DOCSLIB.ORG

Effective Dimensionality Reduction for Word Embeddings

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Effective Dimensionality Reduction for Word Embeddings Effective Dimensionality Reduction for Word Embeddings Vikas Raunak Vivek Gupta Florian Metze Carnegie Mellon University University of Utah Carnegie Mellon University [email protected] [email protected] [email protected] Abstract to inject antonymy and synonymy constraints into vector representations, while (Faruqui et al., 2015) Pre-trained word embeddings are used in tries to refine word vectors by using relational in- several downstream applications as well as formation from semantic lexicons such as Word- for constructing representations for sentences, paragraphs and documents. Recently, there Net (Miller, 1995). (Bolukbasi et al., 2016) tries has been an emphasis on improving the pre- to remove the biases (e.g. gender biases) present trained word vectors through post-processing in word embeddings and (Nguyen et al., 2016) algorithms. One improvement area is re- tries to ‘denoise’ word embeddings by strength- ducing the dimensionality of word embed- ening salient information and weakening noise. dings. Reducing the size of word embed- In particular, the post-processing algorithm in dings can improve their utility in memory (Mu and Viswanath, 2018) tries to improve word constrained devices, benefiting several real- embeddings by projecting the embeddings away world applications. In this work, we present a novel technique that efficiently combines PCA from the most dominant directions and consider- based dimensionality reduction with a recently ably improves their performance by making them proposed post-processing algorithm (Mu and more discriminative. However, a major issue re- Viswanath, 2018), to construct effective word lated with word embeddings is their size (Ling embeddings of lower dimensions. Empirical et al., 2016), e.g., loading a word embedding ma- evaluations on several benchmarks show that trix of 2:5 M tokens takes up to 6 GB memory our algorithm efficiently reduces the embed- (for 300-dimensional vectors, on a 64-bit system). ding size while achieving similar or (more of- ten) better performance than original embed- Such large memory requirements impose signifi- dings. To foster reproducibility, we have re- cant constraints on the practical use of word em- leased the source code along with paper 1. beddings, especially on mobile devices where the available memory is often highly restricted. In this 1 Introduction work we combine the simple dimensionality re- duction technique, PCA with the post processing Word embeddings such as Glove (Pennington technique of (Mu and Viswanath, 2018), as dis- et al., 2014) and word2vec Skip-Gram (Mikolov cussed above. et al., 2013) obtained from unlabeled text cor- In Section2, we first explain the post processing pora can represent words in distributed dense real- algorithm (Mu and Viswanath, 2018) and then our valued low dimensional vectors which geometri- novel algorithm and describe with an example the cally capture the semantic ‘meaning’ of a word. choices behind its design. The evaluation results These embeddings capture several linguistic regu- are presented in section3. In section4, we discuss larities such as analogy relationships. Such em- the related works, followed by the conclusion. beddings are of a pivotal role in several natural language processing tasks. Recently, there has been an emphasis on apply- 2 Proposed Algorithm ing post-processing algorithms on the pre-trained We first explain the post-processing algorithm word vectors to further improve their quality. For from (Mu and Viswanath, 2018) in section 2.1. example, algorithm in (Mrksicˇ et al., 2016) tries Our main algorithm, along with the motivations is 1 https://github.com/vyraun/Half-Size explained next, in the section 2.2. 235 Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), pages 235–243 Florence, Italy, August 2, 2019. c 2019 Association for Computational Linguistics 2.1 Post-Processing Algorithm (Mu and Viswanath, 2018) presents a simple post- processing algorithm that renders off-the-shelf word embeddings even stronger, as measured on a number of lexical-level and sentence-level tasks. The algorithm is based on the geometrical obser- vations that the word embeddings (across all rep- resentations such as Glove, word2vec etc.) have a large mean vector and most of their energy, after subtracting the mean vector is located in a sub- space of about 8 dimensions. Since, all embed- dings share a common mean vector and all embed- dings have the same dominating directions, both of Figure 1: Comparison of the fraction of variance ex- which strongly influence the representations, elim- plained by top 20 principal components of the Original and Post-Processing (PPA) applied Glove embeddings inating them makes the embeddings stronger. De- (300D). tailed description of the post-processing algorithm is presented in Algorithm1 (PPA). Algorithm 1: Post Processing Algorithm PPA(X, D) Data: Word Embedding Matrix X, Threshold Parameter D Result: Post-Processed Word Embedding Matrix X /* Subtract Mean Embedding */ 1 X = X - X; /* Compute PCA Components */ 2 ui = PCA(X), where i = 1,2, ::: ,d. ; /* Remove Top-D Components */ 3 for all v in X do PD T 4 v = v − i=1(ui · v)ui 5 end Figure1 demonstrates the impact of the post- Figure 2: Comparison of the fraction of variance ex- processing algorithm (PPA, with D= 7) as ob- plained by top 20 principal components of the PPA + PCA-150D baseline and Further Post-Processed Glove served on wiki pre-trained Glove embeddings embedding (150D). (300-dimensions). It compares the fraction of variance explained by the top 20 principal com- ponents of the original and post-processed word We first apply the algorithm1 (PPA) of (Mu and vectors respectively 2. In the post-processed word Viswanath, 2018) on the original word embed- embeddings none of the top principal components ding, to make it more ‘discriminative’. We then are disproportionately dominant in terms of ex- construct a lower dimensional representation of plaining the data, which implies that the post- the post processed ‘purified’ word embedding us- processed word vectors are not as influenced by ing Principal Component Analysis (PCA (Shlens, the common dominant directions as the original 2014)) based dimensionality reduction technique. embeddings. This makes the individual word vec- Lastly, we again ‘purify’ the reduced word embed- tors more ‘discriminative’, hence, improving their ding by applying the algorithm1 (PPA) of (Mu and quality, as validated on several benchmarks in (Mu Viswanath, 2018) on the reduced word embedding and Viswanath, 2018). to get our final word embeddings. To explain the last step of applying the algo- 2.2 Proposed Algorithm rithm1 (PPA) on the reduced word embedding, This section explains our algorithm,2 that effec- consider the Figure2. It compares the variance ex- tively uses the the PPA algorithm, along with PCA plained by the top 20 principal components for the for constructing lower dimensional embeddings. embeddings constructed by first post-processing 2 the total sum of explained variances over the 300 principal the Glove-300D embeddings according by Algo- components is equal to 1:0 rithm1 (PPA) and then transforming the embed- 236 dings to 150 dimensions with PCA (labeled as lary) 5. The next subsection also presents results Post-Processing + PCA); against a further post- using word2vec embeddings trained on Google processed version by again applying the Algo- News dataset 6. rithm1 (PPA) of the reduced word embeddings 3.1 Word Similarity Benchmarks 3. We observe that even though PCA has been applied on post-processed embeddings which had We use the standard word similarity benchmarks their dominant directions eliminated, the variance summarized in (Faruqui and Dyer, 2014) for eval- in the reduced embeddings is still explained dis- uating the word vectors. proportionately by a few top principal compo- Dataset: The datasets (Faruqui and Dyer, 2014) nents. The re-emergence of this geometrical be- have word pairs (WP) that have been assigned sim- havior implies that further post-processing (Al- ilarity rating by humans. While evaluating word gorithm1 (PPA)) could improve the embeddings vectors, the similarity between the words is calcu- further. Thus, for constructing lower-dimensional lated by the cosine similarity of their vector repre- word embeddings, we apply the post-processing sentations. Then, Spearman’s rank correlation co- algorithm on either side of a PCA dimensionality efficient (Rho × 100) between the ranks produced reduction of the word vectors in our algorithm. by using the word vectors and the human rankings Finally, from Figures1 and2, it is also ev- is used for the evaluation. The reported metric in ident that the extent to which the top principal our experiments is Rho × 100. Hence, for bet- components explain the data in the case of the re- ter word similarity, the evaluation metric will be duced embeddings is not as great as in the case of higher. the original 300 dimensional embeddings. Hence, Baselines: To evaluate the performance of our al- multiple levels of post-processing at different lev- gorithm, we compare it against different schemes els of dimensionality will yield diminishing re- of combining the post-processing algorithm with turns as the influence of common dominant direc- PCA 7 as following baselines: tions decreases on the word embeddings. Details • PCA of reduction technique is given in Algorithm2. : Transform word vectors using PCA. • P+PCA: Apply PPA (Algorithm1) and then Algorithm 2: Dimensionality Reduction Algorithm transform word vectors using PCA. Data: Word Embedding Matrix X, New Dimension N, Threshold Parameter D • PCA+P: Transform word vectors using PCA Result: Word Embedding Matrix of Reduced and then apply PPA. Dimension N: X /* Apply Algorithm1 (PPA) */ These baselines can also be regarded as ab- 1 X = PPA(X, D) ; lations on our algorithm and can shed light on /* Transform X using PCA */ whether our intuitions in developing the algorithm 2 X = PCA(X) ; /* Apply Algorithm1 (PPA) */ were correct. In the comparisons ahead, we rep- 3 X = PPA(X, D) ; resent our algorithm as Algo-N ,where N is the reduced dimensionality of word embeddings.
Load more

Recommended publications
  • Nonlinear Dimensionality Reduction by Locally Linear Embedding
    R EPORTS 2 ˆ 35. R. N. Shepard, Psychon. Bull. Rev. 1, 2 (1994). 1ÐR (DM, DY). DY is the matrix of Euclidean distanc- ing the coordinates of corresponding points {xi, yi}in 36. J. B. Tenenbaum, Adv. Neural Info. Proc. Syst. 10, 682 es in the low-dimensional embedding recovered by both spaces provided by Isomap together with stan- ˆ (1998). each algorithm. DM is each algorithm’s best estimate dard supervised learning techniques (39). 37. T. Martinetz, K. Schulten, Neural Netw. 7, 507 (1994). of the intrinsic manifold distances: for Isomap, this is 44. Supported by the Mitsubishi Electric Research Labo- 38. V. Kumar, A. Grama, A. Gupta, G. Karypis, Introduc- the graph distance matrix DG; for PCA and MDS, it is ratories, the Schlumberger Foundation, the NSF tion to Parallel Computing: Design and Analysis of the Euclidean input-space distance matrix DX (except (DBS-9021648), and the DARPA Human ID program. Algorithms (Benjamin/Cummings, Redwood City, CA, with the handwritten “2”s, where MDS uses the We thank Y. LeCun for making available the MNIST 1994), pp. 257Ð297. tangent distance). R is the standard linear correlation database and S. Roweis and L. Saul for sharing related ˆ 39. D. Beymer, T. Poggio, Science 272, 1905 (1996). coefficient, taken over all entries of DM and DY. unpublished work. For many helpful discussions, we 40. Available at www.research.att.com/ϳyann/ocr/mnist. 43. In each sequence shown, the three intermediate im- thank G. Carlsson, H. Farid, W. Freeman, T. Griffiths, 41. P. Y. Simard, Y. LeCun, J.
    [Show full text]
  • Comparison of Dimensionality Reduction Techniques on Audio Signals
    Comparison of Dimensionality Reduction Techniques on Audio Signals Tamás Pál, Dániel T. Várkonyi Eötvös Loránd University, Faculty of Informatics, Department of Data Science and Engineering, Telekom Innovation Laboratories, Budapest, Hungary {evwolcheim, varkonyid}@inf.elte.hu WWW home page: http://t-labs.elte.hu Abstract: Analysis of audio signals is widely used and this work: car horn, dog bark, engine idling, gun shot, and very effective technique in several domains like health- street music [5]. care, transportation, and agriculture. In a general process Related work is presented in Section 2, basic mathe- the output of the feature extraction method results in huge matical notation used is described in Section 3, while the number of relevant features which may be difficult to pro- different methods of the pipeline are briefly presented in cess. The number of features heavily correlates with the Section 4. Section 5 contains data about the evaluation complexity of the following machine learning method. Di- methodology, Section 6 presents the results and conclu- mensionality reduction methods have been used success- sions are formulated in Section 7. fully in recent times in machine learning to reduce com- The goal of this paper is to find a combination of feature plexity and memory usage and improve speed of following extraction and dimensionality reduction methods which ML algorithms. This paper attempts to compare the state can be most efficiently applied to audio data visualization of the art dimensionality reduction techniques as a build- in 2D and preserve inter-class relations the most. ing block of the general process and analyze the usability of these methods in visualizing large audio datasets.
    [Show full text]
  • Litrec Vs. Movielens a Comparative Study
    LitRec vs. Movielens A Comparative Study Paula Cristina Vaz1;4, Ricardo Ribeiro2;4 and David Martins de Matos3;4 1IST/UAL, Rua Alves Redol, 9, Lisboa, Portugal 2ISCTE-IUL, Rua Alves Redol, 9, Lisboa, Portugal 3IST, Rua Alves Redol, 9, Lisboa, Portugal 4INESC-ID, Rua Alves Redol, 9, Lisboa, Portugal fpaula.vaz, ricardo.ribeiro, [email protected] Keywords: Recommendation Systems, Data Set, Book Recommendation. Abstract: Recommendation is an important research area that relies on the availability and quality of the data sets in order to make progress. This paper presents a comparative study between Movielens, a movie recommendation data set that has been extensively used by the recommendation system research community, and LitRec, a newly created data set for content literary book recommendation, in a collaborative filtering set-up. Experiments have shown that when the number of ratings of Movielens is reduced to the level of LitRec, collaborative filtering results degrade and the use of content in hybrid approaches becomes important. 1 INTRODUCTION and (b) assess its suitability in recommendation stud- ies. The number of books, movies, and music items pub- The paper is structured as follows: Section 2 de- lished every year is increasing far more quickly than scribes related work on recommendation systems and is our ability to process it. The Internet has shortened existing data sets. In Section 3 we describe the collab- the distance between items and the common user, orative filtering (CF) algorithm and prediction equa- making items available to everyone with an Internet tion, as well as different item representation. Section connection.
    [Show full text]
  • Unsupervised Speech Representation Learning Using Wavenet Autoencoders Jan Chorowski, Ron J
    1 Unsupervised speech representation learning using WaveNet autoencoders Jan Chorowski, Ron J. Weiss, Samy Bengio, Aaron¨ van den Oord Abstract—We consider the task of unsupervised extraction speaker gender and identity, from phonetic content, properties of meaningful latent representations of speech by applying which are consistent with internal representations learned autoencoding neural networks to speech waveforms. The goal by speech recognizers [13], [14]. Such representations are is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being desired in several tasks, such as low resource automatic speech invariant to confounding low level details in the signal such as recognition (ASR), where only a small amount of labeled the underlying pitch contour or background noise. Since the training data is available. In such scenario, limited amounts learned representation is tuned to contain only phonetic content, of data may be sufficient to learn an acoustic model on the we resort to using a high capacity WaveNet decoder to infer representation discovered without supervision, but insufficient information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the to learn the acoustic model and a data representation in a fully kind of constraint that is applied to the latent representation. supervised manner [15], [16]. We compare three variants: a simple dimensionality reduction We focus on representations learned with autoencoders bottleneck, a Gaussian Variational Autoencoder (VAE), and a applied to raw waveforms and spectrogram features and discrete Vector Quantized VAE (VQ-VAE). We analyze the quality investigate the quality of learned representations on LibriSpeech of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately re- [17].
    [Show full text]
  • Unsupervised Speech Representation Learning Using Wavenet Autoencoders
    Unsupervised speech representation learning using WaveNet autoencoders https://arxiv.org/abs/1901.08810 Jan Chorowski University of Wrocław 06.06.2019 Deep Model = Hierarchy of Concepts Cat Dog … Moon Banana M. Zieler, “Visualizing and Understanding Convolutional Networks” Deep Learning history: 2006 2006: Stacked RBMs Hinton, Salakhutdinov, “Reducing the Dimensionality of Data with Neural Networks” Deep Learning history: 2012 2012: Alexnet SOTA on Imagenet Fully supervised training Deep Learning Recipe 1. Get a massive, labeled dataset 퐷 = {(푥, 푦)}: – Comp. vision: Imagenet, 1M images – Machine translation: EuroParlamanet data, CommonCrawl, several million sent. pairs – Speech recognition: 1000h (LibriSpeech), 12000h (Google Voice Search) – Question answering: SQuAD, 150k questions with human answers – … 2. Train model to maximize log 푝(푦|푥) Value of Labeled Data • Labeled data is crucial for deep learning • But labels carry little information: – Example: An ImageNet model has 30M weights, but ImageNet is about 1M images from 1000 classes Labels: 1M * 10bit = 10Mbits Raw data: (128 x 128 images): ca 500 Gbits! Value of Unlabeled Data “The brain has about 1014 synapses and we only live for about 109 seconds. So we have a lot more parameters than data. This motivates the idea that we must do a lot of unsupervised learning since the perceptual input (including proprioception) is the only place we can get 105 dimensions of constraint per second.” Geoff Hinton https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/ Unsupervised learning recipe 1. Get a massive labeled dataset 퐷 = 푥 Easy, unlabeled data is nearly free 2. Train model to…??? What is the task? What is the loss function? Unsupervised learning by modeling data distribution Train the model to minimize − log 푝(푥) E.g.
    [Show full text]
  • Dimensionality Reduction and Principal Component Analysis
    Dimensionality Reduction Before running any ML algorithm on our data, we may want to reduce the number of features Dimensionality Reduction and Principal ● To save computer memory/disk space if the data are large. Component Analysis ● To reduce execution time. ● To visualize our data, e.g., if the dimensions can be reduced to 2 or 3. Dimensionality Reduction results in an Projecting Data - 2D to 1D approximation of the original data x2 x1 Original 50% 5% 1% Projecting Data - 2D to 1D Projecting Data - 2D to 1D x2 x2 x2 x1 x1 x1 Projecting Data - 2D to 1D Projecting Data - 2D to 1D new feature new feature Projecting Data - 3D to 2D Projecting Data - 3D to 2D Projecting Data - 3D to 2D Why such projections are effective ● In many datasets, some of the features are correlated. ● Correlated features can often be well approximated by a smaller number of new features. ● For example, consider the problem of predicting housing prices. Some of the features may be the square footage of the house, number of bedrooms, number of bathrooms, and lot size. These features are likely correlated. Principal Component Analysis (PCA) Principal Component Analysis (PCA) Suppose we want to reduce data from d dimensions to k dimensions, where d > k. Determine new basis. PCA finds k vectors onto which to project the data so that the Project data onto k axes of new projection errors are minimized. basis with largest variance. In other words, PCA finds the principal components, which offer the best approximation. PCA ≠ Linear Regression PCA Algorithm Given n data
    [Show full text]
  • Collaborative Filtering: a Machine Learning Perspective by Benjamin Marlin a Thesis Submitted in Conformity with the Requirement
    Collaborative Filtering: A Machine Learning Perspective by Benjamin Marlin A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Computer Science University of Toronto Copyright c 2004 by Benjamin Marlin Abstract Collaborative Filtering: A Machine Learning Perspective Benjamin Marlin Master of Science Graduate Department of Computer Science University of Toronto 2004 Collaborative filtering was initially proposed as a framework for filtering information based on the preferences of users, and has since been refined in many different ways. This thesis is a comprehensive study of rating-based, pure, non-sequential collaborative filtering. We analyze existing methods for the task of rating prediction from a machine learning perspective. We show that many existing methods proposed for this task are simple applications or modifications of one or more standard machine learning methods for classification, regression, clustering, dimensionality reduction, and density estima- tion. We introduce new prediction methods in all of these classes. We introduce a new experimental procedure for testing stronger forms of generalization than has been used previously. We implement a total of nine prediction methods, and conduct large scale prediction accuracy experiments. We show interesting new results on the relative performance of these methods. ii Acknowledgements I would like to begin by thanking my supervisor Richard Zemel for introducing me to the field of collaborative filtering, for numerous helpful discussions about a multitude of models and methods, and for many constructive comments about this thesis itself. I would like to thank my second reader Sam Roweis for his thorough review of this thesis, as well as for many interesting discussions of this and other research.
    [Show full text]
  • 13 Dimensionality Reduction and Microarray Data
    13 Dimensionality Reduction and Microarray data David A. Elizondo, Benjamin N. Passow, Ralph Birkenhead, and Andreas Huemer Centre for Computational Intelligence, School of Computing, Faculty of Computing Sciences and Engineering, De Montfort University, Leicester, UK,{elizondo,passow,rab,ahuemer}@dmu.ac.uk Summary. Microarrays are being currently used for the expression levels of thou- sands of genes simultaneously. They present new analytical challenges because they have a very high input dimension and a very low sample size. It is highly com- plex to analyse multi-dimensional data with complex geometry and to identify low- dimensional “principal objects” that relate to the optimal projection while losing the least amount of information. Several methods have been proposed for dimensionality reduction of microarray data. Some of these methods include principal component analysis and principal manifolds. This article presents a comparison study of the performance of the linear principal component analysis and the non linear local tan- gent space alignment principal manifold methods on such a problem. Two microarray data sets will be used in this study. A classification model will be created using fully dimensional and dimensionality reduced data sets. To measure the amount of infor- mation lost with the two dimensionality reduction methods, the level of performance of each of the methods will be measured in terms of level of generalisation obtained by the classification models on previously unseen data sets. These results will be compared with the ones obtained using the fully dimensional data sets. Key words: Microarray data; Principal Manifolds; Principal Component Analysis; Local Tangent Space Alignment; Linear Separability; Neural Net- works.
    [Show full text]
  • Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
    JournalofMachineLearningResearch11(2010)451-490 Submitted 4/09; Revised 12/09; Published 2/10 Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization Jarkko Venna [email protected] Jaakko Peltonen [email protected] Kristian Nybo [email protected] Helena Aidos [email protected] Samuel Kaski [email protected] Aalto University School of Science and Technology Department of Information and Computer Science P.O. Box 15400, FI-00076 Aalto, Finland Editor: Yoshua Bengio Abstract Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, al- though the existing methods have been designed for other related tasks such as manifold learning. It has been difficult to assess the quality of visualizations since the task has not been well-defined. We give a rigorous definition for a specific visualization task, resulting in quantifiable goodness measures and new visualization methods. The task is information retrieval given the visualization: to find similar data based on the similarities shown on the display. The fundamental tradeoff be- tween precision and recall of information retrieval can then be quantified in visualizations as well. The user needs to give the relative cost of missing similar points vs. retrieving dissimilar points, after which the total cost can be measured. We then introduce a new method NeRV (neighbor retrieval visualizer) which produces an optimal visualization by minimizing the cost. We further derive a variant for supervised visualization; class information is taken rigorously into account when computing the similarity relationships. We show empirically that the unsupervised version outperforms existing unsupervised dimensionality reduction methods in the visualization task, and the supervised version outperforms existing supervised methods.
    [Show full text]
  • Collaborative Filtering: a Machine Learning Perspective Chapter 6: Dimensionality Reduction
    Collaborative Filtering: A Machine Learning Perspective Chapter 6: Dimensionality Reduction Benjamin Marlin Presenter: Chaitanya Desai Collaborative Filtering: A Machine Learning Perspective – p.1/18 Topics we'll cover Dimensionality Reduction for rating prediction: Singular Value Decomposition Principal Component Analysis Collaborative Filtering: A Machine Learning Perspective – p.2/18 Rating Prediction Problem Description: We have M distinct items and N distinct users in our corpus u Let ry be rating assigned by user u for item y. Thus, we have (M dimensional) rating vectors ru for the N users Task: Given the partially filled rating vector ra of an active a user a, we want to estimate rˆy for all items y that have not yet been rated by a Collaborative Filtering: A Machine Learning Perspective – p.3/18 Singular Value Decomposition Given a data matrix D of size N × M, the SVD of D is D T = UΣV where UN×N and VM×M are orthogonal and ΣN×M is diagonal columns of U are eigenvectors of DDT and columns of V are eigenvectors of DT D Σ is diagonal and entries comprise of eigenvalues ordered according to eigenvectors (i.e. columns of U and V ) Collaborative Filtering: A Machine Learning Perspective – p.4/18 Low Rank Approximation T Given the solution to SVD, we know that Dˆ = UkΣkVk is the best rank k approximation to D under the Frobenius norm The Frobenius norm is given by N M ˆ ˆ 2 F (D − D) = X X(Dnm − Dnm) n=1 m=1 Collaborative Filtering: A Machine Learning Perspective – p.5/18 Weighted Low Rank Approximation D contains many missing values
    [Show full text]
  • Accent Transfer with Discrete Representation Learning and Latent Space Disentanglement
    Accent Transfer with Discrete Representation Learning and Latent Space Disentanglement Renee Li Paul Mure Dept. of Computer Science Dept. of Computer Science Stanford University Stanford University [email protected] [email protected] Abstract The task of accent transfer has recently become a field actively under research. In fact, several companies were established with the goal of tackling this challenging problem. We designed and implemented a model using WaveNet and a multitask learner to transfer accents within a paragraph of English speech. In contrast to the actual WaveNet, our architecture made several adaptations to compensate for the fact that WaveNet does not work well for drastic dimensionality reduction due to the use of residual and skip connections, and we added a multitask learner in order to learn the style of the accent. 1 Introduction In this paper, we explored the task of accent transfer in English speech. This is an interesting problem because it shows the ability of neural network to generate novel contents based on partial information, and this problem has not been explored as much as some of the other topics in style transfer. We believe that this task is very interesting and challenging because we will be developing the state-of-the-art model for our specific task: while there are some published papers about accent classification, so far, we have not been able to find a good pre-trained model to use for evaluating our results. Moreover, raw audio generative models are also a very active field of research, so there is not much literature or implementations for relevant tasks.
    [Show full text]
  • Text Comparison Using Word Vector Representations and Dimensionality Reduction
    PROC. OF THE 8th EUR. CONF. ON PYTHON IN SCIENCE (EUROSCIPY 2015) 13 Text comparison using word vector representations and dimensionality reduction Hendrik Heuer∗† F Abstract—This paper describes a technique to compare large text sources text sources. To compare the topics in source A and source using word vector representations (word2vec) and dimensionality reduction (t- B, three different sets of words can be computed: a set of SNE) and how it can be implemented using Python. The technique provides a unique words in source A, a set of unique words in source B bird’s-eye view of text sources, e.g. text summaries and their source material, as well as the intersection set of words both in source A and and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, B. These three sets are then plotted at the same time. For this, plurality and even semantic concepts like "capital city of". Using dimensionality a colour is assigned to each set of words. This enables the reduction, a 2D map can be computed where semantically similar words are user to visually compare the different text sources and makes close to each other. The technique uses the word2vec model from the gensim it possible to see which topics are covered where. The user Python library and t-SNE from scikit-learn. can explore the word map and zoom in and out. He or she can also toggle the visibility, i.e. show and hide, certain word Index Terms—Text Comparison, Topic Comparison, word2vec, t-SNE sets.
    [Show full text]