Pitfalls in the Evaluation of Sentence Embeddings

TL;DR

This paper identifies key pitfalls in evaluating sentence embeddings, such as size comparison issues and normalization effects, and offers recommendations for more reliable future assessments.

Contribution

It systematically highlights evaluation pitfalls in sentence embeddings and proposes improved practices to enhance assessment reliability.

Findings

Comparison of embeddings of different sizes can be misleading

Normalization significantly impacts evaluation results

Transfer and probing task correlations are often low and inconsistent

Abstract

Deep learning models continuously break new records across different NLP tasks. At the same time, their success exposes weaknesses of model evaluation. Here, we compile several key pitfalls of evaluation of sentence embeddings, a currently very popular NLP paradigm. These pitfalls include the comparison of embeddings of different sizes, normalization of embeddings, and the low (and diverging) correlations between transfer and probing tasks. Our motivation is to challenge the current evaluation of sentence embeddings and to provide an easy-to-access reference for future research. Based on our insights, we also recommend better practices for better future evaluations of sentence embeddings.

Tables

Table 1. Table 1: Sentence encoders used in this work, together with the sizes of the resulting sentence embeddings.

Sentence Encoder	Emb. Size
InferSent Conneau et al. (2017)	4096
Sent2Vec Pagliardini et al. (2018)	700
PMeans Rücklé et al. (2018)	3600
USE Cer et al. (2018)	512
Avg. Glove Pennington et al. (2014)	300
Avg. Word2Vec Mikolov et al. (2013)	variable
SIF-Glove Arora et al. (2017)	300

Table 2. Table 2: Unsupervised cosine similarity + Pearson correlation ( UCP ) on STSBench (test data). Δ Δ \Delta in pp.

	Standard	Normalized	$\Delta$
Glove-300d	0.41	0.62	+21
Word2Vec-300d	0.56	0.65	+9
Word2Vec-800d	0.56	0.67	+11
InferSent-4096d	0.67	0.67	+0
SIF-Glove-300d	0.66	0.67	+1
SIF-Word2Vec-300d	0.67	0.67	+0
USE-512d	0.70	0.70	+0
Sent2Vec-700d	0.67	0.71	+4
PMean-3600d	0.64	0.66	+2

Full text

1 Introduction

The field of natural language processing (NLP) is currently in upheaval. A reason for this is the success story of deep learning, which has led to ever better reported performances across many different NLP tasks, sometimes exceeding the scores achieved by humans. These fanfares of victory are echoed by isolated voices raising concern about the trustworthiness of some of the reported results. For instance, Melis et al. (2017) find that neural language models have been misleadingly evaluated and that, under fair conditions, standard LSTMs outperform more recent innovations. Reimers and Gurevych (2017) find that reporting single performance scores is insufficient for comparing non-deterministic approaches such as neural networks. Post (2018) holds that neural MT systems are unfairly compared in the literature using different variants of the BLEU score metric. In an even more general context, Lipton and Steinhardt (2018) detect several current “troubling trends” in machine learning scholarship, some of which refer to evaluation.

Sentence encoders Kiros et al. (2015); Conneau et al. (2017); Pagliardini et al. (2018) are one particularly hot deep learning topic. Generalizing the popular word-level representations Mikolov et al. (2013); Pennington et al. (2014) to the sentence level, they are valuable in a variety of contexts: (i) clustering of sentences and short texts; (ii) retrieval tasks, e.g., retrieving answer passages for a question; and (iii) when task-specific training data is scarce—i.e., when the full potential of task-specific word-level representation approaches cannot be leveraged Subramanian et al. (2018).

The popularity of sentence encoders has led to a large variety of proposed techniques. These range from ‘complex’ unsupervised RNN models predicting context sentences Kiros et al. (2015) to supervised RNN models predicting semantic relationships between sentence pairs Conneau et al. (2017). Even more complex models learn sentence embeddings in a multi-task setup Subramanian et al. (2018). In contrast, ‘simple’ encoders compute sentence embeddings as an elementary function of word embeddings. They compute a weighted average of word embeddings and then modify these representations via principal component analysis (SIF) Arora et al. (2017); average n-gram embeddings (Sent2Vec) Pagliardini et al. (2018); consider generalized pooling mechanisms Shen et al. (2018); Rücklé et al. (2018); or combine word embeddings via randomly initialized projection matrices Wieting and Kiela (2019).

The embeddings of different encoders vary across various dimensions, the most obvious being their size. E.g., the literature has proposed embeddings ranging from 300d average word embeddings to 700d n-gram embeddings, to 4096d InferSent embeddings, to 24k dimensional random embeddings Wieting and Kiela (2019). Unsurprisingly, comparing embeddings of different sizes is unfair when size itself is crucially related to performances in downstream tasks, as has been highlighted before Rücklé et al. (2018); Wieting and Kiela (2019).

We compile several pitfalls when evaluating and comparing sentence encoders. These relate to (i) the embedding sizes, (ii) normalization of embeddings before feeding them to classifiers, and (iii) unsupervised semantic similarity evaluation. We also discuss (iv) the choice of classifier used on top of sentence embeddings and (v) divergence in performance results which compare downstream tasks and so-called probing tasks Conneau et al. (2018).

Our motivation is to assemble diverse observations from different published works regarding problematic aspects of the emerging field of sentence encoders. We do so in order to provide future research with an easy-to-access reference about issues that may not (yet) be widely known. We also want to provide the newcomer to sentence encoders a guide for avoiding pitfalls that even experienced researchers have fallen prey to. We also recommend best practices, from our viewpoint.

2 Setup

We compare several freely available sentence encoders (listed in Table 1) with SentEval Conneau and Kiela (2018), using its default settings. SentEval trains a logistic regression classifier for specific downstream tasks with the sentence embeddings as the input. We compare 6 downstream tasks from the fields of sentiment analysis (MR, SST), product reviews (CR), subjectivity (SUBJ), opinion polarity (MPQA), and question-type classification (TREC). In these tasks, the goal is to label a single sentence with one of several classes. We also evaluate on the STSBenchmark Cer et al. (2017), which evaluates semantic similarity of pairs of sentences.

3 Problems

Size matters.

Currently, there is no standard size for sentence embeddings and different encoders induce embeddings of vastly different sizes.

For example, the sentence encoders of Conneau et al. (2017), Pagliardini et al. (2018), Rücklé et al. (2018), Cer et al. (2018), Kiros et al. (2015), Subramanian et al. (2018) are 4096, 700, 3600, 512, 4800, 1500/2048 dimensional, respectively. However, Conneau et al. (2017) show that their own model performs better when dimensionality of the embeddings is larger. They hypothesize that the linear model they use for evaluation (logistic regression) performs better with higher dimensional embeddings because these are more likely to be linearly separable. Rücklé et al. (2018) then argued that a comparison to low-dimensional baselines is unfair under this finding and increase the size of the baselines by concatenating different word embedding types or by concatenating different pooling operations (min, max, average). Wieting and Kiela (2019) further extend this idea by enlarging the word embedding size with randomly initialized matrices before averaging. All three works show that performance increases as a concave function of embedding size, when a linear model is used on top of embeddings for evaluation.

We also observe this trend when we merely train higher-dimensional word2vec word embeddings (on Wikipedia) and then average them, see Figure 1. At equal embedding size, some models such as USE and Sent2Vec, have no or very little advantage over average word embeddings. Therefore, we strongly encourage future research to compare embeddings of the same sizes to provide a fair evaluation (or at least similar sizes).

Cosine similarity and Pearson correlation may give misleading results.

The following evaluation scenario is common when testing for semantic similarity: given two inputs (words or sentences), embed each of them, (i) compute the cosine similarity of the pairs of vectors, and then (ii) calculate the Pearson (or Spearman) correlation with human judgments for the same pairs. Both steps are problematic: (i) it is unclear whether cosine similarity is better suited to measure semantic similarity than other similarity functions; (ii) Pearson correlation is known for its deficiencies—e.g., it only measures linear correlation and it is sensitive to outliers. A popular example for failure of Pearson correlation is Anscombe’s quartet Anscombe (1973).

Indeed, using such unsupervised evaluations based on cosine similarity and Pearson correlation (which we denote UCP) may be misleading, as pointed out by Lu et al. (2015). When they normalized word embeddings, their WS353 semantic similarity Finkelstein et al. (2001) scores using UCP increased by almost 20 percentage points (pp). Since normalization is a simple operation that could easily be learned by a machine learning model, this indicates that UCP scores may yield unreliable conclusions regarding the quality of the underlying embeddings.

We wanted to verify if this was also true when comparing different sentence encoders, and therefore used the popular STSBenchmark dataset in the UCP setup. The results in Table 2 show that the outcomes vary strongly, with Glove-300d embeddings performing worst (0.41 correlation) and USE-512d embeddings best (0.70 correlation). We then normalized all sentence embeddings with z-norm111Subtracting the mean from each column of the embedding matrix of the whole data ($2N$ rows, one for each of $N$ pairs, and $d$ columns, where $d$ is the embedding size) and dividing by the standard deviation; after that we normalized each row to have unit length ($\ell_{2}$ norm)., which is given as a recommendation in LeCun et al. (1998) to process inputs for deep learning systems.

With normalization, we observe a reduction in the range of the distribution of UCP scores across the nine systems from 29% to 9%; similarly, the standard deviation decreases from 8.6% to 2.4%. In particular, the worst sentence encoders catch up substantially: e.g., average Glove embeddings improve from 0.41 Pearson to 0.62 Pearson and the improvement of more complex systems over simple averaging baselines appears much less pronounced than before the normalization.

When replacing Pearson by Spearman correlation, we observed very similar trends: e.g., Glove-300d had 0.44 before and 0.58 after normalization.

This shows that that UCP requires specific normalization of the inputs for a fair comparison. Parallel to the suggestion of Lu et al. (2015), we recommend considering to use learned similarity functions and mean-square error (MSE) as an alternative to UCP.

Normalization.

Indeed, we also evaluated the effect of normalization for supervised transfer tasks, i.e., with learned similarity function. To this end, we compared the 9 sentence encoders from Table 2 across 6 transfer tasks (averaged results) and STSBench (learned similarity function on training data instead of cosine similarity).222We estimated normalization vectors for mean and standard deviation on the training data and used these fixed values to normalize the test data.

Figure 2 shows that most techniques profit from normalization (on average), both on the transfer tasks and STSBench, even though gains are often substantially smaller than in the UCP setting. Since the normalization can lead to rank changes (which we, e.g., observe between W2Vec-800, USE, and Sent2Vec) we thus recommend introducing normalization as a binary hyperparameter that is tuned for each sentence encoder and downstream task also in supervised settings with learned similarity function.

Different classifiers for evaluation.

The popular SentEval evaluation tool feeds sentence embeddings into a logistic regression classifier. The underlying assumption is that in order to evaluate the embeddings themselves the classifier used on top of embeddings should be as simple as possible. While the argument has some appeal, one wonders how relevant such an evaluation is when in practice more powerful classifiers would probably be used, e.g., deeper networks (current versions of SentEval also offer evaluating with a multi-layer perceptron (MLP)). In particular, we note here an asymmetry between the extrinsic evaluation of word and sentence embeddings: word embeddings have traditionally been compared extrinsically by feeding them into different powerful architectures, such as BiLSTMs, while sentence embeddings are compared using the simplest possible architecture, logistic regression. While this is cheaper and focuses more on the embeddings themselves, it is less practically relevant, as discussed, and may have undesirable side effects, such as the preference for embeddings of larger size.

A main problem arises when the ranking of systems is not stable across different classifiers. To our knowledge, this is an open issue. We are only aware of Subramanian et al. (2018), who evaluate a few setups both with logistic regression and using an MLP, and their results indicate that their own approach profits much more from the MLP than the InferSent embeddings they compare to ($+$3.4pp vs. $+$2.2pp).

Thus, it is not sufficient to only report results with logistic regression, and evaluations with better-performing approaches would provide a more realistic comparison for actual use-case scenarios. We suggest reporting results for at least logistic regression and MLP.

Correlation of transfer tasks and probing tasks.

Besides transfer tasks, the literature has recently suggested evaluating sentence encoders with probing tasks Conneau et al. (2018) that query embeddings for certain linguistic features, such as to detect whether a sentence contains certain words (WC) or to determine the sentence length (SentLen). Perone et al. (2018) evaluate 11 different sentence encoders on 9 transfer tasks and 10 probing tasks. We plot the Spearman correlation between their transfer task results and their probing task results in Figure 3. The average Spearman correlation is 0.64. The highest average correlation to transfer tasks has SentLen (0.83), and the lowest score has WC (0.04). Taken at face value, this may mean that current transfer tasks query more for superficial sentence features (knowing that embedding A can better predict sentence length than embedding B is indicative that A outperforms B on the transfer tasks) than for actual semantic content, as the embeddings were originally designed for.

Thus, future research might focus on more suitable (difficult) datasets and sentence classification tasks for the evaluation of sentence embeddings, a lesson already learned in other fields Läubli et al. (2018); Yu et al. (2018).

Importantly, depending on the set of evaluated sentence encoders, such correlations can yield contradictory outcomes. For example, Conneau et al. (2018) evaluate more than 40 combinations of similar sentence encoder architectures and observe the strongest correlation with downstream task performances for WC (cf. their figure 2). This is in contrast to the correlations from the results of Perone et al. (2018), where WC had the lowest correlation. Thus, it remains unclear to which extent downstram tasks benefit from the different properties that are defined by many probing tasks.

4 Conclusion

Others have laid out problems with the evaluation of word embeddings Faruqui et al. (2016) using word similarity tasks. They referred to the vagueness of the data underlying the tasks (as well as its annotations), the low correlations between extrinsic and intrinsic evaluations, and the lack of statistical tests. Our critique differs (in part) from this in that we also address extrinsic evaluation and the evaluation techniques themselves,333Faruqui et al. (2016) also discuss some short-comings of cosine similarity. and in that we believe that the comparison between sentence embeddings is not always fair, especially given the current evaluations using logistic regression. This implicitly favors larger embeddings, and may therefore result in misleading conclusions regarding the superiority of different encoders.

As practical recommendations, we encourage future research in sentence embeddings to (1) compare embeddings of the same size; (2) treat normalization as a further hyperparameter; and (3) use multiple classifiers during evaluation, i.e., at least logistic regression and an MLP. We recommend against using unsupervised cosine$+$Pearson evaluations but instead to use a learned similarity function, and to report MSE as an alternative to Pearson/Spearman correlations. If unsupervised evaluation is unavoidable, normalization is even more important. Finally, we think that current transfer tasks for sentence embeddings should be complemented by more challenging ones for which bag-of-words models or random projection models cannot as easily compete.

Acknowledgments

We thank the reviewers for their helpful feedback. This work has been supported by the German Research Foundation (DFG) funded research training group “Adaptive Preparation of Information form Heterogeneous Sources” (AIPHES, GRK 1994/1) and the DFG-funded project QA-EduInf (GU 798/18-1, RI 803/12-1). Some calculations of this research were conducted on the Lichtenberg high performance cluster of the TU Darmstadt.