Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms
Authors: Meghdut Sengupta
Milad Alshomary
Ingrid Scharlau
Henning Wachsmuth
Venue: EMNLP 2023 Findings (2023)
Keywords: Metaphor
Highlighted Aspect
Source Domain
Multitask Learning
Contrastive Learning

Abstract: Metaphorical language, such as ``spending time together'', projects meaning from a source domain (here, $\textit{money}$) to a target domain ($\textit{time}$). Thereby, it highlights certain aspects of the target domain, such as the $\textit{effort}$ behind the time investment. Highlighting aspects with metaphors (while hiding others) bridges the two domains and is the core of metaphorical meaning construction. For metaphor interpretation, linguistic theories stress that identifying the highlighted aspects is important for a better understanding of metaphors. However, metaphor research in NLP has not yet dealt with the phenomenon of highlighting. In this paper, we introduce the task of identifying the main aspect highlighted in a metaphorical sentence. Given the inherent interaction of source domains and highlighted aspects, we propose two multitask approaches - a joint learning approach and a continual learning approach - based on a finetuned contrastive learning model to jointly predict highlighted aspects and source domains. We further investigate whether (predicted) information about a source domain leads to better performance in predicting the highlighted aspects, and vice versa. Our experiments on an existing corpus suggest that, with the corresponding information, the performance to predict the other improves in terms of model accuracy in predicting highlighted aspects and source domains notably compared to the single-task baselines.

Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms
Meghdut Sengupta'
Milad Alshomary'
Ingrid Scharlau
Henning Wachsmuth
Leibniz Universität Hannover PPaderborn University
m.senggpta, m mn.alshomar, h. wachsmuth) ) @ uniuhhannover.de
iingid.scharlar @ uni-paderbomn.de
Abstract
(8) Continual learning
Metaphorical language, such "spending time together", projects meaning from source do- main (here, money) to a target domain (rime), Thereby, it highlights certain aspects of the target domain, such as the effort behind the time investment. Highlighting aspects with metaphors (while hiding others) bridges the two domains and is the core of metaphorical meaning construction. For metaphor interpre- tation, linguistic theories stress that identify- ing the highlighted aspects is important for a better understanding of metaphors. How- ever, metaphor research in NLP has not yet dealt with the phenomenon of highlighting. In this paper, introduce the task of identify- ing the main aspect highlighted in a metaphori- cal sentence. Given the inherent interaction of source domains and highlighted aspects, propose two multitask approaches joint learning approach and continual learning ap- proach based on a finetuned contrastive learn- ing model to jointly predict highlighted aspects and source domains. We further investigate whether (predicted) information about a source domain leads better performance in predict- ing the highlighted aspects, and vice versa. Our experiments on an existing corpus suggest that, with the corresponding information, the perfor- mance to predict the other improves in terms model accuracy in predicting highlighted as- pects and source domains notably compared to the single-task baselines.
fine-tuning phase
contrastive learning encoder
source
domain
metaphorical sentence
best model
fine-tuning phase 2
contrastive learning
highlighted aspect
(b) Joint learning
source domain
metaphorical sentence
contrastive learning encoder
allemating fine-tuning
highlighted
aspect
Figure 1: Proposed multitask approaches predicting the source domain and highlighted aspect of a metaphor- ical sentence: (a) A contrastive learning encoder is fine- tuned continually, first for source domains and then for highlighted aspects (or vice versa).
(b) The encoder is fine-tuned jointly on both an alternating fashion.
as sarcasm, a central challenge with metaphors is that the implicit intended meaning differs from the meaning of the explicit metaphorical expres- sion (Goatly, 1997; Allott and Textor, 2022). The context provided by the neighboring words in the sentence an indicator of this implicit meaning (Stern, 2000). However, for better comprehending the meaning manifested by a metaphor in a given context, further levels of understanding are needed; for example, information about the source and tar- get domain (Lakoff, 1987; Johnson and Pascual- Leone, 1989; Robins and Mayer, 2000). Consider the following metaphorical sentence:
1 Introduction
A metaphor can be defined as a cross-domain con- ceptual mapping from a source domain to target domain (Lakoff and Johnson, 2003). The abun- dance in which metaphors occur in everyday lan- guage (Gibbs, 1992; Ortony, 1993), such as "win- ning someone's heart" or "tax evasion"' presents need for metaphor interpretation and, to that end, the computational decoding of metaphors. Much like in other forms of figurative language, such
"Excessive tax is killing American family business"
Here, the word "killing" is used metaphor, since business tax in physical realms. The intended meaning is manifested by establishing the mapping of two con- ceptual domains, drawing the meaning from the source domain (physical harm) and projecting the
concept cannot be killed by

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meaning into the target domain (laxation). In this metaphorical context, the word killing is the literal representative of the metaphorical meaning con- struction. For simplicitly, we henceforth refer to this word as the literal metaphor.
baseline for both labels. Our results indicate that, in almost all cases, the continual learning approach
outperforms a single-task setup, indicating that the combined information of source domains and high- lighted aspects benefit the models to predict either of them. Our analysis of the results suggests that continual learning particularly learns to differenti- ate between single source domains and a composite source domains well.
According to past research on metaphors (Lakoff and Johnson, 2003; Wolf and Polzenhagen, 2003; Andriessen, 2008; Maxwell, 2015), the core idea of metaphor is to highlight certain aspects of its tar- get domain while hiding others; both aspects have largely been disregarded so far in NLP. The more apparent and deliberate of these is highlighting. In the example above, a highlighted aspect may be threat, since the lax poses a threat in terms of the economic distress it can cause.
Related Work
Highlighted and hidden aspects of metaphors con- tribute to the implicit intention conveyed by the metaphor in the given context. As we discuss in the following, different past research has utilized the source and the target domains in the compu- tational analysis of metaphors. To the best of our knowledge, however, no one has investigated how aspects are highlighted by metaphors yet.
Our research builds on the distinction between source and target domains in line with existing re- search on computational metaphor interpretation (Stowe al., 2021). Beyond prior work, however, we argue based on aforementioned linguistic theo- ries that is also important to have an understand- ing of the aspects highlighted by a metaphor in a given context for better metaphor interpretation. To fill this gap, we provide the following contributions in the paper at hand:
Shutova et al. (2013) defined an ideal metaphor processing system for NLP applications to con- sist of two components: metaphor detection and metaphor interpretation. In line with their work, Shutova al. (2012) previously identified metaphors to then model metaphor interpretation as a paraphrasing task (Witteveen and Andrews, 2019). Similarly, Mao et al. (2018) used WordNet (Miller, 1995) to explore the contextual domains of metaphors to then successively identify and in- terpret metaphors as use case for machine trans- lation. The authors also design the interpretation step as a paraphrasing task.
We assess for the first time how and to what ex- tent the aspects highlighted by metaphor can be predicted computationally. In particular, study the hypothesis that, by concurrently considering source domain and highlighted aspects and by effectively exchanging infor- mation between them, we can enhance their identification. In simpler terms, we investi- gate whether a joint modeling of source do- mains and highlighted aspects improves their predictability.
The majority of computational research on metaphors in natural language has focused on metaphor identification so far, where the task is usu- ally treated as a binary classification task: Given an input word or sentence, decide whether the mean- ing it represents is metaphorical literal (Steen, 2010: Li et al., 2013). One of the pioneering works approuched the identification with unsupervised spectral clustering techniques based on relevant parts of speech, such as nouns and verbs (Shutova et al., 2010). Using seed phrase to learn sim- ilar metaphors associated with particular source domains, the authors adapt the conceptual map- ping from one domain to the other. More recent approaches include that of Li al. (2023) which employs FrameNet (Ruppenhofer al., 2016) identify metaphors where a RoseRTa-based pre- trained language model (Liu et al., 2019) is fine- tuned to encode contextual cues of the concepts
To implement our hypothesis, we develop two multitask contrastive learning approaches to the most highlighted aspect and the source do- main in metaphorical sentences as illustrated in Figure 1: one using continual learning, the other using joint learning. We analyze whether, in this setup, involving the informa- tion of the highlighted aspects benefits the prediction performance of the model on the source domains, and vice versa.
Given the corpus of Gordon et al. (2015) with metaphorical sentences annotated for highlighted aspects and source domains, we evaluate different variations of our approaches against a single-task

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Our first approach follows the idea of continual learning, illustrated Figure (a). Research has shown that continual learning can be helpful in a va- with multitask learning. We start from our con- trastive learning approach from previous work that we shortly summarize in the following subsec- tion. Then, we propose two alternative multitask schemes for the prediction on this basis: continual learning and joint learning, A comparison of the two variants is shown in Figure 1.
(a)
(b)
source domain
highlighted
espect
source
domain
highlighted aspect
metaphorical sentence
metaphorical sentence
3.1 Contrastive Learning for Metaphor Interpretation
contraslive learning
Original embedking space
Leamed embedding space
As discussed in Section 2, Sengupta et al. (2022) demonstrated that modeling similarities between metaphorical sentences and their corresponding source domain through contrastive learning boosts the effectiveness trained models on the source domain prediction task. The core idea is to learn an embedding space where the given input sentence representation and the representation of the most similar source domain is trained to be situated close to each other and the less likely source domain be situated further apart.
Figure 2: Learning to predict the source domain and highlighted aspect for given metaphorical sentence: (a) In the original emhedding space, the similarity of the sentence to the correct domain and aspect may be not be high enough. (b) In the learned space, their similarity is increased, and it is decreased for others. We exploit the intrinsic semantic similarity of source domain and highlighted aspect through multitask learning.
in the data from FrameNet for enhanced model performance. First they obtain sentence repre- sentation with encoder. Next, they employ separate encoder to form FrameNet based represen- tations of the concepts associated with the given literal metaphor. Finally they combine them with the sentence representations to obtain a joint repre- sentation, which is passed through a softmax layer for the final prediction. Unlike all these works, research targets the broader interpretation of metaphors by exploiting components of metaphori- cal meaning construction, like source domains and highlighted aspects.
For the work at hand, we extend their idea to both source domains and highlighted aspects. particular, our hypothesis underlying the multitask approaches presented below is that the mutual se- mantic relations between source domains and high- lighted aspects can be modeled in the learned embedding space for their joint prediction. We illustrate this idea in Figure 2.
3.2 Continual Learning
Based on the founding work of Lakoff and John- son (2003) previous work in metaphor interpre- tation has explored the source and target domains with the usage of FrameNet (Stowe et al., 2021) and in prediction of source domains (Sengupta al., 2022). In the latter, they have proposed to predict source domains in given metaphorical sentences using contrastive learning (Zhang et al., 2022). We incorporate their contrastive learning idea our approach and evaluate on the same metaphor cor- pus (Gordon et al., 2015). However, not only is our main goal to predict highlighted aspects, but we also exploit the semantic connection between source domains and highlighted aspects using mul- titask learning.
riety of downstream tasks with regard to the perfor- mance improvement of machine learning models (Ke et al., 2021).
Fundamentally, continual learning is a machine learning technique where principally model is trained on different tasks in a sequential order (Had- sell al., 2020; Scialom et al., 2022b). This pro- cess directly preserves the information obtained the first task and leverages that information in the next task where the model is finetuned (Scialom et al., 2022a).
We adapt this technique to train our models on the task of predicting the source domains and high- lighted aspects sequentially. In particular, given a of metaphorical sentences as input, we first fine- tune Sentence-BERR (Reimers and Gurevych, 2019) with DeBERTa (He et al., 2020) as the en- coder to learn the most similar source domains.
3 Approach
this section, we present how to predict both source domains and highlighted aspects jointly

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Metaphorical Sentences
Source Domains
Highlighted Aspects
Sentence
Metaphor
Sre. Domain
Dataset
The sad is with the fighting ception of very few firearm organisation doing anything of the slightest value in fight- ing gun control.
Struggle, War
Training set Validation set
1000
120
61
128
56
33
Test set
301
81
49
Full corpus
1429
138
78
This is the historical context victory of Obama's 's election victory.
Competition. Game, War
Table I: The distribution of metaphorical seniences in our experimental setup along with the numbers of source domains and highlighted aspects the sentences refer to.
They attack ""rich people"" attack while enjoying all the spoils of their luck, have zero prob- lems with carned wealth, but these clowns literally lucked out in life.
War
Next, we take the best-periiee pest-performing model from this training phase on the validation set and con- tinue fine-tuning it to predict the most similar high- lighted aspects for the same set of sentences. Both training phases employ contrastive learning (as discussed in Section 3.1) by using the multiple- negatives ranking loss (Henderson al., 2017) like Sengupta et al. (2022) where for a sentence repre- sentation a and a given correct highlighted aspect b for every positive pairs (a,, b.), for negative pair a; for every highlighted aspect bj. j + i, if ||| = B] and S is the similarity score com- puted from the sentence embeddings for the given pairs, the loss is computed as:
Table 2: Example sentences from the dataset having one or more than one concepts grouped as the source domain. The table is reused from Sengupta et al. (2022).
Gurevych (2019), where the training phase is opti- mized round-robin way. For given metaphor- ical sentence, the loss first computed for learning the correct source domain representation. Next, it is backpropagated to compute the loss for learning the representation of the highlighted aspect.
Hence, the training happens in alternating fashion where information about the highlighted aspects and source domains is mutually exploited via a shared encoder with hard parameter sharing (Ruder, 2017). For comparability to the continual learning approach, we employ the same encoder to form the representations in this case well.
C(a, b)
log P Papprox(b,los
S(a;, bi) - log
e b(a),)))
=
Data
For our experiments, we employ the corpus of Gor- don et al. (2015), which is to our knowledge the only metaphor corpus so far that is annotated for both source domains and highlighted aspects. In the following, briefly summarize its characteris- ties and the dataset splits use.
This loss function optimizes the embedding space such that the representation of the correct aspect highlighted and the representation of the sentence are positioned closer to each other while distancing the representation of the sentence from the incor- rect concept representations as shown figure Similarly, we employ the same technique the other way round to first learn on highlighted aspects in order to then predict source domains.
4.1 Corpus
The corpus of Gordon et al. (2015) consists 1771 metaphorical sentences collected from press releases, news articles, weblog posts, online fo- rum discussions, and social media. Each sentence is annotated for several concepts including the source domain, the target domain, and the literal metaphor. For each given metaphorical sentence, highlighted aspect is annotated (referred schema slot in the data). For example, threat is an aspect highlighted by some sentences with the source domain addiction.
3.3 Joint Learning
Our second approach follows the idea of joint learn- ing, as illustrated in Figure (bb). The approach employs joint-lcarning technique where, at the core, it is trained via contrastive learning optimized with multiple-negatin
ranking loss, described in the previous subsection.
We adapt the multitask training procedure of the Sentence-BER architecture of Reimers and

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4.2
Datasets
with the literal metaphor, with the corresponding single-task baseline setups.
We use the same dataset partitions as Sengupta et al. (2022). To obtain clean experimental setting, the authors combined multiple source domains that were assigned to the same sentence into a single source domain (composite source domains) and removed duplicate sentences in the data to finally have 1429 instances. So for example, if a metaphor- ical sentence had multiple source domains such competition, game, and war annotated, in their work the combination competition+ aaappetttoonn-m++++++-- game+ war was treated as a single source domain, which was a dif- ferent source domain from, for example, competi- tion as hown in Table
We compare our multitask approaches to single-task approach, namely use the con- trastive learning approach of Sengupta al. (2022) here, trained separately for each task.
Baseline
In this setup, during training, the input sentence is first provided to the encoder order to obtain the input sentence representation. Similarly, the corresponding label (highlighted aspect or source domain depending on the downstream task) is pro- vided input to the same encoder to get the label representations and hence the corresponding weights are shared.
In the of highlighted aspects, the only com- bination present enemy/side. For consistency in the task design, we also treat it as separate label. So, with 70-30 train-test split, for each of our experiments, we have 1000 training samples, 128 validation samples, and 301 test samples.
After that via contrasting learning, the input sen- tence representation is compared to all the incorrect label representations with paired cosine distance where the training procedure is optimized with mul- tiple negatives ranking loss which learns the em- bedding space of the representations such that the given input sentence and the correct label are situ- ated in close proximity in the embedding space. At inference, the model receives an input sen- tence in the encoder and forms the representation as stated before. Then it compares the sentence representation with all the labels present in the cor- pus for the corresponding downstream task, with paired cosine distance, and ranks all the labels in the order of their similarity. Finally, the top-ranked (most similar) label is chosen be the prediction.
Table shows the data distribution in the final corpus for our experiments. As shown in table the experiment corpus has 1429 with 138 source domains and 78 highlighted aspects respectively emphasizing the sparse label distribution for the downstream tasks of predicting source domains and highlighted aspects. This sparsity is intrinsic to metaphorical language due to its strong diversity.
5 Experiments
For our experiments with the metaphor appended to the input, we perform the training and inference similarly with the literal metaphor appended to the input sentence with with separator token <SEP> in both the cases.
This section describes setup of the experiments we carried out on the data from Section 4 to study the effectiveness of the two proposed multitask learning approaches from Section 3. We present our basic experimental setup, before give details on the single-task baseline that we compare as well as on the two approaches.!
Contrastive Learning Within the contrastive learning setting, we use DeBERTA (He et al., 2020), an enhanced encoder built on top of RoBERTa (Liu et al., 2019), which relies on a dis- entangled attention mechanism and has shown suc- cess in recent NLP research (Zhao et al., 2022). We employ it the encoder for sentence-transffrmmess in all evaluated model configurations.
Task Input A limitation of the approach of Sen- gupta et al. (2022) is that it appends the literal metaphor annotated in the corpus of Gordon al. (2015) to the input metaphorical sentence (with separator token <SEP> in between).
In real-world setting this information may not be available. In our experiments, we predict source domains and highlighted aspects without the literal metaphor as input. However, to see the effective- ness of our approaches, we also report on the results
Multitask Learning To optimize the models of our multitask learning approaches, we performed a hyperparameter search over batch sizes from (4, 8), learning rates in {2 10-5,3 3 10-5 10-3 5 10-53, }, and epochs in {4, 5,
To find the best checkpoint per experiment, we ran each model on each combination over the hy-
'The source code of our experiments can he found here: https:// thw.bubbbtttttbt//e/ee wtm/wwbis-weee det/sdddd///P2222

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Highlighted Aspects
Source Domains
Task Input
Approach
Acc@1 1
Acc@3
Acc@5
Acc@1 1
Acc@3
Acc@5
Metaphorical sentence
Single-task baseline
0.524
0.767
0.831
0.488
0.718
0.797
without literal metaphor
Joint learning (ours)
0.491
0.734
0.847
0.455
0.664
0.734
Continual learning (ours)
0.535
0.767
0.867
0.522
0.718
0.781
Metaphorical sentence with literal metaphor
Single-task baseline
0.581
0.837
0,904
0.522
0.748
0.837
Joint learning (ours)
0,508
0.800
0.910
0,508
0.730
0.827
Continual learning (ours)
0.522
0.817
0.905
0.571
0.764
0.821
Table 3: Evaluation of our two multitask learning approaches in predicting highlighted aspects and source domains.
Without the ground-truth literal metaphor appended to the metaphorical sentence, continual learning outperforms the single-task baseline in both tasks. With the metaphor appended to the input, the ground bround-truth information seems to partly outweigh the impact of multitask learning. The best result per block in each column is marked bold.
perparameters for 20 iterations on the validation set. We then evaluated the optimal configuration in each case on the test set.?
points from (0.524 to 0.535). While the top-3 curacy the same as the baseline, the top-5 accu- racy surpasses it clearly (by 3.6 points), suggesting better overall representational capture continual learning. A value of 86.7 means that it manages to put the right highlighted aspects into the top-5 in almost 7 out of 8 cases.
Metrics Given that the contrastive learning ap- proach creates a ranking, we evaluate all models in terms of top-1, top-3 and top-5 accuracy.
Here, top-1 accuracy means that only the highest-ranked output (that is, a highlighted aspect source domain, respectively) is chosen to be correct. top-3 accuracy and top-5 accuracy, the output is seen as correct, it within the first three and first five ranks, respectively.
With Literal Metaphor as Input Given ground- truth information the literal metaphor, the im- pact of multitask learning largely disappears (ex- cept for top-5 accuracy, where joint learning is strongest with 0.910). This suggests that the infor- mation about the literal metaphor suffices to tackle the task with the straightforward single-task model, while the multitask setting might increase complex- ity unnecessarily.
6 Results
The main results of our experiments shown in Table 3. Overall, our continual learning approach largely outperforms both the single-task baseline and the joint learning approach in predicting both highlighted aspects and source domains. This sug- gests that the information of one of the concepts improves model performance on the other.
6.2 Source Domains
Again, we look at the two task variations one after the other.
The limited performance of the joint learning approach may speak for that learning on two tasks concurrently confused the model in some cases rather than helping it to learn both tasks better. The following subsections discuss our results in detail.
Without Literal Metaphor Input In the case of predicting source domains, the continual learn- ing setup outperforms the single-task baseline with an overall top-1 accuracy increment by 3.4 points (0.522 vs. 0.488), while does not seem to help in the case of top-3 and top-5 accuracies.
6.1 Highlighted Aspects
We discuss the results separately for the two task variations: given only the metaphorical sentence as input, and additionally given the literal metaphor.
With Literal Metaphor as Input Finetuning our model to predict source domains in the set- ting where literal metaphors are added to the input improves the accuracy by 4.9 points, consistent to the case without the metaphor added the input. The high gain over the single-task baseline indi- cates that the direct relationship of the metaphor with its source domain further benefits the learning capabilities of the model.
Without Literal Metaphor as Input Compared to the single-task baseline, the application of con- tinual learning improves the t accuracy by 1.1
'Detailed hyperparameter configurations are provided in the appendix.

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7 Analysis
Sentence
Metaphor
Single Task
Continual
Learning
To further investigate into our results we looked into the predictions of the two tasks for every perimental setup. In order to do that, we obtained the confusion matrices of the outcomes of our ex- periments (provided in the Appendix) and observed particularly which labels have an improvement re- sulting from our continual learning approach.
We cannot allow pathway Texas to go down the big govern- ment pazhwway.
goal
movement
Gun control ad- advances vances in the Scn- ate, Democrats thank MSM for propaganda April
agent
movement
We also looked into the cases where both single- task baseline and continual learning fail to predict the correct outcome, have better idea where the approach can be improved further. We classify our analysis in two parts:
11, 2013
These Are the murdering criminal People Our
victim
Money Is Mur-
dering in Gaza
July 2014
Improvement Incorrect prediction by single-task but correct prediction by continual learning
Table 4: Comparisons outcomes on test set with the literal metaphor appended to the input sentence, to predict highlighted aspects. These examples, show instances where single-task baseline predicts the highlighted aspect correctly (denoted in bold font) but the continual learning predicts them incorrectly.
No Gain Incorrect prediction by both single-task and continual learning
7.1 Predicting Highlighted Aspects
In the following, we consolidate our main findings for highlighted aspects with selected examples."
signed into law earlier this year by Georgia's s Re- publican Gov. Nathan Deal, has advanced in recent months.", both the approaches predict the high- lighted aspect to be agent instead of change.
Improvement We primarily observed that the continual learning approach performs better de- tecting the aspects of threat and threatened, among others. For example, for the sentence "Taxation de- stroys carnings and ability to wwwe/innest..iiflatioo destroys monetary wealth already owned.", ", the con- tinual learning approach predicts the highlighted aspect correctly be threat, unlike destruction potential predicted by the single-task baseline.
Furthermore, for the sentence "The reality is that firearm safety has not meaningfully advanced in the past century.", while the truc label was agent, the single-tase task setup predicts change and the continual learning predicts movement, indicating confusion regarding the three aspects.
7.2 Predicting Source Domains
Continual learning improves performance on the aspect barrier such as in the sentence "This would remove mountain of taxation from the shoulders of labor.", where it correctly predicts the aspect highlighted to be a barrier instead of scale.
Analogously to the previous subsection, we here present our findings on source domains.
Improvement As stated Section 4, we have composite source domains present in the corpus which are combinations of individual source do- mains that are present across the data.
These particular outcomes indicate that, with the knowledge of the source domain in this case, the continual learning procedure captures the broader implicit meaning better while the prediction of the single-task setup is possibly more influenced by the word destroy.
We observe that continual learning can differenti- better between predicting single and composite source domains. An example is the source domain of struggle where for the sentence "How can local governments and civil-society organizations effec- tively fight poverty and promote social responsibil- ity in countries as diverse as Canada, China and Ghana?", continual learning predicts the source do- main of struggle correctly instead of struggle and war by the single-task baseline.
No Gain Both the single-task baseline and con- tinual learning have room for improvements for the aspects of change and agent. For instance, for the sentence "But some pro-gun legislation, includ- ing the sweeping "guns everywhere bill that was
'Detailed outcomes of the experiments on the test set are provided with the code provided.
This holds true for other single and their equiv-

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Our fine-grained analysis on the test data revealed that continual learning particularly improves in dis- tinguishing single and composite source domains. However, they also showed that the single-tast task bascline, in most cases, predicted least one of the concepts in composite source domain correctly. alent composite source domains where in the sen- tence for example "My point was that governments have killed more people the OP did not say the US government but the last time looked the US Govt was in fact a 'government and "governments have killed far more people then individuals not engaged in the service of "government"" the single- task incorrectly predicts crime and physical harm while continual learning predicts the correct source domain physical harm.
Highl. Aspects
Source Domains
Approach
Acc@! 1 Relaxed Acc@1 1 Relaxed
Single-task
0.524
0.548
0.488
0.714
Joint learning
0.491
0.498
0,455
0.724
Continual learning
0.535
0.555
0.522
0.728
Table 5: Evaluation of all approaches in the relaxed setting, comparing the Accuracy@) / from Table 3 to the relaxed accuracy. No literal metaphor given as input.
Another interesting example is where the con- tinual learning approach predicts the highlighted aspect to be victim instead of criminal, as shown in Table 4. While theoretically, it can be argued that both of these aspects are highlighted by the metaphorical usage of murdering in the sentence, in the realm the dataset, the single-task baseline predicts correctly. Given the direct relationship of source domain to the metaphor, these outcomes intuitively make sense, because of the information regarding source domains already present in this case. However, further experiments are required understand them better.
No Gain Overall, continual learning improves considerably over the single-task approach in pre- dicting the souree domains. However both the ap- proaches systematically confused among source domains such as natural physical force and body of water, as in the sentence sweep out the old, then when the fresh rain of democracy came, whole new country would spring up, like mushrooms. or among building low location in the sentence "This rate is an individual's tax floor.".
In the latter, might be even challenging for humans to be certain, because the metaphor tax floor does indicate that the meaning originates from the concept such as low location.
7.4 Relaxed Top-1 Accuracy
7.3 Predicting Highlighted Aspects With Literal Metaphor Input
One possibly unexpected outcome were the top- I accuracy results for predicting the highlighted aspects with the metaphor added to the input as shown in table 3. Upon inspection, found out that the continual learning approach was biased towards the aspect movement as shown in Table 4. In the sentence "We cannot allow Texas to go down the big government pathway"", with the literal metaphor pathway appended to the input, the con- tinual learning approach predicts the highlighted aspect to be movement instead of goal which is correctly predicted by the single-task baseline.
To further investigate the performance of our ap- proach we evaluate our approaches to the baseline with a relaxed top-1 accuracy as shown in Table 5. Unlike our main evaluations, in this setting we consider a prediction to be correct if one of the n-combinations in the true label is correct. So if the correct composite source domain for given metaphorical sentence domain A and source domain B, we consider it to be correct prediction if the model predicts only the concept A as the source domain of this sentence.
combination of source
Similarly, in the sentence "Gun control advances in the Senate, Democrats thank MSM for propa- ganda 1, 2013", the continual learning setup predicts the highlighted aspect to be movement. It is highly likely that with the addition of the lit- eral metaphor to the input, the continual learning approach fails to capture the broader meaning mani- fested by the sentence. On the contrary, without the addition of the literal metaphor the input, in the similar experimental setting, continual learning ap- proach predicts all the aspects correctly, supporting our affrrmennioned theory.
This intuitively also makes sense, given these concepts are combined based on their semantic similarity (Gordon et al., 2015) which means that each of the concepts in a composite source domain is semantically cquivalent to the composite source domain. For example, in the sentence "Across the globe, free markets and trade have helped defeat poverty, and taught men and women the habits of liberty", the metaphor defeat is a meaning manifes- tation of the composite source domain Competition,

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Game, and War. In this case, consider it to be correct prediction if the model predicts War to be the source domain.
tion to what is not put emphasis on by metaphor, which will naturally bring up additional challenges regarding the interpretation of metaphors.
In the relaxed accuracy setting, continual learn- ing improves top-1 accuracies for highlighted as- pects and source domains. Joint learning is more effective than the baseline for source domains, sug- gesting that semantic similarity captured well when considering highlighted aspects, despite the complexity of composite source domains.
A Acknowledgment
This work has been supported by the Deutsche Forschungsgemeinscaatt (DFG, German Research Foundation) under project number TRR 318/1 2021 438445824. We thank the anonymous reviewers for their helpful feedback.
Overall, our analysis not only suggests that con- trastive learning can also be applied to predict high- lighted aspects, but also indicates the performance of continual learning in these downstream task settings in the consistent performance improvement across the experiments.
Limitations
Firstly, one of the major limitations of our work is the nature of our downstream tasks, where from a theoretical standpoint, the number of highlighted aspects for a particular metaphor in a given context are unbounded which means one cannot say with certainty, if certain concept is the one and only correct aspect for given metaphorical sentence While we have tackled this problem by treating the data as and by incorporating real-world set- ting for our experimental setup, is also a limited real-world setting that we employ since we take the aspects annotated in the dataset to be the only possible aspects for the metaphorical sentences. Secondly, while analysis reveals that our pro- posed continual learning approach improves model performance, without any level of explainibility difficult to say exactly to what degree the in- formation from one task does help in the model performance the other.
8 Conclusion
The conceptual mapping from source to target do- main in metaphorical meaning manifestation in- volves the highlighting certain aspects of the target domain. In this study, we have examined the impact of source domain information on predicting highlighted aspects, and vice versa.
To accomplish this, have proposed two multi- task learning approaches within a contrastive learn- ing setting: one utilizing continual learning, the other joint lcarning. We have evaluated the per- formance of our approaches in comparison to ac- cording single-task baselines for predicting either source domains or highlighted aspects.
We have found that continual learning enhances model performance for highlighted aspects and source domains, suggesting mutual improvement. Our fine-grained qualitative analysis further con- firms the effectiveness of our approaches across various experimental setups. Moreover, in a more informal yet potentially more applicable assess- ment, method outperforms the performance of the single-task baseline, demonstrating beneficial outcomes through joint learning.
Finally, to test the generalizability of our ap- proach it is important to test our models on similar datasets. However, to the best of our knowledge, this is the only dataset which fits our task design.
Ethical Statement
To the best of our knowledge, we understand that there are no ethical concerns with our paper. We use relatively transparent approaches publicly available dataset. To the best of our knowledge, it unlikely that a potential harm is posed by either the data or our methods.
We conclude that the aspects highlighted by metaphors can be predicted well in the majority of cases-ase and even more so small set of can- didate aspects (as suggested by our top-5 accu- racy results). We this substantial step to- wards more comprehensive computational interpre- tation of metaphors. Following Lakoff and Johnson (2003), metaphors do not only highlight certain as- pects of a target domain, they also hide others at the same time. Future work should thus pay more atten-
References
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hnttsss//iishaare.com http://gghaaeeccccaaattaaalasasa Corpus_of hww.erRcc..eeeaaoooootttteeeeeee

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Source Domains Without metaphor added to the imput, we find the optimized parameters for the best performing checkpoint to be learning rate of 5 10 batch size of 8, and epochs of With the metaphor added to the input we find the optimized parameters for the best performing checkpoint to be learning rate of 3 10-5, batch size of 8, a epochs of 5.
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9.3 Continual-Learninn
Highlighted Aspects Without metaphor added to the input, in the continual learning approach, based on our hyperparameter search we find the op- timized parameters for the best performing check- point to be a learning rate of 5 10-5, batch size of 8, and epochs 6. With the metaphor added to the input we find the optimized parameters for the best performing checkpoint to be learning rate of 5 10-5 batch size of 8, epochs of 4.
Gerard Steen. 2010. method for linguistic metaphor identification: From MIP to MIPVU, volume 14. John Benjamins Publishing.
Josef Stem. 2000. Metaphor in context. mit Press.
Kevin Stowe, Nils Beck, and Iryna Gurevych. 2021. Exploring metaphoric paraphrase generation. In Pro- ceedings of the 25th Conference Computational Natural Language Learning, pages 323-336, Online. Association for Computational Linguistics.
Source Domains Without metaphor added to the input, we find the optimized parameters for the best performing checkpoint to be learning rate of 10-5, batch size of 8, and an epoch of 5. The encoder for the second phase of training is the best performing model from the single-setup of predict- ing source domains and hence it's optimized with the hyperparameters mentioned above in section 9.2. With the metaphor added the input we find the optimized parameters for the best performing checkpoint to be a learning rate of 1 10 batch size of 8, a epochs of
Sam Witteveen Martin Andrews. 2019. Paraphras- ing with large language models. In Proceedings of the 3rd Workshop Neural Generation and Trans- lation, pages 215-220, Hong Kong. Association for Computational Linguistics.
Hans- Mens-Georg Wolf and Frank Polzenhagen. 2003. Con- ceptual metaphor as idcological stylistic means: An exemplary analysis. Dirven, R., Frank, R. & M. Pütz. Cognitive Models in Language and Thought: Ideol- ogy, Metaphors and Meanings, pages 247-275.
Rui Zhang. Yangfeng Ji, Yue Zhang, and Rebecca J. Passonneau. 2022. Contrastive data and learning for natural language processing. In Proceedings of the 2022 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts, pages 39-47, Seattle, United States. Association for Computational Linguistics.
9.4 Joint-Learning
Highlighted Aspects In the joint learning setup. we find the optimized parameters for the best per- forming checkpoint to be learning rate of 5- 10-5 batch size of 8, and epochs of 6. For the model to identify each of the tasks individually, we add special token of <hghl> for every sentence for the highlighted aspects and <sem> for the source domains. At inference, we test the best performing model on the test set. With the metaphor added to the input we find the optimized parameters for the best performing checkpoint to be a learning rate of 5 10-5 batch size of 8, epochs of 6.
Tianyu Zhao, Junping Du, Zhe Xu, Ang Li, and Zeli Guan. 2022. Aspect-basee sentiment analysis using local context focus mechanism with deberta. arXiv preprint 2xXx:22077002222
9 Appendix
9.1 Hyperparameter Configurations
9.2 Single-Task Setup
Highlighted Aspects Without the literal metaphor added to the input, we find the best- performing checkpoint to result from batch size of 8, learning rate of 5- 10 5 and epochs. With the literal metaphor, 4 epochs are better while the others are hyperparameters identical.
Source Domains The procedure to fine-tune for this task the same mentioned as above, while the only difference being the inference time, where the instead of highlighted aspects we pre- diet source domains. Without metaphor added to the input, we find the optimized parameters for the

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best performing checkpoint to be learning rate of 5 10-5 batch size of 8, and epochs of 6. With the metaphor added to the input we find the optimized parameters for the best performing checkpoint to be a learning rate of 5 10 5 batch size of 8, a epochs of 5.
9.5 Confusion Matrices
Next pages show the confusion matrices for each of the outcomes of the model performances on the test set.

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Figure 3: Confusion matrix of test outcomes of the single-task baseline to predict highlighted aspects. No literal metaphor provided in the input.

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Figure 4: Confusion matrix of test outcomes of the joint learning approach to predict highlighted aspects. No literal metaphor provided in the input.

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Figure 5: Confusion matrix of test outcomes of the continual learning approach to predict highlighted aspects. No literal metaphor provided in the input.

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Figure 6: Confusion matrix of test outcomes of the single-task baseline to predict source domains. No literal metaphor provided in the input.

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Figure 7: Confusion matrix of test outcomes of the joint learning approach to predict source domains. No literal metaphor provided in the input.

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Figure 8: Confusion matrix of test outcomes of the continual learning approach to predict source domains. No literal metaphor provided the input.

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Figure 9: Confusion matrix of test outcomes of the single-task baseline to predict highlighted aspects. Literal metaphor provided in the input.

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Figure 10: Confusion matrix of test outcomes the joint learning approach to predict highlighted aspects. Literal metaphor provided in the input.

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Figure 11: Confusion matrix of test outcomes of the continual learning approach to predict highlighted aspects. Literal metaphor provided the input.

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Figure 12: Confusion matrix of test outcomes of the single-task baseline to predict source domains. Literal metaphor provided in the input.

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Figure 13: Confusion matrix of test outcomes of the joint learning approach to predict source domains. Literal metaphor provided in the input.

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Figure 14: Confusion matrix of test outcomes of the continual learning approach predict source domains. Literal metaphor provided in the input.


Reviews:
[{"review_id": "KRdSnIg2hw", "reviewer": "Reviewer_onsm", "summary": "", "questions": "A. What are the predictions of the texts presented in L560 without the addition of the literal metaphor to the input?", "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 positive judgment about the proposed task and the insights from the multitask learning experiments as well as for your detailed feedback.\n\n**What are the predictions of the texts presented in L560 without the addition of the literal metaphor to the input?**\n\nFiles containing all predictions of all experiments have been submitted with the code, found in the folder /project_metaphor/analysis/ with the files starting with 'confmtrx-' to be the analysis files.\n\nThe predictions of the texts without the literal metaphor added to the input in continual learning comes as per our expectation - where for all three examples from the table, our approach predicted the highlighted aspect correctly - supporting our intuition in the paper (concerning line 560). By literal metaphor, we mean the metaphorical expression, as found in the text; we will clarify this.\n\n**The results are missing statistical significance while the differences in scores are very small.**\n\nWe will add significance results when we revise the paper. Indeed, the smaller differences may not be significant.\n\n**A single dataset was used for experiments**\n\nOur task design investigates how source domains can influence prediction of highlighted aspects and vice versa. As discussed in lines 280-282, to the best of our knowledge, the dataset we used is the only one with annotations of source domains and highlighted aspects.\n\n**judging by the annotations presented in this paper, the inter-annotator agreement should also be discussed.**\n\nSince the corpus comes from peer-reviewed work (Gordon et al., 2015), we did not explicitly question the reliability of the annotations. Gordon et al. reported Cohen's kappa agreement values between 0.42 and 0.65. While they regard these kappa values as good given the complexity of the task, we agree that they might pose a problem. However, this is the only dataset that we can use. Furthermore, it is unlikely that the lack of agreement leads to systematically biased conclusions on our side, but rather that it adds noise.", "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": "* A new task is proposed, that of better understanding the metaphorical text span in a metaphorical sentence. \n* The comparison between single and multi task learning is interesting, and potentially reaching a wider audience.    \n", "reasons_to_reject": "* The results are missing statistical significance while the differences in scores are very small.\n* A single dataset was used for experiments, and, judging by the annotations presented in this paper, the inter-annotator agreement should also be discussed. \n", "questions_to_authors": "A. What are the predictions of the texts presented in L560 without the addition of the literal metaphor to the input?", "paper_topic_and_main_contributions": "Metaphorical words transfer from a source to a target domain and Sengupta et al. (2022) studied contrastive learning for predicting the source domain from a metaphorical sentence. This study extended the work of Sengupta et al. by focusing also on the metaphorical word or text span (aspect). The authors used a baseline a single-task model, predicting either the aspect or the source domain. They developed two multi-task approaches, one based on continual and one based on joint learning. Small differences in accuracy were found, compared to the baseline, which are not enough to draw solid conclusions, at least not with out any statistical significance measurement.\n", "excitement": 2, "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": "- L88: The hypothesis the authors refer to is not clear to me.\n- Figure 1 (caption): L96-97 writes \"or\" which is sort of contradicting. \n- L89: The contribution in proposing these two multitask setups is not very clear. \n- L157: Please consider adding a definition of a literal metaphor.\n- L173: Based on the contributions, the goal is to predict both the aspect and the source domain, but now it's stated differently. This makes the presentation confusing. Also, my understanding is that you do not predict the aspect, but its domain or topic (predicting the aspect would be to predict the word/term).\n- L242: Not clear what is this embedding score, so I am guessing some sort of distance or similarity. \n- L255: Not clear what are these concepts and how are they represented exactly. \n- L268: Not clear how does backpropagation relates to this specific loss computation. Some re-wording would make this clear.\n- L276: What previous approach, the previous work (L181) or continual learning? \n- L300: The exact type of combination is not shared.\n- L332: Where is the difficulty in the real world? (Also, which information?)\n- L351-358: Do you mean that the label is predicted as a next token, with its probability compared to those of alternative labels? \n- L400-403: Not the right conclusion, in my opinion. Without the literal metaphor, in Acc-3, it performs similarly to the baseline for highlights and source; 0.01 up in Acc-1 for highlights; better in Acc-5 for source. With the literal metaphor, the baseline is better in 3 out 6 columns.\n- Table 2: In acc@3 of aspects without the metaphor, the baseline is equally well but not in bold. \n- L474: In my eyes, both are correct while \"destruction potential\" sounds better. Please consider elaborating more regarding your perspective on what's right and wrong.\n- L529: The differences reported do not look considerable to me. \n- L544: Perhaps you can elaborate more on why was this unexpected.\n- L610-612: I see a different pattern. Relaxation reduces the difference in acc@1 between continual learning and the single-task baseline, from 0.011 to 0.007, for highlights, and from 0.034 to 0.014 for source. \n- Table 4: What is presented in bold? (Two per row and two per column...)"}}, {"review_id": "MldLE1OLAu", "reviewer": "Reviewer_hYfy", "summary": "", "questions": "", "limitations": "", "rating": 2, "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 the positive evaluation of the task and insights provided, but also for sharing your concerns.\n\n**The focus of this paper is somewhat narrow in that it depends on a corpus with very specific annotations** \n\nWe point out that the annotations are needed for training and evaluation only, while our approach can be applied to any metaphorical sentence. \n\n**there is no wider comparison. In particular, it would be interesting to see if this work could be used to improve a the more general case of annotation.**\n\nOur broader focus is the interpretation of metaphor as a figurative language as stated in lines 039-051 and 072-080. In order to do that, we have proposed the novel task to model highlighted aspects - a phenomenon of metaphorical meaning construction that was never addressed before although being popular in theoretical linguistics (Lakoff and Johnson, 2003). We consider this to be a starting point, based on which further research can be done in order to expand this to figurative languages even beyond metaphors like sarcasm, or idioms.\n\n**The only baseline is a single-task baseline** \n\nThe reason for having a single-task baseline only is essentially because the task is new and we consider the only other relevant work as stated in the paper. By resorting to MTL, we investigated how the knowledge of one (e.g., source domains) influences the prediction of the other (e.g., highlighted aspects). \n\n**as the MTL set-up is not really novel, the results are not surprising (as it has been well-established that MTL improves peformance).**\n\nConceptually, we agree: Designing the task a multi-task paradigm intuitively makes sense because of the aforementioned reason. Practically, multi-task learning does perform well in certain cases, but it also does not do so throughout (e.g., the joint learning approach does not perform as well as continual learning). In our opinion, the novelty lies in the combination of the new task (as acknowledged by all reviewers) and successively devising an approach that technically utilizes the information in this setting of metaphorical interpretation.", "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": "This is an interesting task and deepens our understanding of automatic approaches to metaphor.\n\nThere is a deep and interesting qualitative analysis of the results.\n\nThe paper is well-written and clear.", "reasons_to_reject": "The focus of this paper is somewhat narrow in that it depends on a corpus with very specific annotations and there is no wider comparison. In particular, it would be interesting to see if this work could be used to improve a the more general case of annotation. \n\nThe only baseline is a single-task baseline, and as the MTL set-up is not really novel, the results are not surprising (as it has been well-established that MTL improves peformance).\n\nThis is a nice paper, but I feel it would be better suited to a focused workshop rather than the EMNLP main conference", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper presents an analysis of metaphor detection with the use of source domain and the aspect that is highlighted in the text. The authors experiment with two multi-task learning training scenarios and show that this joint baseline outperforms the a single-task baseline.", "excitement": 2, "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "\"a principally model\""}, "extra_text": {}}, {"review_id": "qAfVbgz0rE", "reviewer": "Reviewer_u9Nz", "summary": "", "questions": "", "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 encouraging assessment of the paper in terms of the presented task, analyses, and discussion. Regarding the concerns you raised:\n\n**The first contribution is kicking off a new task, and the second contribution is proposing a multi-task learning approach to accomplish this task. The second of these contributions mainly follows an ideological approach that has been used for a long time in the NLP community, and the main innovativeness is focused on the first contribution. However, the analysis part of the experiment is discussed mainly for the second contribution. From my perspective, it's better to focus more on the first contribution instead of the second one.**\n\nWe understand your view and shall elaborate further on the first contribution in the revised version of the paper. \n\n**There is only 1 baseline model in the experimental section.**\n\nAs indicated in the response to Reviewer hYfy already, the reason for having a single-task baseline only is essentially because the task is new and we consider the only other relevant work as stated in the paper. By resorting to MTL, we investigated how the knowledge of one (e.g., source domains) influences the prediction of the other (e.g., highlighted aspects).", "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 newly proposed task is both novel and useful.\n\n2) Good writing.\n\n3) Detailed analysis of experimental results.\n\n4) Good conclusions, especially the \"limitations\" section.", "reasons_to_reject": "1) The paper has two main contributions. The first contribution is kicking off a new task, and the second contribution is proposing a multi-task learning approach to accomplish this task. The second of these contributions mainly follows an ideological approach that has been used for a long time in the NLP community, and the main innovativeness is focused on the first contribution. However, the analysis part of the experiment is discussed mainly for the second contribution. From my perspective, it's better to focus more on the first contribution instead of the second one.\n\n2) There is only 1 baseline model in the experimental section.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a novel contrastive learning method for metaphor interpretation. In particular, this work introduces a new task for predicting the highlighted aspects in metaphors. This proposed task takes advantage of various linguistic research on metaphor, such that metaphor is to highlight certain aspects of its target domain. Such a task has potential in both NLP and linguistic research. The authors propose to use the multitask learning scheme to jointly predict the source domains and the highlighted aspects.", "excitement": 4, "reproducibility": 5, "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 paper focuses on examining highlighting in metaphors, where certain aspects of a target domain are emphasized by the use of the metaphor.  The authors introduce a new NLP task of identifying the highlighted aspect in a metaphorical sentence.  Leveraging a source-target domain framework, the paper explores whether jointly predicting the highlighted aspect and the source domain in a metaphorical sentence improves predictive performance on both tasks.  They find that by performing continual learning, they are able to obtain performance improvements on both source domain and highlight prediction by allowing information to be shared.

On the whole, the reviewers thought the paper was sound and well-written.  One of the main issues in terms of soundness was brought up by reviewer onsm, who noted that the results lacked statistical significance tests despite the different models having similar accuracies across tasks.  The authors discussed including significance tests if they revise the paper but mentioned that their results may no longer be significant after testing.  This, along with the fact that the authors only compare their approach with a simple single-task baseline (noted by reviewers hYfy and u9Nz), does weaken the conclusions the paper draws on jointly predicting source domain and highlighting.  Another soundness critique by reviewer onsm is that the inter-annotator agreement for the dataset used was not acknowledged.  As stated in the rebuttal, agreement on the dataset is quite low, ranging from 0.42 to 0.65, suggesting there may be issues with the dataset used.  However, given that the dataset used is from Gordon et al. (2015), the interannotator agreement does not appear to be an explicit soundness issue with this paper directly.  The paper, however, could be strengthened by acknowledging issues with the single dataset used in the limitations section.

On the other hand, while the paper was well-received in terms of soundness, there is some disagreement over how substantial a contribution it made with its multitask experiments.  While reviewer onsm appreciates that the paper runs experiments examining the difference between single and multitask learning for a metaphor interpretation task, reviewers hYfy and u9Nz regard that comparison as a weakness of the paper.  As reviewer u9Nz notes, the multitask learning experiment “mainly follows an ideological approach that has been used for a long time in the NLP community”.  As a result, the methodological approaches and results in the paper are not surprising, despite operating in a new task setup.

Overall, while the paper is well-written and most of the reported technical details in the experiments are sound, there remain questions about how exciting of a contribution the paper makes.  This is especially the case, given that many of the issues in the paper are due to the limited availability of existing high-quality datasets for metaphor highlighting.  Thus, work that can be done to follow up on the observations and analyses in this paper may be limited.
Decision: Accept-Findings