# Deep Learning-Based Knowledge Injection for Metaphor Detection: A
Comprehensive Review

Cheng Yang, Wenye Zhao, Zhiyue Liu, Qingbao Huang ∗ Cheng Yang (e-mail:
<EMAIL_ADDRESS>and Wenye Zhao (e-mail<EMAIL_ADDRESS>are graduate students of Guangxi University. Zhiyue Liu (e-mail:
<EMAIL_ADDRESS>are assistant professor of Guangxi University. Corresponding
author: Qingbao Huang (e-mail: qbhuang@gxu.edu.cn).

###### Abstract

The history of metaphor research also marks the evolution of knowledge
infusion research. With the continued advancement of deep learning techniques
in recent years, the natural language processing community has shown great
interest in applying knowledge to successful results in metaphor detection
tasks. Although there has been a gradual increase in the number of approaches
involving knowledge injection in the field of metaphor detection, there is a
lack of a complete review article on knowledge injection based approaches.
Therefore, the goal of this paper is to provide a comprehensive review of
research advances in the application of deep learning for knowledge injection
in metaphor detection tasks. In this paper, we systematically summarize and
generalize the mainstream knowledge and knowledge injection principles, as
well as review the datasets, evaluation metrics, and benchmark models used in
metaphor detection tasks. Finally, we explore the current issues facing
knowledge injection methods and provide an outlook on future research
directions.

Deep Learning-Based Knowledge Injection for Metaphor Detection: A
Comprehensive Review

††thanks: ∗ Cheng Yang (e-mail<EMAIL_ADDRESS>and Wenye Zhao
(e-mail<EMAIL_ADDRESS>are graduate students of Guangxi
University. Zhiyue Liu (e-mail<EMAIL_ADDRESS>are assistant professor of
Guangxi University. Corresponding author: Qingbao Huang (e-mail:
qbhuang@gxu.edu.cn).  Cheng Yang, Wenye Zhao, Zhiyue Liu, Qingbao Huang

##  1 Introduction

Metaphors are essentially cognitive mechanisms present in the human mind used
to construct conceptual frameworks Lakoff and Wehling (2012). This phenomenon
works by extracting familiar concepts in the target domain in order to
understand vague and abstract concepts in the source domain Lakoff and Johnson
(2008). As an important linguistic phenomenon, automatic detection of
metaphors is crucial for many practical language processing tasks, including
information extraction Tsvetkov et al. (2013), sentiment analysis Cambria et
al. (2017), machine translation Babieno et al. (2022), and seamless human-
computer interaction Rai and Chakraverty (2021). In the philosophical account
articulated in Maloney (1983), metaphor comprehension involves three distinct
phases: comprehension of the literal interpretation, discovery of
inconsistencies with the literal interpretation, and reasoning to recover the
intended non-literal interpretation. It is important to emphasize that the
focus of this study is to reveal the kinds and ways in which knowledge is
integrated in a metaphor detection task, and therefore only the first two
stages of metaphor comprehension will be explored.

Metaphor detection based on deep learning knowledge injection aims to fuse
deep learning models and external knowledge to automatically identify
metaphorical phenomena in text and improve model performance and
generalization. Recently, researchers have been exploring the application of
knowledge in metaphor detection. Mao et al. (2019) utilized generalized corpus
information as the context with detected words using MIP and SPV paradigms. Le
et al. (2020) attempted to apply dependency tree knowledge to metaphor
detection by constructing graph network adjacency matrices to utilize the
dependency tree structural information. Su et al. (2020) used a cueing
approach to transform metaphor detection into reading comprehension and
introduced local text information. Choi et al. (2021) applied MIP and SPV to
pre-trained models. Recently, Zhang and Liu (2023) achieved SOTA for the
current metaphor detection task by introducing semantic knowledge through
adversarial learning and multi-task learning. However, current knowledge
injection methods ignore the issue of the timeliness of metaphors, i.e., how
to inject knowledge in different contexts and eras, what kind of knowledge to
use, and the criteria for defining the meaning of metaphors. These issues are
crucial for the metaphor detection task.

In the past, although several research surveys on metaphor detection have
existed. Rai and Chakraverty (2021); Ptiček and Dobša (2023) provided an
overview of metaphor theory and computational processing methods, Abulaish et
al. (2020) surveyed six technical approaches to metaphorical language, and
Tong et al. (2021) delved into metaphor processing methods and their
applications. However, none of these surveys has taken the principle of
knowledge infusion as a primary research focus. Against this background, the
present survey aims to fill the gap in this research area. First, we
systematically sorted out the mainstream knowledge types and knowledge
injection principles, and used an innovative categorization method to
organically integrate these studies. Second, we conducted an exhaustive review
and analysis of the current major metaphor datasets, including their different
variants, assessment metrics, and benchmarks. Finally, we provide insights
into the strengths and limitations of different knowledge injection methods,
and offer suggestions and outlooks for future metaphor detection research.

##  2 Knowledge

In this section, we provide an introduction to the types of knowledge that are
commonly used in metaphor detection tasks and how they are used. We categorize
these knowledge types as semantic, syntactic, and affective knowledge.

###  2.1 Syntactic Knowledge

Syntactic knowledge involves rules and information about sentence structure,
grammatical relationships between words, and sentence components in a
language.
Part-of-Speech Tagging Part-of-Speech (POS) is the tagging of each word in a
sentence to indicate its grammatical role or lexical category in the context.
Commonly used POS tag sets include Universal POS tag set Petrov et al. (2011),
which defines a simplified set of lexical tokens with 17 tokens, such as NOUN
(noun), VERB (verb), etc., and Treebank tag set Santorini (1990), which has
more detailed tokens, including JJ (adjective), JJS (adjective with a supreme
ending -est), etc. In metaphor detection tasks, researchers usually combine
POS knowledge directly into the input sequence Song et al. (2021); Feng and Ma
(2022), or construct multitask learning with POS as an auxiliary task Le et
al. (2020).
Dependency Trees A Dependency Tree (DT) is a syntactic structural tree used to
efficiently represent dependency relationships between words in a sentence. In
a Dependency Tree, each word is given a node and is connected by edges to
represent the directional relationship from the dependent (subordinate) word
to its main dependent (head of the subordinate) word. In metaphor detection
tasks, researchers often utilize dependency tree knowledge to improve the
syntactic comprehension of their models. Le et al. (2020) employed Graph
Convolutional Network (GCN), which uses the dependency tree knowledge as an
adjacency matrix in order to build a graphical structure of dependency
relationships between words. Some studies Song et al. (2021); Feng and Ma
(2022), on the other hand, focused on extracting subject-verb-object
relationships in dependency trees to aid in metaphor detection. Song et al.
(2021) processed the output of subject-predicate-object correspondences in
text by combining, averaging, and maximizing to further capture the
associations between structural semantics, while Feng and Ma (2022) use a BERT
Decoder Devlin et al. (2019) to allow the model to generate the start and end
positions of subject-predicate-objects based on the context.

###  2.2 Semantic Knowledge

Semantic knowledge refers to the meaning of words, their denotations, the
relationships between words, and the semantic interpretation of words in
different contexts.
VerbNet VerbNet Schuler (2005) is a verb categorization database containing
nearly 4,000 English verb lemmas (lemma), and its category design refers to
the study of Levin Somers (1994). In VerbNet, each verb is attributed to one
or more categories that describe the semantic roles of the verb, syntactic
constraints, and semantic relations between different categories, etc. VerbNet
provides two main categorization approaches: based on syntactic structure and
based on predicate meaning. In the metaphor detection task, researchers Gong
et al. (2020); Beigman Klebanov et al. (2016) used VerbNet’s class information
to convert each lexical unit into a binary feature vector. In addition to the
syntactic behavior and predicate meaning categories, Beigman Klebanov et al.
(2016) introduced a classification based on verb semantic roles and
restrictive semantic roles on top of that. The experimental results show that
the model with the introduction of syntactic behavior features performs
optimally.
FrameNet The main goal of FrameNet Baker et al. (1998); Lowe (1997) is to
provide sentences with semantic and syntactic annotations for a large part of
the vocabulary in contemporary English. The corpus of this resource is built
on The British National Corpus Consortium et al. (2007).FrameNet employs a
semantic description based on frames (frames), each of which represents a
semantic concept and describes the events, participants, attributes,
relations, etc. associated with that concept. The project Fillmore et al.
(2002) is an extended version of FrameNet, which adds the US National Corpus
resources. In the metaphor detection task, Li et al. (2023c) used the FrameNet
database provided by Fillmore et al. (2002) in the task for frame category
prediction of target and contextual lexical units.
WordNet WordNet Miller (1995); Fellbaum (1998) is a hierarchically structured
lexical database in which each word forms links with other related words to
represent the semantic connections between them. In the metaphor detection
task, Gong et al. (2020); Beigman Klebanov et al. (2016) classified words into
fifteen categories based on the semantic links between words in WordNet and
converted these categories into binary feature vectors. Such feature vectors
can be used to assist the metaphor detection task and improve the performance
of the model. And Zhang and Liu (2023) considered the first of the WordNet
example sentences as literal meanings and used it for multi-task learning.
Dictionary knowledge Dictionary example sentences or paraphrase knowledge are
intended to provide the model with knowledge of the polysemous and
metaphorical meanings of the words to be detected, and help the model better
understand the semantic changes and metaphorical expressions of the words to
be detected in different contexts. In metaphor detection, some researchers
have utilized lexical examples to extract the context-based basic meanings of
the words to be detected Zhang and Liu ; Zhang and Liu (2023), instead of the
traditional approach of directly using the words to be detected as the basic
meanings. Su et al. (2021) combined the lexical paraphrase information into
the model input to achieve knowledge fusion.
Concreteness Concreteness is the degree to which a word is characterized by
the concept or meaning it expresses in a language. In metaphor detection
tasks, researchers often rely on the word specificity rating dataset
constructed by Brysbaert et al. (2014). This dataset is based on the SUBTLEX-
US corpus Brysbaert and New (2009) and covers 37,058 token-level samples. This
dataset was rated using a 5-point scale from abstract to concrete, and the
data was collected with the help of Internet crowdsourcing. In previous
studies Klebanov et al. (2014); Gong et al. (2020); Beigman Klebanov et al.
(2016), lexical units to be detected are transformed into binary feature
vectors depending on their concreteness ratings for further research and
analysis in metaphor detection tasks.
Topic Using the Latent Dirichlet Allocation (LDA) model Blei et al. (2003),
research scholars extracted a model containing 100 topics from the New York
Times (NYT) corpus Sandhaus (2008) to characterize general topics discussed by
the public. In the metaphor detection task, the previous research work
Klebanov et al. (2014); Gong et al. (2020); Beigman Klebanov et al. (2016)
matched and associated the words in each instance with these 100 topics,
followed by the calculation of probability scores for each word under each
topic. These resulting scores were used as topic features in a metaphor
detection task.

SK | SYK | EK | Core Structure | Loss Function | Papers
---|---|---|---|---|---
|  |  | BERT+GRL / BERT+GBM | $\mathcal{L}=\frac{1}{\left|\mathcal{N}^{k}\right|}\sum_{i=1}^{\left|\mathcal{N}^{k}\right|}L_{CE}\left(\hat{y}^{k}_{i},y^{k}_{i}\right)$ |  Zhang and Liu (2023)
Mao and Li (2021)
Mao et al. (2022)
✓ |  |  |  Multi-task learning based on example sentence knowledge or POS labeling, where $|\mathcal{N}^{k}|$ represents the number of samples for the kth task, and $\hat{y}^{k}_{i}$ and $y^{k}_{i}$ represent the predicted and true labeled values for the kth task, respectively.
✓ |  |  | BERT+MIP+SPV / BERT | $\mathcal{L}=\frac{1}{\left|\mathcal{N}\right|}\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{y}_{i},y_{i}\right)$ |  Li et al. (2023b)
Zhang and Liu
|  |  Use dictionary examples, and explanations of the words to be tested as auxiliary inputs. |  Su et al. (2021)
Babieno et al. (2022)
|  |  | BERT | $\scriptstyle\mathcal{L}_{m}=\frac{1}{\left|\mathcal{N}\right|}\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{y}_{i},y_{i}\right);\;\mathcal{L}_{e}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}$ |  Dankers et al. (2019)
Badathala et al. (2023)
|  | ✓ |  Introducing multi-task learning with affective knowledge, $L_{m}$ for metaphor or exaggeration task loss, $L_{e}$ for affective regression task loss
|  |  | BERT | $\scriptstyle\mathcal{L}^{k}=\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{s}^{k}_{i},s^{k}_{i}\right)+\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{e}^{k}_{i},e^{k}_{i}\right)$ |  Feng and Ma (2022)
✓ | ✓ |  |  $k\in(sub,tar,obj)$ is the prediction loss for the main predicate, $\hat{s}^{k}_{i}$ and $\hat{e}^{k}_{i}$ denote the start and end positions of the kth grammatical category, respectively
|  |  | BERT | $\mathcal{L}=\frac{1}{\left|\mathcal{N}\right|}\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{y}_{i},y_{i}\right)$ |  Su et al. (2020)
Gong et al. (2020)
✓ | ✓ |  |  Using semantic knowledge as portfolio input
|  |  | BiLSTM+GCN | $\mathcal{L}\left(x^{t},y^{t}\right)=-\log P^{t}\left(y^{t}\mid x^{t}\right)+\lambda\left\|V^{wsd}-V^{md}\right\|_{2}^{2}$ |  Le et al. (2020)
✓ | ✓ |  |  Multi-task learning based on dependency trees, where $t\in{wsd,md}$. $V^{wsd}$ and $V^{md}$ denote the representation vectors of the same input sentence $x^{t}$, respectively.
|  |  | BERT+MIP+SPV | $\scriptstyle\mathcal{L}^{c}_{k}=-\frac{1}{|\mathcal{N}|}\sum_{i=1}^{|\mathcal{N}|}\sum_{j=1}^{|\mathcal{C}|}y_{ij}\log\left(\hat{y}_{ij}\right);\;\mathcal{L}^{s}=-\frac{1}{\left|\mathcal{N}\right|}\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{y}_{i},y_{i}\right)$ |  Li et al. (2023c)
✓ |  |  |  $\mathcal{L}^{c}$ represents the loss of FrameNet fine-tuning Encoder, where $k\in(target,cls)$ denotes the loss of global Frame and target word Frame; $\mathcal{L}^{s}$ denotes the loss of metaphorical classification.
|  |  | BERT | $\scriptstyle\mathcal{L}_{1}=\frac{1}{\left|\mathcal{N}\right|}\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{y}_{i},y_{i}\right);\;\mathcal{L}_{2}=\sum_{j=0}^{\left|\mathcal{K}\right|}I(f_{i}=g_{i}^{j})\log(\alpha_{i}^{j})$ |  Wan et al. (2021)
✓ |  |  |  Multi-task learning for word sense disambiguation is introduced. Where $f_{i}$ denotes the correct paraphrase of word $word_{i}$ in sentence $s$, $I(X)=1$ denotes when $X$ is true, and $I(X)=0$ denotes when $X$ is false.
|  |  | BERT+MIP+SPV / BERT | $\mathcal{L}=\frac{1}{\left|\mathcal{N}\right|}\sum_{i=1}^{\left|\mathcal{N}\right|}L_{CE}\left(\hat{y}_{i},y_{i}\right)$ |  Wang et al. (2023)
Song et al. (2021)
| ✓ |  |  Guide the model to focus on structural information based on the dependency tree structure of the text
Table 1: Abstract of metaphor detection System Based on Knowledge Injection.
SK: Semantic Knowledge. SYK: Syntactic Knowledge. EK: Emotional Knowledge.
Core Structure: Subject Model Architecture.

###  2.3 Emotional Knowledge

Affective knowledge refers to information and understanding related to
emotions, emotional expression, and emotional attitudes. This section will
review emotional knowledge in metaphor detection tasks.
VAD model VAD Mehrabian (1996) is an affective classification system for
describing and measuring the three main dimensions of human affective
experience: valence, arousal, and dominance.EmoBank corpus Buechel and Hahn
(2017) is a VAD model-based and balanced multi-type 10k English corpus of
sentences, each labeled with one to five ratings on the three VAD dimensions.
In the metaphor detection task, Dankers et al. (2019) introduced the EmoBank
corpus Buechel and Hahn (2017) as an auxiliary task. Sentence-level sentiment
regression was constructed based on each dimension in EmoBank. In its training
process, a batch of metaphor or sentiment task data sampling is randomly
selected for training at each step.
Hyperbole corpus Exaggeration usually involves over- or under-exaggeration of
an emotion, sentiment or attitude. Combining a dataset for hyperbole detection
with a metaphor detection task can make the model more sensitive to capturing
emotions and sentiments in text. In a previous study, Badathala et al. (2023)
introduced two hyperbole corpora, named HYPO and HYPO-L, and subsequently
labeled them with metaphors. The experimental results show that multitask
learning based on hyperbole and metaphor gains in both two-way performance.

##  3 Method

In the metaphor detection task, knowledge injection, as a key method, aims to
improve the model’s understanding and representation of metaphorical phenomena
by integrating external knowledge with deep learning models. This chapter will
comprehensively introduce the current mainstream knowledge injection methods.
Table 1 demonstrates a summary of knowledge injection-based metaphor detection
systems.

###  3.1 Model fine-tuning

Using semantic knowledge to fine-tune the model is a common approach. Li et
al. (2023c) uses two Encoders, which first fine-tune the Conceptual Encoder
model on FrameNet Fillmore et al. (2002). For the output features
$H=(h_{cls},h_{0},...,h_{n},h_{seq})$. We get the Frame distribution of
sentences and targets as follows:

$\displaystyle\hat{y}_{cls}$ $\displaystyle=sig(W_{0}h_{cls}+b_{0})$ (1)
$\displaystyle\hat{y}$ $\displaystyle=softmax(W_{1}H+b_{1}),$ (2)

where $W_{0}$ and $W_{1}$ are learnable parameters and b0 and b1 are biases.
The fine-tuned Conceptual Encoder will be injected with knowledge through MIP
and SPV:

$\displaystyle h_{MIP}=v_{t}\oplus v_{S,t}\oplus h_{t}\oplus h_{S,t}$ (3)
$\displaystyle h_{SPV}=v_{cls}\oplus v_{S,t}\oplus h_{cls}\oplus h_{S,t},$ (4)

where $v_{cls},h_{cls}$ denote the two Encoder cls corresponding to the hidden
layer output, $v_{t},h_{t}$ denote the isolated target word features, and
$v_{S,t},h_{S,t}$ denote the contextual target word features, respectively.

Le et al. (2020) uses the structural knowledge of the dependency tree to
construct the construction of the GCN network adjacency matrix, which is
denoted by the adjacency matrix A as:

$\displaystyle A_{ij}=\begin{cases}1,&\text{ if }i=j\text{ or }j\rightarrow
i\text{ or }i\rightarrow j\\\ 0,&\text{ otherwise }\end{cases}.$ (5)

To enhance and GCN representation, Le et al. (2020) added reverse edges and
the self loops extra edges.

###  3.2 Additional inputs

This type of approach aims to enhance the model’s understanding of the context
by inputting knowledge into the model along with the text to be detected. The
current dominant structure for knowledge-attached input is MIP+SPV.

MIP Group (2007) (Metaphor Identification Program) was originally introduced
by the Pragglejaz Group. Its core logic consists in comparing the difference
between a lexical unit in its original meaning and its meaning in context. For
the text input $S=([cls]w_{0},...,w_{k},....w_{n}[seq])$ and the test word
$w_{k}$, the meaning of a lexical unit in context is defined as:

$\displaystyle V_{S,k}=f_{b}(S)[k+1],$ (6)

where $f_{b}$ denotes the Encoder and $V_{S,k}$ is the corresponding output of
the kth hidden layer in the text to be detected. The vocabulary unit is
intrinsically defined as:

$\displaystyle V_{k}=f_{b}(w_{k}).$ (7)

| VUA ALL | VUA Verb | MOH-X (10 fold) | TroFi (10 fold)
---|---|---|---|---
| Pre. | Rec. | F1 | Acc. | Pre. | Rec. | F1 | Acc. | Pre. | Rec. | F1 | Acc. | Pre. | Rec. | F1 | Acc.
Gao et al. (2018) |  |  |  |  | 53.4 | 65.6 | 58.9 | 69.1 | 75.3 | 84.3 | 79.1 | 78.5 | 68.7 | 74.6 | 72 | 73.7
Gao et al. (2018) | 71.6 | 73.6 | 72.6 | 93.1 | 68.2 | 71.3 | 69.7 | 81.4 | 79.1 | 73.5 | 75.6 | 77.2 | 70.1 | 71.6 | 71.1 | 74.6
Gao et al. (2018) | 71.5 | 71.9 | 71.7 | 92.9 | 66.7 | 71.5 | 69 | 80.7 | 75.1 | 81.8 | 78.2 | 78.1 | 70.3 | 67.1 | 68.7 | 73.4
Mao et al. (2019) | 71.8 | 76.3 | 74 | 93.6 | 69.3 | 72.3 | 70.8 | 82.1 | 79.7 | 79.8 | 79.8 | 79.7 | 67.4 | 77.8 | 72.2 | 74.9
Mao et al. (2019) | 73 | 75.7 | 74.3 | 93.8 | 66.3 | 75.2 | 70.5 | 81.8 | 77.5 | 83.1 | 80 | 79.8 | 68.6 | 76.8 | 72.4 | 75.2
Gong et al. (2020) | 74.6 | 71.5 | 73 |  | 76.7 | 77.2 | 77 |  |  |  |  |  | 72.6 | 67.5 | 69 |
Le et al. (2020) | 74.8 | 75.5 | 75.1 |  | 72.5 | 70.9 | 71.7 | 83.2 | 79.7 | 80.5 | 79.6 | 79.9 | 73.1 | 73.6 | 73.2 | 76.4
Rohanian et al. (2020) |  |  |  |  |  |  |  |  | 80 | 80.4 | 80.2 | 80.5 | 73.8 | 71.8 | 72.8 | 73.5
Leong et al. (2020) | 80.4 | 74.9 | 77.5 |  | 79.2 | 69.8 | 74.2 |  |  |  |  |  |  |  |  |
Su et al. (2020) | 82 | 71.3 | 76.3 |  | 79.5 | 70.8 | 74.9 |  | 79.9† | 76.5† | 77.9† |  | 53.7† | 72.9† | 61.7† |
Song et al. (2021) | 82.7 | 72.5 | 77.2 | 94.7 | 80.8 | 71.5 | 75.9 | 86.4 | 80 | 85.1 | 82.1 | 81.9 | 70.4 | 74.3 | 72.2 | 75.1
Wan et al. (2021) | 82.5 | 72.5 | 77.2 | 94.7 | 78.9 | 70.9 | 74.7 | 85.4 |  |  |  |  | 89.3 | 89.3 | 89.3 | 88.2
Choi et al. (2021) | 80.1 | 76.9 | 78.5 |  | 78.7 | 72.9 | 75.7 |  | 79.3† | 79.7† | 79.2† |  | 53.4† | 74.1† | 62† |
Li et al. (2023c) | 82.7 | 75.3 | 78.8 |  |  |  |  |  | 83.2† | 84.4† | 83.8† |  | 70.7† | 78.2† | 74.2† |
Babieno et al. (2022) | 79.3 | 78.5 | 78.9 |  | 60.9 | 77.7 | 68.3 |  | 81 | 80 | 80.2 |  | 53.2 | 72.8 | 61.4 |
Lin et al. (2021) | 79.3 | 78.8 | 79 | 94.8 | 78.1 | 73.2 | 75.6 | 85.8 | 85.7 | 84.6 | 84.7 | 85.2 | 74.4 | 74.8 | 74.5 | 77.7
Wang et al. (2023) | 80 | 78.2 | 79.1 |  |  |  |  |  | 77∗ | 83.5∗ | 80.1∗ |  | 54.2∗ | 76.2∗ | 63.3∗ |
Zhang and Liu  | 80.4 | 78.4 | 79.4 | 94.9 | 78.3 | 73.6 | 75.9 | 86 | 84 | 84 | 83.4 | 83.6 | 67.5 | 77.6 | 71.9 | 73.6
Feng and Ma (2022) | 81.6 | 77.4 | 79.4 | 95.2 | 81.6 | 71.1 | 76 | 86.4 | 89.5 | 85.2 | 87 | 87.5 | 72.5 | 77.5 | 74.8 | 77.7
Su et al. (2021) |  |  |  |  | 76 | 76 | 76 | 85.7 | 82.9 | 84 | 83.4 | 84.2 | 73.3 | 69.6 | 71.4 | 75.7
Zhang and Liu (2023) | 78.4 | 79.5 | 79 | 94.7 | 78.5 | 78.1 | 78.3 | 87 | 87.4 | 88.8 | 87.9 | 88 | 70.5 | 79.8 | 74.7 | 76.5
Table 2: The table shows the performance of the metaphor detection system on
four datasets, VUA ALL, VUA verb, MOH-X and TroFi, in recent years. Among
them, most of the results on the MOH-X and TroFi datasets are based on ten-
fold cross-validation, and also include some results derived from direct
computation († labeling), as well as some of the models are trained on the
VUA20 dataset (∗ labeling).

SPV (Selectional Preference Violation) was originally introduced to the field
of metaphor detection by Wilks et al. (2013), and its core logic lies in
comparing the semantic differences between lexical units and their surrounding
words. The semantic information of surrounding words can be defined as:

$\displaystyle V_{S}=f_{b}(S)[0],$ (8)

where $f_{b}$ denotes the Encoder and $V_{S}$ denotes the corresponding hidden
layer output of cls in the text to be detected. And the semantic information
of the SPV lexical units is similar to MIP as $V_{S,t}$

In the metaphor detection task, the researcher introduced example sentences
Zhang and Liu ; Li et al. (2023b), word paraphrases Su et al. (2021); Babieno
et al. (2022), or other relevant knowledge Gong et al. (2020); Su et al.
(2020) as additional input to the knowledge.

To the original sentence: $s=(w_{0},...,w_{k},...,w_{n})$, and the word to be
detected is $w_{k}$, Su et al. (2021) combines the first $k$ paraphrases from
the dictionary to the input and combines the output features by the mean-pool
operation:

$\displaystyle h^{l}=\begin{cases}f_{b}(w_{k})&l=0\\\
\frac{1}{N_{l}}\sum_{i=1}^{N_{l}}f_{b}(w^{l}_{i})&l\neq 0\end{cases},$ (9)

where $f_{b}$ denotes the BERT encoder Devlin et al. (2019), $w_{k}$ is the
target word, $l=0$ is the original sentence, $l>0$ is the paraphrase, and all
the outputs will be used for the final classification after cat. While Zhang
and Liu ; Li et al. (2023b); Babieno et al. (2022) separate the sentence to be
tested from the knowledge:

$\displaystyle S_{1}=([cls]w_{0},...w_{k},...w_{n}[seq])$ (10) $\displaystyle
S_{2}=([cls]w^{\prime}_{0},...w^{\prime}_{k},...w^{\prime}_{n}[seq]),$ (11)

where $S_{1},S_{2}$ text and knowledge text to be detected, $w^{\prime}_{k}$
corresponds to the hidden layer output $h^{\prime}_{k}$ will be used as the
basic word paraphrase of the reinforced MIP for the subsequent metaphor
detection.

###  3.3 Output modulation

Pre-defined knowledge information can not only be used as input to the model,
but also direct its attention to specific semantic content or syntactic
structures when adjusting the model output. Wang et al. (2023); Song et al.
(2021) assign different weights to the model output based on the introduced
knowledge.

Wang et al. (2023) measure the distance between the context word and the
target word in terms of the number of jumps between neighboring words. For the
output feature: $H=(h_{1},h_{2},\ldots,h_{n})$, its final output is:

$\displaystyle h^{\prime}_{i}=\frac{1}{n}\sum h_{i},i\in\mathcal{C}_{n},$ (12)

where $h^{\prime}_{i}$ is the final output feature and
$\mathcal{C}_{n},n\in(1,2,3,4)$ denotes the $n$th neighboring word in the
adjacent range of the parse tree centered on the target word.

Song et al. (2021) used three combination approaches (concatenation, average
and maxout) to extract high dimensional features. For example, the contextual
features obtained using the concatenation combination approach are defined as
follows:

$\displaystyle c=c_{subj}\oplus c_{obj}\oplus c_{cls}\oplus c_{bsc},$ (13)

where $c_{k},\;k\in(\text{cls, subj, obj, bsc})$, are the hidden layer outputs
corresponding to the basic meanings of cls, subjects, objects and verbs,
respectively. In order to capture the interaction of target lexical units with
a specific context, Song et al. (2021) used linear, bilinear or a combination
of both. Specifically, the linear and bilinear combination approaches are
defined as follows, respectively:

$\displaystyle r_{vc-linear}=\sigma(V_{r}\begin{pmatrix}v\\\
c\end{pmatrix}+a_{r})$ (14) $\displaystyle r_{vc-
bilinear}=\sigma(v^{\intercal}A_{r}c+b_{r}),$ (15)

where $V_{r}$ and $A_{r}$ are the trainable weight parameters, respectively,
and $a_{r}$ and $b_{r}$ are the bias parameters, $\sigma$ is the sigmoid
function, and the resulting relational features will be used for metaphorical
judgment.

Feng and Ma (2022) Using Decoder to allow the model to predict the position of
the subject-predicate-object in the sentence, for the output text feature
$H=(h_{0},...,h_{n})$, there are:

$h^{\prime}_{t}=f_{d}(w^{\prime}_{x<t},H),\;t\in[1,7],$ (16)

where $h^{\prime}_{t}$ is the $t$ th predicted output, $t\in[1,6]$ corresponds
to the indexes of the beginning and the end of the subject-predicate-object in
the sentence, respectively, and $h^{\prime}_{7}$ is the metaphorical
classification result.

###  3.4 Multi-task based knowledge fusion

Introducing other associated tasks can effectively promote knowledge fusion
between tasks, thus helping to improve metaphor detection performance. In
automatic metaphor detection, the researchers introduced several auxiliary
tasks, including Word Sense Disambiguation (Word Sense Disambiguation) Le et
al. (2020); Wan et al. (2021); Zhang and Liu (2023), lexical annotation Mao
and Li (2021); Mao et al. (2022), and tasks based on VAD sentiment labeling
Dankers et al. (2019).

For metaphor detection and lexical disambiguation input
$s=(w_{0},...,w_{n}),s^{\prime}=(w^{\prime}_{0},...,w^{\prime}_{n})$, Le et
al. (2020) have used different models to extract features with respectively:

$\displaystyle H^{wsd}=f_{b}^{wsd}(s),H^{md}=f_{b}^{md}(s^{\prime}),$ (17)

where $H^{wsd},H^{md}$ are the word sense disambiguation and metaphor
detection output features, respectively, and $f_{c}^{wsd},f_{c}^{md}$ are
their corresponding models, and the output features will be used to obtain the
fusion knowledge through similarity Loss:

$\displaystyle Loss_{similarity}=\lambda||H^{wsd}-H^{md}||.$ (18)

Wan et al. (2021) combines the two types of features by means of a
combination:

$\displaystyle
p^{\prime}_{i}=\sum_{k=0}^{m_{i}-1}\alpha_{i}^{j}p_{i}^{j},\;\alpha_{i}^{j}=\frac{exp(h_{i}p^{j}_{i})}{\sum_{k=0}^{m_{i}-1}exp(h_{i}p^{j}_{i})},$
(19)

where $h_{i}$ metaphorical text feature, $p_{i}^{j}$ is the $j$th paraphrase
of the $i$th metaphorical text, and the weighted combination of paraphrase
features will be concatenated into the metaphorical features.

Zhang and Liu (2023) introduced the Gradient Reversal Layer (GRL) Ganin and
Lempitsky (2015) module for adversarial learning. GRL aims to train on a large
amount of labeled data in the source domain and a large amount of unlabeled
data in the target domain through an inverse-gradient strategy, allowing the
model to learn the target data distribution. Zhang and Liu (2023) constructed
two lossy attempts to incorporate the knowledge of the WSD task into metaphor
detection, for the $i$ th input sample $s_{i}=(w_{0},...,w_{n})$:

$\displaystyle
L^{g}=\frac{1}{\left|\mathcal{D}\right|}\sum\;L_{CE}((W^{g}f_{b}(s_{i})+b^{g}),d_{i})$
(20) $\displaystyle
L^{l}=\frac{1}{\left|\mathcal{D}\right|}\sum\;L_{CE}((W^{l}(\hat{y_{i}}f_{b}(s_{i}))+b^{l}),d_{i}),$
(21)

where $f_{b}$ is the BERT encoder and $\hat{y_{i}}$ is the model output
distribution. $\mathcal{D}=\mathcal{D_{MD}}\cup\mathcal{D_{WSD}}$,
$d_{i}\in(0,1)$ is the task labeling, where $0,1$ are Metaphor Detection and
WSD, respectively. through an adversarial learning approach, the inverse
gradient of $L^{g}$ aims to steer the model for global knowledge fusion, while
$L^{l}$ further fine-tunes the output distribution of the model so that the
metaphor-biased samples are closer to the non-basic samples in WSD, while the
literal samples are closer to the basic samples, thus realizing fine-grained
knowledge fusion.

Mao and Li (2021); Mao et al. (2022) proposed a gating mechanism based on
multi-task learning, whose structure consists of three main parts: reset gate,
update gate, and fusion gate. Specifically, consider the main task output
feature $H^{j}$ of a particular layer and multiple subtask features $H^{m}$
(where $m\neq j$). The reset gate and its filtering features can be
represented as:

$\displaystyle R^{m}=\sigma(W^{m}_{\phi_{R}}H^{m}+b^{m}_{\phi_{R}})$ (22)
$\displaystyle C^{m}=tanh(W^{m}_{\phi_{C}}(R^{m}\odot
H^{m})+b^{m}_{\phi_{C}}),$ (23)

where $W^{m}_{\phi_{R}},b^{m}_{\phi_{R}},W^{m}_{\phi_{C}},b^{m}_{\phi_{C}}$ is
the trainable parameter, $\sigma$ is the sigmoid activation function, and
$\odot$ is the element-wise product. the update gate will learn joint
information of the main task and a certain auxiliary task. For the main task
$H^{j}$ and a certain auxiliary task $H^{m}$, the update gate and its combined
features are denoted as:

$\displaystyle
Z^{m}=\sigma(W^{m}_{\phi_{Z}}H^{j}+b^{m}_{\phi_{Z}}+V^{m}_{\phi_{Z}}C^{m}+d^{m}_{\phi_{Z}})$
(24) $\displaystyle F^{m}=Z^{m}\odot H^{j}+(1-Z^{m})\odot C^{m},$ (25)

where $W^{m}_{\phi_{Z}},b^{m}_{\phi_{Z}},V^{m}_{\phi_{Z}},d^{m}_{\phi_{Z}}$
are the trainable parameters, and $F^{m}$ are the combined features of the
main task and the $m$th auxiliary task. The fusion gate will fuse all the
combined features obtained by the update gate and use them for subsequent
feature extraction or classification, i.e:

$G^{j}=\sigma(W^{j}_{\phi_{G}}(\sum_{m\neq j}F^{m})+b^{j}_{\phi_{G}}),$ (26)

where $W^{j}_{\phi_{G}},b^{j}_{\phi_{G}}$ are the trainable weights and bias,
respectively.

##  4 Dataset and Metrics

This section provides an overview of the current mainstream metaphor detection
datasets and their metrics. The datasets include VUA and its variants VUA ALL
POS, VUA ALL, VUA SEQ, VUAverb, VUA18, and VUA20, as well as two other
commonly used datasets, TroFi and MOH/MOH-X. Table 1 summarizes the
performance of the metaphor detection task on four datasets.

Dataset | #Tok. | #Sent. | %Met.
---|---|---|---
VUAall/SEQ | 205,425 | 10,567 | 11.6%
VUAall/SEQ/tr | 116,622 | 6,323 | 11.2%
VUAall/SEQ/val | 38,628 | 1,550 | 11.6%
VUAall/SEQ/te | 50,175 | 2,694 | 12.4%
VUAallpos | 94,807 | 16,202 | 15.8%
VUAallpos_tr | 72,611 | 12,122 | 15.2%
VUAallpos_te | 22,196 | 4,080 | 17.9%
VUAverb_tr | 15,516 | 7,479 | 27.9%
VUAverb_val | 1,724 | 1,541 | 26.9%
VUAverb_te | 5,873 | 2,694 | 29.9%
MOH-X | 647 | 647 | 48.7%
TroFi | 3,737 | 3,737 | 43.5%

Table 3: Dataset statistics. tr: training set. val: validation set. te: test
set. tokens: number of samples. sent.: total number of sentences, %Met.:
percentage of metaphorical samples

###  4.1 Dataset

VUAMC The VU Amsterdam Metaphor Corpus The VU Amsterdam Metaphor Corpus Steen
et al. (2010) annotates each lexical unit (187,570 in total) in a subset of
the British National Corpus (BNC Consortium, 2007) Consortium et al. (2007)
metaphorically. The corpus tags sentences using the MIPVU metaphor detection
program.VUAMC is the largest publicly available annotated corpus for token-
level metaphor detection, and the only one that investigates the metaphorical
nature of dummy words. Based on VUAMC, many variants of VUA have emerged.
VUA ALL POS VUA ALL POS dataset has been applied to the shared task of
metaphor detection Leong et al. (2018, 2020), which consists of two parts, VUA
ALL POS and VUA Verb. In particular, VUA ALL POS annotates all real-sense
words (including adjectives, verbs, nouns, and adjectives) in a sentence;
while VUA Verb covers only verbs. However, in the studies of Song et al.
(2021); Feng and Ma (2022); Wan et al. (2021); Su et al. (2020), the VUA ALL
POS dataset also includes dummy words. In order to distinguish it from the
shared task of Leong et al. (2018, 2020) in the definition, we name the VUA
ALL POS dataset that includes both real and dummy words as VUA ALL.
VUA Verb Since VUA Verb contains only verbs and no other variants, we used the
dataset reported in the metaphor detection shared task Leong et al. (2018,
2020).. In VUA Verb, 15,516 samples were used for training and 5,873 for
testing.
VUA SEQ VUA SEQ is another dataset constructed based on VUAMC. Compared to VUA
ALL, VUA SEQ has the same number of samples as reported in Gao et al. (2018);
Neidlein et al. (2020). However, VUA SEQ covers all tokens in a sentence, even
punctuation, in the classification task, thus leading to a richer number of
target tokens used than VUA ALL.
VUA18 According to Choi et al. (2021), VUA-18 is very similar to VUA-SEQ and
VUA ALL as they use the same sentences in each subset, 6,323, 1,550, and 2,694
sentences for the training, development, and test sets, respectively. VUA-18
does not consider abbreviations and punctuation as separate tokens, and has
the same labeling rules as VUA ALL. We therefore categorize VUA-18 with VUA
ALL.
VUA20 In the literature Choi et al. (2021); Li et al. (2023c); Wang et al.
(2023), VUA20 labeled 1.2k sentences with real and imaginary words. However,
this does not match the description in the 20-year shared task Leong et al.
(2020). The text states that it uses the same VUA as the 18-year shared task
Leong et al. (2018) (see Section 3.1, lines 8-10) and that both report the
same token count. Given this, we will not list VUA-18 and VUA-20 in the model
performance table.
TroFi TroFi is a verb-target focused dataset containing the literal and
metaphorical usage of 50 English verbs from the 1987-1989 Wall Street Journal
corpus Charniak et al. (2000). The dataset contains a total of 3717 samples,
including 2741 training samples and 968 test samples.
MOH The MOH dataset Mohammad et al. (2016) consists of 1639 sentences
extracted from WordNet, which contains 1230 sentences for literal usage and
409 sentences for metaphorical usage. And it was labeled with metaphors using
crowdsourcing. MOH-X Shutova et al. (2016), on the other hand, is a subset of
the MOH dataset that focuses on collecting samples containing verbs. In MOH-X,
each verb covers multiple semantic meanings, at least one of which is
metaphorical usage.

###  4.2 Metrics

Current mainstream neural models treat metaphor detection as a dichotomous
sequence labeling task. Among their commonly used evaluation metrics,
precision measures the degree of correct prediction, while recall measures the
completeness of the categorization or information retrieval system. The
reconciled mean of precision and recall is known as the F-score, which is high
when both precision and recall are high. Precision, Recall, F-score and
Accuracy are defined respectively:

$\displaystyle Pre=$ $\displaystyle\frac{TP}{TP+FP}$ (27) $\displaystyle Rec=$
$\displaystyle\frac{TP}{TP+FN}$ (28) $\displaystyle F1=$
$\displaystyle\frac{2\times Pre\times Rec}{Pre}$ (29) $\displaystyle Acc=$
$\displaystyle\frac{TP+TN}{TP+TN+FP+FN},$ (30)

where true positives (TP) indicate the number of texts identified as
metaphorical and actually metaphorical, false positives (FP) indicate the
number of texts identified as metaphorical but actually non-metaphorical, and
true negatives (TN) indicate the number of texts identified as non-
metaphorical and actually non-metaphorical.

##  5 Conclusion

In this section, we summarize the problems faced in the metaphor detection
task as follows and explore possible future research directions.
Refining the criteria for defining metaphors Current metaphor definition
criteria ignore the problem of lexical lag. Lexical lag includes dataset
lexical lag and knowledge lexical lag. The dataset lexical lag manifests
itself in the manual labeling of metaphors using outdated labeling methods,
such as the guiding principle of VUA Steen et al. (2010) labeling that treats
literal meanings as the more basic meaning of a word. Whereas, knowledge
lexical lag is manifested in the lack of a clear basis for defining literal
meanings when researchers introduce knowledge, e.g., Zhang and Liu (2023)
consider the first paraphrase in WordNet as the basic meaning; Su et al.
(2021); Zhang and Liu  treat the first k example sentences in the dictionary
as delineated as non-metaphorical expressions. On the other hand, the lexical
lag of the dataset motivates researchers to use the same or approximate
metaphor definition methods. Therefore, refining the metaphor definition
criteria for dataset annotation is the first issue that needs to be addressed.
Improving the way knowledge is injected Most of the past studies inject
knowledge directly into the inputs of the model Su et al. (2021); Zhang and
Liu ; Li et al. (2023b); Babieno et al. (2022); Gong et al. (2020); Su et al.
(2020) or adjust the outputs Wang et al. (2023); Song et al. (2021); Feng and
Ma (2022), however, using this combination alone may not be able to fully
utilize the rich contextual information in the knowledge. In order to improve
the efficiency of knowledge injection, Le et al. (2020) used dependency tree
structure information to construct the adjacency matrix of Graph Convolutional
Network (GCN), Li et al. (2023c) fine-tuned the model with FrameNet to capture
the implicit knowledge, and Mao and Li (2021); Mao et al. (2022) designed a
gating mechanism for extracting the associations between the main task and
several subtasks separately information. Although there have been some
attempts to improve the knowledge injection approach, this area is still an
active research direction.
Exploring Fine-Grained Emotions Many studies have shown that there is a close
connection between textual emotions and metaphors Mohammad et al. (2016); Li
et al. (2023a). Among the previous scholars’ explorations, Dankers et al.
(2019) designed an emotion-based VAD labeled sentiment regression task in
order to assist metaphor detection. Badathala et al. (2023), on the other
hand, skillfully incorporated knowledge from a hyperbole corpus in order to
confirm that multitask learning based on hyperbole and metaphor is expected to
achieve bidirectional efficiency gains. It is worth pointing out that the
above studies have only dealt with the coarse level in terms of the
combination approach, and have not yet delved into more detailed ways of
injecting affective knowledge, such as at the lexical level or at the level of
sentence structure. The practice of introducing fine-grained affective
knowledge has to be weighed against the exact source of the affective
information Strzalkowski et al. (2014). In view of this, there is an urgent
need for further exploration of this topic.
Exploring zero-shot metaphor detection Given the resource overhead problem
associated with supervised metaphor detection. In the past research, some
scholars have made attempts on zero-shot metaphor detection task. Among them,
Mao et al. (2018) introduced WordNet’s superordinate and synonyms, by
calculating the cosine distance between the context and the target word, if it
is greater than a set threshold, it is determined to be a metaphor. Mao et al.
(2022) adopted a similar approach, where the proximity word is selected from
the candidate set, i.e., the word with the highest probability of occurrence
in the BERT context. The above methods provide meaningful explorations in the
direction of zero-shot metaphor detection for knowledge injection and provide
some insights for future research.
Introducing multilingual knowledge An important but little explored research
direction is the construction of multilingual metaphor detection models.
Tsvetkov et al. (2013, 2014) have constructed cross-language metaphor
detection models by training them on English samples and applying them to the
target language. Sanchez-Bayona and Agerri (2022) constructed CoMeta, the
first corpus annotated with Spanish metaphors, and designed two zero-shot
experiments using CoMeta and VUA Steen et al. (2010) as the training and test
sets, respectively, thus demonstrating cross-linguistic consistency between
languages. These studies provide the feasibility and value of exploring cross-
lingual knowledge injection.

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