Riemannian Optimization for Skip-Gram Negative Sampling

04/26/2017 ∙ by Alexander Fonarev, et al. ∙ 0

Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.



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1 Introduction

In this paper, we consider the problem of embedding words into a low-dimensional space in order to measure the semantic similarity between them. As an example, how to find whether the word “table” is semantically more similar to the word “stool” than to the word “sky”? That is achieved by constructing a low-dimensional vector representation for each word and measuring similarity between the words as the similarity between the corresponding vectors.

One of the most popular word embedding models Mikolov et al. (2013)

is a discriminative neural network that optimizes Skip-Gram Negative Sampling (SGNS) objective (see Equation

3). It aims at predicting whether two words can be found close to each other within a text. As shown in Section 2, the process of word embeddings training using SGNS can be divided into two general steps with clear objectives:

  1. [labelindent=15pt,itemindent=0em,leftmargin=!]

  2. Search for a low-rank matrix that provides a good SGNS objective value;

  3. Search for a good low-rank representation in terms of linguistic metrics, where is a matrix of word embeddings and  is a matrix of so-called context embeddings.

Unfortunately, most previous approaches mixed these two steps into a single one, what entails a not completely correct formulation of the optimization problem. For example, popular approaches to train embeddings (including the original “word2vec” implementation) do not take into account that the objective from Step 1 depends only on the product : instead of straightforward computing of the derivative w.r.t. , these methods are explicitly based on the derivatives w.r.t. and , what complicates the optimization procedure. Moreover, such approaches do not take into account that parametrization of matrix

is non-unique and Step 2 is required. Indeed, for any invertible matrix

, we have

therefore, solutions and

are equally good in terms of the SGNS objective but entail different cosine similarities between embeddings and, as a result, different performance in terms of linguistic metrics (see Section 

4.2 for details).

A successful attempt to follow the above described steps, which outperforms the original SGNS optimization approach in terms of various linguistic tasks, was proposed in Levy and Goldberg (2014). In order to obtain a low-rank matrix

on Step 1, the method reduces the dimensionality of Shifted Positive Pointwise Mutual Information (SPPMI) matrix via Singular Value Decomposition (SVD). On Step 2, it computes embeddings

and via a simple formula that depends on the factors obtained by SVD. However, this method has one important limitation: SVD provides a solution to a surrogate optimization problem, which has no direct relation to the SGNS objective. In fact, SVD minimizes the Mean Squared Error (MSE) between and SPPMI matrix, what does not lead to minimization of SGNS objective in general (see Section 6.1 and Section 4.2 in Levy and Goldberg (2014) for details).

These issues bring us to the main idea of our paper: while keeping the low-rank matrix search setup on Step 1, optimize the original SGNS objective directly. This leads to an optimization problem over matrix with the low-rank constraint, which is often Mishra et al. (2014) solved by applying Riemannian optimization framework Udriste (1994). In our paper, we use the projector-splitting algorithm Lubich and Oseledets (2014), which is easy to implement and has low computational complexity. Of course, Step 2 may be improved as well, but we regard this as a direction of future work.

As a result, our approach achieves the significant improvement in terms of SGNS optimization on Step 1 and, moreover, the improvement on Step 1 entails the improvement on Step 2 in terms of linguistic metrics. That is why, the proposed two-step decomposition of the problem makes sense, what, most importantly, opens the way to applying even more advanced approaches based on it (e.g., more advanced Riemannian optimization techniques for Step 1 or a more sophisticated treatment of Step 2).

To summarize, the main contributions of our paper are:

  • We reformulated the problem of SGNS word embedding learning as a two-step procedure with clear objectives;

  • For Step 1, we developed an algorithm based on Riemannian optimization framework that optimizes SGNS objective over low-rank matrix directly;

  • Our algorithm outperforms state-of-the-art competitors in terms of SGNS objective and the semantic similarity linguistic metric Levy and Goldberg (2014); Mikolov et al. (2013); Schnabel et al. (2015).

2 Problem Setting

2.1 Skip-Gram Negative Sampling

In this paper, we consider the Skip-Gram Negative Sampling (SGNS) word embedding model Mikolov et al. (2013), which is a probabilistic discriminative model. Assume we have a text corpus given as a sequence of words , where may be larger than and belongs to a vocabulary of words . A context of the word is a word from set for some fixed window size . Let be the word embeddings of word and context , respectively. Assume they are specified by the following mappings:

The ultimate goal of SGNS word embedding training is to fit good mappings and .


be a multiset of all word-context pairs observed in the corpus. In the SGNS model, the probability that word-context pair 

is observed in the corpus is modeled as a following dsitribution:


where is the number of times the pair  appears in and is the scalar product of vectors and . Number

is a hyperparameter that adjusts the flexibility of the model. It usually takes values from tens to hundreds.

In order to collect a training set, we take all pairs from as positive examples and randomly generated pairs as negative ones. The number of times the word and the context appear in can be computed as

accordingly. Then negative examples are generated from the distribution defined by counters:

In this way, we have a model maximizing the following logarithmic likelihood objective for all word-context pairs :


In order to maximize the objective over all observations for each pair , we arrive at the following SGNS optimization problem over all possible mappings and :


Usually, this optimization is done via the stochastic gradient descent procedure that is performed during passing through the corpus Mikolov et al. (2013); Rong (2014).

2.2 Optimization over Low-Rank Matrices

Relying on the prospect proposed in Levy and Goldberg (2014), let us show that the optimization problem given by (3) can be considered as a problem of searching for a matrix that maximizes a certain objective function and has the rank- constraint (Step 1 in the scheme described in Section 1).

2.2.1 SGNS Loss Function

As shown in Levy and Goldberg (2014), the logarithmic likelihood (3) can be represented as the sum of over all pairs , where  has the following form:


A crucial observation is that this loss function depends only on the scalar product

but not on embeddings and separately:


and is the scalar product , and

are constants.

2.2.2 Matrix Notation

Denote as and as . Let and be matrices, where each row  of matrix is the word embedding of the corresponding word and each row of matrix is the context embedding of the corresponding context . Then the elements of the product of these matrices

are the scalar products of all pairs :

Note that this matrix has rank , because  equals to the product of two matrices with sizes  and . Now we can write SGNS objective given by (3) as a function of :


This arrives us at the following proposition:

Proposition 1

SGNS optimization problem given by (3) can be rewritten in the following constrained form:

subject to

where is the manifold Udriste (1994) of all matrices in with rank :

The key idea of this paper is to solve the optimization problem given by (6) via the framework of Riemannian optimization, which we introduce in Section 3.

Important to note that this prospect does not suppose the optimization over parameters and  directly. This entails the optimization in the space with degrees of freedom Mukherjee et al. (2015) instead of , what simplifies the optimization process (see Section 5 for the experimental results).

2.3 Computing Embeddings from a Low-Rank Solution

Once is found, we need to recover and  such that (Step 2 in the scheme described in Section 1). This problem does not have a unique solution, since if satisfy this equation, then and satisfy it as well for any non-singular matrix . Moreover, different solutions may achieve different values of the linguistic metrics (see Section 4.2

for details). While our paper focuses on Step 1, we use, for Step 2, a heuristic approach that was proposed in

Levy et al. (2015) and it shows good results in practice. We compute SVD of in the form

where and have orthonormal columns, and is the diagonal matrix, and use

as matrices of embeddings.

A simple justification of this solution is the following: we need to map words into vectors in a way that similar words would have similar embeddings in terms of cosine similarities:

It is reasonable to assume that two words are similar, if they share contexts. Therefore, we can estimate the similarity of two words

, as

what is the element of the matrix with indices . Note that

If we choose , we exactly obtain , since in this case. That is, the cosine similarity of the embeddings coincides with the intuitive similarity . However, scaling by  instead of was shown in Levy et al. (2015) to be a better solution in experiments.

3 Proposed Method

3.1 Riemannian Optimization

3.1.1 General Scheme

The main idea of Riemannian optimization Udriste (1994) is to consider (6) as a constrained optimization problem. Assume we have an approximated solution on a current step of the optimization process, where is the step number. In order to improve , the next step of the standard gradient ascent outputs the point

where is the gradient of objective at the point . Note that the gradient can be naturally considered as a matrix in . Point leaves the manifold , because its rank is generally greater than . That is why Riemannian optimization methods map point  back to manifold . The standard Riemannian gradient method first projects the gradient step onto the tangent space at the current point and then retracts it back to the manifold:

where is the retraction operator, and is the projection onto the tangent space.

Although the optimization problem is non-convex, Riemannian optimization methods show good performance on it. Theoretical properties and convergence guarantees of such methods are discussed in Wei et al. (2016) more thoroughly.

3.1.2 Projector-Splitting Algorithm

In our paper, we use a simplified version of such approach that retracts point directly to the manifold and does not require projection onto the tangent space as illustrated in Figure 1:

Figure 1: Geometric interpretation of one step of projector-splitting optimization procedure: the gradient step an the retraction of the high-rank matrix to the manifold of low-rank matrices  .

Intuitively, retractor  finds a rank- matrix on the manifold  that is similar to high-rank matrix  in terms of Frobenius norm. How can we do it? The most straightforward way to reduce the rank of  is to perform the SVD, which keeps largest singular values of it:


However, it is computationally expensive. Instead of this approach, we use the projector-splitting method Lubich and Oseledets (2014), which is a second-order retraction onto the manifold (for details, see the review Absil and Oseledets (2015)). Its practical implementation is also quite intuitive: instead of computing the full SVD of according to the gradient projection method, we use just one step of the block power numerical method Bentbib and Kanber (2015) which computes the SVD, what reduces the computational complexity.

Let us keep the current point in the following factorized form:


where matrices and have  orthonormal columns and

. Then we need to perform two QR-decompositions to retract point

back to the manifold:

In this way, we always keep the solution on the manifold and in the form (8).

What is important, we only need to compute , so the gradients with respect to and are never computed explicitly, thus avoiding the subtle case where is close to singular (so-called singular (critical) point on the manifold). Indeed, the gradient with respect to (while keeping the orthogonality constraints) can be written Koch and Lubich (2007) as:

which means that the gradient will be large if is close to singular. The projector-splitting scheme is free from this problem.

3.2 Algorithm

In case of SGNS objective given by (5), an element of gradient has the form:

To make the method more flexible in terms of convergence properties, we additionally use , which is a step size parameter. In this case, retractor returns instead of onto the manifold.

The whole optimization procedure is summarized in Algorithm 1.

0:  Dimentionality , initialization and , step size , gradient function , number of iterations
0:  Factor
1:   # get an initial point at the manifold
2:  # compute the first point satisfying the low-rank constraint
3:  for  do
4:       # perform one step of the block power method
6:     # update the point at the manifold
7:  end for
9:  # compute word embeddings
10:  return  
Algorithm 1 Riemannian Optimization for SGNS

4 Experimental Setup

4.1 Training Models

We compare our method (“RO-SGNS” in the tables) performance to two baselines: SGNS embeddings optimized via Stochastic Gradient Descent, implemented in the original “word2vec”, (“SGD-SGNS” in the tables) Mikolov et al. (2013) and embeddings obtained by SVD over SPPMI matrix (“SVD-SPPMI” in the tables) Levy and Goldberg (2014). We have also experimented with the blockwise alternating optimization over factors W and C, but the results are almost the same to SGD results, that is why we do not to include them into the paper. The source code of our experiments is available online111https://github.com/AlexGrinch/ro_sgns.

The models were trained on English Wikipedia “enwik9” corpus222http://mattmahoney.net/dc/textdata, which was previously used in most papers on this topic. Like in previous studies, we counted only the words which occur more than times in the training corpus Levy and Goldberg (2014); Mikolov et al. (2013). As a result, we obtained a vocabulary of unique tokens (set of words and set of contexts are equal). The size of the context window was set to for all experiments, as it was done in Levy and Goldberg (2014); Mikolov et al. (2013). We conduct three series of experiments: for dimensionality , , and .

Optimization step size is chosen to be small enough to avoid huge gradient values. However, thorough choice of does not result in a significant difference in performance (this parameter was tuned on the training data only, the exact values used in experiments are reported below).

4.2 Evaluation

We evaluate word embeddings via the word similarity task. We use the following popular datasets for this purpose: “wordsim-353” (Finkelstein et al. (2001); 3 datasets), “simlex-999” Hill et al. (2016) and “men” Bruni et al. (2014). Original “wordsim-353” dataset is a mixture of the word pairs for both word similarity and word relatedness tasks. This dataset was split Agirre et al. (2009) into two intersecting parts: “wordsim-sim” (“ws-sim” in the tables) and “wordsim-rel” (“ws-rel” in the tables) to separate the words from different tasks. In our experiments, we use both of them on a par with the full version of “wordsim-353” (“ws-full” in the tables). Each dataset contains word pairs together with assessor-assigned similarity scores for each pair. As a quality measure, we use Spearman’s correlation between these human ratings and cosine similarities for each pair. We call this quality metric linguistic in our paper.

5 Results of Experiments

First of all, we compare the value of SGNS objective obtained by the methods. The comparison is demonstrated in Table 1.

Table 1: Comparison of SGNS values (multiplied by ) obtained by the models. Larger is better.
Dim. Algorithm ws-sim ws-rel ws-full simlex men
SGD-SGNS 0.719 0.570 0.662 0.288 0.645
SVD-SPPMI 0.722 0.585 0.669 0.317 0.686
RO-SGNS 0.729 0.597 0.677 0.322 0.683
SGD-SGNS 0.733 0.584 0.677 0.317 0.664
SVD-SPPMI 0.747 0.625 0.694 0.347 0.710
RO-SGNS 0.757 0.647 0.708 0.353 0.701
SGD-SGNS 0.738 0.600 0.688 0.350 0.712
SVD-SPPMI 0.765 0.639 0.707 0.380 0.737
RO-SGNS 0.767 0.654 0.715 0.383 0.732
Table 2: Comparison of the methods in terms of the semantic similarity task. Each entry represents the Spearman’s correlation between predicted similarities and the manually assessed ones.
five he main
Neighbors Dist. Neighbors Dist. Neighbors Dist. Neighbors Dist. Neighbors Dist. Neighbors Dist.
lb 0.748 four 0.999 she 0.918 when 0.904 major 0.631 major 0.689
kg 0.731 three 0.999 was 0.797 had 0.903 busiest 0.621 important 0.661
mm 0.670 six 0.997 promptly 0.742 was 0.901 principal 0.607 line 0.631
mk 0.651 seven 0.997 having 0.731 who 0.892 nearest 0.607 external 0.624
lbf 0.650 eight 0.996 dumbledore 0.731 she 0.884 connecting 0.591 principal 0.618
per 0.644 and 0.985 him 0.730 by 0.880 linking 0.588 primary 0.612
Table 3: Examples of the semantic neighbors obtained for words “five”, “he” and “main”.
Neighbors Dist. Neighbors Dist. Neighbors Dist.
akron 0.536 wisconsin 0.700 georgia 0.707
midwest 0.535 delaware 0.693 delaware 0.706
burbank 0.534 ohio 0.691 maryland 0.705
nevada 0.534 northeast 0.690 illinois 0.704
arizona 0.533 cities 0.688 madison 0.703
uk 0.532 southwest 0.684 arkansas 0.699
youngstown 0.532 places 0.684 dakota 0.690
utah 0.530 counties 0.681 tennessee 0.689
milwaukee 0.530 maryland 0.680 northeast 0.687
headquartered 0.527 dakota 0.674 nebraska 0.686
Table 4: Examples of the semantic neighbors from th to th obtained for the word “usa” by all three methods. Top- neighbors for all three methods are exact names of states.

We see that SGD-SGNS and SVD-SPPMI methods provide quite similar results, however, the proposed method obtains significantly better SGNS values, what proves the feasibility of using Riemannian optimization framework in SGNS optimization problem. It is interesting to note that SVD-SPPMI method, which does not optimize SGNS objective directly, obtains better results than SGD-SGNS method, which aims at optimizing SGNS. This fact additionally confirms the idea described in Section 2.2.2 that the independent optimization over parameters and may decrease the performance.

However, the target performance measure of embedding models is the correlation between semantic similarity and human assessment (Section 4.2). Table 2 presents the comparison of the methods in terms of it. We see that our method outperforms the competitors on all datasets except for “men” dataset where it obtains slightly worse results. Moreover, it is important that the higher dimension entails higher performance gain of our method in comparison to the competitors.

To understand how our model improves or degrades the performance in comparison to the baseline, we found several words, whose neighbors in terms of cosine distance change significantly. Table 3 demonstrates neighbors of the words “five”, “he” and “main” for both SVD-SPPMI and RO-SGNS models. A neighbor is marked bold if we suppose that it has similar semantic meaning to the source word. First of all, we notice that our model produces much better neighbors of the words describing digits or numbers (see word “five” as an example). Similar situation happens for many other words, e.g. in case of “main” — the nearest neighbors contain 4 similar words for our model instead of 2 in case of SVD-SPPMI. The neighbourhood of “he” contains less semantically similar words in case of our model. However, it filters out irrelevant words, such as “promptly” and “dumbledore”.

Table 4 contains the nearest words to the word “usa” from 11th to 20th. We marked names of USA states bold and did not represent top-10 nearest words as they are exactly names of states for all three models. Some non-bold words are arguably relevant as they present large USA cities (“akron”, “burbank”, “madison”) or geographical regions of several states (“midwest”, “northeast”, “southwest”), but there are also some completely irrelevant words (“uk”, “cities”, “places”) presented by first two models.

Our experiments show that the optimal number of iterations in the optimization procedure and step size  depend on the particular value of . For , we have , for , we have , and for , we have . Moreover, the best results were obtained when SVD-SPPMI embeddings were used as an initialization of Riemannian optimization process.

Figure 2 illustrates how the correlation between semantic similarity and human assessment scores changes through iterations of our method. Optimal value of is the same for both whole testing set and its 10-fold subsets chosen for cross-validation. The idea to stop optimization procedure on some iteration is also discussed in Lai et al. (2015).

Training of the same dimensional models () on English Wikipedia corpus using SGD-SGNS, SVD-SPPMI, RO-SGNS took minutes, minutes and minutes respectively. Our method works slower, but not significantly. Moreover, since we were not focused on the code efficiency optimization, this time can be reduced.

Figure 2: Illustration of why it is important to choose the optimal iteration and stop optimization procedure after it. The graphs show semantic similarity metric in dependence on the iteration of optimization procedure. The embeddings obtained by SVD-SPPMI method were used as initialization. Parameters: , .

6 Related Work

6.1 Word Embeddings

Skip-Gram Negative Sampling was introduced in Mikolov et al. (2013). The “negative sampling” approach is thoroughly described in Goldberg and Levy (2014), and the learning method is explained in Rong (2014). There are several open-source implementations of SGNS neural network, which is widely known as “word2vec”. 111Original Google word2vec: https://code.google.com/archive/p/word2vec/222Gensim word2vec: https://radimrehurek.com/gensim/models/word2vec.html

As shown in Section 2.2, Skip-Gram Negative Sampling optimization can be reformulated as a problem of searching for a low-rank matrix. In order to be able to use out-of-the-box SVD for this task, the authors of Levy and Goldberg (2014) used the surrogate version of SGNS as the objective function. There are two general assumptions made in their algorithm that distinguish it from the SGNS optimization:

  1. SVD optimizes Mean Squared Error (MSE) objective instead of SGNS loss function.

  2. In order to avoid infinite elements in SPMI matrix, it is transformed in ad-hoc manner (SPPMI matrix) before applying SVD.

This makes the objective not interpretable in terms of the original task (3). As mentioned in Levy and Goldberg (2014), SGNS objective weighs different pairs differently, unlike the SVD, which works with the same weight for all pairs and may entail the performance fall. The comprehensive explanation of the relation between SGNS and SVD-SPPMI methods is provided in Keerthi et al. (2015). Lai et al. (2015); Levy et al. (2015) give a good overview of highly practical methods to improve these word embedding models.

6.2 Riemannian Optimization

An introduction to optimization over Riemannian manifolds can be found in Udriste (1994). The overview of retractions of high rank matrices to low-rank manifolds is provided in Absil and Oseledets (2015). The projector-splitting algorithm was introduced in Lubich and Oseledets (2014), and also was mentioned in Absil and Oseledets (2015) as “Lie-Trotter retraction”.

Riemannian optimization is succesfully applied to various data science problems: for example, matrix completion

Vandereycken (2013), large-scale recommender systems Tan et al. (2014)

, and tensor completion

Kressner et al. (2014).

7 Conclusions

In our paper, we proposed the general two-step scheme of training SGNS word embedding model and introduced the algorithm that performs the search of a solution in the low-rank form via Riemannian optimization framework. We also demonstrated the superiority of our method by providing experimental comparison to existing state-of-the-art approaches.

Possible direction of future work is to apply more advanced optimization techniques to the Step 1 of the scheme proposed in Section 1 and to explore the Step 2 — obtaining embeddings with a given low-rank matrix.


This research was supported by the Ministry of Education and Science of the Russian Federation (grant 14.756.31.0001).


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