Always the Best Fit: Adaptive Domain Gap Filling from Causal Perspective for Few-Shot Relation Extraction
Authors: Ge Bai
Chenji Lu
Jiaxiang Geng
Shilong Li
Yidong Shi
Xiyan Liu
Ying Liu
Zhang Zhang
Ruifang Liu
Venue: EMNLP 2023 Findings (2023)
Keywords: Cross domain
Causal Inference
Few-Shot
Relation Extraction

Abstract: Cross-domain Relation Extraction aims to transfer knowledge from a source domain to a different target domain to address low-resource challenges. However, the semantic gap caused by data bias between domains is a major challenge, especially in few-shot scenarios.
Previous work has mainly focused on transferring knowledge between domains through shared feature representations without analyzing the impact of each factor that may produce data bias based on the characteristics of each domain.
This work takes a causal perspective and proposes a new framework CausalGF.
By constructing a unified structural causal model, we estimating the causal effects of factors such as syntactic structure, label distribution,and entities on the outcome.
CausalGF calculates the causal effects among the factors and adjusts them dynamically based on domain characteristics, enabling adaptive gap filling.
Our experiments show that our approach better fills the domain gap, yielding significantly better results on the cross-domain few-shot relation extraction task.

Always the Best Fit: Adaptive Domain Gap Filling from Causal Perspective for Few-Shot Relation Extraction
Ge Bai, Chenji Lu, Jiaxiang Geng, Shilong Li, Yidong Shi, Xiyan Liu, Ying Liu, Zhang Zhang, Ruifang Liu
Beijing University of Posts and Telecommunications China
Abstract
X: Relation representations
Cross-domain Relation Extraction aims to transfer knowledge from source domain to different target domain to address low-resource challenges. However, the semantic gap caused by data bias between domains is major chal- lenge, especially in few-shot scenarios. Previ- ous work has mainly focused on transferring knowledge between domains through shared feature representations without analyzing the impact of each factor that may produce data bias based on the characteristics of each do- main. This work takes causal perspective and proposes new framework CausalGF. By constructing a unified structural causal model, we estimate the causal effects factors such syntactic structure, label distribution, and entities on the outcome. CausalGF calculates the causal effects among the factors and adjusts them dynamically based on domain character- istics, enabling adaptive gap filling. Our ex- periments show that approach better fills the domain gap. yielding significantly better results on the cross-domain few-shot relation extraction task.
representations
5: Syntactic structure
E: Enities
L: Label
Output loghts
[b3
Figure (a) unified SCM for the task (b) Denoting variable C' by S and E (c) Blocking backdoor path by intervention and counterfactual generation
and entities having different distributions in each domain, resulting in data bias across different do- mains. From a causal perspective, the essence of this bias is that these variables have different causal effects on the results in different domains. Do- main adaptation methods (Ganin et al., 2016; Shen et al., 2018 8) offer new insights to tackle these is- sues by transferring knowledge between domains through shared feature representations extracted from multiple domains. However, these work lies excessively on extracting shared features and label distributions while ignoring the unique fea- ture of each domain, which would lead to inferior results when domains have significant semantic gaps. Therefore, Zhang and Lu (2022) proposed a label prompt dropout approach to climinate the model's over-reliance labels, but it is difficult to adequately capture the critical features of each domain by randomized dropout.
1 Introduction
Relation Extraction (RE) is one of the key tasks of Natural Language Processing (NLP), which aims to identify the relations between given entities. RE models (Zhang et al., 2017; Yamada et al., 2020) have impressive performance through large-scale supervised learning based on BERT (Devlin et al.. 2019) and LSTM (Hochreiter and Schmidhuber, 1997). However, collecting sufficient amounts of data for certain classes may be laborious in prac- tice. Although finetuning prompt-base pre-trained language models (He et al., 2023; Liu et al., 2022) have shown superior performance few-shot RE tasks, they have encountered challenges in dealing with cross-domain problems. The main issue is that variables such as labels, syntactic structure,
To address this issue, our work focuses on iden- tifying and adjusting the causal effects of variables by considering the distinct characteristies of dif- ferent domains. We propose novel framework CausalGF and build unified structural causal model (SCM) (Pearl et al., 2000) to describe the cross moos-domain RE task, as shown in Figure The values and relationship in the graph can be altered
Ruifang Liu is the corresponding author.

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Source Domain:
Prorggt Seneration
Training Stage
COMPARE
Quee
maps arply [ES] learning
Causal Offects
opposed [EZ] backpropagation (1EZ) with pradient descent.
Q & C
[MASK] (MA5K] asply (MA5)) (MA5K with [M4SK] (M45K).
(MASK)
Finel Effects
1
Seff organiling maps: compettivve learning backpropagaaion: grackent descant.
Weights
Daxined
Target Domain: Music
Inference
[ea] Brooks and Dunn [tE1] combined comentiona country with slick rock elements while [F2] kathy Mattee [VE2] updated
Qued
Output Loght
nsctradiiioalit styles.
Final Total Effects
Weights
Final
overview of CausalGF. We utilize counterfactual to acquire representations each variable and estimate their causal effects. The final effects are dynamically adjusted by intervention. Prompt generation, dynamic weighting, and loss function design are used to implement causal operations.
Figure 2:
by intervention and counterfactual generation to study the causal effect of various factors. Further- more, to adapt to the feature of different domains, it is necessary estimate the causal effects of vari- ous factors and adjust them dynamically. CausalGF implements causal operations and dynamically ad- justs the causal effects of variables through prompt generation, dynamic weighting, and loss function improvement, enabling adaptive gap filling based on domain characteristics.
((b). I. denotes the label description. The variable X is the representation of relation for RE which is computed from C, and Y indicates the output logits for prediction. On the edge C X, we fuse the semantic information and linguistic structures into SCM by adopting Transformer (Vaswani al., 2017) to obtain the representation node X. The causal effect of the parent node on Y is obtained by full connectivity with nonlincar transformation. The concepts and theories of causal inference are detailed in Appendix A.
We summarize the contributions follows: To the best of our knowledge, CausalGF is the first work analyzing data bias and the in- fluence of various factors from causal per- spective in cross-domain few-shot RE task. We dynamically estimate and adjust the causal effects of factors in training and inference, enabling adaptive gap filling according to the domain characteristics.
2.2 Causal Operations
In our work, we aim to explore the causal effects of variables on the outcomes. From a causal per- spective, that is, utilizing intervention and counter- factual generation as causal operations to explore the causal effects of the variables S, E, L, and X on Y in SCM G. Since the variables S and E have causal effects both X and Y, their changes in- terfere with the calculation of the causal effect of X + Therefore, counterfactuals S* and E* are generated and intervened on X upon to block the backdoor path (Morgan and Winship, 2014) and eliminate the causal effect of the original S and E on X (Figure I(c)). Mcanwhile, the original S and F. are preserved and restored to the original C to estimate C + Y and maintain the semantic information of the original input.
Extensive experiments different datasets and settings demonstrate the effectiveness of our approach. CausalGF outperforms previ- ous state-of-the methods in all scenarios.
2 Methodology
The overall framework of CausalGF is shown in figure 2. Sections 2.1 and 2.2 describe the structural causal modeling and causal operation. 2.3 and 2.4 describe the implementation of the our method.
2.1 Structural Causal Modeling
For SCM 9, X* and Yx*, counterfactual of X and the original prediction Yx are computed as:
As shown in Figure ((a), cross-domain RE task is represented by unified SCM G. The variable C indicates the contextualized representations of an input text, which is output by the pretrain BERT encoder (Devlin et al., 2019). The variables S and E denote the syntactic structure and the representa- tion of entities in the sentence respectively, which have a direct causal effect on C. Further, as there are no other parent nodes for node C, we can rep- resent it by the nodes S and E, as shown in Figure
X = -X
Yx- = fr X'), E)
(2)
Where fy is the function that computes Y. 2.3 Prompt Generation and Encoding In order to implement the above causal operations, we obtained a representation of the above variables through prompt generation and encoding.

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As shown in Figure 2, variable L is represented by the label description. Counterfactual E ob- tained by extracting entities the sentence and connecting them in sequence. We mask these en- tities to obtain the counterfactual syntactic struc- ture S* Besides, the original input C is retained in prompt, and we add [CLS], [LJ, [S], and [E] placeholder separators between variables. The given entity pair {Cheads Stait) are warped with spe- cial token [E1]. [/E1], [E2] and [/E2] following the approach of Zhang et al. (2019).
During inference, choose the relation y as the prediction by finding the closest class prototype the query sentence's relation representation:
i org max Tk Qrotat
(10)
O=
Where TA is the class prototype of class k. Plotol is representation of the query instance.
3 Experiment
3.1 Datasets and Implementation
We evaluate GausalGF on two cross-domain few- shot RE datasets: CrossRE (Bassignana and Plank, 2022): A manually-curated corpus contains 5265 sentences covering domains with a unified la- bel set of 17 relation types. To assess the domain adaptation of the model, we conducted experiments on CrossRE in single source domain and muitiple source domain scenarios. FewRel: FewRel 1.0 (Han et al., 2018) is collected from Wikipedia arti- cles which contain 100 relations and 700 instances for each relation. FewRel 2.0 (Gao et al., 2019) con- tains test set from the biomedical contains domain 25 relations and 100 instances for each relation. We compare CausalGF with the following base- line methods: Proto-BERT (Snell et al., 2017) is a prototypical network with BERT- (Devlin et al., 2019) serving as the backbone. BERT-PAIR (Gao et al., 2019) is a method that measures the similar- ity of sentence pair. CP (Peng et al.. 2020) pre- trains Proto-BERT using a contrastive pre-training approach that divides sentences into positive pairs and negative pairs. HCRP (Han et al., 2021) equips Proto-BERT with a hybrid attention module and a task adaptive focal loss. Improved Domain Adap- tion (IDA) (Yuan et al., 2022) proposes an encoder learned by optimizing a representation loss and an adversarial loss to extract the relation of sentences in the source and target domain. LPD (Zhang and Lu, 2022) introduces a label prompt dropout ap- proach which adaptable to cross-domain tasks. (7)
Tau = [CLS] L [ C [S] S* [ Ej ... E
(3)
After prompt generation, the entire input in-
stance Tall is fed the encoder:
hL, hgr hg = Encoder(Taa))...
(4)
hcc [ncoder (ancoder[Tau).. (Tau) (Taude
(5)
xx = [hs+
where h is the output embedding for each token in Tal. h1, hs- and hg. represent the respective representation of L, S" and E", which formed by final layer representations of the marker [L], [S] and [E], respectively. Base on SCM G. 1x- is formed by concatenating hi- and hg. hy stands for the representation of the original sentence C which is formed by concatenating the final layer representations of the entity markers [E1] and [F2]..
2.4 Training and Inference
In the SCM G, the parents of the outcome vari- able Y are denoted ε = {x, C) During the training phase, we calculate the causal effect t of each variable in & on Y by utilizing the class prototype r and variable representation h. To flexibly adjust the influence of each variable on the output, we introduce a learnable weight ma- trix W = [wx+, , We, wr.. The final causal effect Qtotal is computed using Formula (8).
= =r h4 = CCC,x,,))
Qiotal
Σ
weight; ef 3EE
We follow the pretraining method of LPD (Zhang and Lu, 2022) which pretrained on the Wikipedia dataset and on top of BERT-base from the Huggingface Transformer library. We perform multiple experiments with different random seeds and report the average accuracy together with the standard deviation. Detailed descriptions of experi- mental settings can found in Appendix C.
th =
Σ Q
Wi
i=X+,C,L
The class prototype r E N is calculated by averaging the relation representations of the N sup- port instances of each class. Where No indicates the number of classes, H indicates the input hidden dimension. We optimize a hinge loss function by introducing the total casual effect Qtotal-
3.2 Results and Analysis
Main Results: Table shows that Causal out- performs all bascline models in CrossRE, achiev- ing an average improvement at least 1.90% and
C En maz(0, Q (totat)
(9)
Nc

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way I shot
5 way shot
10 way I-shot
10 way 5-shot
Models
Multi
Single
Mulii
Single
Mulii
Single
Multi
Single
Mulii
Single
Proto-Bert
67.70+0.5
52.240.7
80.71
64.65+0.8
58.65e0.9
39.86.+122
76.82=1.1
50.82+00.8
70.97
51.83
HCRP*
70.47=1.0
60.3460.9
25.0550.3
70.68e1
59.17+0.5
48 48.5340.6
78.51=10
60,70e0.9
73.30
60.06
CP
78.33+0.9
49.96+007
86 89+1.1
70,70=1.2 :2
70.95+1.1
44.45+0.9
78.36=1
53.82+0.7
78.63
54.73
L.DD*
81.90+0.8
62.35+0.5
86.87+14
75.39=0.5
69.81+1.7
47.39+1.2
78.65=0.5
63,36+0.9
79.30
62.12
CausalGF
84.02:0.7
63.88:1.1
88,35+0.3
76,44=0.6
73.6420.4
49.57=144
78.80=0.8
64.93+0.9
81.20
63.71
Table 1: Accuracy (%) of cross-domain few-shot classification on CrossRE music domain. (+ These works have not been evaluated on CrossRE, so the results are produced by our implementation.)
0.5
5-way I-sbol
5-way S-abol
10-way 1-sbol
10-way 5-abol
0.44L
Model
Jight of oDen
Proto-Bert
40.12
$1.50
26.45
36.93
BERT-PAIR
67.41
78.57
$4.89
66.85
LITZ
HCRP
76.34
83.03
63.77
72.94
IDA 76.30
84.71
67.87
75.84
0.0
CP
84.90
79.80
0.5
now to polites
is makc
sckanac Bicratarz
LPD
82.8120.5
88.9821.4
70.51=1.5
78.7611.6
0
CausalGi
84.14:0.9
91.1021.5
72.90:1.1
83.92+1.3 3
logis logia faal odho
Table 2: Accuracy (%) of cross domsan few-shot clas-
0.27
629
222 2212
ILEE7 0.2%
sification on the FewRel2.0 test set.
Model
mati source
single source
CausalGF
81.20
63.45
0.0 malt togiitics
mulii mase
mult lieeutre scosarios
w/o counterfactual generation
75.6
59.59
Figure 3: The final causal effects of the variable C, E, S and L in six different cross-domain scenarios.
wlo causal effects estimation
77.17
61.71
w/o cansal effects adjustment
75.33
60.29
1.59% for single and multiple source domain sce- narios, respectively. Compared to the previous state-of-the-art LPD, our 10-way- I-sho results show enhancements of 3.83% and 2.18% with sin- gle and multiple sources, respectively. This high- lights that adjusting causal effects adaptively is su- perior to random strategies in reducing the model over-dependence on labeling and data distribution. As shown in Table 2, our upproach significuntly outperforms HCRP, IDA, and CP by at least 4.44% and 4. 10% in 1-shot and 5-shot settings, respec- tively. This indicates that learning the influence of variables based on causal theory is more effective than previous adaptive and contrastive methods in cross-domain few-shot tasks.
Table 3; Ablation study results (%) of our methods CrossRE dataset.
Midel
5-way- 1-shot
10-way- 1-shll
CausalGF
84.14
72,90
w/o counterfactual generation
79.77
68.69
w/o causal effects estimation
81.10
71.35
wlo causal effects adjustment
80.65
70.89
Table 4: Ablation study results (%) of our methods Fewre12.0 dataset.
the w/o causal effect adjustment experiment reveal that improper utilization of causal effects can be counterproducttvee to the model's performance.
3.3 Causal Effects Across Different Domains To verify the ability of our model adaptively filling gaps according different domain characteristics, explored the causal effect of each variable (L, S, E, C) on the results in different cross-domain tasks. Figure shows the normalized average pre- diction logits for the ground truth which obtain from final causal effects Qi (i = L, E, C) un- der six different cross-domain scenarios. The fi- nal causal effects of the same variables differ in different cross-domain scenarios, demonstrating that CausalGF adaptively fills gap by adjusting for causal effects.
Experimental results for other domains can be found in Appendix D.
We construct ablation exper- iments on the music domain of CrossRE. and Fel- re12.0 dataset to investigate the contribution of each component in our approach. We implement a w/o counterfactual generation experiment by removing prompt generation, w/o causal effect estimation by removing the weight matrix W. and w/o causal effect adjustment experiment by initializing fixed W. Table and 4 indicate that removing any part of our approach leads to varying degrees of de- cline model performance. Notably, the results of
Ablation Studies:
For instance, domains with significant dif- ferences, entities are less influential while syntax structure plays a more critical role since entities

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(0) ouss
dicates that CausalGF has universal capability to enhance the model's ability to learn features (b) wio caued effects aijotment
(c) wib cusell effects estimation
(6) wio coumenbettal genenatoon
(e) randontly dropout
Figure 4: The t-SNE visualization results for 100 samples from labels 5-way-1 --sto -hot scenario on CrossRE.
S-way 1-shot
5-way 5-4hot
To-way I-shot
wd-way 5-shot
Proto- Phott-Bert
89.13
94.38
82.77
90.05
BERT PAIR
88.32
93.22
80.63
87.02
and make accurate predictions. It also proves the general significance of the causal effect estimation method in relation extraction tasks.
HCRP
96.42
97.96
93.97
96.46
97.10
LPD
98. 17.0.0
98
08.2910.2
96 6..6660..
96.75.0.2
3.5 Visualization
CausalGF
98.311=0.3
98.5420.5
97.15+0.3
97.04:0.2
During the process of model forwarding, we collect the vector representations of the test samples along with their respective category labels. The t-SNE toolkit (Van der Maaten and Hinton, 2008) is used to map the high- nigh-dimensional feature space of the test samples onto two-dimensional plane, allow- ing for the measurement of sample similarity based these representations. To evaluate the effective- ness of CausalGF, we implemented three ablation experiments and a random dropout method (LPD) to compare with our approach. Figure shows the visualization results for 100 samples from 5 la- bels in a E-way---sot scenario. The results clearly show that CausalGF outperforms other methods in terms of classification effectiveness. This signifies that the inclusion of counterfactual generation and the adaptation of causal effects greatly enhance the ability to learn domain-specific features.
Table 5: Accuracy (%) ) of in-domain few-shot classifi-
cation on the FewRell.0 0 test set.
Model
5-way
5-way
10-way I I-shot
10-way S-shot
s-ohot
5 S-shot
Proto-Ben"
59.69=0.5
73.65+0.9
59.69=0.3
65.04+0.5
HCRPs
65,3420.8
79.39s1.1
56.5820.5
66.8610.6
CP
69.71a1.1
80.7641.0
59.24s0.9
72.18a0.7
LPD*
76.62=1.0
79.70+1.2
69.14
70.69+1.5
CausalGF
78.51=1.3
82.49+1.0
70.90=1.1
72.1940.8
Table 6: Accuracy (%) of in-domain few-shot classifi-
cation on the CrossRE test set. (* are produced by our implementation.)
are domain-specific and semantic information of them is difficult for the target domain to utilize. In contrast, syntactic structure is more universal and applicable across various domains. This phe- nomenon consistent with our intuition. We visu- alize the feature space to further demonstrate the effectiveness CausalGF in Appendix ??.
Conclusion
3.4 In-Domain Experiments
In this paper, we propose CausalGF, novel frame- work based on a causal perspective. By building unified structural causal model, CausalGF esti- mates the causal effects of factors contributing data bias and dynamically adjusts them accom- modate domain characteristics. Our model effec- tively fills the domain gap, outperforming strong baselines in various cross- domainn scenarios.
To further demonstrate the effectiveness of our method, we conducted additional experiments in-domain tasks. We conducted experiments on Fewrel 1.0 and CrossRE datasets, and the results are shown in Table and respectively. The re- sults demonstrate that our method achieves com- petitive performance in do-donain tasks. This in-

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Limitations
ings th Association for Computational Linguistics: ACL-IJCNLP 2021. pages
Some limitations exist in our work. Our effec- tiveness is only examined on the task of relation extraction, while whether this method is able to generalize to other information extraction tasks, such as named entity recognition (NER) and event detection (ED), is not yet explored in this paper. In addition, more fine-grained partition of variables with causal effects on the outcome may enhance the efficacy of counterfactual generation. The above issues will be explored in our future studies.
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference ma- chine learning, pages 1126-113: 135. PMIR.
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Lavi- olette, Mario Marchand, and Victor Lempitsky. 2016. Doweii-adversariall training of neural networks. The journal of machine learning research, 17(1).
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Ethics Statement
Our contribution in this work is fully methodologi- cal, namely novel framework from causal per- spective (CausalGF) to boost the performance of the cross-domain few-shot RE. Hence, there are no direct negative social impacts of this contribution.
Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2019. FewRel 2.0: Towards more challenging few-shot relation classifi- cation. Proceedings of SMNNL-IJCCLL
Madelyn Glymour, Judea Pearl, and Nicholas Jewell. 2016. Causal inference in statistics: A primer. John Wiley & Sons.
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Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019. ERNIE: En- hanced language representation with informative en- tities. In Proceedings of ACL. Association for Com- putational Linguistics.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need, Advances in neural information processing systems, 30.
A Theory of Causal Inference
In this section we present the theory of causal ref- erence involved in our approach.
Zhao Wang and Aron Culotta. 2021. Robustness to spurious correlations in text classification via auto- matically generated counterfactuals. Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1024-14031.
Structural Causal Model (SCM): Figure 5(a) shows an example of SCM. In general, the struc- tural causal model (G {V, U, F)) consists of two sets of variables and a set of functions (Glymour et al., 2016). The variable set V [Vi.. V.) represents the endogenous variables. which are the variables with observable causes (e.g. node X and Y). The variable set U = (UI... Un} repre- sents the exogenous variables, which usually do not
Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, and Fei Wu. 2020. De-biased court's ' view generation with causality. In Proceedings of the 2020 Confer- ence on Empirical Methods in Natural Language Processing (EMNLP), pages 763-780.

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tween X and its parents will be cut off. Meanwhile, the backdoor path from X to Y (X + Z - Y) will also blocked. At this point, variable no longer has a simultaneous causal effect on X and Y. Therefore, intervention can remove the confu- sion between variables and facilitate the estimation of the causal effect between variables.
Intervening
Counterfactual
Counterfactual: Counterfactuals emphasize the outcome of a hypothetical condition if variable is hypothesized under identical conditions of re- ality. The concept of a counterfactual reflects a hypothetical scenario of "what would the outcome be if the variables were different". Unlike inter- ventions which examine the effects on outcomes of implementing certain dispositional observations variables in reality, counterfactuals focus fic- tional scenarios that did not occur. As shown in Figure 5(c), assuming that the variable Z in the case, the estimate the causal effect on X can be expressed as X2-
Figure 5: Example of structural causal model. (a) Structural causal modeling (b) Intervening on variable X (c) Counterfactual generation for variable Z
Target Domain
Single Source
Multiple Source
Music
AI
Domains w/o Music
Al
Music
Domains w/o Al
Literature
Science
Domains w/o Literature
Science
Literature
Domains w/o Science
Politics
News
Domains w/o Politics
B Related Work
News
Politics
Domains w/ News
Cross-Domain Few-Shot Learning: In cross- domain few-shot learning, base and novel classes both drawn from different domains, and the class label sets are disjoint. Although the super- vised paradigm effective fundamental tasks, it suffers from the limitation of insufficient labeled data. To address this issue, previous work has pro- posed a variety of methods for Few-shot learning as well as domain adaptation.
Table 7: Domain segmentation of single source and multiple source on CrossRE dataset.
have parent nodes and whose causes are not usually taken into consideration (c.g. node Z). The func- tion set F is defined as F = (f.,...,n where fi represents the corresponding relationship between variables. The variables V; are determined by the functions as Vi = Ji U,). where A; and U; represent the endogenous and exogenous variables, respectively, which have a direct causal effect on V₁, In terms of causality, the parent node is the cause and the children are the effect. As depicted in Figure 5(a), variable X has direct causal effect on variable Y (X -> Y), while variable Z has a direct causal effect (Z- + Y) and an indirect causal effect (Z + X > Y) on Y. The structural causal model provides a framework for comprehending the causal relationships between variables, allow- ing us conduct experiments, make predictions, and intervene in these relationships.
Data-based few-shot learning methods augment the data with prior knowledge to overcome the dif- ficulty of insufficient data (Gao et al., 2018; Wu et al., 2018; Cong et al., 2021). Algorithm- methods leverage prior knowledge to search for an initial solution that is effective for multiple tasks si- multaneously, which makes it facilitating the adap- tation to new tasks (Finn et al., 2017; Yoo et al., 2018). Metric-based methods employ encoder based metric refine the sentence embedding in the latent space. allowing the learned latent space to generalize to novel relations with few labeled samples in the same domain (Triantafillou et al., 2017; Baldini Soares et al., 2019).
Intervening: Intervening on a variable in a struc- tural causal model involves fixing the value of that variable in order to study its correlation with other variables and its causal effects. Intervention can be represented by the do-calculus. For instance, the intervention on variable X can be denoted as do(X = ,.),, where x" represents the given value (Pearl, 2009). As shown in Figure 5(b), after inter- vening on variable X, the causal relationship be-
Domain adaption studies how to benefit from different but related domains. Shen et al. (2018) introduced Wasserstein distance to improve the gen- eralization ability by constructing domain-invariant space between the source and target domain. Shi et al. (2018) employed an adversarial paradigm to extract class agnostic features different domains.

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S-way-I-shot
S-way-S-shot
10-way-1-shen
10-way-5-shot
Avg.
Models
Mulii
Single
Mulii
Single
Multi
Single
Multi
Single
Multi
Single
Proto-Ben'
52.210.7
48.8811.0
64.6510.8
60.0440.5
39 39.8611.2
38.661008
50.8240.8
47.78&1.1
51.88
48.84
HCRP*
60.34:2099
58.0540.6
70.6811.5
69.82200.5
48.53400.
45.07&1.1
60.70s0.9
54.6920.3
60.06
56.91
CP4
62.58a0.7
43.46a0.9
69.82.1.2
69.68a00.
51.9910.9
41.6340.7
62.37
58.35a0.3
53.28
I.D
76.51+0.5
$9,44 49.4440.3
69.85+0.5
72.00+0.5
68 68.44+1.2
48.9740.8
68 68.99+0.9
60.5 60.54+0.5
70.95
60.24
CausalGF
77.87=1.1
61.34s0.9
72.4340.6
67.9040.3
69.20s1.4
50.6540.8
71.61#0.9
61.71=1.0
72.78
60.40
Table Accuracy (%) of cross-domain few-shot classification on CrossRE Al domain. (* These works have not been evaluated on CrossRE, so the results are produced by our implementation.)
Literature
Science
News
Politics
Models
Multi
Single
Multi
Single
Multi
Single
Multi
Single
Multi
Single
Multi
Single
Proto-Bert*
70,97
51.83
51.88
48. 84
66.78
54,28
67.16
67.53
67.22
62.86
66.20
44.69
HCRP'
73.30
60.06
60.06
56.91
70.38
62.45
68.72
63.97
68.29
61.41
66.70
53.76
CP+
78.63
54.73
61.69
53.28
75.93
55.97
69.77
56.51
61.65
62.85
72.80
48.40
LPD*
79.30
62.12
70.95
60.24
83.28
67.53
75.55
68.42
70.12
62.86
79.09
54.50
CausalGF
81.20
63.71
72.78
60.40
84.15
67.75
77.43
69.60
73.58
65.88
80.02
55.38
Table Average accuracy (%) of cross-domain few-shot classification on CrossRE each domain. (* These works have not been evaluated on CrossRE, so the results are produced by our implementationn)
Yuan et al. (2022) proposed an encoder to extract the relation of sentences in the source and target domain and an adversary loss merge the source domain and target domain.
with the characteristics of each domain.
C Implementation Details
We implemented our model with PyTorch 1.8.1. We use the Adam optimizer and set maximum length 128, learning rate 2e-5, batch size 4, max iteration 20,000 for CrossRE and FewRel 2.0. The learnable weight matrix W [wx-, WC, wt] is initialized to [0.5, 1, and the learning rate for weight matrix = 5c-5.
Different from the above works that are based on conventional approaches, we tackle the cross- domain few-shot RE problem from the perspective of causal inference.
Causal Inference: Causal inference offers new insights for addressing the problem of data bias. In computer vision tasks, previous work has ad- dressed the problem of unbalanced data distribution by causal manipulation of images through detection or segmentation methods (Tang al., 2020; Ab- basnejad et al., 2020). NLP tasks, recent causal models have been applied in various tasks, such as text generation (Wu et al., 2020) and language un- derstanding (Feng et al., 2021). Zeng et al. (2020); Wang and Culotta (2021) generated counterfactuals for weakly-superviis namely entities recognition (NER) and text classifications by replacing the tar- get entity with another entity or their antonyms, respectively. Nan al. (2021) mitigating the spu- rious correlations in long-tailed label distribution for information extraction tasks by counterfactual generation.
During inference, we randomly sample 1,000 episodes from the N-way-K-shot support set and a query instance to evaluate our model. Following previous works (Han et al., 2018; Gao et al., 2019), we set N to 5 and 10, and K to and 5, For all the experiments,
train and test model on the 3090Ti GPU. It takes an average of 2.5 hours to run on the training dataset. All experiments are repeated five times with different random seeds under the same settings.
For Fewrel dataset, we follow the official split to use 64 relations of Fewrel 1.0 for training, 16 for validation and use FewRel 2.0 for testing to evalu- ate the domain adaptation of few-shot models. For CrossRE dataset, we construct our experiment with two scenarios: single source domain and multiple source domain. Domain segmentation on CrossRE dataset shown in Table 7. For single source experiments, we select two different domains for source and tar, respectively. For multiple source experiments, we adopt the leave-one-ous strategy.
Unlike the previous methods, our approach uti- lizes causal operations to bridge the domain gap. We not only generate counterfactuals for cross- domain few-shot RE task, but also estimate and dynamically adjust causal effects to better align

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Label
Instance
Prediction of LPD
Prediction of CausalGF
Causal Effects
opposite
the Kingdom of Judah rebelled against Neo-Babe Coo-Babylonian Empire and was destroyed
win-defeat 0.427 com- 0.233 opposite
opposite 0.720 role
0.454 Qs 0.212 Q 0.079 Qu 0.225
0.101 win-defeat 0.087
0.177
win-defeat United Kingdom lacks the charismatic leader needed keep the country logether and Nazi Germany successfully conquers Great Britain via Operation Sea Lion 1940.
opposite 0.335 win- defeat 0.306 cause
win-defeat 0.681 op- posite 0.091
Qc 0.361 Qs 0.104
named
Qs 0.327 Qr. 0.178
0.197
Table Two cases from CrossRE dataset, their source domain is news and target domain politics.
D Supplementary Experiments on CrossRE
we conducted 12 cross-domain experiments in 6 domains on the CrossRE dataset, as shown in the table 7. Except for the Music domain shown in section 3.2, we present the results of the remaining experiments here. For the domain, we present the detailed results of each few-shot setting in Table 8. For the other domains, we report the average results of their experiments in Table 9. Specifi- cally, CausalGF achieves average improvement of at least 3.42% and 3.02% for single and multi- ple source domain scenarios, respectively. Further- more, our approach achieves the best performance in all of the aforementioned cross-domain experi- ments, demonstrating that CausalGF adapts to do- main characteristics and fills the gap different cross-domaie scenarios.
E
Case Study
As shown in Table 10, we show the content of two cases in the CrossRE dataset, and the prediction results of the previous sota LPD and our method. Since the semantics and application scenarios of the labels (win-defeat and opposite) are relatively similar, LPD, a method that focuses mainly on the semantics of the labels, tends to confuse them, resulting in prediction errors.
Our approach can adaptively fill the domain gap by recognizing and dynamically adjusting the causal effects of different variables in sentence. As result, causalGI will not be overly dependent on a single variable in a sentence be negatively affected by the similarity of single variable in an instance. The table above shows the adjustment of causal effects by our method. can be found that after adjustment, different variables have different causal effects, and our model can make correct and clear predictions in this way.


Reviews:
[{"review_id": "Tmzcec5ieK", "reviewer": "Reviewer_igC2", "summary": "", "questions": "Please refer to \"Reasons To Reject\".", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for the valuable and detailed review of our work. We will address your concerns below and make improvements based on your review.\n1. We apologize for the omission of case study in our submission, and we will show two cases from CrossRE below to further demonstrate the effectiveness of our approach and we will add it to the camera-ready version.\n\n| Label  | Instance | Prediction of  baseline LPD | Prediction of   CausalGF  | Causal Effects  of Variables  |\n| ------ | ------- | -------- | --------- | -------- | \n| opposite        | the **Kingdom of Judah** rebelled against the  **Neo-Babylonian Empire** and was destroyed          | win-defeat  0.427   compare 0.233  **opposite** 0.177  ……   | **opposite**  0.720   role 0.101  win-defeat 0.087  ……    | Q_C 0.454,   Q_S 0.212,  Q_E 0.079,  Q_L 0.225   |\n| win-defeat     | **United Kingdom** lacks the charismatic leader   needed to keep the country together and **Nazi Germany** successfully conquers Great  Britain via Operation Sea Lion in 1940 .         | opposite  0.335   **win-defeat** 0.306  cause 0.197   ……    | **win-defeat**  0.681   opposite 0.209  named 0.091  ……    | Q_C 0.361,   Q_S 0.104,  Q_E 0.327,  Q_L 0.178   |\n\nWe show the content of two cases in the CrossRE dataset (their source domain is news and target domain is politics), and the prediction results of the previous sota LPD and our method. Since the semantics and application scenarios of the two labels（win-defeat and opposite）are relatively similar, LPD, a method that focuses mainly on the semantics of the labels, tends to confuse them, resulting in prediction errors.\n\nOur approach can adaptively fill the domain gap by recognizing and dynamically adjusting the causal effects of different variables in a sentence. As a result, causalGF will not be overly dependent on a single variable in a sentence or be negatively affected by the similarity of a single variable in an instance. The table above shows the adjustment of causal effects by our method. It can be found that after adjustment, different variables have different causal effects, and our model can make correct and clear predictions in this way.\n\n2. On the one hand, from an intuitive perspective, syntactic structure and entities have different characteristics and play different roles in relation extraction tasks. Specifically, entities in a sentence, especially the head and tail entities that need to be identified, contain word-level semantic information that is beneficial for relation extraction while syntactic structure contains more contextual information. However, in cross-domain tasks, the domain-specific entity information is more difficult to be utilized by the target domain while syntactic structure is more universally applicable. On the other hand, from a causal perspective, entities and syntactic structures have different causal effects on the results of cross-domain relation extraction, so it is necessary to separately identify and adjust these effects in the task. In section 3.3 of our paper, we demonstrate the distinct causal effects of different variables in different cross-domain tasks.\n \n3. We're sorry that our typography has inconvenienced your reading. Due to space constraints, we have included implementation details such as hyperparameter tuning and data preprocessing in Appendix C. In the camera-ready version, we will move essential implementation details to the main text.", "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 causal perspective to mitigate the domain gap for cross-domain RE task is sound.\n\n(2) The method is elaborated through theoretical proof, which is interesting.\n\n(3) The method obtains significant improvements over multiple baseline models.", "reasons_to_reject": "(1) Lack of necessary case study to demonstrate why this method works.\n\n(2) The reason why syntactic structure and entities are distinct cross-domain gaps is not clarified.\n\n(3) Detailed experiment settings should be provided such as hyperparameter and data preprocessing.", "questions_to_authors": "Please refer to \"Reasons To Reject\".", "paper_topic_and_main_contributions": "This paper focuses on the cross-domain few-shot Relation Extraction task. It proposes an unified structral causal model called CausalGF from the causal perspective. This model adaptively fills the data bias gap across diffrent domains, using intervention and counterfactual generation causal operation to achieve this purpose. Extensive experiments on different datasets and settings demonstrate the effectiveness of this approach.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "LnxyqRQbk3", "reviewer": "Reviewer_Tq2e", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "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 very much for the comprehensive review of our work,  we will make improvements and refinements based on your review. We will move some important experimental results (such as Appendex D) and implementation details from the appendix to the main text in the camera-ready version.", "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 symbols are clear and the content is correct and comparatively novel.  \n2. It is the first work analyzing data bias and the influence of various factors from a causal perspective in cross-domain few-shot RE tasks. ", "reasons_to_reject": "1. Some content in the appendix needs to be adjusted to the main text to improve clarity and conciseness. For example, consider moving a brief description of the baseline from Appendix D to Section 3.1.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "Previous work has mainly focused on transferring knowledge between domains through shared feature representations without analyzing the impact of each factor that may produce databases based on the characteristics of each domain. In this paper, the authors proposed a new framework CausalGF which is the first work analyzing data bias and the influence of various factors from a causal perspective in cross-domain few-shot RE tasks. The CausalGF outperforms previous SOTA methods in all scenarios. \n", "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": {}}, {"review_id": "PQsr3FbfSf", "reviewer": "Reviewer_8uUD", "summary": "", "questions": "1. In Figure 1, the edge from C to X is blocked and the counterfactual S* and E* are connected to X. However, it’s easy to observe another alternative, which blocks the edge from C to Y and connects S* and E* to Y. The resulting graph will be like S*, E*, L, X connected to Y and C connected to X. In the prompt encoding step, h_S and h_E will be separately weighted and contribute to the total causal effect, which is different to the proposed method. Here’s my question: why are you choosing the graph described in the paper instead of the above alternative? Is concatenating h_S and h_E better than separating them? If yes, would you please explain the reason?\n2. Is this approach capable of migrating to regular cross-domain RE task?\n3. In Figure 2, training on source domain is using S* and E*, but they are not used in inference. I don’t think S* and E* include label information, so would you please explain the reason or point out if I am missing some information?", "limitations": "", "rating": 4, "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 very much for the valuable and detailed review of our work. We will address your concerns below and make improvements based on your review.\n\n1. We choose to intervene on X with S* and E* instead of intervening on Y because we want to preserve the direct causal effect of the original input text C on the output logits Y. The syntactic structure S and entity E are original parts that can mutually affect each other in the original input C, for example, the semantics of the same entity may vary in different contexts. Therefore, we choose to intervene on X, which not only preserves the direct causal effect of the original text but also generates counterfactuals for entities and syntactic structures and considers the causal effects of these two variables separately. We think the idea you propose is very interesting and we will do further experiments next to compare it with our design as well.\n\n    We concatenate h_S and h_E because S and E are two parts extracted from the original sentence C, and in real-world scenarios, these two parts exist as a whole. Therefore, our approach aims to use counterfactual generation during training and enable the model to learn the causal effect of syntactic structure and entities on the outcome. We do not perform counterfactual generation on S* and E* during inference. Instead, we expect the model to recognize the different causal effects of the syntactic structure and entities solely based on the original sentence and make accurate predictions. Besides, we have conducted a comparison of these two settings and found that concatenating h_S and h_E has a better performance. Thank you for the reminder, we will explain this phenomenon in the camera-ready version.\n\n   #\n\n2. Our method is applicable in regular cross-domain relation extraction tasks and can yield competitive results. The main idea of our approach lies in identifying and adjusting the causal effects of different variables in order to fill the domain gap adaptively. Therefore, this causal operation can easily transferred to regular cross-domain RE tasks. \n\n    We apologize for not including experiments on regular cross-domain tasks in our submitted paper. This is because the main focus of our paper is how to effectively transfer knowledge between domains in the resource-limited few-shot scenario. Thank you very much for your suggestions. Due to time constraints, we are showing below the experimental results of the regular cross-domain RE task that we have already completed. We are still continuing to improve the design and analysis of the experiment, which will be showcased in the camera-ready version.\n\n\n\n| Model      | bc    | cts   | wl    | Avg.  |\n| ---------- | ----- | ----- | ----- | ----- |\n| GSN(2018) | 66.38 | 57.92 | 56.84 | 60.38 |\n| DRPC(2019)      | 67.30  | 64.28  | 60.19   | 63.92  |\n| MVC(2020)      | 70.32  | 66.43  | 64.61 | 67.12  |\n|   **CausalGF(ours)**    |   70.50    |    67.64   |    65.92   |    68.02   |\n\n\nThe table above shows the F1 scores of the models on the ACE 2005 dataset over different target domains bc, cts, and wl. Our approach outperforms strong baselines and achieves competitive results.\n\n\nDataset: \n**ACE 2005**[1] dataset include 6 different domains i.e., (bc, bn, cts, nw, un, and wl), covering text from news, conversations and web blogs.\n\nBaselines: \n**GSN**[2] is an adversarial learning model and is trained to learn the genre agnostic features for cross-domain RE.\n**DRPC**[3] is a deep structure-based model which employs dependency trees either as the input graphs to form the computation flow for deep learning models.\n**MVC**[4] simultaneously induces the structures and predicts the relations for the input sentences to obtain better domain adaptation.\n\n[1] Mo Yu, Matthew R Gormley, and Mark Dredze. Combining word embeddings and feature embeddings for fine-grained relation extraction. In NAACL-HLT, 2015.\n\n[2] Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, and Heyan Huang. Genre separation network with adversarial training for cross genre relation extraction. In EMNLP, 2018.\n\n[3] Veyseh, A.P.B.; Nguyen,T.H.;and Dou, D. 2019. Improving cross-domain performance for relation extraction via dependency prediction and information flow control. In IJCAI.\n\n[4] Veyseh, Amir, et al. \"Multi-view consistency for relation extraction via mutual information and structure prediction.\" Proceedings of the AAAI Conference on Artificial Intelligence. \n\n\n3. We're sorry that the descriptions in our article have confused you. S* and E* do not contain labeling information. The label information is contained in the variable L, which plays a role in relationship extraction through prompt generation and causal effect adjustment. We add prompts including label information in the support instance to add relevant information into the model, and the query instance to be recognized does not contain labels.\n    The reason we do not use S and E in the inference process is that our model learns the causal effect of syntactic structure(S) and entities(E) during training, and thus the model can make accurate predictions through dynamic causal adjustment in the inference process, even without counterfactual generation.", "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 paper proposes a novel theoretical framework for classification feature selection and fusion, and experimental results demonstrate the effectiveness of the proposed method.", "reasons_to_reject": "The motivation of graph topology selection is not well presented. See details in the first question in “Question For The Authors”.", "questions_to_authors": "1. In Figure 1, the edge from C to X is blocked and the counterfactual S* and E* are connected to X. However, it’s easy to observe another alternative, which blocks the edge from C to Y and connects S* and E* to Y. The resulting graph will be like S*, E*, L, X connected to Y and C connected to X. In the prompt encoding step, h_S and h_E will be separately weighted and contribute to the total causal effect, which is different to the proposed method. Here’s my question: why are you choosing the graph described in the paper instead of the above alternative? Is concatenating h_S and h_E better than separating them? If yes, would you please explain the reason?\n2. Is this approach capable of migrating to regular cross-domain RE task?\n3. In Figure 2, training on source domain is using S* and E*, but they are not used in inference. I don’t think S* and E* include label information, so would you please explain the reason or point out if I am missing some information?", "paper_topic_and_main_contributions": "This paper focuses on the cross-domain few-shot RE task from causal perspective. The authors propose a novel framework CausalGF based on structural causal model. The structural graph provides a theoretical explanation of feature selection and fusion for classification. Experimental results show that the proposed model outperforms baseline models on two benchmark datasets of this task.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper explores a causal model for cross-domain few-shot relation extraction. The reviewers have several concerns for the paper regarding the lack of clarify and motivation for some model details. However, the rebuttal provides further information and seems to address the major concerns. The authors are encouraged to update the paper according to the discussion with the reviewers.
Decision: Accept-Findings