Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 1: Reject
review_content: Summary
The authors proposed to disentangle syntactic information and semantic information from pre-trained contextualized word representations.

They use BERT to generate groups of sentences that are structurally similar (have the same POS tag for each word) but semantically different. Then they use a metric-learning approach to learn a linear transformation that encourages sentences from the same group to have closer distance. Specifically, they defined a triplet loss (Eq4) and uses negative sampling.

They use 150,000 sentences from Wikipedia to train the transformation. POS tags are obtained from spaCy. To evaluate the learned representations, they provided a tSNE visualization of the original and transformed representations (groups by dependency label); evaluate whether the nearest neighbor shares the same syntactic role; low-resource parsing.

Reasons of rejection:
1. I don't agree with the authors' argument, "we aim to extract the structural information encoded in the network in an unsupervised manner, without pre-supposing an existing syntactic annotation scheme".  First, what do you mean by structural information without a clear definition? Also, in the method, the authors construct a dataset where each group of the sentence share similar syntactic structures (having the same POS tag). It seems there that the structural information just means POS tags.

2. The author failed to convince me that the learned representation is more powerful than just combining POS tags with the original representations. Since POS tags are assumed to be available during training. I think a reasonable baseline in all experiments would be the performance based on POS tags. For example, in Figure 3, although the original EMLo representation does not correlates with the dependency label very much, the POS tags may do. In Figure 4, the authors should compare with delexicalized dependency parsing, which performs pretty well in los-resource setting.

review_title: Official Blind Review #4

final_score: rating: 6: Weak Accept
review_content: CONTRIBUTIONS:
Topic: Disentangling syntax from semantics in contextualized word representations
C1. A method for generating ‘structurally equivalent’ sentences is proposed, based only on the assumption that maintaining function words, and replacing one content word of a source sentence with another to produce a new grammatical sentence, yields a target sentence that is equivalent to the source sentence. 
C2. The ‘structural relation’ between two words in a sentence is modeled as the difference between their vector embeddings.
C3a. The structural relation between a pair of content words in one sentence is assumed to be the same as that between the corresponding pair in an equivalent sentence. 
C3b. The structural relation between any pair of content words in one sentence is assumed to be different from the structural relation between any pair of content words in an inequivalent sentence. 
C4. Given a selected word in a source sentence, to generate an alternative ‘corresponding’ content word for an equivalent target sentence, BERT is used to predict the source word when it is masked, given the remaining words in the source sentence. The alternative corresponding word is randomly selected from among the top (30) candidates predicted by BERT. Given a source sentence, the set of target sentences formed by cumulatively replacing content words one at a time in randomly selected positions defines an ‘equivalence set’ in which words in different sentences with the same left-to-right index are corresponding words. (To promote the formation of grammatical target sentences, a word is only replaced by another word with the same POS.) A pre-defined set of equivalence sets is used for training.
C5. A metric learning paradigm with triplet loss is used to find a function f for mapping ELMo or BERT word embeddings to a new vector space of ‘transformed word representations’. Implementing C2 and C3a, given the indices i and i’ of two content words, the triplet loss rewards closeness of the difference D between the transformed embeddings of the pair of words with these indices in sentence S and the corresponding difference D’ for an equivalent sentence S’. Implementing C3b, the triplet loss penalizes closeness between D and D”, where D” is the difference between transformed word embeddings of a pair of content words in a sentence S” that is inequivalent to S. (Eq. 4).
C6. (Implementing C5.) To form a mini-batch for minimizing the triplet loss, a set of (500) sentences S is selected, and for each a pair of indices of content words is chosen. Training will use the difference in the transformed embeddings of the words in S with these indices: call this D, and call the set of these (500) D vectors B. For each sentence S in B, a ‘positive pair’ (D, D’) is generated, where D’ is the corresponding difference for S’, a selected sentence in the equivalence set of S. Closeness of D and D’ is rewarded by the triplet loss, implementing C3a. To implement C3b, a ‘negative pair’ (D, D”), for which closeness is penalized by the loss, is formed as follows. D” is the closest vector in B to D that is derived from a sentence S” that is not equivalent to S. 
C7. 2-D t-SNE plots (seem to) show that relative to the original ELMo embeddings, the transformed embeddings cluster better by POS (Fig. 3). (No quantitative measure of this is provided, and the two plots are not easy to distinguish.)
C8. Pairs of closest ELMo vectors share syntactic (dependency parse) properties to a greater degree after transformation than before (Table 1). To check that this goes beyond merely POS-based closeness, the syntactic relations that least determine POS are examined separately, and the result remains. Furthermore, the proportion of pairs of closest vectors that are embeddings of the same word (in different contexts) drops from 77.6% to 27.4%, showing that the transformation reduces the influence of lexical-semantic similarity. Similar results hold for BERT embeddings, but to a lesser degree, so the paper focusses on ELMo. 
C9. Few-shot parsing. Two dependency parsers are trained, one on ELMo embeddings, the other on their transformations (under the proposed method). In the small-data regime (less than 200 training examples), the transformed embeddings yield higher parser performance, even when the encoding size of the ELMo embeddings is reduced (from 2048 to 75) to match that of the transformed embeddings by either PCA or a learned linear mapping. (Fig. 4) 
RATING: Weak accept
REASONS FOR RATING (SUMMARY). Using deep learning to create an encoding of syntactic structure with minimal supervision is an important goal and the paper proposes a clever way of doing this. The only ‘supervision’ here comes from (i) the function/content-word distinction (C1 above): two grammatical sentences are structurally equivalent if [but not only if] one can be derived from the other by replacing one content word with another; and (ii) filtering candidate replacement words to match the POS of the replaced word. BERT’s ability to guess a masked word is put to good use in providing suitable content word substitutions. The experimental results are rather convincing.
REVIEW (beyond the summary above)
C1. This assumption is famously not deemed to be true in linguistics, where the structural difference between ‘control’ and ‘raising’ verbs is basic Ling 101 material: see https://en.wikipedia.org/wiki/Control_(linguistics)#Control_vs._raising. This particular structural contrast illustrates how verbs can differ in their argument structure, without there being function words to signal the difference. So substituting *verbs* in particular may be non-ideal for the purposes of this work. Even the third example given by the authors in Sec. 3.1 illustrates a related  point, where function words do signal the contrast:  while the meaning of ‘let’ and ‘allow’ may be very similar, their argument structures differ, so that replacing ‘lets’ with ‘allows’ in the first sentence, or the reverse in the second sentence, produces ungrammatical results: 
*their first project is software that *allows* players connect the company ’s controller to their device
*the city offers a route-finding website that *lets* users to map personalized bike routes
Therefore, contrary to the paper, relative to linguistic syntactic structure, it is not a good result that ‘lets’ in the original version of the first sentence is the closest neighbor in transformed embedding space to ‘allows’ in the second. Rather, it is probably meaning, not structure, that makes ‘let’ and ‘allow’ similar.
It would improve the paper to make note of this general concern with C1 and to provide a response.
On another point, an important premise of the proposed method (C2 above) is that differences in vector space embeddings encode relations; this has been used by a number of previous authors since the famous Mikolov, Yih & Zweig NAACL2013, and that work should be cited and discussed.
review_title: Official Blind Review #3

final_score: rating: 8: Accept
review_content: The authors state a clear hypothesis: it is possible to extract syntactic information from contextualized word vectors in an unsupervised manner. The method of creating syntactically equivalent (but semantically different) sentences is indeed interesting on its own. Experiments do support the main hypothesis -- the distilled embeddings are stronger in syntactic tasks than the default contextualized vectors. The authors provide the code for ease of reproducibility which is nice.

There is a short literature review, but I am wondering if something similar was done for static word embeddings. I understand that they are obsolete these days, but on the other hand, they are better researched, so were there any attempts to disentangle syntax and semantics in the classical static word vectors?

Overall, I have no major concerns with the paper.
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: Summary:
=========
This paper aims to disentangle semantics and syntax in contextualized word representations. The main idea is to learn a transformation of the contexualized representations that will make two word representations to be more similar if they appear in the same syntactic context, but less similar if they appear in different syntactic contexts. The efficacy of this transformation is evaluated through cluster analysis, showing that words better organize in syntactic clusters after the transformation, and through low-resource dependency parsing. 

The paper presents a simple approach to transform word representations and expose their syntactic information. The experiments are mostly convincing. I would like to see better motivation, more engagement with a wider range of related work, and more thorough quantitative evaluations. Another important question to address is also what kind of semantic/syntactic types of information are targeted, and how to handle the tradeoff between them, for instance for different purposes. 


Main comments:
==============
1. Motivation: I found the motivation for the problem understudied a bit lacking. The main motivation seems to be to disentangle semantic and syntactic information. But why should we care about that? Beyond reference to disentangling in computer vision, some more motivation would be good. The few-shot parsing is a good such motivation, although the results are a bit disappointing (see more on this below). Another possible motivation is potential applications of disentanglement in language generation. There is a line of work on style transfer also in language generation, and it seems plausible that the methodology could be applied to such tasks. 
2. The present work is well-differentiated from work on extracting syntactic information from word representations via supervised ways, as the current work does so in an unsupervised way. I don't quite get the terminological differentiation between "mapping" and "extracting" in the introduction, but the idea is clear. 
3. Have you considered alternative representations of word pairs besides the different of their transformations f(x)-f(y)? 
4. I found it interesting that the word representation from BERT is the concatenation of layer 16 with the mean of all the other layers. This is motivated by Hewitt and Manning's findings, and [5] found similar results. However, the different between layer 16 and others is not that large as to warrant emphasizing it so much. Perhaps a scalar mix with fine-tuning may work better, as in [5], or another method. Have you tried other word representations? I also wonder whether it makes sense to use different layers for different parts of the triplet loss, depending on whether to emphasize syntactic vs. semantic similarity. 
5. The introduction lays out connections to some related work, but leaves several relevant pieces missing. See examples below. 
6. The results in 3.3 are limited but useful. The comparison with a PCA-ed and reduced representation is well thought of, because of the risk with low-resource and high dimensionality. That said, I found the gap between the proposed syntax model and the ELMo-reduced disappointingly small. Even in the LAS, it seems like the difference is very small, ~0.5, although it's hard to tell from the figure. Providing the actual numbers and a measure of statistical significance would be helpful here. 
7. Some care should be taken to define what kind of semantics is targeted here. In several cases this is "lexical semantics", but then we have "meaning" in parentheses sometimes (end of intro). Obviously, there's much more to semantics and meaning that the lexical semantics, so a short discussion of how the work views other, say compositional semantics, would be good. 


Other comments:
===============
1. The introduction seeks a representation that will ignore the similarity between "syrup" in (2) and (4). I wonder if "ignoring" is too strong. One may not want to lose all lexical semantic information. Moreover, the proposed triplet loss does not guarantee that information is ignored (and justly so, in my opinion). 
2. In the example, "maple" and "neural" are said to be syntactically similar, although "maple syrup" is a noun compound while "neural networks" is an adjective-noun. Shouldn't they be treated differently then? Unless the notion of syntax is more narrow and just looks at unlabeled dependency arcs. 
3. Some experimental choices are left unexplained, such as k=6 (section 2.1) or mapping to 27 dims (section 2.3); these two seem potentially important. 
4. Section 2.3: do you also back-prop back into the BERT/ELMo model weights? 
5. The dataset statistics in section 3 do not match those in section 2.2. Please clarify. 
6. The qualitative cluster analysis via t-SNE (3.1) is compelling. It could be made stronger by reporting quantitative clustering statistics such as cluster purity before and after transformation. 
7. In the examples showin in 3.1, it would be good to give also the nearest neighbor before the transformation for comparison. 
8. The quantitative results in 3.2 convey the point convincingly. It's good to see also the lexical match measure going down. The random baseline is also a good sanity check to have. It would be good to provide full results with BERT, at least in the appendix and at least for section 3.2, maybe also for 3.3.
9. More related work: 
+ Work that injects syntactic information into word representations in a supervised way, such as [1,2]
+ Work that shows that word embeddings contain different kinds of information (syntactic/semantic), and propose simle linear transformations to uncover them. 
+ Engaging with the literature on style transfer in language generation would be good, as mentioned above for motivation, but also to situate this work w.r.t to related style transfer work. 
+ Another line of work that may be mentioned is the variety of papers trying to extract syntactic information from contextualized word representations, such as constructing trees from attention weights. There were a few such papers in BlackboxNLP 2018 and 2019. 

Typos, phrasing, formatting, etc.:
============================
- Abstract: a various of semantic... task -> various semantic... tasks; use metric-learning approach -> use a metric-learning approach; in few-shot parsing setting -> in a few-shot parsing setting
- Wilcox et al. does not have a year
- Introduction: few-shots parsing -> few-shot parsing
- Method: extract vectors -> extracts vectors; Operativly -> Operatively 
- Section 3: should encourages -> should encourage; a few-shots settings -> a few-shot setting
- 3.2: -- was not rendered properly
- 3.3: matrix that reduce -> reduces 


References
==========
[1] Levy and Goldberg. 2014. Dependency-Based Word Embeddings
[2] Bansal et al. 2014. Tailoring Continuous Word Representations for Dependency Parsing
[3] Artetxe et al. 2018. Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation
[4] Tenney et al. 2019. BERT Rediscovers the Classical NLP Pipeline
[5] Liu et al. 2019. Linguistic Knowledge and Transferability of Contextual Representations
review_title: Official Blind Review #2

Meta-review: This paper aims to disentangle semantics and syntax inside of popular contextualized word embedding models. They use the model to generate sentences which are structurally similar but semantically different. 

This paper generated a lot of discussion. The reviewers do like the method for generating structurally similar sentences, and the triplet loss.  They felt the evaluation methods were clever.  However, one reviewer raised several issues.  First, they thought the idea of syntax had not been well defined. They also thought the evaluation did not support the claims.  The reviewer also argued very hard for the need to compare performance to SOTA models.  The authors argued that beating SOTA is not the goal of their work, rather it is to understand what SOTA models are doing.  The reviewers also argue that nearest neighbors is not a good method for evaluating the syntactic information in the representations.  

I hope all of the comments of the reviewers will help improve the paper as it is revised for a future submission.
Decision: Reject