Certain AI tasks consist in computing a function that is governed by strict rules: e.g., if is the function mapping a mathematical expression to its value (e.g., mapping ‘’ to ), then obeys the rule that for any expressions and . This rule is compositional: the output of a structure (here, ) is a function of the outputs of the structure’s constituents (here, and ). The rule can be stated with full generality once the input is assigned a symbolic structure giving its decomposition into constituents. For a fully-compositional task, completely determined by compositional rules, an AI system that can assign appropriate symbolic structures to inputs and apply appropriate compositional rules to these structures will display full systematic generalization: it will correctly process arbitrary novel combinations of familiar constituents. This is a core capability of symbolic AI systems. Other tasks, including most natural language tasks such as machine translation, are only partially characterizable by compositional rules because natural language is only partially compositional in nature. For example, if is the function that assigns meanings to English adjectives, it generally obeys the rule that , (e.g., ), yet there are exceptions: . On these “partially-compositional” AI tasks, this strategy of compositional analysis has demonstrated considerable, but limited, generalization capabilities.
Deep learning research has shown that Neural Networks (NNs) can display remarkable degrees of combinatorial generalization, often surpassing symbolic AI systems for partially-compositional tasks (Wu et al., 2016), and exhibit good generalization (although generally falling short of symbolic AI systems) on fully-compositional tasks (Lake and Baroni, 2018; McCoy et al., 2019). Given that standard NNs have no obvious mechanisms for representing symbolic structures, parsing inputs into such structures, nor applying compositional symbolic rules to them, this success raises two questions:
Understanding generalization: How do NNs achieve such strong generalization on partially-compositional tasks, and good performance on fully-compositional tasks?
Improving generalization: What new NN architectures can achieve even better generalization on both fully- and partially-compositional tasks?
These two questions are related: understanding the compositional generalization abilities of current NNs can suggest ways to improve their compositional generalization.
Regarding Q1, understanding generalization, McCoy et al. (2019) showed that when trained on highly compositional tasks, standard NNs learned representations that were well approximated by symbolic structures (Sec. 2). Processing in these NNs assigns such representations to inputs and generates outputs that are governed by compositional rules stated over those representations. We will refer to the networks to be analyzed as target NNs, because we will propose a new type of NN (in Sec. 4) — the Role Learner (ROLE) — which is used to analyze the target network, after discussing related methods of network analysis in Sec. 3. In contrast with the analysis model of McCoy et al. (2019), which relies on a hand-specified hypothesis regarding the underlying structure, ROLE automatically learns a symbolic structure that best approximates the internal representation of the target network. Automating the discovery of structural hypotheses provides two advantages. First, ROLE achieves success at analyzing networks for which it is not clear what the underlying structure is. We show this in Sec. 6, where ROLE successfully uncovers the symbolic structures learned by a seq2seq RNN trained on the SCAN task (Lake and Baroni, 2018). Second, removing the need for hand-specified hypotheses allows the data to speak for itself which simplifies the burden on the user who only needs to provide data consisting of input sequences and associated embeddings.
We first consider fully-compositional (hence synthetic) tasks: a simple string-manipulation task in Sec. 5, and the richer SCAN task, which has been the basis of previous work on combinatorial generalization in NNs, in Sec. 6. Discovering symbolic structure within a model enables us to perform precise alterations to the internal representations in order to produce desired combinatorial alterations in the output (Sec. 6.3). Then, in Sec. 7, we turn briefly to partially-compositional tasks in NLP. Regarding Q2, improving generalization, in Sec. 8 we consider how what we have learned about standard NNs can suggest new inductive biases to strengthen compositionality in NN learning.
2 NN embedding of symbol structures
We build on McCoy et al. (2019), which introduced the analysis task DISCOVER (DISsecting COmpositionality in VEctor Representations)
: take a NN and, to the extent possible, find an explicitly-compositional approximation to its internal distributed representations.McCoy et al. (2019) showed that, in GRU (Cho et al., 2014)
encoder-decoder networks performing simple, fully-compositional string manipulations, the medial encoding (between encoder and decoder) could be extremely well approximated, up to a linear transformation, byTensor Product Representations (TPRS) (Smolensky, 1990), which are explicitly-compositional vector embeddings of symbolic structures. To represent a string of symbols as a TPR, the symbols in the string might be parsed into three constituents , where is the role of position from the left edge of the string; other role schemes are also possible, such as roles denoting right-to-left position: . The embedding of a constituent is , where are respectively a vector embedding of the roles and a vector embedding of the fillers of those roles: the digits. The embedding of the whole string is the sum of the embeddings of its constituents. In general, for a symbol structure with roles that are respectively filled by the symbols , .
The example above used the role scheme of linear position from the left edge, but other role schemes are possible, such as linear position from the right edge or position in a tree. McCoy et al. (2019) showed that — for a given seq2seq architecture learning a given string-mapping task — there exists a highly accurate TPR approximation of the medial encoding, given an appropriate pre-defined role scheme. The main technical contribution of the present paper is the Role Learner (ROLE) model, a RNN network that learns its own role scheme to optimize the fit of a TPR approximation to a given set of internal representations in a pre-trained target NN. This makes the DISCOVER framework more general by removing the need for human-generated hypotheses as to the role schemes the network might be implementing. Learned role schemes, we will see in Sec. 6.1, can enable good TPR approximation of networks for which human-generated role schemes fail.
3 Related work
This work falls within the larger paradigm of using analysis techniques to interpret NNs (see Belinkov and Glass (2019) for a recent survey), often including a focus on compositional structure (Hupkes et al., 2019, 2018; Lake and Baroni, 2018; Hewitt and Manning, 2019). Two of the most popular analysis techniques are the behavioral and probing approaches. In the behavioral approach, a model is evaluated on a set of examples carefully chosen to require competence in particular linguistic phenomena (Marvin and Linzen, 2018; Wang et al., 2018; Dasgupta et al., 2019; Poliak et al., 2018; Linzen et al., 2016; McCoy et al., 2019). This technique can illuminate behavioral shortcomings but says little about how the internal representations are structured, treating the model as a black box.
In the probing approach, an auxiliary classifier is trained to classify the model’s internal representations based on some linguistically-relevant distinction(Adi et al., 2017; Conneau et al., 2018; Conneau and Kiela, 2018; Belinkov et al., 2017; Blevins et al., 2018; Peters et al., 2018; Tenney et al., 2019). In contrast with the behavioral approach, the probing approach tests whether some particular information is present in the model’s encodings, but it says little about whether this information is actually used by the model. Indeed, in at least some cases models will fail despite having the necessary information to succeed in their representations, showing that the ability of a classifier to extract that information does not mean that the model is using it (Vanmassenhove et al., 2017).
DISCOVER bridges the gap between representation and behavior: It reveals not only what information is encoded in the representation, but also shows how that information is causally implicated in the model’s behavior. Moreover, it provides a much more comprehensive window into the representation than the probing approach does; while probing extracts particular types of information from a representation (e.g., “does this representation distinguish between active and passive sentences?”), DISCOVER exhaustively decomposes the model’s representational space. In this regard, DISCOVER is most closely related to the approaches of Andreas (2019), Chrupała and Alishahi (2019), and Abnar et al. (2019), who also propose methods for discovering a complete symbolic characterization of a set of vector representations, and Omlin and Giles (1996) and Weiss et al. (2018), which also seek to extract more interpretable symbolic models that approximate neural network behavior.
4 The Role Learner (ROLE) Model
ROLE111Code available at https://github.com/psoulos/role-decomposition produces a vector-space embedding of an input string of symbols by producing a TPR and then passing it through a linear transformation . ROLE is trained to approximate a pre-trained target string-encoder . Given a set of training strings , ROLE minimizes the total mean-squared error (MSE) between its output and ’s corresponding output, .
ROLE is an extension of the Tensor-Product Encoder (TPE) introduced in McCoy et al. (2019) (under the name “Tensor Product Decomposition Network”), which produces a linearly-transformed TPR given a string of symbols and pre-assigned role labels for each symbol (see Appendix A.1 for the TPE architecture). Crucially, ROLE is not given role labels for the input symbols, but learns to compute them. More precisely, it learns a dictionary of -dimensional role-embedding vectors, , and, for each input symbol , computes a soft-attention vector over these role vectors: the role vector assigned to is then the attention-weighted linear combination of role vectors, . ROLE simultaneously learns a dictionary of -dimensional symbol-embedding filler vectors , the column of which is , the embedding of symbol type ; where is the size of the vocabulary of symbol types. The TPR generated by ROLE is thus , where is symbol ’s type. Finally, ROLE learns a linear transformation to map this TPR into , where is the dimension of the representations of the encoder it is learning to approximate.
ROLE uses an LSTM (Hochreiter and Schmidhuber, 1997) to compute the role-assigning attention-vectors from its learned embedding of the input symbols : at each , the hidden state of the LSTM passes through a linear layer and then a softmax to produce (depicted in Figure 1).222Let the LSTM hidden state be ; let the output-layer weight-matrix have rows and let the columns of be , with . Then : the result of query-key attention (e.g., Vaswani et al., 2017) with query to a fixed external memory containing key-value pairs .
Since a TPR for a discrete symbol structure deploys a discrete set of roles specifying discrete structural positions, ideally a single role would be selected for each : would be one-hot. ROLE training therefore deploys regularization to bias learning towards one-hot vectors (based on the regularization proposed in Palangi et al. (2017), developed for the same purpose). See Appendix A.4 for the precise regularization terms that we used.
It is important to note that, while we impose this regularization on ROLE, there is no explicit bias favoring discrete compositional representations in the target encoder : any such structure that ROLE finds hidden in the representations learned by must be the result of biases implicit in the vanilla RNN-architecture of when applied to its target task.
We evaluate ROLE in three ways. The continuous method is as described above, with input symbol assigned role vector . In the snapped method, is replaced at evaluation time by the one-hot vector singling out role : . In the discrete method, we use the one-hot vector to output roles for every symbol in the dataset and then train a TPE which does not learn roles but rather uses the one-hot vector as input during training. In this case, ROLE acts as an automatic data labeler, assigning a role to every input word.
5 A simple fully-compositional task
We first apply ROLE to two target Tensor Product Encoder (TPE) models which are fully compositional by design. Since we know what role scheme each target model deploys, we can test how well ROLE learns these ground-truth roles. The TPEs are trained on the fully compositional task of autoencoding sequences of digits.
We use two types of TPEs: one that uses a simple left-to-right role scheme (e.g., first, second, third) and one that uses a complex tree position role scheme (e.g., left child of the root of the tree, right child of the left child of the left child of the root of the tree), where the trees are generated from digit sequence inputs using a deterministic parsing algorithm (see Appendix A.2 for explanations and examples of the designed role schemes). The left-to-right TPE was paired with a unidirectional RNN decoder, while the tree-position TPE was paired with the tree-RNN decoder used by McCoy et al. (2019). Both of these target models attained near-perfect performance on the autoencoding task (Table 1). Once the encoders are finished training, we extract the encoding for each sequence in the dataset and use this to train ROLE. See Appendices A.3 and A.5 for additional training details.
Table 1 reports the approximation performance of ROLE measured in two ways. Substitution Accuracy is the proportion of the items for which the decoder produces the correct output string when it is fed the ROLE approximation. The V-Measure (Rosenberg and Hirschberg, 2007) assesses the extent to which the clustering of the role vectors assigned by ROLE matches the ground truth role assignments.
The ROLE approximation of the left-to-right TPE attained perfect performance, with a substitution accuracy of 100% and a V-Measure of 1.0, indicating that the role scheme it learned perfectly matched the ground truth. On the significantly more complex case of tree position roles, ROLE achieves essentially the same accuracy as the target encoder and has considerable success at recovering the ground truth roles for the vectors it was analyzing. These results show that, when a target model has a known fully compositional structure, ROLE can successfully find that structure.
|Target model||Target model accuracy||ROLE substitution accuracy||ROLE V-Measure|
6 The SCAN task
We have established that ROLE can uncover the compositional structure used by a model that is compositional by design. We now return to question Q1 of Section 1: How can models without explicit compositional structure (namely, standard RNNs) still be as successful at fully compositional tasks as fully compositional models? Our hypothesis is that, though these models have no constraint forcing them to be compositional, they still have the ability to implicitly learn compositional structure. To test this hypothesis, we apply ROLE to a standard RNN-based seq2seq (Sutskever et al., 2014) model trained on a fully compositional task. Because the RNN has no constraint forcing it to use TPRs, we do not know a priori whether there exists any solution that ROLE could learn; thus, if ROLE does learn anything it will be a significant empirical finding about how these RNNs operate.
We consider the SCAN task (Lake and Baroni, 2018), which was designed to test compositional generalization and systematicity. SCAN is a synthetic sequence-to-sequence mapping task, with an input sequence describing an action plan, e.g., , being mapped to a sequence of primitive actions, e.g., (see Sec. 6.3 for a complex example). We use to abbreviate , sometimes written ; similarly, we use for . The SCAN mapping is defined by a complete set of compositional rules (Lake and Baroni, 2018, Supplementary Fig. 7).
6.1 The compositional structure of SCAN encoder representations
For our target SCAN encoder , we trained a standard GRU with one hidden layer of dimension 100 for 100,000 steps (batch-size 1) with a dropout of 0.1 on the simple train-test split. (The hidden dimension and dropout rate were determined by a limited hyper-parameter search.) achieves (full-string) accuracy on the test set. Thus provides what we want: a standard RNN achieving near-perfect accuracy on a non-trivial fully compositional task.
After training, we extract the final hidden embedding from the encoder for each example in the training and test sets. These are the encodings we attempt to approximate as explicitly compositional TPRs. We provide ROLE with 50 roles to use as it wants (additional training information can be found in Appendix A.6). The substitution accuracy that this learned role scheme provides is evaluated with the three evaluation methods defined in Sec. 4. For comparison, we also train TPEs using a variety of hand-crafted role schemes: left-to-right (LTR), right-to-left (RTL), bidirectional (Bi), tree position, Wickelrole (Wickel), and bag-of-words (BOW) (additional information provided in Appendix A.2). The substitution accuracy from these different methods is shown in Table 2. All of the predefined role schemes provide poor approximations, none surpassing accuracy. The role scheme learned by ROLE does significantly better than any of the predefined role schemes: when tested with the basic, continuous role-attention method, the accuracy is . The success of ROLE tells us two things. First, it shows that the target model’s compositional behavior relies on compositional internal representations: it was by no means guaranteed to be the case that ROLE would be successful here, so the fact that it is successful tells us that the encoder has learned compositional representations. Second, it adds further validation to the efficacy of ROLE, because it shows that it can be a useful analysis tool in cases of significantly greater complexity than the autoencoding task.
6.2 Interpreting the learned role scheme
Based on an analysis of the roles assigned to the sequences in the SCAN training set, we created a symbolic algorithm for predicting which role will be assigned to a given filler. This algorithm is described in Appendix A.7. Though the algorithm was created based only on sequences in the SCAN training set, it is equally successful at predicting which roles will be assigned to test sequences, exactly matching ROLE’s predicted roles for 98.7% of sequences, suggesting that the algorithm is an accurate symbolic description of the role scheme discovered by ROLE.
The details of this algorithm illuminate how the filler-role scheme encodes information relevant to the task. First, one of the initial facts that the decoder must determine is whether the sequence is a single command, a pair of commands connected by , or a pair of commands connected by ; such a determination is crucial for knowing the basic structure of the output (how many actions to perform and in what order). We have found that role 30 is used for, and only for, the filler , while role 17 is used in and only in sequences containing (usually with as the filler bound to it). Thus, the decoder can use these roles to tell which basic structure is in play: if role 30 is present, it is an sequence; if role 17 is present, it is an sequence; otherwise it is a single command.
Once the decoder has established the basic syntactic structure of the output, it must then fill in the particular actions. This can be accomplished using the remaining roles, which mainly encode absolute position within a command. For example, the last word of a command before (e.g., ) is always assigned role 8, while the last word of a command after (e.g., ) is always assigned role 46. Therefore, once the decoder knows (based on the presence of role 17) that it is dealing with an sequence, it can check for the fillers bound to roles 8 and 46 to begin to figure out what the two subcommands surrounding look like. The identity of the last word in a command is informative because that is where a cardinality (i.e., or ) appears if there is one. Thus, by checking what filler is at the end of a command, the model can learn whether there is a cardinality present and, if so, which one.
This description of how the decoding could take place does not necessarily match how it actually does take place; for example, it is likely that some of the steps we have described as occurring serially, for expository simplicity, actually occur in parallel. We leave for future work the question of which operations are actually being performed and how those operations are instantiated in an RNN.
6.3 Precision constituent-surgery on internal representations to produce desired outputs
The substitution-accuracy results above show that if the entire learned representation is replaced by ROLE’s approximation, the output remains correct. But do the individual words in this TPR have the appropriate causal consequences when processed by the decoder?333Historically, this question has had considerable significance: the original compositionality challenge to neural network models of cognition by Fodor and colleagues (Fodor and Pylyshyn, 1988) insisted that constituents of cognitive representations must individually be causally efficacious in order for those constituents to provide an explanation of the compositionality of cognition (Fodor, 1997; Fodor and McLaughlin, 1990). That TPRs meet the challenge was argued in Smolensky (1987, 1991).
To address this causal question (Pearl, 2000), we actively intervene on the constituent structure of the internal representations by replacing one constituent with another, and see whether this produces the expected change in the output of the decoder. We take the encoding generated by ROLE for an input such as , subtract the vector embedding of the constituent, add the vector embedding of the constituent, and see whether this causes the output to change from the correct output for ( to the correct output for ().
We extract word categories from the SCAN grammar (Lake and Baroni, 2018, Supplementary Fig. 6) by saying that word A and word B belong to the same category if every occurrence of word A in all grammatical sequences could be replaced by word B and still yield a grammatical sequence, and if every occurrence of word B in all grammatical sequences could be replaced by word A and still yield a grammatical sequence. Based on our analysis of the learned role scheme in Appendix A.7, we do not replace occurrences of and since the presence of either of these words causes substantial changes in the roles assigned to the sequence. Such surgery can be viewed as a more general extension of the analogy approach used by Mikolov et al. (2013) for analysis of word embeddings. An example of applying a sequence of five such constituent surgeries to a sequence are shown in Figure 2 (left).
The proportion of cases for which a random sequence of such surgeries produced the correct output at each step is shown in Figure 2 (right). The discrete version gives the best surgical accuracy, approximately ranging from about for one to for eight successive surgeries.
7 Partially-compositional NLP tasks
The previous sections explored fully-compositional tasks where there is a strong signal for compositionality. In this section, we explore whether the representations of NNs trained on tasks that are only partially-compositional also capture compositional structure. Partially-compositional tasks are especially challenging to model because a fully-compositional model may enforce compositionality too strictly to handle the non-compositional aspects of the task, while a model without a compositional bias may not learn any sort of compositionality from the weak cues in the training set.
We test four sentence encoding models for compositionality: InferSent (Conneau et al., 2017), Skip-thought (Kiros et al., 2015), Stanford Sentiment Model (SST) (Socher et al., 2013), and SPINN (Bowman et al., 2016). For each of these models, we extract the encodings for the SNLI premise sentences (Bowman et al., 2015). We use the extracted embeddings to train ROLE with 50 roles available (additional training information provided in Appendix A.9).
As a baseline, we also train TPEs that use pre-defined role schemes (additional training information in Appendix A.8). For all of the sentence embedding models except for Skip-thought, ROLE with continuous attention provides the lowest mean squared error at approximating the encoding (Table 3). While ROLE performs better than the hand-crafted role schemes, most of the results are within the same order of magnitude, suggesting that the sentence embedding models are not representing compositional structure. The bag-of-words (BOW) role scheme represents a TPE that does not use compositional structure by assigning the same role to every filler; for each of the sentence embedding models tested except for SST, bag-of-words performs similarly to the learned role scheme. Parikh et al. (2016) found that a bag-of-words model scores extremely well on Natural Language Inference despite having no knowledge of word order, showing that structure is not necessary to perform well on the sorts of tasks commonly used to train sentence encoders. Overall, these results do not suggest that these sentence embedding models rely on compositional representations.
8 Future work
TPRs provide NNs an aspect of the systematicity of symbolic computation by disentangling fillers and roles. The learner needs to learn to process fillers — providing, essentially, what each input constituent contributes to the output — and to process roles — essentially, how these contributions are used in the output. In fully-compositional tasks, the processing of fillers and roles can be encoded (and hence in principle learned) independently. In partially-compositional tasks, the processing of fillers and roles may be approximately encoded independently, with key interactions when a task deviates from full compositionality. In a fully-compositional mapping, the weights processing a TPR factor into weights processing fillers and weights processing roles. This can be illustrated with a simple example from the SCAN task.
One of the compositional rules for the mapping defined by the SCAN task is: . Suppose that, given the input string , a NN encoder produces a representation that is approximated by the TPR of a single-constituent structure in which the filler is assigned the role “argument of ”: . So to generalize correctly to the input , producing output , the system needs to learn two things: how to map the filler, , and how to map the role: (the roles of the first and second constituents of the output structure). Now if , are respectively computed over embedding vectors through weight matrices , in a NN, then the weight tensor will correctly map the embedding of to the embedding of : . This suggests that greater compositional generalization might be achieved by future models that are explicitly biased to utilize TPRs as internal representations and explicitly biased to factor their processing weights into those that process fillers (independently of their roles) and those that process roles (independently of their fillers), as in above.
We have introduced ROLE, a neural network that learns to approximate the representations of an existing target neural network using an explicit symbolic structure. ROLE successfully discovers symbolic structure both in models that explicitly define this structure and in an RNN without explicit structure trained on the fully-compositional SCAN task. When applied to sentence embedding models trained on partially-compositional tasks, ROLE performs better than hand-specified role schemes but still provides little evidence that the sentence encodings represent compositional structure. Uncovering the latent symbolic structure of NN representations on fully-compositional tasks is a significant step towards explaining how they can achieve the level of compositional generalization that they do, and suggests types of inductive bias to improve such generalization for partially-compositional tasks.
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Appendix A Appendix
a.1 Tensor Product Encoder (TPE) Architecture
a.2 Designed role schemes
We use six hand-specified role schemes as a baseline to compare the learned role schemes against. Examples of each role scheme are shown in Table 4.
Left-to-right (LTR): Each filler’s role is its index in the sequence, counting from left to right.
Right-to-left (RTL): Each filler’s role is its index in the sequence, counting from right to left.
Bidirectional (Bi): Each filler’s role is a pair of indices, where the first index counts from left to right, and the second index counts from right to left.
Tree: Each filler’s role is given by its position in a tree. This depends on a tree parsing algorithm.
Wickelroles (Wickel): Each filler’s role is the filler before it and the filler after it. (Wickelgren, 1969)
Bag-of-words (BOW): Each filler is assigned the same role. The position and context of the filler is ignored.
|Bidirectional||(0, 3)||(1, 2)||(2, 1)||(3, 0)||(0, 5)||(1, 4)||(2, 3)||(3, 2)||(4, 1)||(5, 0)|
|Bag of words||r||r||r||r||r||r||r||r||r||r|
a.3 TPEs trained on digit sequence tasks
We trained two TPEs end-to-end with an RNN decoder for our target networks on the digit sequence tasks. The left-to-right (LTR) TPE used a left-to-right role scheme applied to each element in the input and was connected to a unidirectional GRU decoder. The tree TPE used tree positions for each element in the input and was connected to a tree GRU decoder; here, the digit strings were parsed as a binary tree by a deterministic algorithm given in McCoy et al. (2019, App. C). The filler and role dimensions were both 20 for the LTR TPE. The filler dimension was 20 for the Tree TPE, and the role dimension was 120. We used a hidden size of 60 for the GRU decoders. We used a patience of 2 for early stopping. The left-to-right TPE achieves 100% accuracy on the test set and the tree TPE achieves 98.62% on the test set.
a.4 ROLE regularization
Letting , the regularization term applied during ROLE training is , where
is a regularization hyperparameter and:
Since each results from a softmax, its elements are positive and sum to 1. Thus the factors in are all non-negative, so assumes its minimal value of 0 when each has binary elements; since these elements must sum to 1, such an must be one-hot. is also minimized when each is one-hot because when a vector’s norm is 1, its norm is maximized when it is one-hot. Although each of these terms individually favor one-hot vectors, empirically we find that using both terms helps the training process. In a discrete symbolic structure, each position can hold at most one symbol, and the final term in ROLE’s regularizer is designed to encourage this. In the vector , the element is the total attention weight, over all symbols in the string, assigned to the role: in the discrete case, this must be 0 (if no symbol is assigned this role) or 1 (if a single symbol is assigned this role). Thus is minimized when all elements of are 0 or 1 ( is similar to , but with squared terms since we are no longer assured each element is at most 1). It is important to normalize each role embedding in the role matrix R so that small attention weights have correspondingly small impacts on the weighted-sum role embedding.
a.5 ROLE trained on digit sequence tasks
Once the TPEs in Sec. A.3 were trained, we extracted the hidden embedding for each item in the training, dev, and test sets.
For both ROLE models trained on the digit sequence task, we used a bidirectional 2-layer LSTM (Hochreiter and Schmidhuber, 1997) with filler dimension of 20, and regularization constant . For training, we used the ADAM (Kingma and Ba, 2015) optimizer with a learning rate of .001, batch size 32, and an early stopping patience of 10. The ROLE model trained on the LTR TPE was given 20 roles each of dimension 20. The ROLE model trained on the Tree TPE was given 120 roles each of dimension 120.
a.6 ROLE trained on SCAN
For the ROLE models trained to approximate the GRU encoder trained on SCAN, we used a filler dimension of 100, a role dimension of 50 with 50 roles available. For training, we used the ADAM (Kingma and Ba, 2015) optimizer with a learning rate of .001, batch size 32, and an early stopping patience of 10. The role assignment module used a bidirectional 2-layer LSTM (Hochreiter and Schmidhuber, 1997). We performed a hyperparameter search over the regularization coefficient using the values in the set [.1, .02, .01]. The best performing value was .02, and we used this model in our analysis.
a.7 SCAN Role Analysis
The algorithm below characterizes our post-hoc interpretation of which roles the Role Learner will assign to elements of the input to the SCAN model. This algorithm was created by hand based on an analysis of the Role Learner’s outputs for the elements of the SCAN training set. The algorithm works equally well on examples in the training set and the test set; on both datasets, it exactly matches the roles chosen by the Role Learner for 98.7% of sequences (20,642 out of 20,910).444This figure of 98.7% is so constant across datasets presumably because the synthetic nature of the SCAN dataset means that any reasonably-sized sample from it will be similarly representative of the entire dataset.
The input sequences have three basic types that are relevant to determining the role assignment: sequences that contain and (e.g., jump around left and walk thrice), sequences that contain after (e.g., jump around left after walk thrice), and sequences without and or after (e.g., turn opposite right thrice). Within commands containing and or after, it is convenient to break the command down into the command before the connecting word and the command after it; for example, in the command jump around left after walk thrice, these two components would be jump around left and walk thrice.
Sequence with and:
Elements of the command before and:
Last word: 28
First word (if not also last word): 46
opposite if the command ends with thrice: 22
Direction word between opposite and thrice: 2
opposite if the command does not end with thrice: 2
Direction word after opposite but not before thrice: 4
Direction word after around: 2
Direction word between an action word and twice or thrice: 2
Elements of the command before and:
First word: 11
Last word (if not also the first word): 36
Second-to-last word (if not also the first word): 3
Second of four words: 24
Sequence with after:
Elements of the command before after:
Last word: 8
Second-to-last word: 36
First word (if not the last or second-to-last word): 11
Second word (if not the last or second-to-last word): 3
Elements of the command after after:
Last word: 46
Second-to-last word: 4
First word if the command ends with around right: 4
First word if the command ends with thrice and contains a rotation: 10
First word if the command does not end with around right and does not contain both thrice and a rotation: 17
Second word if the command ends with thrice: 17
Second word if the command does not end with thrice: 10
after: 17 if no other word has role 17 or if the command after after ends with around left; 43 otherwise
Sequence without and or after:
Action word directly before a cardinality: 4
Action word before, but not directly before, a cardinality: 34
thrice directly after an action word: 2
twice directly after an action word: 2
opposite in a sequence ending with twice: 8
opposite in a sequence ending with thrice: 34
around in a sequence ending with a cardinality: 22
Direction word directly before a cardinality: 2
Action word in a sequence without a cardinality: 46
opposite in a sequence without a cardinality: 2
Direction after opposite in a sequence without a cardinality: 26
around in a sequence without a cardinality: 3
Direction after around in a sequence without a cardinality: 22
Direction directly after an action in a sequence without a cardinality: 22
To show how this works with an example, consider the input jump around left after walk thrice. The command before after is jump around left. left, as the last word, is given role 8. around, as the second-to-last word, gets role 36. jump, as a first word that is not also the last or second-to-last word gets role 11. The command after after is walk thrice. thrice, as the last word, gets role 46. walk, as the second-to-last word, gets role 4. Finally, after gets role 17 because no other elements have been assigned role 17 yet. These predicted outputs match those given by the Role Learner.
This algorithm may seem convoluted, but a few observations can illuminate how the roles assigned by such an algorithm support success on the SCAN task. First, a sequence will contain role 30 if and only if it contains and, and it will contain role 17 if and only if it contains after. Thus, by implicitly checking for the presence of these two roles (regardless of the fillers bound to them), the decoder can tell whether the output involves one or two basic commands, where the presence of and or after leads to two basic commands and the absence of both leads to one basic command. Moreover, if there are two basic commands, whether it is role 17 or role 30 that is present can tell the decoder whether the input order of these commands also corresponds to their output order (when it is and in play, i.e., role 30), or if the input order is reversed (when it is after in play, i.e., role 17).
With these basic structural facts established, the decoder can begin to decode the specific commands. For example, if the input is a sequence with after, it can begin with the command after after, which it can decode by checking which fillers are bound to the relevant roles for that type of command.
It may seem odd that so many of the roles are based on position (e.g., “first word" and “second-to-last word"), rather than more functionally-relevant categories such as “direction word." However, this approach may actually be more efficient: Each command consists of a single mandatory element (namely, an action word such aswalk or jump) followed by several optional modifiers (namely, rotation words, direction words, and cardinalities). Because most of the word categories are optional, it might be inefficient to check for the presence of, e.g., a cardinality, since many sequences will not have one. By contrast, every sequence will have a last word, and checking the identity of the last word provides much functionally-relevant information: if that word is not a cardinality, then the decoder knows that there is no cardinality present in the command (because if there were, it would be the last word); and if it is a cardinality, then that is important to know, because the presence of twice or thrice can dramatically affect the shape of the output sequence. In this light, it is unsurprising that the SCAN encoder has implicitly learned several different roles that essentially mean the last element of a particular subcommand.
a.8 TPEs trained on sentence embedding models
For each sentence embedding model, we trained three randomly initialized TPEs for each role scheme and selected the best performing one as measured by the lowest MSE. For each TPE, we used the original filler embedding from the sentence embedding model. This filler dimensionality is 25 for SST, 300 for SPINN and InferSent, and 620 for Skipthought. We applied a linear transformation to the pre-trained filler embedding where the input size is the dimensionality of the pre-trained embedding and the output size is also the dimensionality of the pre-trained embedding. This linearly transformed embedding is used as the filler vector in the filler-role binding in the TPE. For each TPE, we use a role dimension of 50. Training was done with a batch size of 32 using the ADAM optimizer with a learning rate of .001.
To generate tree roles from the English sentences, we used the constituency parser released in version 3.9.1 of Stanford CoreNLP (Klein and Manning, 2003).
a.9 ROLE trained on sentence embedding models
For each sentence embedding model, we trained three randomly initialized ROLE models and selected the best performing one as measured by the lowest MSE. We used the original filler embedding from the sentence embedding model (25 for SST, 300 for SPINN and InferSent, and 620 for Skipthought). We applied a linear transformation to the pre-trained filler embedding where the input size is the dimensionality of the pre-trained embedding and the output size is also the dimensionality of the pre-trained embedding. This linearly transformed embedding is used as the filler vector in the filler-role binding in the TPE. We also applied a similar linear transformation to the pre-trained filler embedding before input to the role learner LSTM. For each ROLE model, we provide up to 50 roles with a role dimension of 50. Training was done with a batch size of 32 using the ADAM optimizer with a learning rate of .001. We performed a hyperparameter search over the regularization coefficient using the values in the set . For SST, SPINN, InferSent and SST, respectively, the best performing network used .