A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification
Authors: Xiang Hu
XinYu KONG
Kewei Tu
Venue: ICLR 2023 poster (2023)
Keywords: Interpretability
natural language processing
text classification
unsupervised learning
structured language model
multiple instance learning
recursive neural network

Abstract: Deep neural networks based on layer-stacking architectures have historically suffered from poor inherent interpretability. Meanwhile, symbolic probabilistic models function with clear interpretability, but how to combine them with neural networks to enhance their performance remains to be explored. In this paper, we try to marry these two systems for text classification via a structured language model. We propose a Symbolic-Neural model that can learn to explicitly predict class labels of text spans from a constituency tree without requiring any access to span-level gold labels. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self-interpretable classification model. Our experiments demonstrate that our approach could achieve good prediction accuracy in downstream tasks. Meanwhile, the predicted span labels are consistent with human rationales to a certain degree.

Published as a conference paper at ICLR 2023
A MULTI-GRAINED SELF-INTERPRETABLE SYMBOLIC-NEURAL MODEL FOR SINGLE/MULTI-LABELE TEXT CLASSIFICATION
Xiang Hu" Xinyu Kong', Kewei Tu 22
1 Ant Group @ShanghaïTech University
ABSTRACT
Deep neural networks based on layer-stacking architectures have historically suf- fered from poor inherent interpretability. Meanwhile, symbolic probabilistic mod- els function with clear interpretability, but how to combine them with neural net- works to enhance their performance remains to be explored. In this paper, try to marry these two systems for text classification via a structured language model. We propose Symbolic-Neurge model that can learn explicitly predict class labels of text spans from a constituency tree without requiring any access to span- level gold labels. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self- celeppeterpee classification model. Our experiments demonstrate that our ap- proach could achieve good prediction accuracy in downstream tasks. Meanwhile, the predicted span labels are consistent with human rationales to certain degree.
INTRODUCTION
Lack of interpretability is an intrinsic problem in deep neural networks based on layer-stacking for text classification. Many methods have been proposed to provide posthoc explanations for neural networks (Lipton 2018 Lundberg & Lee 2017 Sundararajan et al. 2017). However, these methods have multiple drawbacks. First, there only leve-level attribution but no high-level attribution such as those over phrases and clauses. Take sentiment analysis an example, in addition to the ability to recognize the sentiment of sentences, an ideal interpretable model should be able to identify the sentiment and polarity reversal at the levels of words, phrases, and clauses. Secondly, argued by Rudin (019)), models should be inherently interpretable rather than explained by a posthoc model.
A widely accepted property of natural languages is that "the meaning of a whole is function of the meanings of the parts and of the way they are syntactically combined" (Partee 1995). Compared with the sequential outputs of layer-stacked model architectures, syntactic tree structures naturally capture features of various levels because each node in a tree represents a constituent span. Such a characteristic motivates to think about whether the representations of these internal nodes could be leveraged to design an inherently constituent-leww interpretable model. One challenge faced by this idea is that traditional syntactic parsers require supervised training and have degraded performance on out-of-domain data. Fortunately, with the development of structured language models (Tu et al.) 2013 Maillard al. 2017; Choi et al. [0188] Kim et al. 2019), we are now able to learn hierarchical syntactic structures in an unsupervised manner from any raw text.
In this paper, we propose general self- interpretable text classification model that can learn to predict span-level labels unsu- pervisedly as shown in Figure [ Specif- ically, we propose a novel label extrac- tion framework based tive bias for inference. During training, we maximize the probability summation of all potential trees whose extracted labels are consistent with a gold label set via dynamic programming
request, address Give me the address and direct me there, well worn situations
navigate
(:) Our model can learn to predict span-level labels without access to span-level gold labels during training. In examples (a) and (b), only raw texts and sentence-level gold labels (request_addee _addeess, navigate) and {negative) are given.
Figure 1:
simple induc-
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com and

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Published as a conference paper at ICLR 2023
with linear complexity. By using structured language model as the backbone, are able to lever- age the internal representations of constituent spans as symbolic interfaces, based on which we build transition functions for the dynamic programming algorithm.
The main contribution of this work is that we propose a Symbolic-Neural model, a simple but general model architecture for text classification, which has three advantages:
1. Our model has both competitive prediction accuracy and self-interpretabiiityy whose ratio- nales explicitly reflected the label probabilities of each constituent.
2. Our model can learn predict span-level labels without requiring any access to span-level gold labels.
3. It handles both single-label and multi-label text classification tasks in a unified way instead transferring the latter ones into binary classification problems (Read et al. 2011 in conventional methods.
To the best of our knowledge, are the first propose a general constituent-level self self-interpretable classification model with good performance on downstream task performance. Our experiment shows that the span-level attribution consistent with human rationales certain extent. We argue such characteristics of our model could be valuable in various application scenarios like data mining, NLU systems, prediction explanation, etc, and we discuss some of them in our experiments.
PRELIMINARY
2.1 ESSENTIAL PROPERTIES OF STRUCTURED LANGUAGE MODELS
Structured language models feature combining the powerful representation of neural networks with syntax structures. Though many attempts have been made about structured language models (Kim it al. 2019; Drozdov et al. 2019: Shen et al. 2021), three prerequisites need to be met before a model selected as the backbone able syntax structure in an unsupervised manner. Secondly, it computes an intermediate representa- tion for each constituency node. Thirdly, has a pretraining mechanism improve representation performance, Since Fast-R2D2 (Hu et al. [2022] (021) satisfies all the above conditions and also has good inference speed, we choose Fast-R2D2 as our backbone.
our method. Firstly, should have the ability learn reason-
2.2 FAST-R2D2
Overall, Fast-R2D2 is type of structured language model that takes raw texts as input and outputs corresponding binary parsing trees along with node representations as shown in Figure 3a). The representation ei,j representing text span from the ith to the juh word is computed recursively from its child node representations via a shared composition function, i.c., ex = f(e,,s ex+1J). where is the split point given by the parser and f(.) is an n-layered Transformer encoder. When i j. exg initialized as the embedding of the corresponding input token. Please note the parser is trained in supersupe manner, human- annotatdd parsing trees are required.
SYMBOLJCC-EEURAL MODEL.
3.1 MODEL
There are two basic components in the Symbolic-Neural model:
1. A Structured LM backbone which is used to parse a sentence to a binary tree with node representations.
2. An MLP which is used to estimate the label distribution from the node representation.
For Structured LMs that follow bottom-up hierarchical encoding process (such our default LM Past-RRDD), context outside a span is invisible to the span, which may make low-level short spans unable to predict correct labels because of a lack of information. So introduce optional module to allow information to flow in parse trees from top to down.
The overall idea is to construct top-down process to fuse information from both inside and outside of spans. For a given span (). we denote the top-down representation as c'-- We use the Trans- former as the top-down encoder function f'. The top-down encoding process starts from the root and functions recursively the child nodes. For the root node, we have e(, = =f' f'((eroat, e1,,.)
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where Groot is embedding of the special token [ROOT] and is the sentence length. Once the top-down representation <'. is ready, we compute its child representations recursively via ( = [1(4 Ct,k, Cx+1,]) as illustrated in Figure 2
We denote the parameters of the model V, the parameters used in the Structured LM as & and the parameters used in the MLP layer and the top-down encoder as θ Thus = (0, O).
d'u
Transformers
xN
3.2 LABEL EXTRACTION FRAMEWORK FOR INFERENCE
elu
Ch+
[PR]]
[LERT]
[RIHH]]
During inference, we first use Fast-R2D2 produce parsing tree, then predict the label of each node in the parse tree and output a final label set by the yield function introduced below.
Figure 2: [PRT], [LEFT], [RIGHT] role emheddings for the corre- sponding inputs.
Inductive bias. Through observing cases in single/multi- label classification tasks, we propose inductive bias that constituent in text corresponds to at most one label. As constituents could be seen as nodes in a binary parsing tree, we can asso- ciate the nodes with labels. Nodes with multiple labels could be achieved by assigning labels to non-overlapping child nodes. Please note such an inductive bias is not applicable for special cases in which minimal semantic constituent of a text is associated with multiple labels, e.g., the movie "Titanic" could be labeled with both 'disaster' and 'love'. However, we argue that such cases are rare because our inductive bias works well on most single/mullii tasks as demonstrated in our experiments.
Label Tree. label tree transferred from a parsing tree by associating each node with a label. A label tree example is illustrated in Figure Bb... During inference, we predict a probability distri- bution of labels for each node and pick the label with the highest probability. To estimate the label distribution, we have Pe (w(-In..)) enabled, we replace 6i,5 with G.
(eltmax(MLP(ee)).. Please note if the top-down encoder is
Algorithm Definition of Yield func- tion
I:
function YIELD())
ELS
2:
S (}
F4.5
3:
9 [Eroot]
4:
> The list of nodes to visit
E5.3
C4.4
5:
while len(g) 0 do
Stacturdd
Models
rotresting
Miy + 9-)pp(()) if labell Our then
(4)
(H)
7:
8:
if not is then
Figure 3: (a) A parsing tree. (b) A label tree transferred from the left parsing tree. For the given label tree, y returns {A.C). Y stops traversing at terminal nodes in shallow gray whose ancestor labels are all ØNT and the nodes in dark gray are not visited.
9:
g.append
10:
9 gnappen((ng.right)
11:
else
12:
[abylabel]] S S= S (Ør}
13:
14:
return
Yield function. We design a yield function that traverses a label tree in a top-down manner and extracts labels. For brevity, we use y short for the yield function. We divide the labels into two categories: terminal labels and non-terminal labels, which indicate whether y should stop or con- tinue respectively when it traverses to a node. Considering some nodes may not be associated with any task-defined labels, we introduce empty labels denoted as Φr and ΦNT for terminal and non- terminal ones respectively. For simplicity, we do not discuss nesting case in this paper, so there is only one unique non-terminal label which is dNT and all task-defined labels are terminal labels. However, our method can be naturally extended handle nesting cases by allowing non-terminal labels to be associated with task labels. As defined by the pseudo-code in Algorithm [ y traverses a label tree from top to down starting with the root; when it sees ΦNT, it continues to traverse all
For example, in aspect-based sentiment analysis, span corresponding to the sentiment may be nested in span corresponding to the aspcct
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its children; otherwise, when it terminal label, it stops and gathers the task-defined terminal label of the node. Figure illustrates how y traverses the label tree and gathers task defende labels.
3.3 TRAINING OBJECTIVE.
During the training stage, though the Structured LM can predict tree structures, the difficulty here is how to associate each node with a single label without span-level gold labels. We define our training objective as follows:
Training objective Given sentence S whose length is [S] and its gold label set T= (41,..... .... Im): t is its best parsing tree given by the unsupervised parser of Fast-R2D2 and i is a label tree transferred from t. gl denotes satisfying condition C. The training objective is to maximize the probability of a given tree transferring to label tree yielding labels that are consistent with the ground-truth labels, which could be formalized as minimizing - log Pv (())))=7(())
Before we get into the specifics, several key aspects are defined as follows:
(1) Denotations: h.j denotes the subtree spanning from to j (both indices are inclusive), whose root, left and right subtree are Ri.j 44 and [x+1 respectively in which & is the split point.
(2) Symbolic Interface: Ps (4)))) is the probability of single node ni, being associated with the specified label Thus, the probability of transferring to a specific label tree is the product of all the probabilities of nodes being associated with the corresponding labels in f.
THE
P(shurmmu
Rij
NNT
PNT
NTT
PNT
vt,
fpTs.//
Eik
C
Cx+1//
nyJ
p())..)
valid label trees: 1
Vix
Figure 4: To ensure that the yield result of f4,, contains label I, node ni. needs to associated with either NN or I, whose probabilities are ( n.,,) and Pv (()m))) respectively. If associated with 1, it satisfies the condition. associated with ΦNT, least one of its children's yield results should contain I. Here we use \/ denote that the yield result does not contain label 7. In conclusion, Yi could be estimated recursively by Equation
Obviously, it is intractable to exhaust all potential to estimate Py Py (ED'())=T [t). Our core idea is to leverage symbolic interfaces to estimate Po (111 via dynamic programming. We start with an elementary case: estimate the probability that the yield result of 4, contains given label i.e.,
Po (( For brevity, we denote it as Yi As the recursive formulation illustrated in Figure we have:
Yi Po (1lma) Pv (ONT(nus) (1 (1-(1- Y(u) (1 Yk41,)) (1)ma))
if < if =
However, for given label set M, if we try estimate Py (i (())))))))) (4 in the same way, will inevitably exhaust all potential combinations as illustrated in Figure 5(a) which will lead to exponential complexity?
To tackle the problem of exponential complexity, we try to divide the problem of estimating (t.;) to estimating Yi, for each label / in M. Let F denote the set union of all
Pe (C..)) (GD(0)=M) the task labels and {Φr, dNT), and let O denote F T. By assuming that the states of labels are independent of each other, where the state of label indicates whether the label is contained in the
Detaail about the dynamic programming algorithm with exponential complexity to estimate Ps ((D())=1]))) is included in Appendix A.2
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M (a, b)..
P(GnrtnL)
NNT
Rd
RiJ
Pur
NNT
Ra
ORT
(a.b), M (b.c).
[R+LJ
(4), M, - (b,c).
M,
M, (N.c).
Ce+tJ/
nul
st.M, M.-
I
M
V
M
M,
(1
kel
(a)
(b)
Figure 5: (a) Potential valid yield results of left and right children for M ba,b, (b) Valid label constraint.
trees when we include a
yield resul
we have:
(e( Pv Pv ((
Po (ITTEM fonDi0= (t) Po (CTEDD011)) P
II Pa (A = -II vi,, Pa ( = Pv ((tor LET
We do not approximate Pv (( as it could be computed directly. The above function premises that multiple non-overlapping spans could associate with the same label. In some cases, there is mutual-exclusivent ciate with the same task label as shown in Figure (bb), the function becomes:
constraint that two non-overlapping spans are not allowed to asso-
Y (ollm)))) Po(oNr|mu) (ms) (Yil (1 Yk+1s) Yk+io (1-V;,,)) (1lle))))
ifi j if if
(3)
Regarding Pw ((00))0)))))) y(f) containing any label I E O would satisfy the condition. We denote as yi in short. Similar Equation we have:
Eieo Po(()as))) + Po (ONT(Ra)) (1-(1- Yik) (1- yo xet1))) Eieo (o(()nis)
if < j if i j
x
(4)
Thus Pv ((D (t)
l1EET V 11ss (1-
---Y 1.1s/) and the objective function given a parsing tree is:
Co (V) log Pv ((D))))TTI1)
Σ
log Yi log(1 (,|s))
(5)
IET
Because it has been verified in prior work Hu al. (2022) that models could achieve better down- stream performance and domain-adaptivity by training along with the self-supervised objective Leeels ()), we design the final loss follows:
L Ciaa( + Loels
(6)
EXPERIMENTS
4.1 DOWNSTREAM TASKS
In this section, compare our interpretable symbolic-Neural model with models based dense sentence representation to verify our model works as well as conventional models. All systems are trained on raw texts and sentence-level labels only.
Data set. We report the results on the development set of the following datasets: SST-2, CoLA (Wang et al. 2019). ATIS (Hakkani- et al., 2016), SNIPS (Coucke et al. 2018). Stan- fordLU (Eric et al. 2017). Please note that SST-2, CoLA, and SNIPS are single-label tasks and ATIS, StanfordLU are multi-label tasks. There are three sub-fields in StanfordLU including naviga- tor, scheduler, and weather.
Baselines. To fairly compare our method with other systems, all backbones such as Fast-R2D2 and BERT (Devlin al. 2019) are pretrained on the same corpus with the same vocabulary and epochs. We record the best results of running with 4 different random seeds and report the mean of
Details about the conditional independence assumption could he found Appendix A.8

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them. Because of GPU resource limit and energy saving, pretrain all models Wiki- (Mer- ity et al., 2017). which contains 110 million tokens [ To compare our model with systems only using whole sentence representations, our baselines. To study the reliability of the unsupervised parser, we include systems with a super- vised parser /Zhang parser/Zhang et al. (2020) that uses BERT or a tree encoder the backbone. For the former, we take the average pooling on representations of words in span (i.j as the representation of the span. For the latter, we use the pretrained R2D2 tree encoder as the backbone. To compare with methods dealing with multi-instance learning (MIL) but without structure constraints, we extend the multi-instance learning framework proposed by [Angelidis & Lapata (2018) to the multi-instance multi-label learning (MIMLL) scenario. Please find the details about the MIL and MIMLL in Ap- the
include BERT and Fast-R2D2 using root representation in
pendix A.7 We also conduct ablation studies on systems with or without the top-down encoder and
constraint. For the systems using root or [CLS] representations on multi- label tasks, outputs are followed by a sigmoid layer and filtered by a threshold that is tuned on the training set.
Hyperparamettes. Our BERT follows the setting in Devlin et al. 2019). using 12-layer Trans- formers with 768-dimensional embeddings, 3,072-dimensional hidden layer representations, and 12 attention heads. The setting of Fast-R21D2 follows Hu et al. (2022). Specifically, the tree encoder uses 4-layer Transformers with other hyper-parameters same BERT and the top-down encoder uses 2-layer ones. The top-down parser uses a 4-layer bidirectional LSTM with 128-dimensional embeddings and 256-dimensional hidden layers. We train all the systems across the seven datasets for 20 epochs with a learning rate of 5 x 10 - for the encoder, 1 x 10 for the unsupervised parser, and batch size 64 on A100 GPUs.
Backbone Multi-Label%%
Arch.
#Param.
SST-2
CoLA
SNIPS
ATIS
Nav.
Schc.
Wca.
0
1.69
26.54
24.86
5.39
BERT(Wiki-103)
Sent.
116M
89.54
34.99
98.86
98.56
89.25
94.14
96.98
BaRS
S.N.
172M
89.11
9.46
99.00
93.50
80.06
81.77
95.22
Treeen.
Sent.
128M
88.53
11.77
99.04
97.52
89.50
93.74
96.98
Treeen
S.N.
128M
88.19
21.49
98.93
95.77
88.94
86.95
96.50
Fast-R2D2
MIL
62M
89.22
34.05
98.66
Fast-R2D2
MIMLL
62M
93.29
84.21
89.66
94.39
Fast-R2D2
Scnt.
62M
89.96
36.31
99.00
98.11
89.25
93.68
96.70
Fast-R2D2
S.N. Sp
62M
98.61
89.38
91.18
96.96
Fast-R2D2
S.N. SP
67M
98.45
88.91
93.46
97.13
Fast-R2D2
S.N.
62M
89.91
35.02
98.86
98.78
88.18
91.94
97.43
Fast-R2D2 exclusive
S.N.
62M
89.45
36,68
99.00
98.27
87.86
89.87
96.47
Fast-R2D2 topdoww
S.N.
67M
89.45
36.49
99.14
98.50
90.82
93.47
97.21
Past-R2D2
S.N.
67M
89.91
35.31
99.14
98.16
90.75
93.80
96.94
We report mean accuracy for SST-2, Matthews correlation for CoLA, and F1 scores for the rest. We use "S.N." to denote the systems based on the Symbolic-Neural architecture, and "Sent." to denote those using only whole sentence representations. We use subscript fp for the models based on full permutation, topdown, and erclusive for those with the top-down encoder and the mutual-exclusiveness constraint. Please find the details of S.N.fp in Appendix A.2 A.2
Table 1:
Results and discussion. We make several observations from Table T Firstly, We find that our models overall achieve competitive prediction accuracy compared with strong baselines including BERT, especially on multi-label tasks. The result validates the rationality of our label-constituent association inductive bias. The significant gap compared to MIMLL fully demonstrates the supe- riority of building hierarchical relationships between spans in the model. Secondly, when using sentence representation, the models with the unsupervised parser achieve similar results to those with the supervised parser on most tasks but significantly outperform the latter on CoLA. A possible reason for the poor performance of the latter systems CoLA is that there are many sentences with grammar errors in the dataset which are not covered by the training set of the supervised parser. While the unsupervised parser can adapt those sentences as Csum and LxL are included in the final loss. The result reflects the flexibility and adaptability of using unsupervised parsers. Thirdly, Tre in Symbolic-No Neural architectures does not perform as well as pparer+TTreeEnn
'Our model could also be tuned based on the public version of pretrained Fast-R2D2 which is available wtttttt:///// q/ thub. thub. for pretraining BERT and Fast-R2D2 from scratch used in this paper could he found in Appendix A.3
com/a al pay/St SSt.uucuurre rectureedll. I.M.M KTDT/re / Taaeeee ttag///tt/////// tag/fast fast R2D22 Details

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using sentence representation, while the systems using the unsupervised parser show opposite sults. Considering that the Symbolic-Neure Neural model relies heavily on the representation of inner con- stituents, suppose such results ascribe to the tree encoder having adapted to the trees given by the unsupervised parser during the pretraining stage of Fast-R2DD2, which leads to the self-consistent in- termediate representations. This result also verifies the structured language model that learns latent tree structures unsupervisedly is mature enough to be the backbone of our method.
4.2 ANALYSIS OF INTERPRETABILITY.
Bastings al. 2022) propose method that "poisons" classification dataset with synthetic short- cuts, trains classifiers on the poisoned data, and then tests if a given interpretability method can pick up on the shortcut.
Sctup. Following the work, we define two shorteuts with four continuous tokens access the faithfulness of predicted span labels: #0#1#2#3 and #4#5#6#7 indicate label and 0 respectively. We select SST2 and CoLA as the training sets, with additional 20% synthetic data. We create synthetic example by (1) randomly sampling an instance from the source data, (2) inserting the continuous tokens at random positions, and (3) setting the label as the shortcut prescribes.
Verification steps. The model trained on the synthesis data could achieve 100% accuracy on the synthetic test data and the model trained on the original dataset achieves around 50% on the synthetic test set.
Sorting tokens. Since our model does not produce a heatmap for input tokens, it lacks an intuitive way to get top K tokens required in the shorteut method. So propose simple heuristic tree-based ranking algorithm. Specifically, for a given label, we start from the root denoted as n and compare P(Umetft) (eftt) and P([[rright) where Meft and nlightt are its left and right children. If P(](meet)) > P(l]nright), all descendants of the left node are ordered before the descendants of the right child, and vice versa. By recursively ranking according to the above rule, could have all tokens ranked. We additionally report the precision of shortcut span labels in the predicted label trees. shorteut span label is correct only if the continuous shortcut tokens are covered by the same span and the predicted label is consistent with the shortcut label.
From the table 2 we have an interesting finding that the pre- cision declines with the increase of training epochs. We think the rea- son for this phenomenon is that short- cut spans are the easiest to learn at early epochs, almost all top tokens are short-cut tokens. With continuous training, the model gradually learns the semantics of texts from the origi- nal data. Although the label for sen- tence in the synthetic data is random,
Results.
top4 prec.
span label prec.
Models
epochs
SST2
Col.A
SNT2
Col.A
Symbolic Neuura
93.31%
99.11%
100%
100%
Symbolic-Neural
95%
99.30%
100%
100%
Symbolic-Nor Neural
5
93.46%
98.95%
100%
100%
Symbolic-Neural Symbolic-Neure
7
93.23%
98.25%
100%
100%
87.44%
98.83%
100%
100%
Symbolic-Neural... Lopdonm
97.31%
99.23%
100%
100%
Symbolie-Neural Neurallugsooww
3
98.76%
99.95%
100%
100%
77.06%
99. 99.64%
100%
100%
Symholic Neuraltagsfoum
93.03%
100.00%
100%
100%
Symbolie-eurrllouddor
80.76%
100.00%
100%
100%
(mert_(mss
72.94%
99, 68%
10uert (maxt//00)))
3
65.1
96.91%
1Guert
69.21%
90.01%
IG
57.03%
86.36%
there is still around 50% probability that it is semantically consistent with the text and hence the label probabil- ity of certain span may exceed the probability of the shortcut span. Please note the precision of shorteut span labels predicted by our model is 100%. Such results demonstrate again that model is self-interprettall and could flect the model's rationales by span labels. Samples of label trees with shortcut tokens are shown in Appendix A.5
10 (mank)p00))
9
66. 2056
26.29%
Figure 6: Top precision and span label precision.
4.3 CONSISTENCY WITH HUMAN RATIONALES
To evaluate the consistency of the span labels learned by our model with human rationales. we design constituent-leeel attribution task. Specifically, we hide the gold span positions in NER and slot-filling datasets to see whether our model is able to recover gold spans and labels. So only raw text and sentence-level gold labels are visible to models. We then train models as multi-label classification tasks and evaluate span positions learned unsupervisedly by models.
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fromloc.city_namme name
depart_time popart_time.period_ of _o__day Find a flight between Denver and Oakland the flight should be in the afternoon
toloc.city_namee
prrive_time time
flight_stop
and arrive close to 5 pm the flight should be nonstop
Figure 7: A sample of our method on semi-supervised slot filling. The ground truths are Denver, Oakland, afternoon, 5 pm, nonstop for each slot correspondingly. However, the last three rea- sonable even though different from the ground truths.
Data set. We report Fl scores on the following data sets: ATIS (Hakkani-Tur et al. 2016 MITRestaurant (Liu et al. 2013a) and MITMovie (Liu al. 2013b). ATIS is a slot-filling task and the others are NER tasks.
Baselines. We include two baselines with attribution ability on multi-label tasks: integrated- gradient(IG) (Sundararajan et al. 2017) and multi-instance learning (Angelidis & Lapatal 2018). and
We follow the setup in 4. and report the results of the last epoch. For IG, we set the interpola- tion steps as 200 and use the same BERT in the last section the encoder, filter the attribution of each token by a threshold and select filtered positions as outputs. We use zero vectors and [MASK] embeddings the baselines for IG as [Bastings al. (2022) find the latter one could significantly improve its performance. Considering IG scores not having explicit meaning, we allow IG to adjust thresholds according to the test datasets. We report the best results of both baselines and correspond-
ing thresholds. Please find the full version of the table in Appendix A.4 For MIMLL, we select the span with the max attention score for a specified label. Please find details in Appendix A..7
Metries. We denote the predicted span set as P and gold span set as & and the overlap of P and G with the same labels as O. Then we have:
Enco j.j 0.i +
recall Enco o.j 0.1 +
* (pree- rocall)
prec
ΣpEp P.j p.i +
Eneg J.j g.i
F1
(prec + recall)
Model
Thres.
Slot-filling
Thres.
sls-movie-en
length
all
1
2-3
3-5
5
1-2
2-5 5
>>
ratio
100
95.84
3.70
0.46
100
55.60
42.75
1.65
(
0,3
50.28
51.13
37.15
13.87
0.2
57.19
60.07
55.79
34.36
IG (serooo))))))0
0.4
56.62
57.42
46.15
18.46
0.3
47.59
50.53
46.12
23.80
MIMLL
N.A.
11.11
10.84
17.37
16.74
N.A.
14.55
14.11
14.76
17.43
Symbolic-Neuron
N.A.
35.30
35.38
33.78
34.01
N.A.
53.04
50.61
$4.99
57.77
Symbolic-Ncuronercde
N.A.
32.13
32.37
30.62
16.33
N.A.
52.89
50.45
54.55
61.15
Symbolic-Neuree
N.A.
32.86
32.91
32.06
32,88
N.A.
53.15
51.59
$4.21
59.08
N.A.
42.01
42.28
38.69
34.95
N.A.
57.82
56.54
58.87
59.82
Model
Thres.
Al-movie-trvvne
Thres.
sls-restaurant
length
all
1 [
2 2- 5
>> 5
all
2
2 5
>>
ratio
100
7.57
57.07
35.36
100
40.87
57.89
1.24
[GBERT
0.02
47.69
38.63
45.27
50.83
0.2
50.10
51.06
50.11
37.73
IGBERT (scroooo)))
0.1
42.34
31.20
39.00
46.57
0.2
43.65
45.85
43.07
30.23
MIMLL
N.A.
61.77
42.48
52.03
69.02
N.A.
7.58
7.40
7.73
6.08
Symbolic-Neuron
N.A.
67.30
41.11
60.18
75.18
N.A.
48.07
42.93
50.89
45.60
Cymbolic-Ncuron.rc.e
N.A.
63.60
44.75
58.89
68.80
N.A.
49.46
44.28
52.22
48,20
N.A.
68.55
41.62
60.74
77.07
N.A.
47.43
43.42
49.83
40.41
Numboli-Neuurongp..ce
N.A.
70.83
45.92
64.32
77.73
N.A.
52.52
49.14
54.67
44.27
Table 2: F1 scores for semi-supervised slot filling and NER whose golden span positions are hidden. "Thres." is short for threshold.
Results and discussion. From Table 2 one observation is that models with the mutual- exclusiveness constraint achieve better Fl scores. Such results illustrate that a stronger inductive bias more helpful for models to learn constituent-label alignments. Besides, we find the Neural- Symbolic models significantly outperform the MIMLL and IG baselines on the NER datasets but trail the IG on the slot-filling task. Through studying the outputs of our method, with a sample shown in Figure 7 we find that our model tends to recall long spans while the ground truths in ATIS tend MIMLL significantly outperforms IG. So
to be short spans. We also find that on als-movie-triviee hypothesize that the distribution of golden span lengths may affect results. We divide sentences into buckets according to the average golden span length and compute Fl scores for each bucket, as shown in Table 2 Interestingly, we find that the scores of IG decline significantly with increasing
8

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Published as a conference paper at ICLR 2023
span lengths, while method performs well on all the buckets. In addition, argue that the Fl scores on the NER datasets can reflect interpretability more objectively, because the boundaries of proper nouns are clear and objective, while the choice of slots is relatively ambiguous about whether to include prepositions, modal verbs, etc.
4.4 CASE STUDY & POTENTIAL APPLICATIONS
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ctm
cmm
show
ote
ctn
show show fhur chow chow
chow
mng
inf
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inf
yes,
please
send
on my screen and pick route
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Figure 8: A sample of the symbolic-neural model on Navigator with the top-down encoder.
O+
Ver
dronk
<ronk
gaining
the
around
C pub
dry
pub
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Figure 9: Samples of the symbolic-neural model with the top-down encoder.
We output the label trees generated by our model trained on the Navigator, SST-2, and CoLA to observe whether the model has sufficient interpretability. From Figure g we can find our method is able to learn potential alignments of intents and texts and show them explicitly. This can be used in multii-ntent NLU systems to help determine the attribution of slots to corresponding in- tents. We also study the difference between generated label trees of the vanilla Symbolic-Neural model and the vanilla Symbolic-Neural model fails to deal with multi-intent cases. Such observation verifies the necessity of introducing the top-down encoder. For SST-2, as there are no neutral samples, we randomly sampled sentences from Wiki-103 neutral texts and force all nodes to be ΦNT by the mean squared eтrоr loss. Figure9ia shows the sentiment polarity of each constituent and the polar- ity reversal of "never". Such characteristic could be used for text mining by gathering the minimal spans of specified label. We also study the generated label trees on Col.A, a linguistic acceptance data set. We transfer the task to a grammar error detection problem by converting the label "I" to as "I" means error is found in sentence. Figure ( shows it's able to detect incomplete constituents and may help in applications like grammar error location. More cases could be found in the Appendix.
The cases could be found in Appendix A.12 We find the
CONCLUSION & LIMITATION
In this paper, we propose a novel label extraction framework based on simple inductive bias and model single/multi-kat label text classification in a unified way. We discuss how build a probabilistic model to maximize the valid potential label trees by leveraging the internal representations of structured language model as symbolic interfaces. Our experiment results show our method achieves inherent interpretability
various granularities. The generated label trees could have potential values in various unsupervised tasks requiring constituent-leve outputs.
Regarding to the limitation of our work, we require that the labels corresponding to the texts in the dataset have a certain degree of diversity, thus forcing the model to learn self- consisten constituent- label alignments. For example, in ATIS, almost all training samples have the same labels like "from- loc.city name" and "toloc.city name". That's 's why our model fails to accurately associate these two labels with correct spans in Figure

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Published as a conference paper at ICLR 2023
6 REPRODUCIBILITY STATEMENT
In the supplemental, we include a zip file containing our code and datasets downloading linkage. We've also included in the supplemental the scripts run all basclines and the Symbolic-Neural models.
ACKNOWLEDGEMENT
This work supported by Ant Group through CCR-Ant Research Fund. We thank the Aliyun EFLOPS team for their substantial support in designing and providing a cutting-edge training plat- form to facilitate fast experimentation this work. We also thank Jing Zheng for his help in paper revising and code reviewing.
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A APPENDIX
A.1 RELATED WORKS
Structured language models. Many attempts have been made to develop structured language models. Pollack (1990) proposed to use RvNN as a recursive architecture to encode text hierar- chically, Socher et al. (2013) showed the effectiveness of RvNNs with gold trees for sentiment analysis. However, both approaches require annotated trees. Guubel--Tree-LS"'
(Choi et al.) 2018) construct trees by recursively selecting two terminal nodes to merge and learning composition probabilities via downstream tasks. CRvNN (Chowdhury & Caragea 202I) makes the entire pro- cess end-to-end differentiable and parallel by introducing continuous relaxation. However, neither Gumbel- Tee--Teee---SSMMM nor CRvNN mention the pretraining mechanism in their work. URNNG (Kim) et al. 2019) proposed the first architecture to jointly pretrain a parser and encoder based on RNNG Dyerr et al. 2016). However, its O(n") time and space complexity makes it hard to pretrain on large-scale corpora. ON-LSTM and StructFormer (Shen et al., 2019 202I) propose a series of methods to integrate structures into LSTM or Transformer by masking information in differentiable ways. As the encoding process is still performed in layer-stacking models, there are no interme- diate representations for tree nodes. [Mailardd et al. (2017) propose an alternative approach, based on differentiable CKY encoding. The algorithm differentiable by using soft-gating approach, which approximates discrete candidate selection by probabilistic mixture of the constituents avail- able in a given cell of the chart. While their work relies on annotated downstream tasks to learn structures Drozdov et al. (2019) propose novel auto-encoder-like pretraining objective based on the onside-outside algorithm Baker (1979); Casacubertal (1994) but is still of cubic complexity. To tackle the (n²²) limitation of CKY encoding. Hu et al. [2021) propose an MLM-like pretraining objective and a pruning strategy, which reduces the complexity of encoding to linear and makes the model possible to pretrain large-scale corpora.
Multi-Instane Learning. Multi-Instance learning (MIL) deals with problems where labels are associated with groups of instances or bags (spans in our case), while instance labels are unobserved. The goal is either to label bags Keeler et al. (199)); Dietterich et al. (1997); 9)3 Maron & Ratan (1998) or to simultaneously infer bag and instance labels Zhou al. (2009) : Kotzias et al. (2015). Angelidis & Lapata (2018) apply MII. to segment-level sentiment analysis based on an attention based scoring method. In our work, we refine instances to different semantic granularities and consider hierarchical relationships between instances.
Model Interpretability. In the line of work on model interpretability, many approaches have been proposed. Ribeiro et al. (2016); Lundberg & Lee (2017) try generate explanation for prediction. Baehrens et al. (2010); Simonyan et al. (2014); Sundararajan et al. (2017) analyze attribution by gradients. The above-mentioned methods are all posthoc. Kim et al. (2020); [Cao al.] (2020); Chen & Ji (2020) apply masks on the model input in text classification to obtain token weights, but single-dimensional weights are not enough reflect multi-label interpretation. Alvarez-Melis & Jaakkola (2018;; Rudin (2019) argue interpretability should be an inherent property of deep neural network and propose corresponding model architectures. However, all the above-mentioned methods are not able to generate constituent-level interpretability.
A.2 DYNAMIC PROGRAMMING BASED ON FULL PERMUTATION
A naive way to estimate POEDMM (t.s) is to enumerate all possible state spaces and sum them up via dynamic programming. We use X,M short for P(ED)MM)) (t.)). Let M, and M, denote a pair of sets subject to Mt U.M, = M. Let C(M) denote the set containing all valid Mi and M, pairs. Figure 5 a) discusses all potential combinations of Mi and M, when [M] > 1.
If M > 1, let C(M) be the set of all potential pairs where )'(f..)) ()(((+2))) = M.
If M = 1, it's similar to the case described in Figure 4
If M di Maj could only be associated with or or ΦNT with Mi Φ and Mr &.
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Finally the transition function for tag where < is:
P(oNTincs) MAMaM X.Mi X.M,
|M| >
iik k+ly
x P(mlnas) + P(oNr|may (X X.M + X.M X
XMXX
.M {m}
P(or(mag) + P(ØNT| (X X x
.MM =
(8)
When = j. we have:
|M| .M = {m} .M = Φ
X
P(m|mas) P(or(naa)) + P(GNTinas)
(9)
The transition function works bottom-up manner and iterates all possible M CT. XT,ss the final probability. Even though, iterating C(M) and all M C T is of exponential complexity, so only works when [T] small.
A.3 PRETRAIN BERT AND
FROM SCRATCH
The dataset WikiBooks originally used to train BERT (Devlin et al. 2019) is combination of En- glish Wikipedia and BooksCorpus (Zhu et al. 2015). However, BooksCorpus is no longer publicly available. So it's hard to pretrain Fast-R2D2 fairly with the publicly available BERT model. Considering the limited GPU resources, we pretrain both BERT and F2st-R2D2 from scratch W03i-103. We train BERT from scratch following the tutorial by Huggingface S with the masked rate set to 15%. The vocabulary of BERT and Fast-R2D2 kept the same as the original BERT. As demonstrated in RoBERTa (Liu task is harmful and longer sentence is helpful to improve performance in downstream tasks, we remove the NSP task and use the original corpus that is not split into sentences as inputs. For Fast- R2D2, WikiText103 is split at the sentence level, and sentences longer than 200 after tokenization discarded (about 0.04%e of the original data). BERT pretrained for 60 epochs with learning rate of 5 x 10 and batch size 50 per GPU on 8 A 1100 GPUs. Fast-R21D2 is pretrained with learning rate x 10-5 for the transformer encoder and size of Fast-R2D2 is dynamically adjusted to ensure the total length of sentences in batch won't 't exceed a certain maximum threshold, to make the batch size similar to that of BERT, the maximum threshold is set to 1536. Because the average sentence length is around 30 for Wiki103, the average batch size of
the same corpus, making it impossible to compare
2019) that the NSP
10-3 for the parser. Please note that the batch
around 50 which is similar to that of BERT.
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A.4
THE FULL VERSION OF THE SPAN ATTRIBUTION TASK.
Model
Thres.
Slot-filling
Thres.
sis- sls-movie-ene
length
all
1
2 3
3
all
1 1- 2
5
>
ratio
100
95.84
3.70
0,46
100
55.60
42.75
1.65
TG (
0.2
47.80
48.35
40.51
17.72
51.40
51.57
51.74
42.69
50.28
51.13
37.15
15.87
0.2
$7.19
60.07
$5.79
34.36
IG SEWT [mask]]00)
49.82
51.03
10.53
56.84
62.13
26.15
IG #EW7 [scro]200)
53.88
54.33
23.68
45.57
45.72
31.88
IG BERTTER (scro(800)
56.62
57.42
46.15
18.46
47.59
46.12
23.80
0.5
56.24
57.44
39.09
13.79
46.01
51.47
42.61
12.01
MIMLL
N.A.
11.11
10.84
17.37
16.74
N.A.
14.55
14.11
14.76
17.43
Symbolic- Neuron
N.A.
35.30
35.38
33.78
34.01
N.A.
53.04
50.61
54.99
57.77
N.A.
32.13
32.37
30.62
16.33
N.A.
52.89
50.45
54.55
61.15
Caboolic-NuurOo-sppooor
N.A.
32.86
32.91
32.06
32.88
N.A.
53.15
51.59
54.21
59.08
Symbolic-Ness Neubie--eeuronop.eeeedd
N.A.
42.01
42.28
38.69
34.95
57.82
56.54
58.87
59.82
Model
Thres.
sls-movic-trivial
Thres.
sls-restaurant
length ratio
all
2-
5
>>
100
57.07
35.36
57.89
IG (P
0.02
47.30
30.68
41.50
54.73
0.1
46.70
45.45
47.58
41.34
IG (mm20()))))))
0.05
47.69
38.63
45.27
50.83
0.2
50.10
51.06
50.11
37.73
IG (me4)00))))
0.1
44.35
43.91
46.00
42.91
0.3
49.04
51.67
48.46
31.33
IG (
0.05
41.78
25.79
36.29
49.00
0.1
40.01
39.19
40.72
32.92
IG [
0.1
42.34
31.20
39.00
46.57
0.2
43.65
45.85
43.07
30.23
IG E
0,2
37.26
36.49
38.01
36.68
0.3
43.07
45.45
41.10
25.95
MIMLL
N.A.
61.77
42.48
52.03
69.02
N.A.
7,58
7.40
7.73
6,08
Symbolic-Neuron
N.A.
67.30
41.11
60.18
75.18
N.A.
48.07
42.93
50.89
45.60
NeurOn
N.A.
63.60
44.75
58.89
68.80
N.A.
49.46
44.28
52.22
48,20
Symbolie-Neuroo...poo
Neuron
N.A.
68.55
41.62
60.74
77.07
N.A.
47.43
43.42
49.83
40.41
Symbolic-Neuron.oog
Neuron
N.A.
70.83
45.92
64.32
77.73
N.A.
52.52
49.14
54.67
44,27
A.5 SAMPLES OF LABEL. TREES WITH SHORTCUT TOKENS.
Samples of label trees with shortcut tokens are shown follows:
0
turkey
been
the cold
#3
would
#0
ve far
better
title
#1
#2
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a
journey
charming
and often
affecting #4 #5 #6 #7
A.6 MULTI-LABEL LEARNING BASED FAST-R2D2
We adopt a canonical multi-instance learning framework used in text classification proposed by Angelidis & Lapata (2018), which each instance has representation and all instances fused by attention. The original work produces hidden vectors h; for each segment by GRU modules and computes attention weights a, as normalized similarity of each h; with ht.
erp(h, 0: erp(h/h.) P, Nallmax(Waslll + bets) PPd (e) = Σ Eap CE
(10)
where C is the total class number, is the individual segment label prediction, is document level predictions. They the negative log-likelihood of the prediction objective function:
logp! Leis Ea log Pd (za), We simply replace segment representations with span representations in our work the experiment baseline. Specifically, use the top-down representation e/.,, the tensor to be attended to and predict the label by eigi
exple',, hh))
ai.j =
Pi. = softmax(Wase1) + bets),
EmNED exple haa)
(II)
Ps
(c)
Σ
am.nP m.n
CE 11
M,nDD
where D is the span set for parsing tree. Please note the MIL model in our baselines is trained together with Loam and LKL, whose final loss Lets + Ceelf.
A.7 MULTI-LABEL. MULTIIINSTANCE LEARNING BASED ON FAST-R2D2
To support multi-label multi-instance learning, refactor the above equations enable them to support attention on different labels. For each label there is vector (e) based on which attention weights a. is computed as following: (c)
exple'T (eple(T,hho))
(.) sigmoid(W eijj + ((0))
EmmeD eap(eT m.n ((0))
(12)
p(()
(m) (c) CE [1.
M.DD
The final objective function is L - log EcEPy log(1-p(o)) In the semi-supervised slot-filling and NER tasks, we let the model predicts labels first and then pick the span with the max attention weight for each label.
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A.8 ABOUT THE CONDITIONAL INDEPENDENCE ASSUMPTION
We argue the independence assumption used in our objective actually is weaker than the one used in conventional multi-label classification tasks. Formally, conventional multi-label classification is the problem of finding a model that maps inputs x to binary vectors y; that is, it assigns a value of 0 for each element (label) in y. So the objective of multi-label classification to mini- mize: - log P(N EE = 1.N J.n V/ = 0fzr, where T denotes the indices for golden labels and O denotes the indices not in T. It's impossible to tractably estimate it without introducing some con- ditional independence assumption. By assuming the states of labels are independent of each other, we have:
P( P(N Di = 1, N M, = 0/z)
P( P(N
Vi 1/z)
P(
M5 = 0/2)
(13)
iET
jEO
iET
JEO
log P( ( y: 1|x) ~ log II
Plys 1/x) Σ
log P(ys 1xx
(14)
iET
GET
iET
log P( yj = 0/ ~ log II
P(y = 0/5) = Σ
log P(y; = 0/z)
(15)
JE0
JEO
JET
which could finally be reformulated the well-known binary cross entropy loss E, 91 log y4 + (1 - 9:) log(1 - -y.), where ÿ is the ground truth and y is the output probability of model.
The logic of Equation is similar to the above equations. P( is equivalent to P(NiET ys 1| and P((lenxx()+11p) is equivalent to P(N Vjj 0|r). But we don't 't
quire the independence assumption to estimate the latter.
A.9 REAL LABEL TREES SAMPLED FROM SYMBOLIC-NEURAN t/-c ON SST-2
NT
NT
NT
NT
O
NT
NT
by
far the NT
NT
NT
NT
situations
worst NT NT
NT
NT
NT
NT
worn
movie NT NT
##tri
##ved
well
of
NT
NT
the
year
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NT
NT
contains
NT
NT
NT
NT
NT
NT
NT
NT
like
NT
NT
NT
NT
laughs and
NT
80
much
young
NT
very few NT
NT
robert
NT
NT
less surprises
den
##iro
NT
NT
NT NT NT
NT
NT NT NT NT of digital filmmaking
NT
NT
outrageous the promise
##dda
##cioos
NT
NT
NT NT
NT
NT
NT
NT NT #tiiglyy brilliant NT NT
director
castwood
NT NT be NT NT entertaining
and
is
##wil ##der
off
NT
his
A. REAL LABEL TREES SAMPLED FROM SYMBOLIC-NEURAL -/-- ON SST-2
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NT
0
NT
NT
NT
0
NT
NT
by far the NT
NT
NT
NT
situations
worst NT NT
con
NT
NT
NT
NT
worn
movie NT NT ##trr
##vedd
well
of
NT NT
the
year
NT
NT
contains
NT
NT
NT
NT
NT
NT
NT
NT
like
NT
NT
NT laughs
NT much
so
young
NT
very few cvcn
NT
NT
robert
NT
NT
less
surprises
den
##iro
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NT Published as a conference paper at ICLR 2023
NT
NT
NT NT
NT
NT NT
NT NT of
digital filmmaking
NT
NT
outrageous the promise
##da
##cious
NT
NT
NT NT
NT
NT
NT
NT NT
#tingly brilliant NT NT
18 ##wwil ##der director
castwood
NT
NT
be
NT
NT
and entertaining
off
NT
his
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A.11
REAL LABEL TREES SAMPLED FROM SYMBOLIC-NEURAL-V/--
CoLA
T
0
T
T
T
T
T
the professor
0
T
the professor
talked us into
T
talked us
T
T
T
0
stu
##por
T
0
they
T
they
T
T
T
T
T
made him
into
T
made him
to
T
T
T
exhaustion
a
monster
A.12 SAMPLED LABEL TREES IN ATIS
We sample label trees from Neural-Symbolic-.yy and Neural-Symmolic--e-- respectively for ob- servation. Ground truths annotated in brackets.
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fighh((iight+aiifare))
flight
flight
flight airfare flight
flight
flight
flight
flight airfare flight flight
flight
flight
first
flights
and
fares
from
flight
flight
flight
flight
pittsburgh
airport
flight
thursday
atlanta
Figure 10: The label tree generated by Neural-Symbolic w/o the topdown encoder.
NT(ftight-airfare
flight
airfare
flight
flight
airfare
airfare
first
flights
flight airfare flight
flight
and
fares flight flight
flight
flight
from
flight
flight
flight
flight
pittsburgh
flight flight_no
a
thursday
atlanta
Figure 11: The label tree generated by Neural-Symmollien...oomm
A.13 SAMPLED LABEL TREES NAVIGATOR
2.request.route 3rappreciate, 4:rqqesst 6:navigate
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fight(flight-iirffre)
flight
flight
flight flight
flight
flight
flight
airfare
flight.
airfare flight
flight
flight
flight
flight
flight
the
flights
flight
airfare
on
flight
flight
from
airfare
flight
give
december
airfare
flight
indianapolis
airport
flight
twenty
seventh
orlando
Figure 12: The label tree generated by Neural-Symbolic
aT(llight-airfare)
flight
airfare
flight
light airfare
airfare
flight
flight nlihhts flight airfare flight
flight
flight
flight
the
and fares flight night
flight
flight
give
me
on
flight
night
flight
flight
flight
flight
december
flight
flight,
from
indianapolis
to
orlando
twenty seventh
Figure 13: The label tree generated by Neural-Symmollie..doowm
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Published as a conference paper at ICLR 2023
NT
NT
NT
ok
NT
NT
NT NT
NT
please 4
6
NT
6
6
6
give me the address and 6 NT there
2
NT
direct me
2
via
2
NT
quick
route
Figure 14: The label tree generated by Neural-Symmollieropdorr
NT
NT
NT
NT
NT
NT
NT
mmm
NT
NT
NT
NT
2
NT
patience
NT
NT
please
NT
NT
NT 2
NT
send
me
a
NT NT
there
3
quick
route
to
3
NT
thank you
Figure 15: The label tree generated by Neural-Symmoolienppoomm
26


Reviews:
[{"review_id": "vqT0lExwrld", "reviewer": "Reviewer_1uUN", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In this paper, the authors propose a Symbolic-Neural (SN) interpretable model for text classification that uses a structured language model Fast-R2D2 as the backbone and during training, maximizes the probability summation of all potential trees whose extracted labels are consistent with a gold label set via dynamic programming with linear complexity.  Given a sentence and parse tree, the model generates a labeled tree which explain constituent spans in the sentence.  The authors conduct experiments on 6 datasets and compare different representation architectures ( uninterpretable dense Sentence Representation and their interpretable Symbolic Neural constituent representation ) showing their method matches or surpasses dense non-interpretable methods.  Additionally, they conduct a quantitative analysis comparing the interpretability of SN to Integrated Gradients and Multi-instance learning by designing a constituent-level attribution task to see how close the results learned by the models in an unsupervised fashion are to human-annotated results.", "summary_of_the_review": "The empirical results of their method are impressive and thorough as is the analysis of explainability from a quantitive standpoint. My biggest qualms is with clarity of writing and keeping the paper self contained.\n", "strength_and_weaknesses": "**Strengths**  \nThe empirical results of their method are impressive and pretty thorough and the analysis of explainability from a quantitive standpoint is also interesting. \n\n**Weaknesses**   \nThis paper is too dense and not self contained ( ie, references to charts, chart encoders for example, etc ).  \nrequired me going to other cited papers to be clear on certain aspects of the paper.\n\nSection 3.2 and in particular page 4 are very dense and hard to follow initially as is and really could be improved by having a table for variables and a more fully guided example ( another figure in the style of Fig 2 ) \n\nAdditionally, I think conclusion does a better job of motivating and describing the paper than the intro does, so you may want to take parts of that to help guide the reader, in particular when it comes to understanding when supervision is needed vs not.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "axhzKk1ng9g", "reviewer": "Reviewer_AUue", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "[REVISED]\n\nTask: The paper considers the relatively unexplored task of making multi-label text classification interpretable by learning a model that induces implicit labeled spans given only sentence-level labels as supervision.\n\nModel: The paper assumes a \"backbone\" model for unsupervised parsing that gives an embedding $e_{i,j}$ for each span $(i,j)$. A conditional label distribution $P(l|n_{i,j})$ is defined at every considered node $n_{i,j}$ based on $e_{i,j}$. In experiments, the embeddings are augmented to incorporate top-down information.\n\nTraining: The model is trained by optimizing $L + L^t$ where $L$ is the usual loss for the backbone model and $L^t$ is the new loss. The new loss fixes the predicted tree from the backbone model $t$ (which changes dynamically during training), then computes the likelihood of $t$ yielding labels that are consistent with the ground-truth labels. Naively, this requires summing over all possible label configurations of $t$ which is exponential in the label size. The paper makes conditional independence assumptions to derive a dynamic programming to approximate this loss. \n\nExperiments: The paper considers multiple single-/multi-label text classification datasets (SST-2, ATIS, etc.). The paper shows that (1) the proposed model works as well as conventional models and (2) the model recovers gold spans and labels in slot-filling/NER tasks.\n\n[OLD REVIEW]\n\nThe paper proposes using a structured language model for interpretable classification. The method is based on Fast-R2D2. It proposes a variant of the inside algorithm to compute marginal probabilities of subtrees with gold labels and adds the resulting classification loss to the original objective of Fast-R2D2. Experiments on single- and multi-label classification datasets show that the method is competitive in performance but can yield meaningful trees.", "summary_of_the_review": "[REVISED]\n\nThe paper considers a relatively unexplored problem of interpretable text classification and a new model and training scheme. However, the paper is quite vague and confusing in communicating the model and the training scheme, which makes it very difficult to appreciate these contributions. Even after revising and appreciating the submission much more than before, I think the paper needs substantial restructuring before publication.\n\n\n[OLD REVIEW]\n\nThe paper proposes using a structured language model for interpretable classification, but the method is not surprising and relies on existing structured LMs. ", "strength_and_weaknesses": "[REVISED]\n\nSTRENGTHS\n- The proposed problem is relatively unexplored and interesting. \n- The proposed algorithm for computing the label-consistent loss conditioned on a MAP tree is technically novel.\n- There is a clear practical benefit. The model works as well as conventional classification models but also induces labeled spans.\n\nWEAKNESSES\n- The paper is written *very* confusingly, which is the reason I (and the reviewer Vws4) found it almost impossible to judge the quality of the submission. It's not just one problem but several, which makes the reader wonder \n   - What is the model? What parameters are being optimized? The model is never precisely defined but kind of sketched on top of Fast-R2D2. The reader doesn't necessarily know about Fast-R2D2 and has to wonder if this specific to Fast-R2D2 or similar models. \n   - What is the new loss? Again despite many equations that technically define the loss, the objective is simply defined in $P(\\hat{t}^{[\\mathcal{Y}(\\hat{t})=\\mathcal{T}]} |t)$ and I found the current explanation both verbose and confusing. My main feedback is to really transform the model and training section. Precisely define the model and the objective. The detailed derivation should follow only after the model/objective is crystal clear. I think I've parsed the paper more correctly at this point, but this should never be the burden of the reader. \n- I stand by my one previous weakness, which is: the case for the usefulness of label trees needs to be made with more systematic studies instead of qualitative analyses only.\n\n\n[OLD REVIEW]\n\nSTRENGTHS\n- The important problem of developing interpretable neuro-symbolic models is considered.\n\nWEAKNESSES\n- The method is somewhat straightforward and relies on an existing structured language model. \n- The performance results are underwhelming. Table 1 shows that the method attains little or very slight gains over the Fast-R2D2 baselines. \n- The case for the usefulness of label trees needs to be made with more systematic studies instead of qualitative analyses only, it seems. ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "954ftL2-Cc", "reviewer": "Reviewer_Vws4", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper describes a method of training a classifier based on a pretrained syntax tree extractor called a structured LM. The structured LM produces contextualized representations of each node through a learned composition function applied to the node's children. They then apply an MLP and softmax to produce label probabilities for each node. The authors also define a yield function to gather task-relevant node labels based on the tree structure and node labeling.\n\nHowever, I don't understand how the optimization objective is supposed to work. The authors mention Tau as representing a \"gold label set\" associated with a single example, suggesting Tau is supposed to be a set of gold labels on the level of individual nodes / words. However, the authors apply their method to datasets that (to my knowledge) don't have such fine-grained labels, such as CoLA.\n\nThe authors say the objective is to \"maximize the probability summation of {t_hat | yield(t_hat) = Tau}\". I do not understand what this means. What's the probability we're summing up, and how is it computed? Later text mentions how to compute per-node label probabilities, but those seem like different things than what's being summed here. In fact, I don't even understand how there's anything here to sum over. It seems like t_hat is a specific set of possible labels for a specific parse tree, and Tau is either another set of labels for that specific parse tree, or a single label for the entire sentence. Either way, I don't see how there's a space to sum over.\n\nWhat does it mean for t_hat to be conditioned on yield(t_hat) = Tau? I'd assume it means that we're restricting the space from which we're drawing t_hat to only include elements whose yield is equal to Tau. However, the previous text just called t_hat one of the possible label trees associated with the best syntax tree of a sentence, with no indication that t_hat is at all restricted. ", "summary_of_the_review": "I tentatively recommend rejecting this paper. My top issues with this paper are its lack of clarity (particularly regarding the optimization objective, which is its primary contribution) and the weakness of its experimental protocol. \n\nLack of clarity:\n- I was unable to follow key aspects of the paper.\n- There seem to be many opportunities to improve the explanation of the paper's technical contribution, as I highlighted in the summary section.\n\nExperimental weakness: \n- I described my concerns with the interpretability experiment in the weaknesses section.\n- I am concerned about the lack of detail provided in the training process of the BERT baseline, and the lack of comparison to the publicly available BERT model, as mentioned in the weaknesses section.", "strength_and_weaknesses": "Strengths:\n- Focuses on interpretability from the start, rather than slapping some ad-hoc method on at the end.\n- Provides class predictions at each node of the syntax tree. You can see the overall prediction evolving as you walk up the tree, which is neat.\n- I appreciate the authors' effort to provide a fair comparison of their approach by training their own BERT model trained with comparable resources.\n\nWeaknesses:\n- The authors do not go into much detail regarding the training process of their BERT model, and they do not include the standard pretrained BERT among their comparisons. This makes it difficult to judge the performance of the method relative to the alternatives that are available to a potential user. It would also have been nice to see comparisons of the amount of compute required to train a Fast-R2D2 model as compared to a BERT model.\n- I think (though am not entirely sure) that the authors evaluate their method's interpretability by comparing the degree to which highly attributed words in a classified input overlap with human rationales for that input. I think this way of evaluating a method's interpretability is very questionable for two reasons:\n    1. Interpretability methods should highlight a model's true basis for making a classification. That basis might be very different from human rationales. The degree of rationale overlap depends on *both* the degree to which the model's rationales overlap with human rationale and the degree to which the highlighted text represents the model's true rationale. \n    2. The quality of baselines for rationale overlap metrics is quite erratic, and can vary between different tasks, datasets and models. Integrated gradients and attention scores may be poorly suited to the task at hand. In particular, attention scores can do very poorly as explanation unless scaled by the magnitude of the associated value vectors. See (https://arxiv.org/abs/2004.10102). Failing to apply this correction leads to attention-based attribution scores seeming to highly weight CLS and punctuation tokens, which may be degrading the performance of the author's attention-based baselines. Additionally, the authors do not mention what they used as the baseline for integrated gradients, which (https://arxiv.org/abs/2111.07367) find can have a significant influence on the method's performance.\n\n    - I'd suggest the authors consider another approach to demonstrating the interpretability advantage of their method. E.g., (https://arxiv.org/abs/2111.07367) \"poison\" a classification dataset with synthetic shortcuts, train classifiers on the poisoned data, and then test if a given interpretability method can pick up on the shortcut.\n\n    - If the above approach represents too much of a burden, an alternative would be to include a wider range of attribution baselines such as:\n        - Integrated gradients (preferably with the [MASK] token as the baseline)\n        - Attention (while scaling by the attention value vector)\n        - L2 norm of the gradients\n        - Sum( gradient x input embeddings )\n        - ||Max( gradient x input embeddings, 0 )||, a method called NormGrad selective. See (https://arxiv.org/abs/2004.02866)\n    - Note that the above alternative only addresses the weakness of using integrated gradients / attention as baselines, not the deeper conceptual issue of rationale overlap being a poor way to evaluate interpretability, regardless of the baselines used for comparison.\n- The method seems to require a class of language model different from those currently in common use. This requires potential users to either train their own structured language model (expensive) or to use a publicly released structured language model, of which there seem to be very few.\n    - It might be helpful if the authors could demonstrate that their finetuning method works on a publicly released structured language model, which is what potential users would realistically be starting from themselves.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster