Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation
Authors: Xulang Zhang
Rui Mao
Kai He
Erik Cambria
Venue: EMNLP 2023 Findings (2023)
Keywords: sentiment analysis
neuro-symbolic AI
word sense disambiguation

Abstract: Sentiment analysis is a task that highly depends on the understanding of word senses. Traditional neural network models are black boxes that represent word senses as vectors that are uninterpretable for humans. On the other hand, the application of Word Sense Disambiguation (WSD) systems in downstream tasks poses challenges regarding i) which words need to be disambiguated, and ii) how to model explicit word senses into easily understandable terms for a downstream model. This work proposes a neurosymbolic framework that incorporates WSD by identifying and paraphrasing ambiguous words to improve the accuracy of sentiment predictions. The framework allows us to understand which words are paraphrased into which semantically unequivocal words, thus enabling a downstream task model to gain both accuracy and interpretability. To better fine-tune a lexical substitution model for WSD on a downstream task without ground-truth word sense labels, we leverage dynamic rewarding to jointly train sentiment analysis and lexical substitution models. Our framework proves to effectively improve the performance of sentiment analysis on corpora from different domains.

Neurosymbolic Sentiment Analysis with Dynamic Word Sense Disambiguation
Xulang Zhang Rui Mao Kai He* and Erik Cambria
Nanyang Technological University, Singapore National University of Singapore, Singapore
xulang001 @e. ntu. edu. sg; {rui mao, cambria iabriiano Jrntu. eew.ttu.edu.... sg; kai mai_he@nus.edt
Abstract
In traditional black-box sentiment analysis, the representations of word meanings are generated by neural networks (Mao and Li, 2021). As such, the disambiguation process is implicit and opaque. It is challenging to investigate what implicit senses are assigned to ambiguous words by neural network model for it to make accurate predictions. Coupling symbolism and sub-symbolism, neurosymbolic approach to sentiment analysis is transparent and interpretable, regarding the extraction and distinc- tion of word meaning as a sub-problem (Cambria et al., 2017). However, research that explicitly deals with ambiguous words in sentiment anal- ysis is limited (Xia et al., 2014, 2015; Cambria et al., 2015). Sense-annotated resources such as SentiWordNet (Baccianella al., 2010) have been utilized, but only to detect and score sentimental words in the input (Hung and Lin, 2013; Nassir- toussi et al., 2015; Hung and Chen, 2016). The disambiguation of polysemous words in sentiment analysis inputs remains under-explored. Thus, we are motivated to develop a neurosymbolic frame- work for sentiment analysis with explicit Word Sense Disambiguation (WSD).
Sentiment analysis is task that highly depends on the understanding of word senses. Tradi- tional neural network models are black boxes that represent word senses as vectors that are uninterpretable for humans. On the other hand, the application of Word Sense Disambigua- tion (WSD) systems in downstream tasks poses challenges regarding which words need be disambiguated, and ii) how to model explicit word senses into easily understandable terms for downstream model. This work proposes a neurosymbolic framework that incorporates WSD by identifying and paraphrasing ambigu- ous words to improve the accuracy of sentiment predictions. The framework allows us to under- stand which words paraphrased into which semantically unequivocal words, thus enabling a downstream task model to gain both accu- racy and interpretabilityy. To better fine-tune lexical substitution model for WSD on a down- stream task without ground- trutt word sense la- bels, we leverage dynamic rewarding to jointly train sentiment analysis and lexical substitution models. Our framework proves effectively improve the performance of sentiment analysis on corpora from different domains.
Traditional WSD aims to classify which sense of the target word is appropriate according to its context (Huang al., 2019; Blevins and Zettle- moyer. 2020; Bevilacqua and Navigli, 2020; Song et al., 2021; Barba et al., 2021a.b). However, the limitations (&) their task setups are that (E1) the output of those WSD systems, whether sense la- bel (Bevilacqua and Navigli. 2020) or gloss defini- tion (Blevins and Zettlemoyer, 2020; Barba et al., 2021a), is difficult to integrate into downstream input; (#2) the target words must be pre-defined for WSD systems disambiguate, making it difficult to apply them directly downstream tasks; and (83) the reliability of sense distinction of the bench- mark has been questioned (Ramscy, 2017; Maru et al., 2022), as word senses are not discrete.
1 Introduction
To improve the accuracy of sentiment analysis, it is crucial to disambiguate polysemous words according to the context (Cambria al., 2023). A word may have senses that carry opposite sentiments, e.g., "fine" has the positive meaning of being delicate and refined, and the negative meaning of monetary penalty. Additionally, words may appear in positive, negative, or neutral senses in different domains. For instance, the word "bearish" often used in negative sense to describe the stock market in the financial domain, while more likely to be used neutrally to describe hulking figures in other domains.

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Attempts have been made incorporate the dis- ambiguation of word senses into downstream ap- plications. Nonetheless, they are either impractical in weal-world settings (Farooq et al., 2015; Pu et al., 2018), or only applicable to a specific downstream task, i.c., machine translation (Hangya et al., 2021; Campolungo et al., 2022). To address these issues, we leverage dynamic WSD in our neurosymbolic framework. We regard WSD a lexical substi- tution task (for ε1), where ambiguous words in sentiment analysis input are paraphrased into ones that cause less sentiment confusion using a lexical substitution model with dynamic WSD.
Our model is examined with three publicly avail- able datasets from movie review, finance, and Twit- ter domains, outperforming strong baselines by 1.23% on average. The contributions of our work are twofold: (1) We propose a neurosymbolic senti- ment analysis framework that leverages WSD as an auxiliary lexical substitution task, achieving state- of-the-art performance on examined sentiment analysis datasets from different domains; (2) We design a transparent mechanism select WSD tar- get words, based on a) the importance of words the learning and inference of a downstrcam task, and b) word sense diversity.
Our dynamic WSD method comprises two dis- tinct aspects: the dynamic selection of the target word for disambiguation, based on downstream contextual information (for f2); and the dynamic learning of the most suitable lexical substitutions, based on the prediction accuracy of the downstream model (for (3). To identify target words that need to be substituted, we first adopt an attention -eased ex- plainable encoder to rank input words by the level influence they have on the output, motivated by the assumption that not all polysemous words af- feet sentiment predictions (Cao et al., 2015). Then, the top-ranking words with the most diverse senses are selected to be targets, based our hypothesis that ambiguous word may coincide with signifi- cant disparities in the semantic space between the word itself and its potential substitution candidates.
2 Related Work
Word Sense Disambiguation is task that assigns the appropriate meaning to an ambiguous word according to its context. Recent WSD systems at- tained Fl scores higher than 80% on the benchmark datasets (Song et al., 2021; Bevilacqua and Navigli, 2020; Barba et al., 2021a,b), which is regarded as the estimated human performance since it is the highest inter-annotator agreement (Edmonds and Kilgarriff, 2002; Palmer et al., 2007). However, research on the application of WSD systems on downstream tasks is limited. Pu al. (2018) uti- lized clustering algorithms to obtain sense embed- dings by inferring from the similarity of the context vectors of a target word. The heavy requirement for disambiguating every homographs prior to down- stream applications makes their method less fea- sible in many real-world situations. Campolungo et al. (2022) achieved the disambiguation of word sense in neural machine translation by leveraging a multilingual WSD system to establish a sense- tagged parallel corpus. Nonetheless, their approach relied on cross-lingual word alignments for reliable annotations, meaning that it cannot be adopted by other types of downstream tasks.
As such, we can trace back which words are se- lected as targets, and which substitute words are used to disambiguate them, making our method in- terpretable and transparent. Furthermore, we fine- tune the lexical substitution model whilst training the sentiment analysis model by adopting dy- namic rewarding mechanism that does not require gold standard substitution labels. As such, the sub- stitution model can be adjusted to provide more appropriate output for the downstream task as well as the target domain. Our model achieves more accurate sentiment analysis with explicit lexical- substinution-based WSD, despite not specifically trained on WSD and sentiment parallel datasets. Since the disambiguated word senses are repre- sented as the lexical substitutions of the original words and an explainable encoder is used for re- trieving the most contributing words, the neurosym- bolic sentiment inference process is more inter- pretable for humans compared to black box mod- els.
Sentiment Analysis can be generally defined as the task that determines a piece of text expresses positive, negative, or neutral opinion (Mao et al., 2023c). Word sense information has been applied in sentiment analysis in the form of sentiment- annotated lexicons (Devitt and Ahmad, 2007; Ohana and Tierncy, 2009; Hung and Lin, 2013; Nassirtoussi al., 2015), such as SendiWordNet.
However, these methods incorporate the sentiment information of word without disambiguating its word sense according to the context, but resort- ing to the emotional score of the most frequent

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Sentiment
(7]a
Analysis
(8)a
activation
(9)u
lass weiights
[8/b
HAN
19/b
[7]a
(3)
Encoder
Euclidear
distance
Diversily
Average
(1)
Ranking
(10Ib
[6]b
(4)
Diversity
16]a
Lexical
Substitution
Bank by diversity scores
WL
W. W,
Figure I: Architecture of the proposed disambiguation framework for sentiment analysis. A dotted line means backpropagation. Green block means that the original word is replaced by word provided by the lexical substitution model. The darker the green, the higher prohability it is assigned to by the model. Orange block indicates the words with top ranking attention scores produced by HAN.
sense, or the average score of all senses. Addi- tionally, the main objective of this approach is to identify sentimental words in sentence, instead of disambiguating polysemous words that might affect model predictions. Hung and Chen (2016); Farooq et al. (2015); Liu et al. (2018) attempted to disambiguate according to target domain and context, but either costed extensive human labour, fail to incorporate word senses explicitly and transparently.
the downstream prediction from To resolve the potential ambiguity, we extract M of its syn- onyms from WordNet (Fellbaum, 1998) candi- dates e (Ci,..., (.l....,Ca)). forming the input (s) as

; un, u2, ........ b...., WT. 

c1, 
 02 
 CM 
.

 and 
 are special tokens that were defined by our employed pre-trained language model. The lexical substitution model should identify the best candidate from as the substitute which retains the original meaning of in the context by using contrast learning. Thus, is the symbolic represen- tation of in context w. Then, the paraphrased in- put w = (wi...., ....., w1) is fed into a neural network classifier to predict sentiment label. The objective is that the paraphrased input w increases the probability of correct sentiment prediction.
To address these limitations, our neurosymbolic framework aims to identify ambiguous target words in the context and replace them with clarifying ones automatically, making them interpretable. We also leverage dynamic rewarding mechanism so that the substitutions can be fine-tuned better serve the downstream task and the text domain.
3 Task Definition
Methodology
this work, we regard word sense ambiguity as
Our proposed neurosymbolic framework can be viewed Figure 1. There are two models in our framework, namely sentiment analysis and lexical substitution models. The sentiment analysis model consists of an encoder, an interpretable attention module, and multiple feedforward neural networks. First, through Figure (1) and (2), the original input fed into the encoder and the interpretable attention module to obtain the top tokens with the highest attention weights, which contribute the most to sentiment prediction.
potential cause of errors a downstream task, e.g., sentiment analysis. Inspired by the work of Mao et al. (2023b), we reframe WSD as a lexical substi- tution task, aiming to replace ambiguous word with synonym that not only represents the true sense of the original word in the context (Arthur al.. 2015), but also reduces misclassifications in the downstream task. Given an input sentence
w (Wi, u2..... wL), we first aim to identify the target word that is likely troublesome for

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In (3), the sense diversity of each token is com- puted as the average distance of the token's 's hidden state to those of its substitution candidates. Then, through (4), top J tokens with the most diverse senses are selected as disambiguation targets, be- cause these target tokens may have very different senses in different contexts. Subsequently, the pre- trained lexical substitution model is applied to pro- vide the best substitution to replace each target token, as shown in (5) and (6)a. The new imput sen- tence is passed onto the sentiment analysis model for final prediction ((7)a and (8)a), which is used for the sentiment model backpropagation ((9)a).
Table 1: Notation table.
Description
Input sequence length
Number of words with top ranking attention
scores to be potential larget words
Number of words with top ranking diversity
scores be potential larget words Number of candidates used for finetuning lexical
N
substitution via dynamic rewarding Number of candidates for training lexical substitution model
K
W
The original input sentence The hidden states of
g
The ground-truth sentiment label The final input sentence where ambiguous words in w are substituted
ws
yf
The predicted sentiment label with w as input The /-th word in w The attention weight of the f-th word in w The word in w with the i-th highest attention weight
In order to fine-tune the lexical substitution model to provide better substitutions that improve the accuracy of sentiment analysis, we adapt a dy- namic rewarding mechanism (Ge et al., 2022) that was proposed for multi-task learning. As shown in Figure 1 (5) and (6)b, for each target word, we choose the top N candidates. Each of them is seen as a substitute candidate to calculate the probability of correct sentiment prediction after the replace- ment ((7)b and (8)b). Then, each probability is used to dynamically compute the loss weight when the corresponding candidate is learned by the lexi- cal substitution model as a ground truth (99)b and (10)b). If the replacement by candidate leads to a higher chance of predicting the right sentiment, the candidate more likely given a higher rank by the lexical substitution model. Otherwise, the model would be less likely to select the candidate as the best replacement. The important notations used in this paper are summarized in Table 1. The detailed training process can be seen in Algorithm 1.
wy
wit
The hidden state of w
The numher of synonyms of oof with the same sart-cf-speech that are provided by the WordNet The hidden states of the M WordNet synonyms The hidden state of the m-th WordNet synonyms of wn!
M
9m
The average Euclidean distance between of and gl, 9o.. .9r The j-th target word in w to be substituted A set of candidate words from WordNet. The top N candidates ranked by the lexical substitution model
d:
The top 1i-h candidate for replacing Ly The resulting sentence when replacing in w with ε
The loss weight when éy.n. is treated as the gold standard substitution
0,.0
The formatted input to lexicul substitution model The hidden state matrix of the input sentence in a
U
The hidden state of the /-th input word in s The hidden state matrix of the candidates in The hidden state of the k-th candidate in for training
R
Tk
The temperature
T
the lexical substitution model
The hyper-parameter for balancing the sentiment analysis loss and fine-tuning loss
4.1 Sentiment Analysis with Interpretability
B
C(sa)
The sentiment amalysis loss
Given input sentence -wi.---y WL), the goal is to predict the correct sentiment label y. The input is first fed into a pre-trained encoder:
Citune)
The loss for finetuning the pretrained language model into a lexical substitution model
(ila)
The lexical substitution loss when čy.. is treated as the gold standard suhstitution
V Encoder(w))
glls)
The final weighted lexical substitution loss for hackpropagation
where V is hidden states. Next, we aim to find the tokens that contribute the most to sentiment inference. We adopt interpretable attention mod- ule called Hierarchical Attention Network (HAN, Han et al., 2022), which effectively encodes hid- den states with multiple non-linear projections and ranks the most influential tokens based on attention. Following the setup of Han et al. (2022), we stack two blocks of HAN to form our attention module.
where vector q is the yielded hidden state. a is the attention weights, indicating the contribution of cach token to the final sentiment prediction. To obtain the sentiment prediction, q is passed on two layers of feed-forward neural networks (FNN) to obtain the probability distribution of sentiment prediction, with the first one being activated by (3) ReLU (Agarap, 2018), and the second by softmax.
4, a - (N

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Algorithm I: Sentiment analysis with word sense disambiguation using dynamic re- warding mechanism.
h = ReLU(FNNN()) 9 = (oftm
(4)
(5)
Initialize sentiment analysis model as 4, pre-trained lexical substitution model as V
We denote the prediction of the sentiment analysis model if when the input is w which denotes paraphrased w where all selected target tokens are replaced by sense-clarifying substitutions. Thus, the sentiment analysis loss is computed as:
2 Initialize hyperparameiers, B. I.J.N:
3 while not done do
4
Sample a sentence w = W1, W2, Wpi
5
for 1=1:L do Compute the attention weight of token wx;
end
=a + Top I of w ordered by attention weights 8
L (sa) Crosssntropp /f .))
(6)
a= 01 0g. ug....,0... ag;
for i=1:1 do
10
c c all possible candidates with the same part-of-speech type wi' provided by WordNet:
4.2 Disambiguation by Lexical Substitution
To obtain a lexical substitution model, we fine-tune the pre-trained language model ALM (Mao et al., 2023a)' with benchmark datasets from McCarthy and Navigli (2007); Kremer et al. (2014). We use the candidates from the datasets to formulate our input as in Formula 1. The candidate with the highest score is set as the ground truth substitution č Given the input s, the model encodes as:
Compute the average Eulidean distance d, between the hidden states of w, and c. produced hy the encoder in 4;
end
= (h, t2 ........................... ..,)) + Top of wat ordered by d di d2,,d,..... dy: wn w
for j=1:J do
Input into to produce top candidates éj = ( -... &3,N) v target word i,,
ordered by probability;
U, R (s),
Replace in wf with ē5.1;
(<S>, Wi...., ".t............. f....,, Wr. 
, Gy. 
.... C,NN, </<>); for n=1:N do
where U [u...... "r] ] is the hidden states of the ...,,,KK is the hidden
input sentence, and .r...... rx] states of the candidates. We denote the representa- tion the target word as u, (t E LJ).
w Replace 4 in w with dj
Input whn into & to obtain the probability of correct sentiment
prediction P( = g)...
Our training objective is to a) close the distance between the representation of the ground truth candidate Tkk (k: E (1,...,)) with and b)
05.m 6- SP = 9)
Compute c/0²) by feeding a with &, labeled true substitute into V (ie)) in 0 Cla))
push away incorrect candidates representations ry
(lla).
G {1...., KIj + k)) from u, Namely, (rk, u.)
end
will be regarded a positive pair, while (rj. u.)
end
will be regarded as a negative pair. We follow the
28
((4))
+ ClN cll +... D
InfoNCE loss (Oord et al., 2018) to achieve these
Compute sentiment analysis loss ((ae) using w as imput;
goals, which can be formulated as
L <- ((0)) + C(,))
(ep(d(u,rrk/T))
30
(tune) = Σ log Σ, crp(d(u,
(8)
end
where i E {1,..., K}, is temperature hyper- parameter, and d(.) is Euclidean distance.
During the inference stage, we choose the can- didate è whose corresponding hidden state i'ss is the most similar to ut, measured by Euclidean dis- tance:
'ALM employed a novel pre-training paradigm, termed Anomalous Language Modeling, ALM was pre-trained to de- tect anomalous substituted words from sequence and retrieve the original words from set of candidates that contains a pos- itive sample (an original word) and multiple hard negative samples (the synonyms of the original word) via contrastive learning. Thus, it is more suitable for fine-tuning lexical substitution model in our task, because we also aim to detect appropriate substitution words from set of candidates.
é é= arg min (d(u,, rk))
(9)
We then use the resulting lexical substitution model to find replacements for the selected target words in sentiment analysis input. To determine which words are selected as disambiguation tar-

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gets, select the top words in the input with the highest a produced by Equation 3, forming the set watt, We denote their corresponding representa- tions as Van = {19th ...., 1gth).
Dataset
Split
Samples
SemEval 2007 Task CoinCo
2,010
15,629
Train
8,530
For each efth we compute its average Euclidean distance to all of its candidates' hidden states. The candidates consist of WordNet synonyms of all of its possible senses un- der the same part-of-speech type, which are trans- formed into hidden states G {g1,.... 9M) using Equation 2. . represents the number of candidates provided by WordNet. The top words in watt with the largest corresponding average distance are considered to be the ones with the most diverse word meanings, and thus are more likely to require disambiguation. We will demonstrate the success of such an ambiguous word selection method in the later ablation study (see Section 6.1).
Rotten Tomatoes
Dev
1,066
Test 1,066
Financial PhraseBank STS-Gold
4,846
2,026
Table 2:
Datasets used for experiments.
We formulate the input s² to lexical substitution model with sentence w and candidates dj. using Formula 1. Similar to Equation 8, the loss j.n of the lexical substitution model when ēy,, is con- sidered as gold standard is computed as follows:
jim - log car(dd(u,, (Tm)TT
----log
E
(13)
Σ, crrp(d(u, G5)/T)
Hence, target words (4,,...,4) are se- lected by finding each corresponding ug
Finally, the total loss is computed as:
egth arg max
Σ
d(utt .mm)). (10)
L = [(xa) + +1,1 + .... + O2, C (14)
M
Experiments
Following the lexical substitution benchmarks, only words whose part-of-speech tags fall under the general categories of noun, verb, adjective, and adverb are considered as targets.
5.1 Datasets
To fine-tune the pretrained lexical substitution model, we combined SemEval 2007 Task 10 (Mc- Carthy and Navigli. 2007) and CoinCo (Kremer et al., 2014). The former consists of 2,010 example sentences for 201 polysemous words. The latter contains 2,474 sentences with multiple words be substituted. We used 70% of the concatenated dataset for training, 30% for testing.
4.3 Dynamic Rewarding Mechanism
To fine-tune the lexical substitution model on the downstream sentiment analysis task without know- ing WSD gold standard truth, for each target word lj. we utilize the top N candidates 2j &,j.....,,jj...... Ny.N) produced by the lexical substitution model. The new input resulting from 1 being replaced by candidate en,n is denoted as win.. Same with Equation 5, the probability distribution of sentiment prediction from w is:
For sentiment analysis, we used three datasets from different domains: Rotten Tomatoes (Pang and Lee, 2005) consists of 5,331 positive and 5,331 negative sentences collected from Rotten Toma- toes movie reviews. Financial PhraseBank (Malo et al., 2014) contains 4,846 sentences from English financial news, each labeled negative, positive, or neutral. As the dataset has no standard split, We adopted split of 70% and 30% for the training and testing sets. STS-Gold (Saif et al., 2013) contains 2,026 tweets, annotated as negative or positive. As the dataset has no standard split, We adopted split of 90% and 10% for the training and testing sets. Details of the datasets are listed in Table 2.
P(j)i, = softmaz(FNNNatt>
(11)
where h is the hidden states of w outputted by Equation in the sentiment analysis model.
To adjust the model in such a way that a more accurate sentiment prediction results in a higher re- ward for the corresponding substitution output, we compute the loss weight for ēj.m being the correct substitution prediction as:
0j.n = 3P = 1)
(12)
5.2 Baselines
where 9 is hyperparameter for balancing the sen- timent analysis and lexical substitution losses, ÿ is the ground truth sentiment label as defined above.
Since we aim to improve a sentiment analysis clas- sifier with explicit WSD in a neurosymbolic fash- ion, vanilla RoBERTa-larg (Liu et al., 2019) is

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considered the most direct baseline, because we also used it for sentiment classification. To put our model's performance into perspective, we further include published baselines with the highest re- ported accuracy on the experiment datasets, which consist of various pre-trained language models fine- tuned on the target dataset, i.e., FinBERT (Araci, 2019), DeBERTa (He et al., 2020; Sileo, 2023); and ones trained with additional resources, namely, DeBERTa+tarksouree (Sileo, 2023) which is pre- trained on unified dataset for 500 English tasks, and SentieNet 7 (Cambria et al., 2022) which is a XLNet enhanced by sentiment knowledge.
Model
RT
FPB
STS-Gold
Avg
DeBERTa
90.42
84.48
87.45
Htasssource
90.99
85.20
88.10
FinBERT
86.00
SentieNet 7 RoBERTa
90.08
91.31
87.09
97.14
91.85
Ours
91.87*
88.32
99.05
93.08
Table Comparisons with baselines. RT stands for Rotten Tomatoes. FPB stands for Financial PhraseBank. Bold font denotes the best results. The RoBERTa base- line was implemented by denotes the improvement is statistically significant (p < 0.01 on a two-tailed t- test), against the highest baseline score. Our results are averaged over 5 runs.
5.3 Setups
standard expressions that are semantically clearer. Hence, our model obtains a marginal increase of 0.56% on Rotten Tomatoes. Despite the im- provement being marginal on average, i.c., 1.23%, would like to highlight that this improve- ment is significant considering the context of error propagation from the lexical substitution model. Our framework consistently demonstrates improve- ments across all datasets, substantiating its ability to achieve higher gains in accuracy while minimiz- ing any potential loss. Furthermore, we can inter- pret the WSD outcomes and the sentiment analy- sis decision decision-making process before and after lexi- cal substitutions from the proposed neurosymbolic framework. We will demonstrate the interpretabil- ity of the framework in Section 6.2.
To obtain the lexical substitution model, we fine- tune ALM with the lexical substitution datasets for 20 epochs with batch size of 20 and learning rate of 1e-8. The pre-trained model we used as the encoder for sentiment analysis is RoBERTa-large. The hidden size for both models is set to 1024. We use the NLTK pfrt-of-specch tagger (Bird, 2006) to label the sentiment analysis input.
To train our proposed framework, we adopt I = 5, J = 2, K 15, N T 0.05, and | 0.0001. We set the learning rate to 1- c6. The models are trained for 50 epochs with batch size of 10, using early stopping. We used Adam (Kingma and Ba, 2014) as the optimizer. Ex- periments are run on RTX A6000 GPU. Following the setups in the baselines, we adopt accuracy as an evaluation metric.
6.1 Ablation Study
To prove the effectiveness of each component our proposed framework, we compare with two variations: w/o WSD, where sentiment prediction is done by the sentiment analysis model without any modifications to the input; w/o DR, where the lexical substitution model is not fine-tuned during sentiment analysis training. To prove hypothe- sis in WSD target word selection, we compare the proposed framework with two variations: Top-2, where the top 2 words the input with the highest attention weights are automatically chosen as target words to be disambiguated; Rand, where 2 out of the most attended 5 words are randomly selected to be the target words. The comparison of perfor- mance is shown in Table 4. It can be observed that our full model outperforms w/o WSD by 0.28% Rotten Tomatoes, 1.03% on Financial PhraseBank, and 1.43% on STS-Gold
6 Results
We compare our model with the basclines in Ta- ble 3. Our model outperforms existing models with the best-published results, achieving state-of-the the- art performance on the three datasets. Compared to the RoBERTa baseline, our model is able to improve the accuracy of sentiment analysis cor- pora from different domains varying extent. We hypothesize that the extent of improvement de- pendent on domain types. For instance, Twitter language is informal and brief, with frequent use of short homographs. Thus, the integrated disam- biguation model is able to obtain the most signifi- cant increase of 1.91% % accuracy on STS-Gold. In the financial domain, metaphors and less common word senses are commonly used, accounting for our 1.23% gain on Financial PhraseBank.
Movie reviews, on the other hand, often use
The extent of gains in different domains is in

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Model
RT
FPB
STS-Gold
Avg
DG22 DGC COS
w/o WSD
91.59
87.29
97.62
92.17
w/o DR
91.40
87.27
96.67
91.78
Top-2
91.60
87.81
97.62
92.34
fave
nave
cinp
(pr
cing
exerdse
Rand
89.72
86.30
97.14
91.05
matr
pram
derrased ls
Ours
91.87
88.32
99.05
93.08
b
de
neatly
neadyy
undeate
Table 4: Ablation study results.
that
that
abo
55
butt
line with our hypothesis in the previous section. It shows that our disambiguation framework is indeed able to improve the accuracy of sentiment unalysis by selecting the appropriate ambiguous words and paraphrasing them into semantically clearer ones. Our model also achieves higher accuracy than w/o DR on all three datasets, proving the validity and effectiveness of our dynamic rewarding mechanism. Noticeably, without dynamic rewarding and joint fine-tuning, the performance of w/o DR is weaker than w/o WSD, showing that dynamic rewarding- based fine-tuning with data from another task can effectively alleviate error propagation from lexical substitution.
lwwer
Ieser
ts
1M0
jolo
gaad
good
<
<1 <t0 <57 <pp
(a)
(b)
(c)
Figure 2: The most attended word demonstration before
and after paraphrasing in sentiment analysis predictions. The lighter the color, the higher the attention weight.
word substitutions that aid the correct sentiment predictions. From Figure 2 (b), we can observe a rather evenly distributed attention weights in the original sentence, meaning that the attention mod- ule cannot identify the words that significantly af- feet sentiment. After our framework replaces the word decreased to fell, the model is able to as- sign higher weights to the sentiment-loaded phrase profit fell. From Figure 2 (c), we can see that af- ter replacing the word mediocre with average, the sentiment related words including the paraphrased word and unaware all received higher attention. By attending the straightforward paraphrases pressing opinions, the sentiment analysis model can yield correct predictions for all three cases.
Our proposed framework outperforms the Top-2 selection method on all datasets. It proves our as- sumption that words that are highly diverse in their senses are more likely to cause ambiguity for sen- timent prediction. Rand performs the worst all three datasets compared to w/o WSD, Top-2, and our model, indicating i) the necessity of selecting the appropriate target words to disambiguate so as not to introduce noise, and ii) the validity of choos- ing target words based on both their importance to the decision and their sense diversity level.
6.3 Case Study
6.2 Interpretability Demonstration
Since an interpretable encoder (HAN) is used in our framework to learn sequential representations, we can understand which words contribute the most to sentiment analysis prediction by visualizing the attention weights of HAN.
In Table 5, we study the ability of our framework to identify and paraphrase ambiguous words into unequivocal ones. In the first instance, albeit not affecting the prediction, the word delicious is dis- ambiguated by our framework into its figurative meaning delighted instead of its literal one. the second instance. the word succeeds has a more pos- itive word sense equating to win, and a more neutral one equating to follow. Our framework is able to paraphrase it into the latter. In the last instance, the word visibility falls under the word sense of degree of exposure public notice in this context. Our framework correctly paraphrases into pro- file, which is the synonym under the same sense in WordNet. can be concluded that our framework can disambiguate word senses in a transparent and interpretable way for sentiment analysis.
As shown in Figure 2, conduct a study on the change of attention weights before and after paraphrasing. In Figure 2 (a), the words that receive the most attention in the original sentence are not, a, sore, etc., among which not has significantly higher weight. This indicates that the model is mostly relying on negation to make inference. As not does not have diverse meaning and a is an article, sore is selected target word and replaced with painful. Our attention module assigns much higher weight the negative adjective after paraphrasing. This shows that our model is able to find suitable

---

Setup
Content
Setup
Content
Label
a delicious and delicutely funny look at the residents of a copenhagen neighborhood coping with the befuddling comphcations losses Ihem.
tour de force of modern ciocina.
Original
Positive
Original
lour de power of modern cinema.
Paraphrased
Posilive
a delighted and delicutely funny look the residents of copenhagen neighhorhood coping with the befuddling complications life losses Ihem.
Is just at work for the day -sigh- I have headache
Original
Negative
Puraphrused
Is good work for the day
Paraphrased
Positive
-sigh- have headache
Le Lay succeeds Walter G++enter and will be basedin Finland.
Original
the skills of calculos major
m.i.t are required to balance all formulaic equations
Le Lay fallows Walter O++nler will he based Finland.
Paraphrased
Original
Negative
long- winded heist comedy who is cletis tout?
Strong brand visibility nationally and regionally primary importance in home sales, vehicle and consumer advertising. Strong brand profile nationally and regionally is of primary importance in home sales, vehicle and consumer advertising.
Original
the science of calculus major at m.it. are required to balance all the formuluic equations the
Paraphrased
Positive
Paraphrased
long- winded heist comedy who is cletis tout?
Table Case study of disambiguation as paraphrasing.
Table 6: Error analysis. Red indicates errors made by proposed framework.
6.4 Error Analysis
Additionally, to fine-tune the lexical substitution model to provide better disambiguating substitutes for sentiment analysis with no ground-truth word sense labels, we adopt the dynamic rewarding mechanism to jointly train the sentiment analysis and lexical substitution models. Experiments show that our framework indeed effective improving the performance of sentiment analysis, obtaining state-of-the-as performance on three corpora from different domains.
Finally, we perform a brief error analysis (Ta- ble 6). In the first sentence, the model wrongly paraphrases force into power, because it does not recognize the multi-word idiom tour de force. It shows the model's ' limitation in understanding the semantic meaning of infrequent multi-word idiom. In the second sentence, the word just is wrongly paraphrased as good, which negatively influenced the sentiment prediction. This mainly due to the part-of-speecl tagger wrongly classifying the word just into a adjective, instead of an adverb. Thus, the lexical substitution module might be subjected to error propagation of the part-of-speech tagger and introduce noise into the input. In the last sentence, the word skills paraphrased as science. is likely that the model mistakes the word major as a field study instead of a student studying field. Thus, the substitution makes sense in the local context the science of calculus major, but not in the whole context, meaning that the model fails to understand the semantic meaning of the sentence.
Limitations
The framework proposed in this paper have to be seen in light of some limitations. First, the performance of sentiment analysis to some extent depends on decent-performing pre-trained lexical substitution model. Currently, polysemous samples in lexical substitution data are not as abundant as in WSD data, which might limit its ability in disambiguating less common polysemies. Second, the effectiveness of our proposed model is only tested on English corpora, as it relies the sense knowledge from WordNet. The disambiguation target selection and paraphrasing methods might work on languages with more complex segmentation limited sense lexicon.
7 Conclusion
this work, we propose a neurosymbolic frame- work for sentiment analysis that incorporates the disambiguation of word senses by identifying and paraphrasing ambiguous words in the input, which is realized by leveraging pre-trained lexical sub- stitution model. The proposed framework not only improves the accuracy of sentiment analysis, but also achieves interpretabiliiyy by enabling us to trace back which words are paraphrased and what words they are replaced with.
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Reviews:
[{"review_id": "rNG30PgFPq", "reviewer": "Reviewer_xJze", "summary": "", "questions": "1) Is the reliance on WordNet a limitation of the work since WordNet may not potentially capture all sense of a word?\n\n2) The proposed approach replaces one word at a time. However, WSD is known to be a task where the meaning of a word is influenced by the context. How does the proposed model take into account the importance of context in WSD?\n\n3) The idea of using WSD in conjunction with sentiment analysis is not new: \n(a) Rentoumi, Vassiliki, George Giannakopoulos, Vangelis Karkaletsis, and George A. Vouros. \"Sentiment analysis of figurative language using a word sense disambiguation approach.\" In Proceedings of the International Conference RANLP-2009, pp. 370-375. 2009.\n(b) https://aclanthology.org/W15-2916.pdf\nHow does the current work \n\n4) How does the model take into account the requirement that the substituted word must also be a high-attention word for the classification task?\n\n5) What is neuro-symbolic about the approach - that allows it to be distinguished from, say, multi-task learning? It may be useful to highlight that using prevalent paradigms for neurosymbolic methods. \n\n6) Related to 5a: The idea of using a model for a certain task in order to serve as input (or vectors) for another task is not new. For example: https://arxiv.org/abs/1906.05466 . How does this model improve upon such approaches in being neuro-symbolic?\n\n7) What is the intuition behind using Euclidean distance to choose candidate substitutions?", "limitations": "", "rating": 3, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of the novelty and effectiveness of our work. We hope our work can provide a new perspective of disambiguating word senses for downstream tasks that is effective and explainable.\n\nQ1. The reliance on WordNet is a limitation of the WSD task as a whole. To the extent of our knowledge, supervised WSD was commonly trained and tested on datasets annotated using static WordNet sense definitions, while most unsupervised WSD also relied on existing knowledge bases [1, 2]. The generation of word senses is a challenge in WSD that is outside the research scope of this paper.\n\nQ2. It is worth noting that, in most WSD systems (Blevins and Zettlemoyer, 2020; Bevilacqua and Navigli, 2020; Song et al., 2021; Barba et al., 2021a), a target word in a sentence is disambiguated individually without considering the word sense of other target words in the sentence, which is essentially the same as our setting. Therefore, (1) our model learns the semantic context of the input sentence implicitly via pretrained language model, same as in other WSD works, (2) replacing of a target word independently from other target words has the advantage of minimizing error propagation, where a wrong substitute negatively affects the substitution of the other target word.\n\nQ3. We appreciate the suggestion of related works. The question is incomplete, so this answer is based on our assumption on what the question is. In work (a), the polarity of each word is assigned on sense-level. However, a word with neutral sense might have a positive or negative meaning in the context of the sentence, which our model would be able to discern. Work (b) has the same limitation as the works we discussed in Line 170-183.\n\nQ4. The model does not require the substituted word to be high-attention. Our aim is to replace important words with diverse sense so that the input would be less ambiguous to the model. The high attention score of substituted word shown in Fig.2 is the outcome of the disambiguated input, but not the goal of our model. There are also instances where the high-attention words in the original sentence likely should not receive a high attention score due to its prevailing sense, e.g., the second example sentence in Table 4. \n\nQ5&6. Our model is neuro-symbolic in the sense that the word senses are explicitly disambiguated for the downstream task with other symbolic representations, which contrasts with the exclusive reliance on neural networks [3]. By definition, the work mentioned in Q6 can be considered neuro-symbolic. The aim of our paper is to propose a new paradigm where the disambiguation of word sense can be integrated into sentiment analysis (and other downstream classification task) in an intuitive and explainable way. We appreciate the suggestions but such comparisons are very much outside of our research scope.\n\nQ7. As stated in Line 301-302, to choose the appropriate substitution, we utilize ALM and adopt  similar training process from Mao et al. (2023), where using Euclidean distance as the similarity measure performs the best.\n\n“Line 212”. We do not implement a special token to indicate which word is replaced. We use an index sequence to keep track of the position of target words, same as in Mao et al. (2023).\n\n[1] Bevilacqua, Michele, et al. \"Recent trends in word sense disambiguation: A survey.\" Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. International Joint Conference on Artificial Intelligence, Inc, 2021.\n\n[2] Maru, Marco, et al. \"Nibbling at the hard core of Word Sense Disambiguation.\" Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.\n\n[3] Cambria, Erik, et al. \"Sentiment analysis is a big suitcase.\" IEEE Intelligent Systems 32.6 (2017): 74-80.", "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": "1) The paper models WSD as a word substitution task - and shows that doing so can improve the performance of sentiment analysis.", "reasons_to_reject": "1) It is not clear what makes the approach 'neuro-symbolic'. Please refer to questions 3, 5,6 below.\n\n2) The work relies on a static resource such as WordNet. Incompleteness of WordNet may be a limitation of the work.\n\n3) The idea of modeling WSD as a substitution is interesting. However, WSD must take into account other words in the context of the sentence. It is not clear how and if the model does this.\n\n4) The baselines seem rather weak in comparison to the proposed approach. The ablation study offers comparable models - and shows promise. However, other methods where WSD is used to aid sentiment analysis would have made for a stronger comparison.", "questions_to_authors": "1) Is the reliance on WordNet a limitation of the work since WordNet may not potentially capture all sense of a word?\n\n2) The proposed approach replaces one word at a time. However, WSD is known to be a task where the meaning of a word is influenced by the context. How does the proposed model take into account the importance of context in WSD?\n\n3) The idea of using WSD in conjunction with sentiment analysis is not new: \n(a) Rentoumi, Vassiliki, George Giannakopoulos, Vangelis Karkaletsis, and George A. Vouros. \"Sentiment analysis of figurative language using a word sense disambiguation approach.\" In Proceedings of the International Conference RANLP-2009, pp. 370-375. 2009.\n(b) https://aclanthology.org/W15-2916.pdf\nHow does the current work \n\n4) How does the model take into account the requirement that the substituted word must also be a high-attention word for the classification task?\n\n5) What is neuro-symbolic about the approach - that allows it to be distinguished from, say, multi-task learning? It may be useful to highlight that using prevalent paradigms for neurosymbolic methods. \n\n6) Related to 5a: The idea of using a model for a certain task in order to serve as input (or vectors) for another task is not new. For example: https://arxiv.org/abs/1906.05466 . How does this model improve upon such approaches in being neuro-symbolic?\n\n7) What is the intuition behind using Euclidean distance to choose candidate substitutions?", "paper_topic_and_main_contributions": "Summary: The paper presents an approach to incorporate word sense disambiguation into sentiment analysis. WSD is modeled as a word substitution task. The claim of the paper is that by substituting the high-attention yet ambiguous words can make the sentence easier as an input for sentiment analysis. The authors report results on fine-tuned RoBERTa, FinBERT etc. The paper also presents a qualitative analysis of sentences where words are correctly and incorrectly substituted.\n\nContribution: Using substitution of words for a NLP task is an interesting recent idea. It has been shown to work for detection of AI-generated text (to an extent; in the DetectGPT paper).", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Figure 1 is quite loaded with labels on the edges being explained in the description. I would suggest to simplify Figure 1 and extend the details to a new figure.\n\n\nLine 212: Is there a special token to indicate which word was replaced? The equation does not seem to say so.\n\nEulidean -> Euclidean (in the algorithm)\n\nIt may be useful to add a section on neuro-symbolic methods in the related work section."}}, {"review_id": "67ffGtJ3Dx", "reviewer": "Reviewer_PXHm", "summary": "", "questions": "1) It would be better to analyze the time efficiency of the preposed framework. \n2) In Line 16 Algorithm 1, does only the word t_j is feed into pre-trained lexical substitution model?\n3) In my opinion, after word selection, fusion information of synonyms via attention mechanisms is a simple way to use WordNet. Could this method be used as a baseline?\n4) There are so many symbols in this paper that it is not very readable. \n", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of the novelty and contributions of our work. We hope our work can provide a new perspective of disambiguating word senses for downstream tasks that is effective and explainable.\n\n“time-consuming”. It's a trade-off between gaining explainability with a neuro-symbolic method and a pure neural network-based method during the training stage. To mitigate error propagation of lexical substitutions, we have to take an extra step to train the model with multi-lexical substitutions and dynamic rewarding to learn the most robust one. This is because there is no parallel dataset with word sense and sentiment labels that allows us to learn both tasks in a multitask learning fashion. However, during the inference stage, the output can be achieved with a single feedforward propagation. Additionally, the inference phrase is actually quite efficient, because the lexical substitution model no longer needs computational time to be finetuned, and the time needed to replace target words in an input sentence is quite marginal, compared to other related works (Pu et al., 2018; Campolungo et al., 2022) that use a WSD system to disambiguate every word in the sentence.\n\n“Line 16 Algorithm 1”. Thank you for pointing out our careless mistake! The correct step would be: “Input $w$ into $\\Psi$ with $t_j$ as target to produce top $N$ candidates $\\hat{c} = (\\hat{c}_1, \\dots, \\hat{c}_N)$”. We further make modification of L15-27 so that the notations in Alg 1 will be exact match of the notations in text.\n\n    For $j=1:J$\n        Input $w$ into $\\Psi$ to produce top $N$ candidates $\\hat{c}_j=(\\hat{c}_{j,1},\\dots,\\hat{c}_{j,N})$ for target word $t_j$, ordered by probability;\n        Replace $t_j$ in $w^f$ with $\\hat{c}_{j,1}$;\n        $s \\leftarrow (\\textrm{
},w_1,\\dots,t_i,\\dots,w_L,\\textrm{
}, \\hat{c}_{j,1},\\textrm{
},\\dots, \\hat{c}_{j,N},\\textrm{
})$;\n        For $n=1:N$\n                $w^{j,n} \\leftarrow$ Replace $t_j$ in $w$ with $\\hat{c}_{j,n}$;\n                Input $w^{j,n}$ into $\\Phi$ to obtain the probability of correct sentiment prediction $P(\\hat{y}=\\tilde{y})_{j,n}$ ;\n                $\\theta_{j,n} \\leftarrow \\beta P(\\hat{y}=\\tilde{y})_{j,n}^2$ ;\n                Compute $\\mathcal{L}^{(ls)}_{j,n}$ by feeding $s$ with $\\hat{c}_n$ labeled as true substitute into $\\Psi$ ;\n                $\\mathcal{L}^{(ls)}_{j,n} \\leftarrow \\theta_{j,n}\\mathcal{L}^{(ls)}_{j,n}$\\;\n\n“baseline”. As stated in Line 412-413, the aim of our experiment is to prove that our framework of incorporating WSD, specifically lexical substitution as explainable explicit WSD, can improve the performance of a sentiment analysis model. We include other baselines mainly to put our model’s performance into perspective. (Please note that DeBERTa+tarksource uses a unified dataset containing much more training samples than ours.) Since WordNet is essentially a word sense dictionary, it is difficult to incorporate it without some measure to disambiguate senses, which is the problem our paper is trying to address. If my understanding of the baseline suggestion is correct, fusing synonyms of the target word under all of its sense, albeit via attention mechanism, would incorporate more “noisy” word senses into the input context, which does not quite make sense regarding the disambiguation goal.", "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": "1) The disambiguation of polysemous words in sentiment analysis remains under-explored. The proposed framework is a reasonable way to boost sentiment classification with WSD. \n2) With the lexical substitution, the framework is more explainable and interpretable.\n", "reasons_to_reject": "1) The proposed framework is very time-consuming during both the training and inference phases.\n2) The experimental results are not very convincing. None of the chosen baselines in Table 2 uses the extra resource WordNet. Additionally, the strongest baseline is a vanilla RoBERTa model.\n", "questions_to_authors": "1) It would be better to analyze the time efficiency of the preposed framework. \n2) In Line 16 Algorithm 1, does only the word t_j is feed into pre-trained lexical substitution model?\n3) In my opinion, after word selection, fusion information of synonyms via attention mechanisms is a simple way to use WordNet. Could this method be used as a baseline?\n4) There are so many symbols in this paper that it is not very readable. \n", "paper_topic_and_main_contributions": "This paper proposes a novel framework for sentiment classification, which leverages word sense disambiguation (WSD) as an auxiliary lexical substitution task. The proposed framework can pick up the important and polysemous words for sentiment classification, and replace them with semantically unequivocal words. To do this, authors develop an interpretable attention module for word selection and introduce a pre-trained language model ALM for lexical substitution. Additionally, a self-supervised dynamic rewarding mechanism is designed to further fine-tune ALM. The experimental results show that the proposed framework is more accurate and interpretable.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "YSHKRG7AYV", "reviewer": "Reviewer_PBHS", "summary": "", "questions": "- QA: What's the \"dynamic rewarding mechanism\"?\n- QB: Alg. 1:\n\t- I'm very confused by L15-27. There seem to be some discrepancies to the text in Sec. 4. I may be misunderstanding a lot here, so please take the following with a grain of salt. Here's what I think should be corrected:\n\t- L16: According to Sec. 4.2, $\\psi$ isn't supposed to \"produce top N candidates$ but rather representations if $w^{j,n}$ and $c^hat_{j,n}$.\n\t- $s$ in L18 is never used.\n\t- L21: According to Sec. 4.3 $w^s$ should be $w^{j,n}$ and $c^hat_n$ should be $c^hat_{j,n}$.\n- QC: Looking at the hyperparameters in Sec. 5.3 I assume you ran some kind of hyperparameter search. How was that done? Given that the Financial PhraseBank and STS-Gold dataset don't have a dev set it would be problematic to tune on the test set.\n- QD: L450: Reported results are not comparable to previous work, if the splits are different. That being said, previous work on Financial PhraseBank shows that SotA performance is far above 90% (https://paperswithcode.com/sota/text-classification-on-financial-phrasebank). This in itself is not a reason to reject the paper, but the statement should be toned down to reflect current state of the art. I further suggest adding the actual SotA results for all datasets into the table.\n- QE: Sec. 6.1.\n\t- What's the difference between the \"w/o WSD\" model and the RoBERTa baseline?\n\t- A reasonable baseline would be to use a standard WSD system instead of the substitution model.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your recognition of the novelty and contributions of our work. We hope our work can provide a new perspective of disambiguating word senses for downstream tasks that is effective and explainable.\n\nQA: As we describe in Section 4.3, since we do not have gold standard truth annotation for which substitution candidate is the most appropriate, we compute the loss of each candidate being the “truth” (best substitute) respectively, but weight each loss with the probability of correct sentiment prediction when the original target word is replaced by the corresponding candidate. In other words, if the substitution of a candidate word makes the sentiment analysis model more likely to make the correct prediction, we reward the lexical substitution model for ranking it higher by increasing the corresponding loss weight. This mechanism is used to further finetune the lexical substitution model so that it can adjust its output according to the downstream task and the text domain.\n\nQB: (1) “L16”. Thank you for pointing out our careless mistake! We attach the correct version of L15-27 for preview. Since in Alg 1 all candidates $\\hat{c}$ is only used locally in the $j=1:J$ loop (L15-27), we omitted the subscript $j$ for a more elegant algorithm presentation in the original script, but we can see how different expression from the main text might rise confusion. We will modify the $j=1:J$ loop so that all notation matched with the text, as shown below. (2) “s in L18” is used in L24, which correspond to the $s^j$ Line 380-382 in text. \n\n    For $j=1:J$\n        Input $w$ into $\\Psi$ to produce top $N$ candidates $\\hat{c}_j=(\\hat{c}_{j,1},\\dots,\\hat{c}_{j,N})$ for target word $t_j$, ordered by probability;\n        Replace $t_j$ in $w^f$ with $\\hat{c}_{j,1}$;\n        $s \\leftarrow (\\textrm{
},w_1,\\dots,t_i,\\dots,w_L,\\textrm{
}, \\hat{c}_{j,1},\\textrm{
},\\dots, \\hat{c}_{j,N},\\textrm{
})$;\n        For $n=1:N$\n                $w^{j,n} \\leftarrow$ Replace $t_j$ in $w$ with $\\hat{c}_{j,n}$;\n                Input $w^{j,n}$ into $\\Phi$ to obtain the probability of correct sentiment prediction $P(\\hat{y}=\\tilde{y})_{j,n}$ ;\n                $\\theta_{j,n} \\leftarrow \\beta P(\\hat{y}=\\tilde{y})_{j,n}^2$ ;\n                Compute $\\mathcal{L}^{(ls)}_{j,n}$ by feeding $s$ with $\\hat{c}_n$ labeled as true substitute into $\\Psi$ ;\n                $\\mathcal{L}^{(ls)}_{j,n} \\leftarrow \\theta_{j,n}\\mathcal{L}^{(ls)}_{j,n}$\\;\n\nQC: We follow the setting of $\\tau$ in Mao et al. (2023) and learning rate of the best published baselines. $\\beta$ is fixed across experiment on all datasets.\n\nQD: Thank you for your suggestion. We did not consider unpublished results when including baselines. We will update the SOTA.\n\nQE: (a) “w/o WSD” is Roberta with the addition of two HAN blocks (see the sentiment analysis component in Fig. 1).\n\n(b) Standard WSD systems face the following gaps to be integrated into sentiment analysis: 1) target words in the input must be predefined, 2) the direct output of current SOTA WSD systems is the gloss definition of a target word (Song et al., 2021; Barba et al., 2021a; Barba et al., 2021b), which is difficult to embed into sentiment analysis model in an explainable way, and 3) there is no straightforward way to finetune a standard WSD system to the target domain due to lack of sense annotations in downstream task corpora. In contrast, our framework consists of interdependent components that aim to bridge these gaps.", "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": "The presented approach allows to interpret the importance of words w.r.t. polarity and their replacement candidate.", "reasons_to_reject": "- The presented approach is complex and requires 2 models and a lexical resource.\n- Generally, I had trouble understanding Sec. 4 and the algorithm. Unfortunately, Fig. 1 didn't help in shedding more light to the algorithm.\n- I had trouble following the notation throughout the paper. A table with all used symbols would have helped, because the variable definitions are spread over multiple pages. Some of the notation is inconsistent (more details below).\n- It's difficult to judge how well the presented system works given that only 1 out of 3 of the datasets has a published train/dev/test split where numbers can be compared to previous research. On that dataset the improvement is marginal (but still significant).", "questions_to_authors": "- QA: What's the \"dynamic rewarding mechanism\"?\n- QB: Alg. 1:\n\t- I'm very confused by L15-27. There seem to be some discrepancies to the text in Sec. 4. I may be misunderstanding a lot here, so please take the following with a grain of salt. Here's what I think should be corrected:\n\t- L16: According to Sec. 4.2, $\\psi$ isn't supposed to \"produce top N candidates$ but rather representations if $w^{j,n}$ and $c^hat_{j,n}$.\n\t- $s$ in L18 is never used.\n\t- L21: According to Sec. 4.3 $w^s$ should be $w^{j,n}$ and $c^hat_n$ should be $c^hat_{j,n}$.\n- QC: Looking at the hyperparameters in Sec. 5.3 I assume you ran some kind of hyperparameter search. How was that done? Given that the Financial PhraseBank and STS-Gold dataset don't have a dev set it would be problematic to tune on the test set.\n- QD: L450: Reported results are not comparable to previous work, if the splits are different. That being said, previous work on Financial PhraseBank shows that SotA performance is far above 90% (https://paperswithcode.com/sota/text-classification-on-financial-phrasebank). This in itself is not a reason to reject the paper, but the statement should be toned down to reflect current state of the art. I further suggest adding the actual SotA results for all datasets into the table.\n- QE: Sec. 6.1.\n\t- What's the difference between the \"w/o WSD\" model and the RoBERTa baseline?\n\t- A reasonable baseline would be to use a standard WSD system instead of the substitution model.", "paper_topic_and_main_contributions": "The paper describes a multi-step approach for Word Sense Disambiguation (WSD) specifically for Sentiment Analysis (SA) (although the approach is task agnostic). In a first step, words that have high importance for the SA task are identified using an attention mechanism. For the most important words they identify synonyms via WordNet and rank them via a dedicated \"lexical substitution model\". The highest ranking candidate then substitutes the original target word and the so modified input sentence is used as input for a sentiment classifier.", "excitement": 2, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "- Related work: Fine-Grained Contextual Predictions for Hard Sentiment Words, Ebert and Schuetze, EMNLP 2014"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Fig. 1: \"Lexical Substitution\" has the \"n\" in an extra line.\n- There seems to be some inconsistency in the use of $K$ and $N$. L210, L316 use $K$ as number of candidates, but Fig. 1 and L255 use $N$.\n- L413: typo in \"neurosymbolic\"\n- Missing a reference to Tab. 1 to Sec. 5.1."}}]

Author rebuttal:

Meta-review: The paper presents a novel neuro-symbolic approach that models WSD as a word substitution task to improve sentiment analysis performance. It identifies the ambiguous words and substitutes them using candidates from WordNet. 
The approach is considered reasonable by the reviewers. It offers interpretability advantages and improves the sentiment analysis performance. However, the reviewers raised concerns regarding the reliance on static resources like WordNet, limited baselines, the time-consuming nature of this approach, and the experimental results. The authors addressed some of these concerns in their response by providing further clarification, however, the reviewers are still quite ambivalent about the excitement of this work.
Decision: Accept-Findings