Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining (2024)

Abstract: Metaphor detection aims to identify whether a linguistic expression in text is metaphorical or literal. Most existing research tackles this problem either using word-pair or token-level information as input, and thus treats word-pair and token-level metaphor detection as distinct subtasks. Benefited from the simplified structure of word pairs, recent methods for wordpair metaphor detection can provide intermediate explainable clues for the detection results, which remains a challenging issue for tokenlevel metaphor detection. To mitigate this issue in token-level metaphor detection and take advantage of word pairs, in this paper, we make the first attempt to bridge word-pair and tokenlevel metaphor detection via modeling word pairs within a sentence as explainable intermediate information. As the central role of verb in metaphorical expressions, we focus on tokenlevel verb metaphor detection and propose a novel explainable Word Pair based Domain Mining (WPDM) method. Our work is inspired by conceptual metaphor theory (CMT). We first devise an approach for conceptual domain mining utilizing semantic role mapping and resources at cognitive, commonsense and lexical levels. We then leverage the inconsistency between source and target domains for core word pair modeling to facilitate the explainability. Experiments on four datasets verify the effectiveness of our method and demonstrate its capability to provide the core word pair and corresponding conceptual domains as explainable clues for metaphor detection.

heading: 1 Introduction
text: Metaphor is not just a figurative expression but a pervasive phenomenon in human thought, perception, and reasoning (Lakoff and Johnson, 1980). As defined in Merriam-Webster Dictionary, metaphor is “a figure of speech in which a word/phrase literally denoting one kind of object or idea is used in
*Corresponding author
place of another to suggest a likeness or analogy between them”. Metaphor detection, as a fundamental research task in natural language processing (NLP), focuses on distinguishing metaphorical expressions from literal expressions in text. It can benefit a variety of other NLP tasks, which require the understanding of implicit semantics, such as machine translation (Mao et al., 2018), sentiment analysis (Mao and Li, 2021), and conversational dialogue (Sun et al., 2023).
Previous studies on metaphor detection mainly focus on two distinct subtasks, word-pair metaphor detection (Ge et al., 2022; Tian et al., 2023) and token-level metaphor detection (Choi et al., 2021; Li et al., 2023). The former considers word pair as the fundamental unit conveying metaphorical meaning and classifies word pairs into metaphorical or literal categories, while the latter identifies words within sentences that imply metaphorical meaning. For example, in Figure 1, digests news can be classified as a metaphorical word pair and the word digests within the sentence is a token-level metaphor conveying metaphorical meaning.
Early research on word-pair metaphor detection employs machine learning or deep learning methods based on linguistic features related to metaphor
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(Tsvetkov et al., 2014; Shutova et al., 2016; Rei et al., 2017). Recent research is inspired by conceptual metaphor theory (CMT) (Lakoff and Johnson, 1980), which reveals the underlying process of metaphor understanding in human cognition and argues that a metaphor implies the inconsistency between source and target domains. These works (Ge et al., 2022; Tian et al., 2023) propose methods to model the intermediate explainable domain information for better word-pair metaphor detection.
To detect token-level metaphors, early studies employ statistical or RNN-based methods (Li et al., 2013; Wu et al., 2018; Mao et al., 2019). Most recent studies (Choi et al., 2021; Zhang and Liu, 2022; Li et al., 2023) have developed methods based on the elements and relations described in metaphor identification theories, including selection preference violation (SPV) (Wilks, 1975, 1978) and metaphor identification procedure (MIP) (Pragglejaz Group, 2007).
Benefited from the simplified structure of word pairs, Ge et al. (2022) can capture intermediate explainable clues including source and target domains to enhance word-pair metaphor detection. However, modeling such explainable information for better detection remains a challenging issue for token-level metaphor detection. As tokenlevel metaphor detection involves complex semantic structure and sentence-level context, it brings greater research challenges for both detection and explanation compared to word-pair metaphor detection. Moreover, token-level and word-pair metaphor detection are treated as distinct subtasks in existing research. The intermediate information and explainable clues that can be derived from word pairs have never been explored in previous token-level research, nor are they properly utilized in the token-level detection and explanation processes. For example, Figure 1 illustrates a tokenlevel metaphor, indicating that the core word pair and corresponding source and target domains can serve as valuable clues for understanding and detecting token-level metaphors.
In this paper, we take the first step to bridge word-pair and token-level metaphor detection and propose an explainable Word Pair based Domain Mining (WPDM) method for token-level verb metaphor detection. Since in a token-level metaphorical sentence, there are several candidate context words that can form word pairs with the focus word, it is non-trivial to identify the core word pair that conveys the primary metaphorical mean-
ing. Thus, inspired by CMT, we mine the domain concepts associated with each word pair utilizing semantic role correspondence and domain granularity assessment for core word pair selection. Specifically, we first devise an approach to mine source and target domain information via mapping semantic roles based on VerbNet (Schuler, 2005) and leveraging domain concepts from cognitive, commonsense and lexical knowledge resources. We then exploit the inconsistency between conceptual source and target domains to determine core word pair attentions for facilitating explainable metaphor detection. The main contributions of our work are as follows:
• We make the first attempt to connect word-pair and token-level metaphor detection for more fine-grained identification and understanding of metaphors at the sentence level.
• To facilitate explainable token-level metaphor detection, we propose a novel word pair based domain mining method inspired by CMT, which consists of semantic role mapping and conceptual domain mining for core word pair modeling based on cognitive, commonsense and lexical resources.
• Extensive experiments on four datasets verify the effectiveness of our method and also demonstrate its capability to identify the core word pair and corresponding conceptual domains as explainable results for token-level metaphor detection.

heading: 2 Related Work
text: Metaphor detection aims to distinguish metaphorical and literal expressions in text. Existing studies focus on two subtasks: word-pair metaphor detection and token-level metaphor detection. The former determines whether a word pair is metaphorical or literal, and the latter concentrates on identifying metaphorical words within sentences.
Word-Pair Metaphor Detection Traditional research on word-pair metaphor detection utilizes machine learning techniques based on linguistic or external knowledge related to metaphor, such as abstractness (Turney et al., 2011), imageability (Tsvetkov et al., 2014), visibility (Shutova et al., 2016) or property norm (Bulat et al., 2017). After that, some studies exploit neural networks modeling the similarity between words in a word pair (Rei
et al., 2017) or the concreteness constructed from images (Su et al., 2020a) to detect metaphors. In addition, Shutova et al. (2017) perform distributional clustering methods to identify metaphors. Recently, some researchers have utilized well-founded cognitive theory, conceptual metaphor theory (CMT) (Lakoff and Johnson, 1980), to benefit this task. Ge et al. (2022) design a method to generate explainable source and target domains to help identification of metaphors. Tian et al. (2023) further propose an attribute Siamese network to capture similar attributes between source and target concepts for metaphor detection.
Token-Level Metaphor Detection Inspired by a theory, selectional preference violation (SPV) (Wilks, 1975, 1978), which indicates that a metaphor contains violations between its context and its frequent usage of contexts, early work (Shutova et al., 2010; Li et al., 2013; Mao et al., 2018) develops statistical methods to capture cooccurrence information between words and their contexts based on various corpora and knowledge bases for token-level metaphor detection.
After that, some studies employ RNN-based models to extract contextual representations to detect token-level metaphors (Gao et al., 2018; Mao et al., 2019; Le et al., 2020). As these methods primarily rely on static word embeddings, they struggle to capture the intricate contextual meaning implied by metaphors. In contrast, pre-trained models (Devlin et al., 2019; Liu et al., 2019) can provide dynamic contextualized word embeddings and become popular backbones (Su et al., 2020b; Li et al., 2020) for current studies in metaphor detection. Among them, some studies employ the multitask framework to learn shared embeddings from other tasks related to metaphor (Mao and Li, 2021; Li et al., 2020; Zhang and Liu, 2023; Badathala et al., 2023). Other studies utilize external resources to enhance performance, such as multiword expressions (Rohanian et al., 2020) and word definitions (Su et al., 2021). In addition, some works employ data augmentation methods to expand limited metaphor datasets (Lin et al., 2021; Feng and Ma, 2022).
Recently, some studies (Choi et al., 2021; Zhang and Liu, 2022; Wang et al., 2023; Li et al., 2023) have developed methods to model the elements and their relations in metaphor identification theories, including SPV and metaphor identification procedure (MIP) (Pragglejaz Group, 2007), achieving
promising results.
Computational Work for Both Metaphor Detection and Explanation Metaphor explanation aims to interpret the implicit meaning conveyed by metaphorical expressions, using paraphrased expressions (Shutova, 2010) or conceptual domains (Wachowiak and Gromann, 2023) as explanatory information. Previous works tackling both metaphor detection and explanation (Li et al., 2013; Mao et al., 2018) mainly employ sequential methods to detect metaphors and then explain them based on contextual frequency clues guided by SPV. In fact, metaphor detection and explanation are inherently associated, and intermediate explainable information can benefit metaphor detection. To bridge the gap between metaphor detection and explanation, Ge et al. (2022) propose a model to generate source and target domain for better wordpair metaphor detection guided by a more explainable theory CMT. However, their approach takes word pairs as inputs and relies on the simplified word-pair structure for domain mining. In contrast, token-level metaphor detection is a more general but challenging task involving complex sentence context. To mitigate this gap, we aim to advance token-level metaphor research by upgrading wordpair metaphor detection with explainable sentencelevel conceptual domain modeling based on CMT.

heading: 3 Problem Definition
text: Formally, Dtr = {(sk, wkv , bk)}Ntrk=1 is the training dataset with Ntr instances, where s is a sentence, wv is a focus verb within s, and b is the label (metaphorical or literal) for wv. Dte = {(sk, wkv , bk)}Ntek=1 is the test dataset with Nte instances. The goal of token-level verb metaphor detection is to predict the label of the focus verb wv in Dte by training a model with Dtr.

heading: 4 Method
text: We propose an explainable Word Pair based Domain Mining (WPDM) method for token-level verb metaphor detection. Figure 2 illustrates the overview of our method, which contains four components: (1) Literal Example Sentences Construction, which collects example sentences for the literal meaning of the focus verb; (2) Semantic Role Mapping, which organizes words labeled with the same semantic role into a semantic group in the input sentence and literal example sentences, respectively; (3) Conceptual Domain Mining, which
employs a domain mining algorithm on words in each semantic group to mine the conceptual source and target domains based on cognitive, commonsense, and lexical resources; and (4) TokenLevel Metaphor Detection with Word Pair Modeling, which evaluates the importance of word pairs in the sentence based on the inconsistency between conceptual source and target domains, and then gives more attention to the important core word pair during metaphor detection training.

heading: 4.1 Literal Example Sentences Construction
text: To obtain the context for the focus verb wv using its literal meaning and establish a solid foundation for the following domain mining, we construct a literal example sentence set Se = {ske}|Se|k=1 with |Se| sentences by gathering all the example sentences associated with the first sense of the focus verb wv in dictionaries.1

heading: 4.2 Semantic Role Mapping
text: The semantic role of a verb, such as “agent” and “theme”, describes the underlying relation between an argument (a phrase/word) and the verb in the sentence (Schuler, 2005). The argument labeled
1We use the first sense of a word in a dictionary to capture its literal usage, for the reason that dictionaries typically list word senses chronologically by starting with the original meaning (see https://www.merriamwebster.com/help/explanatory-notes/dict-definitions and https://www.oxfordlearnersdictionaries.com/faq).
… …
meat milk
beverage
food
organism
plant
Root Node
living thing
entity
physical entity
meal
nutriment
Hyper.
Hypo.
WordNet Synsets
Hyper.
Hypo.Hypo.
Hypo.
Hyper.
Hypo. Hypo.
Figure 3: Illustration of the hierarchical structure of synsets in WordNet. A hypernym of a synset represents a broader or more general semantic field than the synset itself, while a hyponym of a synset conveys a more specific meaning. For example, “entity” is a hypernym of “physical entity” and “organism” is a hyponym of “living thing”. Hyper. denotes hypernym and hypo. denotes hyponym.
with a semantic role, which is closely related to the focus verb in semantics, can form a word pair with the focus verb to convey the implicit metaphorical meaning. To find these word pairs, we utilize VerbNet Parser to annotate the arguments (e.g. “public” in Figure 2) and their semantic roles associated with the focus verb wv in both the input sentence sin and the literal example sentences Se. We then select the overlapping roles in sin and Se to construct the semantic role set R = {rk}|R|k=1. Arguments labeled with the same semantic role in sin
construct a semantic group. We can get a collection of semantic groups Ain = {Aiin} |R| i=1 for sin, where the set of the arguments labeled with the semantic role ri is denoted as Aiin = {(aiin)k} |Aiin| k=1 . Similarly, we can obtain a collection of semantic groups Ae = {Aie}|R|i=1 for Se, where the semantic group containing arguments labeled with ri is denoted as Aie = {(aie)k}|A i e| k=1. The semantic groups A i in and Aie are mapped using semantic role ri for the subsequent target and source domain mining.

heading: 4.3 Conceptual Domain Mining
text: Humans can formulate the taxonomy of domains from various perspectives, such as cognitive processes that provide the underlying framework for how humans think and categorize (Lakoff and Johnson, 1980), commonsense knowledge that offers categories of general facts and relationships (Speer et al., 2017), and lexical resources that reflect the linguistic aspect of concept organization (Gelman et al., 1989). To mine the conceptual target and source domains for the aforementioned mapped semantic groups, we develop a conceptual domain mining approach based on knowledge at cognitive, commonsense and lexical levels.
Domain Concept List We first construct a domain concept list that represents the taxonomy of domains established on human cognitive processes and commonsense knowledge, using two resources: Master Metaphor List (MML) (Lakoff et al., 1991), constructed by cognitive linguists, containing paired conceptual source and target domains of metaphorical understanding in cognitive processes, and OpenCyc (Lenat et al., 1985), a knowledge base consisting of large-scale commonsense concepts and relations. The combination of domain concepts from these resources constitutes the domain concept list, which is shown in Appendix A.
Domain Mining Algorithm A large lexical semantic database WordNet (Miller, 1995) organizes words into hierarchical structures through synsets and conceptual relations (hypernym and hyponym). Figure 3 shows the hierarchical structure of synsets in WordNet. Given the richness and transitivity of hypernym relations, WordNet is a valuable resource to identify candidate conceptual domains.
For a semantic group A = {ai}|A|i=1, we regard the hypernyms along the hypernym path from ai to the root node in WordNet as the candidate con-
Algorithm 1 Conceptual Domain Mining
Input: A = {ai}|A|i=1: a semantic group with |A| arguments. Require: (1) Ld: the domain concept list; (2) Comb(A, k):
a function to generate the combinations of k nonoverlapping groups from the elements in the set A; (3) WordNet.
Output: D = {dj}|D|j=1: the conceptual domain set. 1: for all i← 1 to |A| do 2: for all Â = [Â1, . . . , Âi] ∈ Comb(A, i) do 3: for all Âj ∈ Â do 4: if Âj contains only one argument â then 5: Traverse h in hypernym path P of â 6: if ∃ (h ∈ P and h ∈ Ld) then 7: Obtain h as the domain for Âj 8: else 9: Obtain P [0] as the domain for Âj 10: else 11: Obtain the least common ancestor hyper-
nym of all arguments in Âj as the domain for Âj 12: Compute (θabs)j ▷ Eq. (1) 13: Compute θdg ▷ Eq. (2) 14: Obtain the domain set with the maximum θdg as D
ceptual domains of ai. Our goal is to construct a domain set D = {dj}|D|j=1 and divide A into |D| subsets Â = {Âj}|D|j=1, where every argument in the j-th subset Âj belongs to the j-th domain dj . To assess how abstract a domain (hypernym) is relative to an argument, we introduce the abstraction level l, calculated by the relative distance between them in the hypernym path (e.g. the abstraction level of domain food relative to the argument meal is 2 in Figure 3). (θabs)j represents the average abstraction level of dj for the arguments in Âj , which is calculated by
(θabs)j = 1
|Âj |
|Âj |∑
i=1
lij , (1)
where lij is the abstraction level of dj relative to the i-th argument in Aj . The domains in D should strike a balance by capturing the broad meanings shared among the arguments in A without being overly abstract. For example, in Figure 3, food and living thing are more appropriate domains for the arguments (i.e. plan, meal, meat, and milk) than the excessively abstract domain physical entity. To evaluate how well D maintains this balance, we design the domain granularity metric θdg, which is calculated by
θdg = 1
|D|
|D|∑
k=1
|Âk| (θabs)k . (2)
We develop a conceptual domain mining algorithm to find all possible collections of subsets Â for arguments in A and their related conceptual domains D based on the domain concept list and WordNet. We obtain the ones with the highest domain granularity metric θdg as our final results. Algorithm 1 shows the pseudocode of this algorithm.
We apply Algorithm 1 on every Aiin ∈ Ain and Aie ∈ Ae to obtain the conceptual target domain set Diin for A i in and the conceptual source domain set Die for A i e, respectively, where i ∈ [1, |R|] is the index of semantic role.

heading: 4.4 Token-Level Metaphor Detection with Word Pair Modeling
text: According to conceptual metaphor theory (Lakoff and Johnson, 1980), metaphor implies the inconsistency between source and target domains. Inspired by this, we utilize the inconsistency scores between above conceptual source and target domains to mine core word pairs that convey primary metaphorical meaning within the input sentence, and give more attention to core word pairs during the training process for explainable metaphor detection.
Candidate Word Pair Weighing In the input sentence, each word labeled with a semantic role and the focus verb can constitute a candidate word pair. For the k-th such word wkp labeled with the semantic role rk, we have mined its conceptual target domain dkt and its set of conceptual source domain Dks derived from the words labeled with the same semantic role rk in literal example sentences, using our domain mining algorithm. According to CMT (Lakoff and Johnson, 1980), a metaphor implies the inconsistency between source and target domains. Thus, the candidate word pair showing high inconsistency between its target domain and all its source domains is likely to be the core word pair conveying primary metaphorical meaning. We assign an attention to each candidate word pair based on the minimum inconsistency score between its target domain and all source domains. Specifically, the inconsistency scores βk between the target domain and all source domains for k-the candidate word pair and the attentions α for all the candidate word pairs are calculated by
βk = (Sim(d k t , D k s )) −1 ∈ R|Dks |, (3) β̂k = Min(βk), (4) α = Softmax([β̂1, . . . , β̂np ]) ∈ Rnp , (5)
where Sim(·) calculates the path distance similarity in WordNet between dkt and every domain in Dks , (·)−1 denotes the operation of element-wise inverse, Min(·) obtains the minimum score in a vector, Softmax(·) is the softmax function, and np is the number of candidate word pairs.
Encoding To train our model from a good start of text embedding, we use the pre-trained model RoBERTa (Liu et al., 2019), as the text encoder. Given the input sentence sin and candidate word pairs P = {pk}npk=1, the input T is constructed by
T = 
 sin 
 p1 
 p2 . . .
 pnp , (6)
where 
 and 
 are the global and separation tokens, respectively. We divide T into tokens and feed them into RoBERTa to obtain contextual embeddings H ∈ RN×d and the contextual embedding of global token h
:
H = RoBERTa(T ) = [h1, . . . ,hN ] ⊤, (7)
h
 = h1 ∈ Rd, (8) where d is the dimension of embedding, N is the number of tokens in T and hi is the embedding for the i-th token in T . We also obtain the embedding of the focus verb hv in the input sentence, and the embeddings of the focus verb hkv as well as the other target word hkw in the k-th candidate word pair. If a word is cut into tokens, its embedding is calculated by averaging its token embeddings.
Word Pair Layer The embedding for the k-th candidate word pair is computed as
ĥ k wp = CrossAtt(h k v ,h k w) ∈ Rd, (9)
where CrossAtt(·) denotes lt Transformer blocks (Vaswani et al., 2017). In this module, hkv serves as the query vector and hkw serves as both key and value vectors. The aggregated word pair embedding hwp ∈ Rd is computed by
hwp =
np∑
k=1
ĥ k wpαk, (10)
where αk is the k-th element in α.
Classification Finally, we feed the global embedding h
, verb embedding hv and word pair embedding hwp into a classifier, and adopt a crossentropy loss function to compute the loss Lclass:
l̂ = Softmax(MLP(h
⊕hv⊕hwp)), (11) Lclass = −(l)⊤ log l̂, (12)
where ⊕ is the concatenation operation, MLP(·) is a multilayer perceptron with hidden dimension d′, l̂ ∈ R2 is the predicted probability for all the labels, and l ∈ R2 is the ground truth.

heading: 5 Experiments

heading: 5.1 Datasets
text: We constructed a new verb metaphor detection dataset LCC-Verb based on LCC (Mohler et al., 2016), which is one of the representative benchmark datasets for metaphor detection. The LCCVerb construction process is shown in Appendix B. We also conducted experiments on three publicly available verb metaphor detection datasets, which are as follows: (1) MOH-X (Mohammad et al., 2016), containing 647 sentences where only a verb labeled as metaphorical or literal in each sentence; (2) TroFi (Birke and Sarkar, 2006), another verb metaphor detection dataset collected from Wall Street Journal Corpus; and (3) VUA-Verb (Leong et al., 2020), the largest publicly available dataset for verb metaphor detection drawn from VU Amsterdam Metaphor Corpus (VUA) (Steen et al., 2010). Table 1 shows the statistics of these datasets.

heading: 5.2 Baselines
text: We compare our method with several representative methods for token-level metaphor detection, which are as follows: (1) RNN_ELMo (Gao et al., 2018) is a BiLSTM-based method using ELMo embeddings to model metaphorical words in context; (2) RNN_HG and RNN_MHCA (Mao et al., 2019) make the first attempt to apply linguistic theories (MIP & SPV) on BiLSTM-based network design for metaphor detection; (3) MUL_GCN (Le et al., 2020) exploits a multi-task learning framework to transfer knowledge from word sense disambiguation to metaphor detection; (4) MrBERT
(Song et al., 2021) regards metaphor detection as a relation classification task via extracting dependency relations and utilizing them for metaphor detection; (5) MisNet (Zhang and Liu, 2022) proposes a linguistics enhanced network inspired by MIP and SPV for metaphor detection; (6) AdMul (Zhang and Liu, 2023) proposes a multi-task learning framework to transfer knowledge from basic sense discrimination to metaphor detection via adversarial training; (7) BasicBERT (Li et al., 2023) proposes a method to mine the concise basic meaning of the word based on literal annotation from training set inspired by MIP.

heading: 5.3 Implementation Details
text: We use three English dictionaries to extract literal example sentences in our methods, including Longman Dictionary of Contemporary English1, Oxford Advanced Learner’s Dictionary2, and Collins English Dictionary3. We use F1 score and accuracy for evaluation. Following the convention of previous studies (Zhang and Liu, 2022, 2023), we take the model that achieves the best F1 score on the validation set to test on VUA-Verb testing dataset, and we calculate the average over the best F1 scores and corresponding accuracy scores in total 10 folds for MOH-X, TroFi and LCC-Verb datasets. We use RoBERTabase (Liu et al., 2019) as the encoder. Given MUL_GCN (Le et al., 2020) did not release their code, we reproduced their method according to their paper and tested it on our four datasets. For a fair comparison, we replaced ELMo in MUL_GCN, BERTbase in MrBERT and DeBERTabase in AdMul with RoBERTabase in our experiments. All experiments are done on NVIDIA RTX 3090 GPUs. Other details are illustrated in Appendix C.4

heading: 5.4 Main Results
text: From the experimental results shown in Table 2, we can see that our proposed method outperforms previous state-of-the-art performances across most datasets, which verifies the effectiveness of our core word pair modeling based on conceptual domain mining for token-level metaphor detection. Methods utilizing pre-trained models, which incorporate extensive world knowledge through unsu-
1https://www.ldoceonline.com/ 2https://www.oxfordlearnersdictionaries.com/ 3https://www.collinsdictionary.com/dictionary/english 4Our code is available at https://github.com/
TIAN-viola/WPDM.
are underlined. † indicates that these methods use the same RoBERTabase as the encoder. Average denotes the average results on four datasets. The symbols ∗ and ∗∗ indicate that our method’s results are statistically significantly different from those of the second-best methods, with p ≤ 0.05 and p ≤ 0.01, respectively.
pervised learning, perform better than earlier RNNbased models. Our proposed method achieves performance gains across most datasets compared to previous methods inspired by SPV and MIP, showing the superior advantage of the theory CMT utilized in our method over SPV and MIP.

heading: 5.5 Ablation Study
text: We conduct ablation study to evaluate the impact of components in our method. Table 3 shows the experimental results of the ablation study. Using average attentions to replace word pair attentions in candidate word pair weighing reduces the perfor-
mance, verifying that candidate word pair weighing based on the inconsistency between source and target domains can help our model focus on the core word pair and benefit the explainable detection of metaphors. We further directly remove the word pair embedding from our method, resulting in greater performance drops across all datasets compared to only removing candidate word pair weighing, which verifies the effectiveness of word pair information in our method. When the global embedding is aborted, the performance decreases, thus demonstrating the importance of global context information for metaphor detection. In addition, as verb contains the central information for verb metaphor detection, the removal of verb embedding leads to the most significant performance declines across all the datasets.

heading: 5.6 Human Evaluation on Explainable Results
text: Our method can not only identify metaphorical verbs but also provide the core word pair along with conceptual source and target domains as explainable results. To evaluate these explainable results, we randomly sampled 100 metaphorical instances from MOH-X, LCC-Verb, TroFi and VUAVerb’s test dataset, respectively. We then invited two annotators to evaluate the core word pair as well as its source and target domains identified by our method using accuracy. The core word pair for each focus verb is the candidate word pair in the sentence with the highest word pair attention, and the corresponding paired domains used for the calculation of this attention are considered the source and target domains.
Core Word Pair Selection For the evaluation of core word pair selection, we compare our method with two baselines. The baseline Random Words randomly selects a word in each sentence to form a core word pair with focus verb. The other baseline Random Words w/ Semantic Roles randomly selects a word from the words labeled with semantic roles, which are related to the focus verb in the sentence, to form a core word pair with the focus verb. From the experimental results shown in Figure 4, we can see that our method outperforms baselines across all the datasets, verifying that our method can provide explainable intermediate clues in the form of the core word pair during metaphor detection, which is a capability lacking in previous methods.
Conceptual Domain Mining From the human evaluation results in Table 4, we can see that our method performs well on both target and source domain mining, with most metrics exceeding 80%, verifying the effectiveness of our conceptual domain mining algorithm. When comparing between
the target and source domain mining, our method achieves better performance on mining target domains than source domains. This is because information related to source domains is often absent in the context of the focus verb, making it more challenging to identify source domains. In contrast, the information related to target domains is typically explicitly present in the context of the focus verb. Despite the difficulty in mining source domains, our method still achieves good performance across most datasets.

heading: 5.7 Case Study
text: Table 5 shows several cases for explainable results mined by our method on MOH-X dataset. For example, the word drown typically has a semantic relation of theme with nouns in the domain PERSON. However, in the third sentence of Table 5, its theme is trouble, which belongs to the target domain DIFFICULTY. The core word pair drowned trouble and the inconsistency between the source and target domains indicate a metaphorical use of the verb drown in this sentence. These cases further show the effectiveness of our method for explainable metaphor detection.

heading: 6 Conclusion
text: In this paper, we propose an explainable word pair based domain mining method for tokenlevel metaphor detection. Inspired by conceptual metaphor theory, our method leverages semantic role mapping and conceptual source and target domain mining for core word pair modeling to facilitate explainable metaphor detection. Experimental results not only verify the effectiveness of our method for metaphor detection but also demonstrate its capability to determine the core word pair and conceptual domain information as explainable clues for the prediction of metaphors.
Limitations
Our work has some limitations. Currently, our method only focuses on detecting verb metaphors and providing explainable core word pair with corresponding source and target domains. Our future work needs to further explore other types of metaphors, such as noun metaphors and adjective metaphors. In addition, since our method utilizes external resources, our method may perform less effectively if verbs or context words cannot be found in these resources. Also, our method might exhibit performance decrease due to the fact that the external resources may be of lower quality in other languages.
Ethics Statement
As shown in previous studies (Navigli et al., 2023; Bartl et al., 2020), pre-trained language models exhibit biases in several respects, such as gender, ethnicity and race. Our method is fine-tuned on the pre-trained language model RoBERTabase and thus may inherit these biases.

heading: Acknowledgments
text: This work is supported in part by the National Natural Science Foundation of China under Grants #72293575 and #62206287. We thank the anonymous reviewers for the valuable comments.

heading: A More Details on Method
text: A.1 Semantic Role Mapping We use a publicly available tool1 as the VerbNet Parser in our method. The list of semantic roles that we use is shown in Table 6. We split arguments containing conjunctions like "or" or "and" into separate arguments using these conjunctions and then perform lemmatization on them.
If the VerbNet Semantic Parser fails to label the semantic roles, we use Stanford dependency parser2 as a substitute. Specifically, we use dependency relations, including nsubj, nmod:agent, obl:agent, nmod:agent and agent, to label the semantic role Agent, and dependency relations, including obj, dobj, nsubjpass, nsubj:xsubj, nsubj:pass and nsubj:passdir, to label the semantic role Theme.
1https://github.com/jgung/verbnet-parser 2https://stanfordnlp.github.io/CoreNLP/
A.2 Conceptual Domain Mining
The domain concept list is shown in Table 7. When constructing the domain concept list, we filter out overly abstract domains (e.g., THING). We also exclude overly abstract hypernyms in WordNet, including physical_entity, abstraction, and entity. When determining the hypernym path of a word w in our conceptual domain mining algorithm, if the word w can be annotated with a name entity label (i.e. person, location, organization, money, number, ordinal, percent, date, time, duration), we obtain the connection of this label and its hypernym path in WordNet as the hypernym path for w; Otherwise, we directly obtain the hypernym path of w in WordNet.
In our method, we use the function path_similarity(·) in NLTK python packages1 as the function Sim(·) in Eq. (3) to calculate the path similarity of two words in WordNet.
1https://www.nltk.org/howto/wordnet.html
path_similarity(·) returns a score denoting the similarity between two word senses, based on the shortest path that connects the senses in WordNet. The score is in the range of 0 to 1.

heading: B Dataset Construction
text: LCC-Verb LCC (Mohler et al., 2016) is a metaphor detection dataset with metaphoricity ratings of 0 to 3. We extracted verb instances to create LCC-Verb, classifying those labeled 0 as literal and those labeled 2 or 3 as metaphorical. We discarded instances labeled 1, which are possible metaphors.
C Implementation Details
We rerun the released code of baselines on our newly constructed LCC-Verb dataset. As BasicBERT has never been tested on the datasets that we use, we rerun their code2 on four datasets in our
2https://github.com/liyucheng09/BasicBERT/tree/master
experiments. We also rerun the code of MisNet1 and AdMul2 on LCC-Verb in our experiments. We use AdamW3 as our optimizer with a weight decay of 0.01. The dropout rate is 0.5. The dimension of the text embedding d is 768. The hidden dimension of MLP is 2304. The layer of Transformer blocks is 2. We train our method for 15 epochs. Table 8 shows other hyper-parameter values. We utilize Stanford CoreNLP4 for named entity tagging in our method.
The average Cohen’s kappa coefficients κ (Cohen, 1960) of the inter-rater agreement in human evaluations of explainable results on MOHX, LCC-Verb, TroFi, and VUA-Verb datasets are 0.75, 0.75, 0.70, and 0.73, respectively (note that 0.6 ≤ κ ≤ 0.8 means substantial agreement).

heading: D Experiments on ChatGPT
text: Implementation Details We also compare our method with ChatGPT. We use GPT-3.5 (gpt-3.5turbo-0613)5 and GPT-4 (gpt-4-0125-preview)6 as
1https://github.com/SilasTHU/MisNet 2https://github.com/SilasTHU/AdMul 3https://pytorch.org/docs/stable/generated/torch.optim.
AdamW.html 4https://stanfordnlp.github.io/CoreNLP/ner.html 5https://platform.openai.com/docs/models/gpt-3-5 6https://platform.openai.com/docs/models/gpt-4-and-gpt4-turbo
baseline models. We explore two prompting strategies, including the standard zero-shot prompting and the standard few-shot prompting (Brown et al., 2020). Table 9 summarizes prompts used for the experiments on ChatGPT. We randomly sampled 200, 250, 400, and 600 instances from MOH-X, LCC-Verb, TroFi and test set of VUA-Verb, respectively, as the testing datasets for ChatGPT, and then we randomly sampled 5 instances from the remaining data as the few-shot instances. To cleanse the answer, we select the initial string of either "yes" or "no" in the response provided by ChatGPT, following the process of converting all uppercase characters to lowercase in the answer string.
Experimental Results From the experimental results in Table 10, we can see that GPT-4 performs better than GPT-3.5 for metaphor detection. Although the 5-shot prompting strategy leads to performance improvements for both GPT-3.5 and GPT-4 compared to the standard zero-shot prompting strategy, our method outperforms all ChatGPT baselines, which verifies the effectiveness of our method and indicates the difficulty and challenge inherent in the task of metaphor detection.

heading: E Licenses of Scientific Artifacts
text: Table 11 shows the licenses of the scientific artifacts used in this paper.

heading: Future Work
text: Our future work needs to further explore other types of metaphors, such as noun metaphors and adjective metaphors. In addition, since our method utilizes external resources, our method may perform less effectively if verbs or context words cannot be found in these resources. Also, our method might exhibit performance decrease due to the fact that the external resources may be of lower quality in other languages