setting (1) lDataset: lthe Toronto book corpus (70M sentences) lWikipedia sentences and tweets lDropout: lFor each sentence they use dropout on its list of n-grams ! " ∖ {%(")}, where %(") is the set of all unigrams contained in sentence ". lThey find that dropping K n-grams (n > 1) for each sentence is giving superior results compared to dropping each token with some fixed probability. lRegularization lApplying L1 regularization to the word vectors. lOptimizer lSGD
- 12. 11 Evalua&on Tasks lTransfer tasks: lSupervised lMR: movie review senti-ment lCR: product reviews lSUBJ: subjectivity classification lMPQA: opinion polarity lTREC: question type classifi-cation lMSRP: paraphrase identification lUnsupervised lSICK: semantic relatedness task lSTS : Semantic textual similarlity
- 16. 15 Conclusion lIn this paper, they introduce a novel, computationally efficient, unsupervised, C-BOW-inspired method to train and infer sentence embeddings. lOn supervised evaluations, their method, on an average, achieves better performance than all other unsupervised competitors with the exception of Skip-Thought [Kiros et al., 2015]. lHowever, their model is generalizable, extremely fast to train, simple to understand and easily interpretable, showing the relevance of simple and well-grounded representation models in contrast to the models using deep architectures. lFuture work could focus on augmenting the model to exploit data with ordered sentences.