Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning
Authors: Sapan Shah
Sreedhar Reddy
Pushpak Bhattacharyya
Venue: EMNLP 2023 Main (2023)
Keywords: Emotion
Contrastive Learning
Retrofitting

Abstract: We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa. Our method updates pre-trained network weights using contrastive learning so that the text fragments exhibiting similar emotions are encoded nearby in the representation space, and the fragments with different emotion content are pushed apart. While doing so, it also ensures that the linguistic knowledge already present in PLMs is not inadvertently perturbed. The language models retrofitted by our method, i.e., BERTEmo and RoBERTaEmo, produce emotion-aware text representations, as evaluated through different clustering and retrieval metrics. For the downstream tasks on sentiment analysis and sarcasm detection, they perform better than their pre-trained counterparts (about 1% improvement in F1-score) and other existing approaches. Additionally, a more significant boost in performance is observed for the retrofitted models over pre-trained ones in few-shot learning setting.

Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning
Sapan Shah
Sreedhar Reddy and Pushpak Bhattacharyya
TCS Research, Tata Consultancy Services, Pune Indian Institute of Technology Bombay, Mumbai (sapan.hs, www.bhsssseeehh..reeee @tcs.com) pb @ iitb.ac.nn
Abstract
ments with the same emotion label to have similar embeddings the PLMs are cognizant of emotion content. However, as evident from Table 1, the co- sine distance (computed using BERT [CLS] embed- dings) between pairs exhibiting the same emotion is anomalously higher than those between different emotion categories. This observation suggests that PLMs do not adequately capture emotion aspects that are inherently present in natural language text, Incorporating them into PLM representations can significantly benefit NLP applications that are affec- tive nature, such as sentiment analysis, sarcasm detection, empathetic agents, etc.
We present novel retrofitting method to induce emotion aspects into pre-trained lan- guage models (PLMs) such BERT and RoBERTa. Our method updates pre-trained network weights using contrastive learning so that the fragments exhibiting similar tions are encoded nearby in the representation space, and the fragments with different emo- tion content it also ensures that the linguistic knowledge al- ready present in PLMs is not inadvertently per- turbed. The language models retrofitted by method, i.e., BERTEmo and RoBERTaEEmo, produce emotion-awee aware text representations, evaluated through different clustering and retrieval metrics. For the downstream tasks on sentiment analysis and sarcasm detection. they perform better than their pre-trate pre-trained coun- Fl-score)
pushed apart. While doing so,
One straightforward way to inject emotions into PLMs is through transfer learning by fine-tuning them on emotion recognition task. Multi-task learn- ing is also potential alternative wherein the af- fective task of interest is paired with the emotion recognition task and optimized jointly. However, these approaches are not that effective in capturing emotion aspects (see section 5). The community has focussed on using resources such as sentiment lexicons, emoticons, etc., to impart affective as- pects into PLMs, by modifying pre-training ob- jectives such as masked language modeling (Zhou et al., 2020; Aduragba et al., 2021) and next sen- tence prediction (Babanejad et al., 2020). These ap- proaches either learn PLMs from seratch contin- ually pre-train them on domain-specific corpora. A few works (Suresh and Ong. 2021; Yin et al., 2020) have also explored attention based network mod- ules over contextualized embeddings to capture affective semantics. The approaches mentioned above, however, are highly sensitive to the training corpus and the lexical resources used, and do not generalize well across tasks. Retrofitting methods that external knowledge to improve representa- tions have been well explored in the community for static embeddings (Mrkšić al., 2016; Shah al., 2020). There are also attempts to retrofit PLMs to learn robust contextualized word embeddings
terparts (about % improvement and other existing approaches. Additionally, a more significant boost in performance is observed for the retrofitted models over pre- trained ones in few-shot learning setting.
1 Introduction
Despite the emergence of powerful models like GPT4 and LaMDA, lightweight pre-trained lan- guage models (PLMs) such as BERT and RoBERTa remain relevant since they are computationally less expensive and open. Being trained on massive amount of data available over the web, these mod- els are extremely good at encoding general lan- guage properties, resulting in highly accurate word, sentence, and paragraph representations. These representations are then typically fine-tuned for given tasks using task-specific datasets. However, the representations learned using these PLMs general purpose, and they do not capture the af- fective aspects (such as emotions, affects, etc.) of human communication well. Consider a few exem- plar pairs of text fragments, along with the emotion they evoke, in Table We expect the pairs frag-

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Text fragment
Text fragment 2
BERT
Ours
She sounds abusive and narcissistic. Delete your face- book, hit the gym and kill her. -anger
0.2013
0.1085
Worst udvice ever. This would piss me off. -anger
love how this sub gender less -joy
0.0702
0.5254
Glad to see we are still around :). -joy
0.1063
0.1746
I'm glad watched this entire thing. That's incredible. -joy
I'm sorry it happened like that for you. I really can't imagine. -sadness
0.082
0.3429
Table I: Cosine distance between pairs of text fragments exhibiting same and different emotions. With BERT, the distances between text fragments are not in agreement with their emotion content. Our method fixes this by retrofitting BERT for emotions (Ours BERTEmo)
(Shi al., 2019), sentence embeddings (Cai et al., 2022), etc. However, PLM retrofitting relatively less explored for human affects domains.
ensuring that sentences with similar emotions have high cosine similarity and sentences with different emotions have low similarity. The sentence embeddings from BERT and RoBERTa do not have this property (quan- titatively shown in Table 3).
Recently, contrastive learning has been actively explored in self-supervised setting to learn better sentence representations using data augmentation (Gao et al., 2021; Fang et al., 2020). Supervised contrastive learning (SCL) (Khosla et al., 2020) improves further by generating informed train- ing data using label information. SCL. has been shown to learn robust sentence classification mod- els (Gunel et al., 2021; Sedghamiz et al., 2021). It has also been applied to affective tasks such as arousal classification (Pinitas et al., 2022), emotion recognition in conversation (Song et al., 2022), and on. A common theme across all these methods is that SCL has been applied only in single task setting. Despite being fundamentally a representa- tion learning tool, it has neither been explored in transfer learning nor in retrofitting settings.
2. A detailed evaluation (Table 6) showing that the *Emo models perform better than their pre-trained counterparts (about 1% statisti- cally significant improvement in Fl-score) and other approaches, such as transfer learn- ing and multi-task learning, on sentiment anal- ysis and sarcasm detection tasks. In limited data setting, they perform exceedingly better than BERT and RoBERTa, as exemplified by the few-shot learning experiments on senti- ment analysis task (Figure 2).
Related Work
In this work, we present a novel retrofitting method to learn emotion-aware PLMs using su- pervised contrastive learning as transfer learn- ing tool. We use go_ emotions (Demszky et al., 2020), the largest publicly available emotion recog- nition dataset, retrofitting corpus. Our method updates PLM network weights such that the text fragments exhibiting similar emotions are encoded nearby in the representation space, and fragments with different emotion content are pushed apart. While doing so, it also ensures that the linguistic knowledge originally present in the PLM is pre- served. We refer to the resulting emotion-awan models *Emo'_ Our contributions are:
2.1 Affect-aware PLMs
Resources such as sentiment lexicons, emoticons, etc., have been actively used to learn affect- aware PLMs by updating masked language modeling (MLM) objective. For instance, SentiX (Zhou et al., 2020) learns the BERT model from scratch using reviews/ratings (Yelp, Amazon) dataset by increasing the masking probability for sentiment words and emoticons; EmoBERT (Aduragba al., 2021) continually pre-trains by masking emotion- bearing words in tweet dataset; and so on. Unlike MLM, Babanejad et al. (2020) generate emotion- feature vector using EmoLex (Mohammad and Tur- ney, 2013) and use it for the next sentence predic- tion (NSP) task in continual pre-training setting. CARER (Saravia al., 2018) is graph-based ap- proach for learning emotion-aware contextualized representations. SentiBERT (Yin al., 2020) adds an attention network based semantic composition module over BERT representations, and fine-tunes
1. A novel retrofitting method to learn emotion- aware PLMs using supervised contrastive learning (section 4). The *Emo models pro- duce emotion-aware sentence representations
Wee use >Emo to refer to both BERTEmo and
RoBeRTaEmo combinedly.

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it on sentiment analysis task using constituency trees. few approaches use affective resources directly during end-task fine-tuning. For instance, KEA (Suresh and Ong. 2021) uses token-level va- lence, arousal, and dominance scores to learn at- tention weighted sentence representations. The affect-aware approaches described above. however, are highly sensitive to the training corpus and the lexical resources used, and may not generalize well across tasks.
analysis tasks. Unlike using LLMs during infer- ence, Deng et al. (2023) use ChatGPT with CoT prompt to obtain labeled data for sentiment analy- sis and then use the weakly labeled data to learn an accurate student model. This approach, however, has only been tested on social media content in the finance domain.
2.3 Contrastive Learning
Contrastive learning (CL) aims to learn an embed- ding space such that similar data points are mapped close to each other while dissimilar points are pushed apart. Self-supervised contrastive learning uses data augmentation techniques such as lexical editing (Wu et al., 2018), back translation (Fei et al., 2020; Fang et al., 2020), dropout (Gao et al., 2021), cut-off (Shen et al., 2020), etc., to generate similar (positive) data points for the given anchor. The dissimilar (negative) points for the anchor are then selected randomly from in-batch examples. Su- pervised contrastive learning (SCL) (Khosla et al., 2020) takes this idea further by using labeled data to generate positives from the same class as the anchor, thereby providing more variability than data augmentation. SCL has been recently applied to text classification problems, e.g., learning with SCL and cross-entropy (CE) loss (Gunel al., 2021), SCL followed by standard fine-tuning using CE in a pipeline fashion (Sedghamiz et al., 2021). has also been applied for emotion recognition in conversations (Song et al., 2022), affect-infused representations for arousal classification (Pinitas et al., 2022), and so on. However, the approaches described above have applied SCL only in sin- gle task setting. In contrast, we explore SCL transfer learning tool induce emotion aspects into PLMs in retrofitting setting.
Retrofitting is a post-processing method that updates pre-trained network weights to respect constraints extracted from external knowledge re- sources. It is well explored for static embeddings, e-g., retrofitting for relations such as synonymy, and hypernymy (Mrkšić et al., 2016; Shah et al., 2020), for affective lexicons (Shah et al., 2022b,a), and so on. Shi et al. (2019) retrofit PLM in ELMo using paraphrase context to learn robust contextual- ized word embeddings. Other notable approaches include retrofitting sentence embeddings with ab- stract meaning representations in multilingual set- con-
ting (Cai al., 2022), learning ditional mask language model for contextual data augmentation (Wu et al., 2018), and so on. PLM retrofitting, however, is relatively less explored for the human affects domain.
2.2 Large Language Models
The research community has recently been actively applying large language models (LLMs), especially ChatGPT, to sentiment analysis tasks (Zhong et al., 2023; Wang et al., 2023). These works have ex- plored a variety of approaches, including zero- shot and in-context learning, as well as prompt- ing methods such as chain-of-thought (CoT). Al- though these works have considered aspects such polarity shift detection, ispect-based analysis, and sentiment inference, for the standard sentiment classification task, they experiment only with the Stanford sentiment treebank binary classification (SST2 dataset). While the reported results are com- parable to fine-tuned BERT and RoBERTa, the SST2 dataset is a more straightforward, coarse- grained classification task with SOTA accuracy of 97.5%. A more detailed evaluation with fine- grained sentiment classes, non-standard English, etc., required before establishing the efficacy of LLMs. To this end, Zhang al. (2023) perform an cxhaustive set of experiments with ChatGPT using zero-shot and in-context learning on 13 sentiment
Retrofitting Corpus: go go_emotions
To retrofit PLMs for emotions, we need corpus that provides emotion annotations for text frag- ments, i.e., emotion recognition datasets. Such datasets, varying in size, labeling scheme, domain, etc., have been proposed in the field (see Bostan and Klinger 2018 for review). Being the largest publicly available dataset, use so_emotions (Demszky al., 2020) in this work. It provides fine- fine-grained emotion annotations (27 categories) for 54,263 English Reddit comments. The dataset has been carefully created to avoid offensive, iden- tity and religion terms. Though the text fragments

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go emotions
Training batch sampled using,
label
Lid: Supervised NT-Xent loss (with XBM)
2. Encoder:
3. Projection head:
<l am happy for you: joy>
<Text Label>
MPerClassSampler
Text
PLM
CLS
R
R
Streter
Encoder Rxeed
L rap Lupp:Vector Vector Space
4. Loss function:
PLM (frozen)
CLS)
Preservation
I. Lest Aleep
Training data generation
Figure Architecture for learning emotion-awan PLMs in retrofitting setting
Number of examples
58,009
Ig-normalized vector &g E R4. i.c..
Number of raters per example
3
Proj([CIS),) = 24. We consider two variants for Proj: (1) Linear: [CLS], projected linearly to R64,
Number of examples with 2+
54,263
raters agreeing on least label
(2) MLP: [CLS], projected non-linearly 1264 us-
Number of emotions
27+Neutral
ing a single hidden layer size 768.
Number of examples with unique labels
45,446
4. Loss function: In self-supervised contrastive learning, the normalized temperature-scaled cross entropy (NT-Xent) loss proposed by Chen et al. (2020) has been shown to learn robust latent rep- resentations by implicitly providing hard posi- tive/negative mining. Khosla et al. (2020) extended further for SCL by incorporating label informa- tion into the loss function. We use the supervised version the loss function which is as follows,
Table 2: go_ emotions dataset: examples with single emotion label are used as a retrofitting corpus
in the dataset are marked with multiple emotion categories, we only use a subset of 45,446 ex- amples that are annotated with a single emotion label. Table 2 shows the summary statistics for so_emotions.
e(arzp)/T
Led
log PEP()
Σ elecRol/T (1) bCB(1)
P(i)
4 Retrofitting Method
Here, B(i): be BY(i: P(i): {o E B: w and 7: temperature scaling factor. Setting 7 to a low value gives more importance to hard posi- tives/negatives, whereas high value weighs all pairs nearly equally.
The application of supervised contrastive learning our retrofitting method is inspired by (Khosla et al., 2020; Sedghamiz. et al., 2021), albeit applied in a transfer learning setting, Our method also en- sures that the linguistic knowledge already present in PLM network weights is not inadvertently per- turbed. Figure shows the architecture for our retrofitting method.
Vector space preservation (VSP): Being trained on a vast amount of data, PLMs are extremely good encoding lexical, syntactic, and semantic relations, concept similarities, and so on (Jawahar et al., 2019; Lin et al., 2019). We do not want to lose such valuable information while retrofitting them for emotions. While retrofitting literature on static embeddings has recognized and addressed this issue by introducing regularization term that preserves the topology of pre-trained vector space (Mrkšić et al., 2016: Mrkšić al., 2017), such reg- ularization has not been considered by retrofitting methods for PLMs. To address this, we use the following regularization term,
1. Training data generation: We sample train- ing examples from go_emotions to create a mini- batch B of size N (i.e., 3i text fragment; = label). We consider two sam- pling alternatives: (1) MPerClasSSamller sam- ples equal number of examples from each emo- tion category; (2) StratifiedSampler samples ex- amples from an emotion category proportional to the total number of examples of that category in go co__cmoioon.
2. Encoder: The PLM under consideration is treated as an encoder function. It takes text fragments 2; from batch B input and returns the (CLS), embeddings output, i.e.. Enc(x;) [CLS)
Leusp Σ ||]n((;;)) Encixedd (2)
2 Contrastive learning on deep networks generally requires large mini-batch sizes for training stability (Radford et al., 2021; Chen et al., 2020). To support this, we use cross- cross-batch memory (XBM) (Wang et al., 2020) with the loss.
3. Projection head: It maps [CLS); to

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in equal While Enc(-) comes from step 2, Encixee (+) is a fixed version where the network weights from PLM are frozen, providing the snapshot of the PLM before the application of contrastive learning. With this regularization, the network weights in Enc(-) are updated such that the sentence embeddings in [CLS] do not deviate much from their pre-trained version. The final loss function used by our method is then: L Lgg oesp. where À is hyper- parameter that determines how strictly the original pre-trained vector space is preserved.
cation heads, one for the end task under consid- cration and the other for the emotion recognition task. While training, both tasks are given equal weightage by sampling their proportions.
Contrastive methods: with BeRtenne-BERTTRRBBRRTa (SNT) (Reimers and Gurevych, 2019). It fine-tunes BERT and RoBERTa on natural language inference task using Siamese network architecture, with the objective to learn semantically meaningful sentence repre- sentations. Next, we compare our approach with two SCL methods: (1) SCL-Joint (Gunel et al., 2021): proposed for sequence classification tasks, defines the loss function as an affine combination of CE and SCL loss. (2) SupCLSeq (Sedghamiz et al., 2021): a pipelined approach that first updates PLM network weights using SCL loss and dropout- based data augmentation. The updated PLM is then further fine-tuned on end tasks using CE loss. Both methods apply the CE and SCL losses only for the task under consideration, i.e., single-task setting. They do not take any explicit affective signals into account.
We compare our approach
Post training, we discard the projection head. While the encoder Enc(-), with its updated weights, provides us with emotion-aware PLM.
5 Experiments
We evaluate our retrofitting method on two PLMs in BERT (Devlin et al.. 2019) and RoBERTa (Liu et al., 2019) [base, uncased versions). The emotion-aware PLMs retrofitted by our method are referred to *Emo, i.e., BERTEmo and RoBERTaEmo. To select the best hyper-parameter configuration, we consider two aspects: (1) quality #Lmm in learning emotion -wwore embeddings, measured using clustering and retrieval metrics (re- fer section 5.2 for details)
Affect-aware methods: We compare the *Emo models with two affect-aware PLMs: (1) KEA (Suresh and Ong, 2021): enriches contextualized word embeddings using valence, arousal, and dom- inance scores in the NRC VAD lexicon (Moham- mad, 2018). The [CLS) embeddings are first used as a query vector to learn sentence embeddings in terms of attention weighted VAD-enriched contex- tualized embeddings. A classification head over the sentence embeddings is then used to fine-tune end tasks; (2) SentiX (Zhou et al., 2020): learns sentiment-aware BERT from scratch using large- scale review datasets. In addition to MLM and NSP, it adds additional pre-training objectives at to- ken and sentence level using emoticons, sentiment lexicons, and review ratings.
(2) vector space preser- vation: mean cosine distance between sentence em- beddings obtained from =Emo and its pre-trained version (cosine distance 1 cosine similarity; Range= 2D). We first filter vector space pre- serving configurations. From the filtered set, we then choose the configuration with the highest AMT (clustering quality metric) as the best configura- tion. We detail this process and report the complete hyper-paramete grid search in Appendix A.
5.1 Compared Work
The BERT and RoBERTa models are considered pre-trained baseline. For downstream tasks, we add a linear classification head over the [CLS] embeddings and jointly fine-tune both the PLM network weights and the classification head using cross-entropy (CE) loss (referred to as standard fine-tuning).
5.2 Evaluating Emotion-awarenes
The question we posed to evaluate language mod- els for their emotion content is: Do text fragments that evoke the same emotion have similar sentence embeddings? In other words, fragments with similar emotion content clustered together in the embedding space? Our retrofitting corpus (i.e., un- seen test set in go_emotions provides the required test bed for this study. We perform K-means cluster- ing (#means = 28), considering [CLS] embeddings
For transfer learning (TLearn), we first add a classification head over the [CLS] embeddings and perform fine-tuning on the emotion recognition task using the go_emotions dataset. After training for emotions, the updated PLM further fine-tuned on end tasks using a linear classification head. For multi-task learning (MTL), we add two classifi-

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Clustering Metrics
Retrieval Metrics
Representations
Emb
AMI
ARI
FMSt
MRR
P@ 1
MAP@rf
BERT
0.011
0.001
0.076
0.339
0.193
0.043
0
BERT
0.079
0.011
0.089
0.414
0.264
0.06
0.209
SentiXt
0.041
0.006
0.088
0.355
0.207
0.052
1.211
TLEaMBERT
0.378
0.133
0.229
0.625
0.503
0.28
0.941
BERTEmo
0.299
0.102
0.194
0.584
0.446
0.189
0.055
RoBERTa
0.05
0.014
0.101
0.374
0.228
0.051
0
RoBERT 0.117
0.021
0.099
0.436
0.289
0.065
0.731
BeRR 0.402
0.158
0.255
0.635
0.514
0.305
0.945
RoBERTaEmo 0.377
0.142
0.237
0.614
0.488
0.257
0.172
Table 3: Evaluating sentence embeddings for emotion content using clustering and retrieval metries (t: higher values are better). The last column Aemb reports the mean cosine distance between sentence embeddings obtained from emotion-awee aootion-wwwre models and their pre-trained versions. To ensure that the linguistic knowledge already present in PLMs is not inadvertently perturbed, this value should be as low as possible. BERTEmo and RoBERTaEmo are not only emotion-aware (high values for clustering and retrieval metries) but also preserve the topology sentence embeddings space (low values for Aemb). While the compared methods take emotion aspects into account, they are not good at vector space preservation (high values for emm)). {SentiXt: applicable only for BERT)
of text fragments as features. Since the emotion labels are available, we apply external cluster va- lidity indices such as adjusted mutual information (AMI), adjusted rand index (ARI), and Fowlkes Mallows score (FMS) (refer to Scikit-learn user guide) to measure clustering quality. In addition to clustering, we also consider retrieval-base met- rics? such mean reciprocal rank (MRR), preci- sion @1 (P@ I), and mean average precision at (MAP@r). These metrics directly probe the nearest neighbors of given text fragments for their consis- tency in emotion labeling. While retrofitting PLMs for emotions, we want to preserve the lexical, syn- tactic, and semantic knowledge already contained in them. To quantify this property, we compute the mean cosine distance between sentence em- beddings obtained from emotion-awart language models and their pre-trained counterparts (referred as Amtii lower values are better).
prisingly, even though SentiX is trained senti- ment data, which has some affective aspects, it is not good at capturing emotion aspects. The straight- forward way incorporating emotions into PLMs by fine-tuning them on emotion recognition task, i.e., TLearn, drastically improves the clustering and retrieval metrics. However, it excessively alters the topology of sentence embedding space (very high emb)) and may end up overfitting the embeddings for emotions. The * *Emo models retrofitted by our method are not only emotion aware (high values for clustering and retrieval metrics) but also pre- serve the topology of the embedding space (low values for Aemb).
Appendix D shows 2-dim UMAP plots for text fragments in the go_ emotions test set. The frag- ments from all emotion categories are completely interleaved for BERT and RoBERTa. On the other hand, BERTEmo and RoBERTaEmo provide a good separation between different emotion cat- egories. The cosine distances computed using BERTEmo are well calibrated for emotion content, as evident from the exemplar pairs in Table 1.
We investigate" +Emo, SNT, TLearn, SentiX, and their pre-trained counterparts. As shown in Table the BERT and RoBERTa baselines have extremely low scores across metrics. This because emotion aspects have not been explic- itly taken into account during their pre-training phase. The sentence embeddings in SNT slightly improve the clustering and retrieval metrics. Sur-
5.3 Evaluation on Downstream Tasks
We evaluate our method on two affective down- stream tasks: (1) Sentiment analysis Stanford sentiment treebank (sentence level) with both the graded (SST5) and binary (SST2) variants; and SemEval 2017 task 4A (SE) containing tweet mes- sages; (2) Sarcasm detection using Mustard++ dataset (Mus) that contains sit-com utterances.
'refer to Appendix C for details metrics. 'Not applicable for remaining methods: MTL and KEA include emotion signals only during end task fine-tuning; SCI--loint and Seq do not consider emotion signals at all.

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Task
Dataset
#class
size
#token
length
Type
Source
SST5
5
11855
199120
25:11
sentence
(Socher et al., 2013)
Sentiment
SST2
2
9613
162783
25=11
sentence
(Socher et al., 2013)
analysis
SE
3
61854
1174626
25+77
tweet
(Rosenthal et al., 2017)
Sarcasm
Mus
2
1202
14219
15+8
utterance
(Ray et al., 2022)
detection
Table 4: Dataset statistics for downstream tasks: (1) Sentiment analysis on SST5, SST2 and SemEval 2017 task 4a (SE) datasets; (2) Sareasm detection on Mustard++ (Mus) dataset [length= #tokens per instance)
Models
SST5
SST2
SE
SupCLSeq led to slightly improved F1 Fi-scores on both tasks. It should be noted that these approaches 0.508
Mus
BERT 0.342
0.706
0.51
BERTEmo 0.443
0.854
0.63
0.55
have not taken any emotion signals into account during fine-tuning.
RoBERTa 0.366
0.724
0.535
0.571
RoBERTaEmo 0.498
0.881
0.653
0.558
Being pre-trained on large review datasets such as Yelp and Amazon that mainly contain sentiment signals, SentiX attains the highest F1 Fl-sc on sen- timent analysis task (for BERT). However, it does not generalize to other tasks (e.g., inferior results on sarcasm detection). The knowledge (valence, arousal, dominance) embedded approach in KEA unexpectedly does not perform well any task. The PLMs retrofitted for emotions using transfer learning (TLearn) and multi-task learning (MTL) perform better than the pre-trained baselines on both tasks, exhibiting the positive impact the exter- nal emotion signals provide. In this work, we seek to learn emotion-aware PLMs, but not at the ex- pense of displacing existing linguistic knowledge (by overfitting them for emotions). While the VSP loss helps in preserving the topology of the tence embedding space, the contrastive loss brings robustness to our method. This helps Emmo models achieve better results than other approaches, with As 1% statistically significant improvements (p-val < 0.01 for one-tailed student's (-test with sample size 30) over pre-trained basclines.
Table 5: Micro-FI scores for KNN classification using sentence embeddings as features: *Emo models per- form significantly better than pre-trained baselines
Table 4 details the statistics of these datasets.
While showed the intrinsic efficacy of emotion-aware sentence representations in section 5.2, are they effective for downstream tasks? To study this, we learn KNN classifier' for end tasks, treating [CLS) embeddings as fea- tures, Being amotion-aware, the retrofitted * #Emo models achieve significantly better results (>v 10% improvements in Fl-score) than baselines on both tasks (refer Table 5). This suggests that incor- porating emotion content into PLMs is beneficial for end tasks that are affective in nature. Next, we perform fine-tuning experiments where we update all network parameters during training.
Table 6 reports the micro Fi-scores (averaged over 30 runs) on the downstream tasks for the fine- tuning experiments. The standard fine-tuning of BERT and RoBERTa using CE loss seems to be hard baseline to beat for both tasks. The sentence embeddings in SNT improves slightly for the sen- timent analysis task. Though ubiquitous, the CE loss sometimes leads to poor generalization (Liu et al., 2016) and may not be robust to noisy labels (Zhang and Sabuncu, 2018). Therefore it has re- cently been supplemented with the SCL loss either in joint or pipeline architecture. Though the joint learning approach in SCL-Joint could not perform well, we observed that the pipelined architecture in
Comparison with ChatGPT: Zhang et al. (2023) have reported results gpt-3.5-turbo with zero- shot and in-context learning for 13 sentiment anal- ysis tasks. Table 7 compares these results with the fine-tuned RoBERTaEmo (our method). As we can see, RoBERTaEmo performs significantly better than gpt-3.5-turbo on the sentence-level sentiment classification datasets considered in this work. It will be interesting to compare RoBERTaEmo with fine-tuned gpt-3.5-turbo. We will leave this for future work.
"The network weights are fixed only for the KNN classifi- cation experiments. The rest of the experiments in this section jointly update parameters of hoth the PL .M and classification head (i.c,, standard fine-tuning)
5.3.1 Ablation Study
Vector space preservation: Retrofitting methods for static embeddings have consistently used a reg-

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BERT
RoBERTa
Models
SST5
SST2
SE
Mus
SSTS
SST2
SE
Mus
pre-trained
0.5291
0.9103
0.6983
0.5935
0.5591
0.9335
0.7022
0.5708
SNT
0.5356
0.9139
0.6989
0.5731
0.5634
0.9368
0.7064
0.5698
Sep
0.5304
0.9139
0.6834
0.5987
0.568
0.9449
0.6975
0.5768
SCL-Joint
0.5074
0.9125
0.6916
0.5526
0.5572
0.9445
0.6981
0.5014
TLearn
0.5347
0.9136
0.702
0.5869
0.5643
0.9374
0.7078
0.5734
MTL.
0.5294
0.9104
0.6884
0.6041
0.5684
0.9407
0.7006
0.5942
Sentixt
0.5471
0.9186
0.6934
0.5638
KEA
0.5316
0.9060
0.6977
0.5913
0.5492
0.9368
0.7074
0.5828
*Emo
0.5364
0.9141
0.7026
0.6119
0.5688
0.9454
0.7097
0.614
*Emox=0 0.5249
0.9092
0.7010
0.5987
0.5655
0.8417
0.7090
0.5670
Table 6: Micro F1-Scores for fine-tuning experiments on sentiment analysis (SST3, SST2, SemEval 2017 task 4a (SE) and sareasm detection (Mus): compares *Emo with pre-trained baselines in BERT, RoBERTa, and other approaches
(SNT) and SCL methods (SupCLSeq and SCL-Joint) not take explicit emotion sigmals into account; Middle block: transfer learning (TLearn), multi-task learning (MTL), SentiX, and KEA take affective signals into account and are emotion-aware; Last block: emotion-aware models learned by our method. *Emo statistically significantly better than pre-trained versions with p-eal 0.01 as computed using one-tailed student's s f-test (sample size=30). (SentiXt; applicable only for BERT)
highest; Bold: next highest). Top block:
Model
SST5
SST2
SE
(Gunel et al., 2021; Sedghamiz et al., 2021) apply contrastive loss directly on the encoder output, i.c., [CLS] embeddings (or Identity (I) projection head), However, for images, as suggested by Chen et al. (2020), applying contrastive loss directly on the en- coder (ResNet) output inadvertently results in loss of information that may be useful for downstream tasks. To avoid such unintended effects, we first map encoder output to a 64-dim vector space using two projection head variants, i.e., Linear and MLP, as described in section 4. Table shows the F1- scores on end tasks with RoBERTaEmo variants that are learned using different projection heads. The results indicate that the Linear and MLP pro- jection heads perform better than directly using the encoder output".
ChatGPT shot
0.48
0.9360
0.6940
ChatGPT, sew-
0,5187
0.9527
0.6647
RoBERTaEmo 0.5688
0.9454
0.7097
Table 7: Micro-F1 scores on sentiment analysis. The fine-tuned RoBERTaEmoo performs better than Chat- GPT in both zero-shot and few-shot in-context learning
Proj I 0.5677
SST5
SST2
SE
Mus
0.9425
0.7055
0.6059
Linear
0.5694
0.943
0.7105
0.5964
MLP
0.5688
0.9454
0.7097
0.614
Table Micro-Fl scores on end tasks. RoBERTaEmo projection head varied as: I: projection; Linear: (CLS) projected to 6 MLP: [CLS] projected to 166 with a non-linear hidden layer size 768
5.3.2 Few-shot Learning Experiments
We perform few-shot learming experiments on the sentiment analysis task. For all datasets, we first sample train data of various sizes from the orig- inal set such that the #training examples are in [20, 50, 100, 500], keeping the original label dis- tribution intact. We then fine-tune BERTEmo and BERT on these data sizes and compare their micro-F1 scores on the original test set. As shown in Figure 2, the emotion-aware BERTEmo out- performs its pre-trained counterpart BERT across
ularization term to preserve the topology of pre- trained vector space. However, such term has been considered in the context of PLMs. By con- straining sentence representations to be closer to their pre-trained version, The Teep term in our loss function guides weight updates for emotions such that the linguistic knowledge already present in PLMs is not distorted. When retrofitted BERT and RoBERTa without vector space preservation (xEmox=0 in Table 6), the performance both the end tasks consistently deteriorated.
"The MI.P head is more robust. The variance in Fl-scores across multiple runs is relatively lower with MLP than Lincar.
On projection head: The SCL methods for text

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0.9
Limitations
In this work, we have used the go_ ro_emotions dataset primarily due to its large size. The dataset has been created from English Reddit comments. Be- ing retrofitted solely on go__motions, +Emo may contain Reddit-speciie nuances. It will be interest- ing to learn jointly from multiple emotion recog- nition datasets spanning different genres and do- mains such as news, tweets, blogs, health, politics, etc. However, this may also bring in additional complexities. For instance, we do not know which domain or genre should be given more importance, how to handle datasets that vary lot in size, etc.
0.8
0.7
F1-Score
0.6
0.4
Model
1957s, to
cssts, ki
0.3
(se,
idt.
20
50
100
500
#Training Examples
We demonstrated the effectiveness of *Emo models two downstream tasks. We believe these models can generalize to other affective tasks such as hate speech detection, bias detection, and empa- thetic agents. However, difficult to comment on their effectiveness on general NLP tasks such as entity extraction, grammatical error correction, etc. Though the vector space preservation term in our loss function keeps check on the PLM network weights that the existing linguistic knowledge is not inadvertently perturbed, few changes are bound to happen to accommodate the emotion as- pects. It will be interesting to compare *Emo with their pre-trained counterparts on general bench- marks such as GLUE and SuperGLUE.
Figure 2: Few-shot learning experiments on the senti- ment analysis task: BERTEmo (+Y) performs signifi- cantly better than BERT NON)
all datasets by significant margin (similar ob- servation for RoBERTaEmo; refer to Figure 3 in Appendix B). The difference in performance grad- ually decreases with an increase in #training exam- ples. This suggests that the affective knowledge on emotions, as captured by our retrofitting method, helps improve end tasks, especially in few-shot learning scenario.
Ethics Statement
6 Conclusion and Future Work
The go_eggmmmmm emotions dataset has been created from En- glish Reddit comments which are known to contain toxicloffensive language. These comments are also demographically biased toward young male users. Though the creators of go_ go_emotions have taken ex- tensive care in data filtering, pre- pre-processing, and masking steps to address the bias and offense- related issues, the dataset might still inadvertently contain inappropriate content. This might then flow directly into * +Emo models, degrading their quality.
We present novel retrofitting method to learn emotion-aware PLMs in contrastive learning ting using emotion labeling in the go_emotions (Demszky et al., 2020) corpus. It updates PL.M network weights such that the text fragments ex- hibiting similar emotions are encoded nearby in the representation space, and fragments with dif- ferent emotions are pushed apart while preserving the linguistic knowledge originally captured during the PLM pre-training phase. The emotion-awand aware models (+Emo) learned by our method perform bet- ter than their pre-trained counterparts (about 1% improvement in F1 Fl-score) and other benchmarks, with significant gains in few-shot learning setting.
References
Olanrewaju Tahir Aduragba, Jialin Yu, Alexan- dra Ioana Cristea, and Lei Shi. 2021. Detecting fine-grained emotions social media during ma- jor disease outbreaks: Health and well-being fore and during the covid-19 pandemic. AMIA Annual Symposium proceedings. AMIA Symposium. 2021:187-196.
Going forward, we want extend our retrofitting method other affective resources such as the NRC VAD lexicon, emoticons, etc. We also plan to investigate affective content in large lan- guage models such as GPT4.
Nastaran Babanejad, Heidar Davoudi, Aijun An, and Manos Papagelis. 2020. Affective and contextual

---

embedding for sareasm detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 225-243, Barcelona, Spain (On- line). International Committee on Computational Linguistics.
Hao Fei, Yafeng Ren, and Donghong Ji. 2020. Retrofitting structure-awes aware transformer language model for end tasks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Lan- guage Processing (EMNLP), pages 2151-2161, On- line. Association for Computational Linguistics.
Laura-Ana-Marie Bostan and Roman Klinger. 2018.
An analysis of annotated corpora for emotion clas- sification in text. In Proceedings of the 27th Inter- national Conference on Computational Linguistics, pages 2104-2119, Santa Fe, New Mexico, USA. As- sociation for Computational Linguistics.
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple contrastive learning of sentence embeddings. In Proceedings of the 2021 Conference on Empirical Methods Natural Language Process- ing, pages 6894-6910, Online and Punta Cana, Do- minican Republic. Association for Computational Linguistics.
Sven Buechel and Udo Hahn. 2023, Emotion embeddings- learning stable and homogeneous ab- stractions from heterogeneous affective datasets.
Beliz Gunel, Jingfei Du, Alexis Conncau, and Veselin Stoyanov. 2021. Supervised contrastive learning for pre-trained language model fine-tuning. In Interna- tional Conference on Learning Representations.
Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing. and Wai Lam. 2022. Retrofitting multilingual sen- tence embeddings with Abstract Meaning Represen- tation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6456-6472, Abu Dhabi, United Arab Emi- rates. Association for Computational Linguistics.
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019. What does bert learn about the structure of language? pages 3651-3657, Florence, Italy, Asso- ciation for Computational Linguistics.
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Su- pervised contrastive learning. Advances in Neural Information Processing Systems, volume 33, pages 18661-18673. Curran Associates, Inc.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 1597-1607. PMLR.
Yongjie Lin, Yi Chern Tan, and Robert Frank. 2019. Open Sesame: Getting inside BERT's Linguistic Knowledge. In Proceedings of the 2019 ACL Work- shop BlackboxNI.P. Analyzing and Interpreting Neu- ral Networks for NLP, pages 241 253,, Stroudsburg. PA, USA.
Dorottya Demszky, Dana Movshovitz-Attias, Jeong- woo Ko. Alan Cowen, Gaurav Nemade. and Sujith Ravi. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meet- ing the Association for Computational Linguisties. pages 4040 4054, Online. Association for Computa- tional Linguisties.
Weiyang I Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016. Large-margin softmax loss for convolu- tional neural networks. In Proceedings of the 33rd International Conference on International Confer- ence on Machine Learning Volume 48, ICML' 16, page 507-516 JMLR.org.
Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, and Michael Bendersky. 2023. Lims to the moon? reddit market sentiment analysis with large language models. In Companion Proceedings of the ACM Web Conference 2023, WWW '23 Com- panion, page 1014-1019, New York, NY, USA. As- sociation for Computing Machinery.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach.
Jacob Devlin. Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume I (Long and Short Papers), Minneapolis, Minnesota. Associ- ation for Computational I. Linguistics.
Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In International Con- ference on Learning Representations.
L. MeInnes, Healy, and J. Melville. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv e-prints.
pages
Gonçalo Azevedo Mendes and Bruno Martins, 2023, Quantifying valence and arousal in text with multi- lingual pre-trained transformers. In Advances in In- formation Retrieval, pages 84-100, Cham. Springer Nature Switzerland.
Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, and Pengtao Xie. 2020. Cert: Contrastive self-supervised learning for language understanding. ArXiv, abs/2005.12766.

---

Saif M. Mohammad. 2018. Obtaining reliable hu- man ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of The An- mual Conference of the Association for Computa- lional Linguistics (ACL), Melbourne, Australia.
Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natu- ral Language Processing (EMNI.--ICCNLP)) pages 3982-3992, Hong Kong, China. Association for Computational Linguistics.
Saif M. Mohammad and Peter D. Turney, 2013. Crowd- sourcing word-emotion association lexicon. Com- putational Intelligence, 2963):436-465.
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017. SemEval-2017 task 4: Sentiment analysis in Twit- ter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) pages 502-518, Vancouver, Canada. Association for Computational Linguistics.
Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašié, Anna Korho- nen, and Steve Young. 2017. Semantic specializa- tion of distributional word vector spaces using mono- lingual and cross-lingual constraints. Transactions of the Association for Computational Linguistics, 5:309-324.
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018. CARER: Con- textualized affect representations for emotion recog- nition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pages 3687-3697, Brussels. Belgium. Association for Computational Linguistics.
Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thom- son, Milica Gašić, Lina M. Rojas-Barahome Pei- Hao Su. David Vandyke, Tsung-Hsiee Hsien Wen, and Steve Young. 2016. Counter-fitinng word vectors linguistic constraints. In Proceedings of the 2016 Conference of the North American Chupter of the Association for Computational Linguistes: Human Language Technologies, pages 142-148, San Diego, California, Association for Computational Linguis- tics.
Scikit-learn user guide. Clustering performance evalu- ation. Online; accessed 01 O1-February-200
Hooman Sedghamiz, Shivam Raval, Enrico Santus, Tuka Alhanai, and Mohammad Ghassemi. 2021. SupCL-Seq: Supervised Contrastive Learning for downstream optimized sequence representations. In Findings of the Association for Computational Lin- guistics: EMNLP 2021, pages 3398-3403 Punta Cana, Dominican Republic. Association for Compu- tational Linguistics.
Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. 2020a. A metric learning reality check. In Com- puter Vision ECCV 2020, pages 681-699, Cham. Springer International Publishing,
Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. 2020h. Pytorch metric learning. ArXiv,
Sapan Shah, Sreedhar Reddy, and Pushpak Bhat- tacharyya. 2020. retrofitting model for incorpo- rating semantic relations into word embeddings. In Proceedings of the 28th International Conference on Computational Linguisties, pages 1292-1298, Barcelona, Spain (Online). International Committee Computational Linguistics.
abs/2008.09164.
Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis, and Georgios N. Yannakakis. 2022. Su- pervised contrastive learning for affect modelling. In Proceedings of the 2022 International Confer- ence on Multimodal Interaction, ICMI '22, page 531-539, New York, NY, USA. Association for Computing Machinery.
Sapan Shah, Sreedhar Reddy, and Pushpak Bhat- tacharyya. 2022a. Affective retrofitted word embed- dings. In Proceedings of the 2nd Conference of the Chapter of the Association for Compu- tational Linguistics and the 12th International Joint Conference on Natural Language Processing (Vol- ume I: Long Papers), pages 550-561, Online only. Association for Computational Linguistics.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learn- ing transferable visual models from natural language supervision. Proceedings of the 38th Interna- tional Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages
Sapan Shah, Sreedhar Reddy, and Pushpak Bhat- tacharyya. 2022b. Eemotion enriched retrofitted word embeddings. In Proceedings of the 29th In- ternational Conference on Computational Linguis- tics, pages 4136-4148, Gyeongju, Republic of Ko- rea. International Committee on Computational Lin- guistics.
PMLR.
Anupama Ray, Apoorva Nunna, and Pushpak Bhat- tacharyya. 2022. A multimodal corpus for emotion recognition in sareasm. In Proceedings of the 13th Edition of the Language Resources and Evaluation Conference (LREC-2022), Marseille, France.
Dinghan Shen, Mingzhi Zheng, Yelong Shen, Yanru Qu, and Weizhu Chen. 2020. A simple but tough-to-beat data augmentation approach for natu- language understanding and generation. CoRR. abs/2009.13s 13818.
Nils Reimers and Iryna Gurevych. 2019. Sentence- BERT: Sentence embeddings using Siamese BERT- networks. In Proceedings of the 2019 Conference on

---

Weijia Shi. Muhao Chen, Pei Zhou, and Kai-Wei Chang. 2019. Retrofitting contextualized word em- beddings with paraphrases. In Proceedings of the 2019 Conference on Empirical Methods in Natu- ral Language Processing and the 9th International Joint Conference on Natural Language Processing (FMNLP-JJCNLP), pages 199--1203, Hong Kong, China. Association for Computational Linguistics.
Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023. Can chatgpt understand too? comparative study on chatgpt and fine-tuned bert. ArXiv, abs/2302.101198
Jie Zhou, Junfeng Tian, Rui Wang, Yuanbin Wu, Wenming Xiao, and Liang He. 2020. SentiX: A sentiment-aware pre-trained model for cross-domain sentiment amalysis. In Proceedings of the 28th Inter- national Conference Computational Linguisties. pages 568-579, Barcelona, Spain (Online). Interna- tional Committee on Computational Linguistics.
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over sentiment tree- bank. In Proceedings of the 2013 conference on empiricul methods in natural language processing, pages 1631-1642.
Training Details
In this section, we provide the complete hyper- parameter grid search details and the best combi- nations selected thereof. As described in section 4, we treat the PLMs in BERT and RoBERTa as en- coding function Enc(-). The [CLS] embeddings obtained from the Enc(-) function are then passed to a projection head Proj(.). We consider three variants for Proj(.): [Identity, Linear, MLP). For both Enc(.) and Proj(.), we set weight-decay to 0.01 and dropout to 0.1. The temperature factor in NT-Xent loss in eq. 1 is varied as (0.05, 0.1, 0.2}. We set the batch size B to 64, with cross- batch memory in XBM (Wang et al., 2020) varied as 1512, 1024, 2048). The regularizer for vector space preservation loss, X, is varied (0.01, 0.05, 0.1, 0.5). The hyper-parameters relevant for train- ing are: ReLU activation; Adam (Loshchilov and Hutter, 2019) optimizer with Cosine decay and warm-up (3 steps); learning rate varied as ( (5e-06, le-05): and 30 epochs with early stopping using official validation set. The training batches are generated using two sampler variants: MPerClass- Sampler and StratifiedSampler.
Xiaohui Song. Longtao Huang, Hui Xue, and Songlin Hu. 2022. Supervised prototypical contrastive learn- Pro-
ing for emotion recognition in conversation. ceedings the 2022 Conference on Empirical Meth- ods in Natural Language Processing, pages 5197- 5206, Abu Dhabi, United Arab Emirates. Associa- tion for Computational Linguistics.
Varsha Suresh and Desmond C. Ong- 2021. Us- ing Enowledge-embeddedd attention to augment pre- trained language models for fine ane-ggrined emotion recognition. 2021 9th International Conference on Affective Computing and Intelligent Interuction (ACII), pages 1-8.
Xun Wang, Haozhi Zhang, Weilin Huang, and Matthew R Scott. 2020. Cross-batch memory for embedding learning. CVPR.
Zengzhi Wang, Qiming Xie, Zixiang Ding, Yi Feng, and Rui Xia. 2023. Is chatgpt a good sentiment ana- lyzer? A preliminary study. CoRR, abs/2304.04339,
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu, 2018. Conditional bert contextual augmentation. In International Conference Con- ceptual Structures.
For experimentation, used Nvidia DGX A100 GPUs with memory size of 20GB RAM. Each configuration, on average, took 17 minutes to run.
Da Yin, Tao Meng, and Kai-Wei Chang. 2020. Sen- tiBERT: A transferable transformer-basse architec- ture for compositional sentiment semantics. In Pro- ceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 3695- 3706, Online. Association for Computational Lin- guistics.
hyperparameter sampler
BERTEmo
RoBERTaEmo
MPerClass
MPerClass
X temperature T Proj(-)
0.05
0.01
0.05
0.1
Linear
MLP
Wenxuan Zhang. Yue Deng, Bing-Quan Liu, Sinno Jialin Pan, and Lidong Bing. 2023, Sen- timent analysis in the of large language models: reality check. ArXiv, abs/2305. 15005.
batch size
64
64
memory size
1024
512
learning rate
5c-06
5c-06
Zhilu Zhang and Mert R. Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. In Proceedings of the 32nd Inter- national Conference on Neural Information Process- ing Systems, NIPS' 18, page 8792-8802, Red Hook, NY, USA. Curran Associates Inc.
Table The best hyper- syper-parameter configurations for emotion-aware PLMMs: BERTEmo and RoBERTaFmoo
We find the best parrmm-e configuration setting in the following way. Post training, for

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each combination, compute metrics: (1) the clustering metric in AMI to measure the emotion- awareness of the retrofitted PLM; (2) the mean cosine distance between sentence embeddings ob- tained from the retrofitted PLM and its pre-trained version, i.c., Aemb. to measure the vector space preservation quality. We first filter configurations for which emmb is less than 0.1 for BERT and 0.2 for RoBERTa. We then choose the configuration with the highest AMI from the filtered set. We consider the performance on the SST5 dataset (sen- timent analysis task) for tie-breaking, Table re- ports the best hhper--ppaameeeeee parameter configurations for BERTEmO and RoBERTaLimo. To implement our retrofitting method, we used Pytorch Metric Learn- ing library (Musgrave et al., 2020b).
0.9
0.8
0.7
F1-Score
0.6
0.5
0.4
Model
0.3
06. V
(66
20
50
100
500
# #Training Examples
Figure Few-shot learning experiments with the senti- ment analysis task: RoBERTaEmo (+)) performs sig- nificantly better than RoBERTa
B Training Details: Downstream tasks
For downstream tasks, add a linear classification head over the sentence embeddings in [CLS] and jointly fine-tune both the PLM network weights and the classification head using cross-entropy loss. We implement this using Auto Classes? from the huggingface library. The relevant hyper-parameten are: Adam W optimizer with learning rate in (cc-05, 2e-05, 3e-05), dropout=0.1, 1, and batch size=32.
text fragments for their consistency in emotion la- beling. The precision @ metric checks the imme- diate neighbor of the queried text fragment. The mean reciprocal rank (MRR) metric considers the reciprocal rank of the closest fragment with the same emotion label as the query fragment. The mean average precision at (MAP@r) metric con- siders average precision till rank where r is the number of text fragments with the same emotion label as the query fragment. Refer (Musgrave et al., 2020a) for details.
While section 5.3.2 compares BERT with the emotion-awand aware BERTEmo on few-shot learning ex- periments for the sentiment analysis task, here we report results for RoBERTa and its emotion- -wware version in RoBERTaEmo. As can be seen from Figure 3, RoBERTaEmo performs significantly bet- ter than its pre-trained version RoBERTa across all datasets in the limited data setting.
D Evaluating Emotion-awareness: UMAP plots
We visualize 2-dim UMAP (Mclnnes et al., 2018) plots for text fragments in the go_emotions test set, comparing Emo with their pre-trained coun- terparts. We learn the 2-dim embeddings using the umap-learn library with the following hyper- parameter setting: #neighbors=15; min 1i distance metric=Fuclidean. As we can see from Figure 4 and Figure 5, The text fragments from all emotion categories are completely interleaved for BERT and RoBERTa. On the other hand, BERTEmo and RoBERTaEmo provide good sep- aration between different emotion categories.
C Evaluating Emotion-awarence Metries
As described in section 5.2, we use clustering and retrieval metrics for evaluating emotion aware- ness. The existing labeling of text fragments in the go_ emotions test set provides us with true clus- tering. Whereas the clustering induced by the K-means algorithm gives the predicted clustering. The partition of text fragments provided by the true and predicted clustering is then used compute the clustering validity indices such as adjusted mu- tual information (AMI), adjusted rand index (ARI), and Fowlkes Mallows score (FMS) (refer to Scikit- learn user guide). Unlike clustering, the retrieval- based metrics directly probe the neighborhood of
"ecquence classification huggingface

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admiration
anger
approval
suppeinttinn
popproval
disqust
emb massment
soci mocitament
kyy
love
mervocseme Mas
pride
refief
diste0.1
BERT [CI.S] emheddings
admration
amusement
cariing
maniusin
curlostty
desire
diaa-pointment
discpproval
grae
grief
Pry
lova
optinism
realiaation
remorce
sectnss
surp
UMAP WWR.nnneighhnn 15 minddst-0.1
(b) BERTEmo (CLS) embeddings
Figure 4: 2-dim UMAP plots for text fragments in the go_ emotions test set: compares BERT with BERTEmo

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acmretion
amuvettent
anger
Quriosity
desits
dsacpcintment
disepproval
diqust
grat
grtet
joy
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norvousness
apbimiem
Socinoss
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UMW neightess- min distro.1
RoBERTa (CLS) embeddings
edmiration
anger
oyance
speroval
Caring
contusion
curiosity
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disqust
ambhsersooment
eeckommnnt
Reer
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1ew.oone mindist-0.1
(b) RoBERTaFmo [CI.S] emheddings
Figure 5: 2-dim UMAP plots for text fragments in the emotions test set: compares RoBERTa with RoBERTaEmo


Reviews:
[{"review_id": "DdrcsM89Yi", "reviewer": "Reviewer_MQD5", "summary": "", "questions": "Question A: As you mentioned in Line 331, the classifier, as well as the PLM, are jointly optimized. On the other hand, you use the KNN classifier (which means the PLMs are fixed since [CLS] embeddings are regarded as features?) described in Line 454. What is the correct one?\n\nQuestion B: In classification tasks, a simple yet strong baseline is adding a classifier with a single softmax layer and optimising the parameters from the classifier and PLM. Upon this method, the cosine distance between different instances is not as important as claimed in this paper because this method will map the [CLS] embeddings into another space (the output distribution). Thus, how to prove the importance of the cosine distance of [CLS] embeddings?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "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 valuable feedback. We appreciate it and will take it into account as we continue to improve our work. \n### Question A:\n###### The classifier, as well as the PLM, are jointly optimized (Line 331). The main results reported in Table 6 for sentiment analysis and sarcasm detection have both the classifier and the PLM jointly optimized. \n###### With the KNN classifier (Line 454), we wanted to investigate the following: emotion-awareness of PLMs in retrieval scenarios, e.g., fetching similar/alternative sentences from data-store for empathetic agents. We investigated this by comparing pre-trained BERT/RoBERTa with their emotion-aware versions using the KNN classifier. Given a test sentence, we retrieve its k closest neighbors in the embedding space using cosine similarity and use their emotion labels to determine the test sentence's category. Our results clearly show that the emotion-aware embeddings perform much better than their pre-trained counterparts. The PLMs are fixed only for this experiment (Table 5). In this sense, the KNN experiments shall be explained in section 5.2, i.e., \"evaluating emotion-awareness\"\n###### Thanks a lot for pointing this out. We will clarify this and present it better in the revised version.\n\n### Question B:\n###### Agree that it is reasonable to assume that a simple baseline approach of adding a classifier with a single softmax layer and optimizing the parameters from the classifier and PLM would generally work well. After a model is fine-tuned for a given task, the [CLS] embeddings would anyway become task-specific and dataset-specific. In that case, the initial cosine distances between [CLS] embeddings should not matter much post fine-tuning. But this assumption is precisely what we wanted to investigate. The question is: When you start the training, how are the sentences with different emotion categories placed in the vector space (i.e., [CLS] embeddings)? Can you eventually learn better models for affective end-tasks by apriori making the [CLS] embeddings emotion-aware? Our results (Table 6) show that this is indeed the case. The emotion-aware [CLS] embeddings indeed help learn better models for end-tasks compared to the simple baseline. \n\n### On retrofitting data size:\n###### As you rightly mentioned, retrofitting methods do not learn from scratch. They take an existing PLM and update it for the stated objective. This means they do not require as much training data as methods that learn PLMs from scratch. The corpus (go_emotions) that we have used for retrofitting has 58,009 sentences marked for their emotion categories. We believe that this is a sizeable corpus for retrofitting setting, as we are not learning PLMs from scratch.\n\n### On Novelty:\n###### In NLP, supervised contrastive learning (SCL) has been primarily used in a task-specific setting (either in a pipelined or joint-learning fashion). It has not been explored as a representation learning tool. To the best of our knowledge, our work is the first to apply SCL as a representation learning tool for retrofitting language representations.\n###### In retrofitting setting, the SCL loss (NT-Xent) needs to be appropriately regularized to prevent the inadvertent perturbation of linguistic knowledge (semantic relations, concept similarities, etc.) already present in PLMs. The vector space preservation loss introduced in this work provides this much-needed regularization. As reported in Table 6 (last row), an emotion-aware model learned using only the SCL loss does not perform well on downstream tasks (performance comparable to the pre-trained baseline, or even less in some cases). On the other hand, including the vector space preservation loss results in a robust emotion-aware model that outperforms pre-trained baselines as well as other compared work.\n\n### On \"The performance improvement is not significant\":\n###### We experimented with gpt-3.5-turbo (model behind ChatGPT) on the SST5 dataset (sentiment analysis task) in a zero-shot setting. As of 9th June, we obtained an F1-score of 0.498 vs. 0.529 for the pre-trained BERT baseline (mentioned on Line no 576). In fact, a recent paper [1] has reported results for zero-shot and few-shot experiments with gpt-3.5-turbo (model behind ChatGPT) on the same sentiment analysis dataset we have considered. The RoBERTaEmo (our method) has better accuracy than both the zero-shot and the few-shot gpt-3.5-turbo. The following table shows the comparison.\n\t\n\t\t\t\t\t\t\tSST5\tSST2\tSE(SemEval-Twitter)\n\tRoBERTaEmo (ours)\t0.5688\t0.9454\t0.7097\n\tChatGPT[1]\t\t0.48\t0.9360\t0.6940\n\tChatGPT_few-shot[1]\t0.5187\t0.9527\t0.6647\nWe will include these findings in the revised version.\n\n####\n###### Lastly, thank you for your comment on the exhaustive experimentation and appreciating our domain-specific work on emotion-aware language models.\n### References:\n###### [1] Zhang, W., Deng, Y., Liu, B., Pan, S.J., & Bing, L. (2023). Sentiment Analysis in the Era of Large Language Models: A Reality Check. ArXiv, abs/2305.15005.", "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. Orthogonal methods for a specific domain are welcomed.\n2. Experiments are exhaustive and the results are positive on most datasets.", "reasons_to_reject": "1. Retrofitting method is a kind of fine-tuning strategy which is not new. The loss function in this paper is also not new. Therefore the novelty is limited.\n2. The performance improvement is not significant.\n3. The corpus for retrofitting method is not large (compared with other large-scale unlabeled corpus). Therefore this work is more likely to be an adaptation method in the emotional domain.", "questions_to_authors": "Question A: As you mentioned in Line 331, the classifier, as well as the PLM, are jointly optimized. On the other hand, you use the KNN classifier (which means the PLMs are fixed since [CLS] embeddings are regarded as features?) described in Line 454. What is the correct one?\n\nQuestion B: In classification tasks, a simple yet strong baseline is adding a classifier with a single softmax layer and optimising the parameters from the classifier and PLM. Upon this method, the cosine distance between different instances is not as important as claimed in this paper because this method will map the [CLS] embeddings into another space (the output distribution). Thus, how to prove the importance of the cosine distance of [CLS] embeddings?", "paper_topic_and_main_contributions": "This paper proposes a retrofitting method with supervised contrastive learning to retrofit the PLM into an emotional-aware one. The retrofitting objective has two parts: a supervised contrastive loss and a vector space preservation loss. This paper post-processes the PLMs (BERT and RoBERTa) in an emotional corpus utilizing the retrofitting objective and then fine-tuning the new PLM on downstream tasks. The experiment result shows that this method has about a 1% improvement in F1-score compared with baseline methods.", "excitement": 3, "reproducibility": 4, "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": "kWTI4kPS0Z", "reviewer": "Reviewer_tXoc", "summary": "", "questions": "Do you invision the approach scaling across to other languages? Or, even non standard English?\nIs the approach really worth it given the performance discrepancy evident between newer generative models and PLMs?\n\nWhat about other loss functions outside of contrastive? I do feel the choice of loss functions could be better justified.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "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 valuable feedback. It really helps us improve our work. \n\n### On Scaling:\n###### 1. To non-standard English: Yes, it seems to scale. In fact, one of the sentiment analysis datasets (SemEval 2017 Task 4A) we have experimented with has tweets as input. Oftentimes, tweets have non-grammatical sentences and spelling mistakes and variations.\n###### 2. To other languages: There could be multiple threads to explore here: (a) If the target language has a sizeable retrofitting corpus, the proposed approach can be directly applied. (b) Considering English as a source language, retrofitting corpus for the target language can be obtained using an off-the-shelf Machine Translation (MT) system. The quality of the generated corpus will depend on how accurate the MT model is for the given <source, target> language pair. Even when it is reasonably accurate, MT itself may bring in noise. (c) It is worth checking the applicability of multilingual BERT/RoBERTa (e.g., bert-base-multilingual from Google Research). Does a multilingual PLM, retrofitted for emotions using English, accurately scale to other languages? \n###### Thank you for pointing us to the language aspect. It opens up a new line of exploration for us.\n\n### On the choice of loss function:\n###### We wanted to retrofit PLMs using a corpus that provides sentence-level emotion annotations, i.e., input: sequence; output: label. We had initially considered two loss functions: cross-entropy loss and contrastive loss. \n###### We chose contrastive loss for our method for two reasons. First, cross-entropy loss is not robust to noisy data [1] (it may perform poorly if the training data has errors/inconsistencies). Second, cross-entropy loss sometimes does not generalize well [2] due to learning poor margins. Contrastive loss is more robust to noisy data and generalizes better than cross-entropy loss. It focuses on learning the relative distances between different emotion categories rather than the absolute probabilities of each category, making it less sensitive to errors in the training data. We will explain this in more detail in the revised version.\n\n### Comment on newer generative models:\n###### We experimented with gpt-3.5-turbo (model behind ChatGPT) on the SST5 dataset (sentiment analysis task) in a zero-shot setting. As of 9th June, we obtained an F1-score of 0.498 vs. 0.529 for the pre-trained BERT baseline (mentioned on Line no 576). We are currently evaluating it on the remaining datasets. In fact, a recent paper [3] has reported results for zero-shot and few-shot experiments with gpt-3.5-turbo on the same sentiment analysis dataset we have considered. The RoBERTaEmo (our method) has better accuracy than both the zero-shot and the few-shot gpt-3.5-turbo. The following table shows the comparison.\n\t\n\t\t\t\t\t\t\tSST5\tSST2\tSE(SemEval-Twitter)\n\tRoBERTaEmo (ours)\t0.5688\t0.9454\t0.7097\n\tChatGPT[3]\t\t0.48\t0.9360\t0.6940\n\tChatGPT_few-shot[3]\t0.5187\t0.9527\t0.6647\n###### We will include these findings in the revised version. With each passing day, we are witnessing new developments in generative LLMs. Future versions might fare better on affective end-tasks. However, the method proposed in this work is generic and can also be applied to LLMs.\n\n####\n####\n###### Lastly, thank you for your comments on our extensive experimentation. The availability of open source and computationally less expensive PLMs are essential for many domain-specific applications. Thank you for acknowledging our efforts in building such PLMs for the human affects domain.\n### References:\n###### [1] Zhilu Zhang and Mert Sabuncu. Generalized cross-entropy loss for training deep neural networks with noisy labels. In Advances in neural information processing systems, pages 8778–8788, 2018.\n###### [2] Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio. Large margin deep networks for classification. In Advances in neural information processing systems, pages 842–852, 2018.\n###### [3] Zhang, W., Deng, Y., Liu, B., Pan, S.J., & Bing, L. (2023). Sentiment Analysis in the Era of Large Language Models: A Reality Check. ArXiv, abs/2305.15005.", "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": "Openly available and computationally less expensive PLMs can be retrofitted to outperform other established approaches to making the models more affect aware. The experiments are extensive, valid, and well performed.", "reasons_to_reject": "The only potential reason would the lacking comparison between these retrofitted models and newer models e.g. GPTs.\n", "questions_to_authors": "Do you invision the approach scaling across to other languages? Or, even non standard English?\nIs the approach really worth it given the performance discrepancy evident between newer generative models and PLMs?\n\nWhat about other loss functions outside of contrastive? I do feel the choice of loss functions could be better justified.", "paper_topic_and_main_contributions": "The paper proposes retrofitting to improve affect aware representation of well established PLMs. Multiple experiments are performed to support the improvement seen in PLM through the proposed retrofitting. ", "excitement": 4, "reproducibility": 4, "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": "Nothing major missing perhaps approaches not involving PLMs.", "Typos_Grammar_Style_and_Presentation_Improvements": "Nothing major bug a few typos and edits needed."}, "extra_text": {}}, {"review_id": "MLjKIuZNyE", "reviewer": "Reviewer_HtpC", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "###### We would like to start by thanking you for your positive comments on our experimentations and well-written content. We have carefully considered your comments in the \"Reasons to Reject\" section and have provided point-wise responses below.\n\n### On Innovation:\n###### In NLP, supervised contrastive learning (SCL) has been primarily used in a task-specific setting (either in a pipelined or joint-learning fashion). It has not been explored as a representation learning tool. To the best of our knowledge, our work is the first to apply SCL as a representation learning tool for retrofitting language representations.\n###### In retrofitting setting, the SCL loss (NT-Xent) needs to be appropriately regularized to prevent the inadvertent perturbation of linguistic knowledge (semantic relations, concept similarities, etc.) already present in PLMs. The vector space preservation loss introduced in this work provides this much-needed regularization. As reported in Table 6 (last row), an emotion-aware model learned using only the SCL loss does not perform well on downstream tasks (performance comparable to the pre-trained baseline, or even less in some cases). On the other hand, including the vector space preservation loss results in a robust emotion-aware model that outperforms pre-trained baseline as well as other compared work.\n\n### Comparison with latest SOTA:\n###### We experimented with gpt-3.5-turbo (model behind ChatGPT) on the SST5 dataset (sentiment analysis task) in a zero-shot setting. As of 9th June, we obtained an F1-score of 0.498 vs. 0.529 for the pre-trained BERT baseline (mentioned on line no 576). We are currently evaluating it on the remaining datasets. In fact, a recent paper [1] has reported results for zero-shot and few-shot** experiments with gpt-3.5-turbo on the same sentiment analysis dataset we have considered. The RoBERTaEmo (our method) has better accuracy than both the zero-shot and the few-shot gpt-3.5-turbo. The following table shows the comparison.\n\n\t\t\t\t\t\t\tSST5\tSST2\tSE(SemEval-Twitter)\n\tRoBERTaEmo (ours)\t0.5688\t0.9454\t0.7097\n\tChatGPT[1]\t\t0.48\t0.9360\t0.6940\n\tChatGPT_few-shot[1]\t0.5187\t0.9527\t0.6647\nWe will include these findings in the revised version.   \n###### ** The few-shot results on gpt-3.5-turbo vary across 1-shot, 5-shot, and 10-shots with no indication of improved results with increase in #few-shots.\n\n### Related Work - no paper from 2023:\n###### We have systematically scanned all 2023 papers on ACL Anthology. The following table shows the #publications containing the word 'sentiment' in the paper title across ACL venues (2023).\n\t\t\tACL+Findings\tEACL\tSemEval\t\tstarsem\tTACL\tOtherWorkshops/Events\n\t#papers\t66\t\t9\t29\t\t1\t1\t38\n###### For sentiment analysis (SA), the research community has been mainly focussing on the following sub-topics: Multi-modal SA, Aspect-based SA, User-Product-based SA, SA for low-resource/language setting, and African SA (due to SemEval 23 shared task). However, in the current work, our focus is on the standard multi-class sentiment classification task. This is relatively less explored at ACL venues (2023), because of which we could only find one related 2023 paper from the anthology [2]. However, that paper is also  contextually not relevant to our work as it mainly focuses on designing prompt templates. We will explore other non-ACL venues and include a comparison if we find a suitable related work. \n\n### On space utilization (comment that the paper spends too much space on subsection 5.1, Table 4 ...):\n###### We will look at this again to see what we can improve. We will consider moving Table 4 to Appendix given that the datasets for sentiment analysis and sarcasm detection tasks are widely known.\n\n### On Reproducibility:\n###### 1. Hyper-parameter setting: We have provided the complete hyper-parameter grid search details, including network architecture, loss functions, and optimizer parameters, in the Appendix A (including the hardware details and runtimes). It also reports the best hyper-parameter configurations. To the best of our knowledge, we have discussed all relevant parameter details. However, if the details are inadequate on some of the parameters, we will be happy to expand.   \n###### 2. Training/evaluation data: The corpus used for retrofitting (i.e., go_emotions) and the datasets used for downstream tasks are widely available (on author websites, huggingface/datasets). We have cited the respective papers and used the author suggested train/validation/test splits. \n###### 3. The micro-F1 scores reported in Table 6 are averaged over 30 runs. The emotion-aware models retrofitted by our method perform better than pre-trained BERT and RoBERTa with statistical significance (p-value < 0.01) as computed using one-tailed student's t-test (sample size = 30) (Line no 502).\n\n### On Soundness:\n###### The claims/arguments in our paper are supported by extensive experimentation, including ablation study (Sec. 5.3.1) and few-shot learning experiments (Sec. 5.3.2). The retrofitting method in Section 4 provides a clear explanation of the training data generation, neural network architecture, loss functions, and regularizer. It would be helpful if the reviewer could point to any specific technical or methodological problems with our work.\n\n####\n####\n###### In view of the point-wise responses mentioned above, we respectfully request that the reviewer consider increasing the score.\n###### Lastly, thank you for your valuable feedback. We appreciate your suggestions, as they will help us improve our work. \n\n### References:\n###### [1] Zhang, W., Deng, Y., Liu, B., Pan, S.J., & Bing, L. (2023). Sentiment Analysis in the Era of Large Language Models: A Reality Check. ArXiv, abs/2305.15005.\n###### [2] A Simple Yet Strong Domain-Agnostic De-bias Method for Zero-Shot Sentiment Classification (Zhao et al., Findings ACL 2023)", "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 authors conduct sufficient experiments to verify the effectiveness of their method.\n\n2. This paper is clearly organized and well-written. The description of the method is easy to understand.", "reasons_to_reject": "1. The method in this paper is limited in innovation.\n\n2. The authors did not compare their method with the latest SOTA.\n\n3. The papers cited by the authors in the related work section lacks the most recent papers (there is no paper for 2023).\n\n4. I don't think it is necessary to spend too much space on the description of some existing work, such as subsection 5.1 and Table 4. Instead, the space should be devoted to the analysis of experimental results.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper presents a novel retrofitting method to induce emotion aspects into pre-trained language models, which updates pre-trained network weights using contrastive learning.", "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": {}}]

Author rebuttal:

Meta-review: The reviewers agree that this is a solid paper which contains a comprehensive evaluation. The evaluation results indicate that the proposed method works well.  

The main reserve expressed by the reviewers is the limited novelty of the method. However, the authors' answer emphasise the fact that even though supervised contrastive learning has been used before, this is the first time when it is used as a representation learning tool. In response to questions from the reviewers, the authors have experimented with GPT-3.5-turbo in a zero shot setting for sentiment analysis. This information can be included in the paper if it is accepted. 

The reviewers ask a few other questions related to methodology/technical details, which are successfully answered by the authors. In response to one of the reviewers' observation that there are no papers from 2023 in the related works section, the authors have conducted a quick survey of work from 2023 from venues other than ACL. This information can be added in the final version of the paper. 

Overall, the reviewers indicate that this is a strong and interesting paper.
Decision: Accept-Main