Improving Named Entity Recognition via Bridge-based Domain Adaptation
Year: 2023

Abstract:
Recent studies have shown remarkable success in cross-domain named entity recognition (cross-domain NER). Despite the promising results, existing methods mainly utilize pretraining language models like BERT to represent words. As such, the original chaotic representations may challenge them to distinguish entity types of entities, leading to entity type misclassification. To this end, we attempt to utilize contrastive learning to refine the original representations and propose a model-agnostic framework named MoCL for cross-domain NER. Additionally, we respectively combine MoCL with two distinctive cross-domain NER methods and two pre-training language models to explore its generalization ability. Empirical results on six domains show the effectiveness and good generalization ability of MoCL.

heading: 1 Introduction
text: Given a sentence, named entity recognition (NER) aims to extract entities and classify them into predefined entity types (Zhu and Li, 2022; Wang et al., 2020). As shown in Table 1, given the sentence S1, a NER model needs to extract the entity “Nova” and classify it into the entity type person. Most existing NER models rely on massive annotated data, making it hard to directly apply them to datalimited domains. To this end, many researchers started to explore cross-domain named entity recognition (cross-domain NER) methods (Yang et al., 2022; Chen et al., 2022). This paper focuses on the supervised setting, which generalizes effective representations learned from the source domain to the target domain with small annotated samples of the target domain (DAUME III, 2007).
According to the tagging scheme, previously supervised cross-domain NER approaches can be
†Corresponding author. ‡The results are predicted by a state-of-the-art model
(Zhang et al., 2022a).
grouped into two types: (1) compositional labelingbased methods that utilize the monolithic tags to train models, where each token is labeled by a composition tag (e.g., B-person) (Liu et al., 2021; Zheng et al., 2022); (2) modular learning-based approaches that decompose the composition tag into two tags, where each token is labeled by an entity boundary tag (e.g., B) and an entity type tag (e.g., person) (Zhang et al., 2022a).
Despite the promising results, both types of approaches mainly leverage pre-training language models like BERT to represent words. As such, the original chaotic representations (Li et al., 2020) may bring challenges for models to distinguish en-
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tities with different entity types of corresponding domains, leading to entity type misclassification. Let us consider S1 again. Through visualization shown in Figure 1, we observe that the representations of entities with entity types “person” of the source domain and “musicalartist” of the target domain are mixed. As such, as shown in Table 1, even the state-of-the-art method may struggle to successfully distinguish entities belonging to the entity types “person” and “musicalartist”, and hence wrongly classify the entity “Nova” into the incorrect entity type “musicalartist” rather than the correct entity type “person”. Recently, contrastive learning has achieved remarkable success in computer vision, which could generate discriminative representations based on queries and keys (He et al., 2020; Chen et al., 2020). Motivated by this, we attempt to utilize contrastive learning to solve entitytype misclassification faced by the above two kinds of methods by refining the original chaotic representations.
(a) orignial representations (b) refined representations
pull push
musical artist
labels under the modular learning
framework
labels under the sequence labeling
framework
person B-person
Source
Target
domain shared
B-musicalartist
Figure 2: The illustration of our proposed framework MoCL. Different shapes and colors (e.g., red, blue, and purple) represent the entity types and domains of entities, respectively. The tags adopted by the existing two types of mainstream models (i.e., compositionallabeling (Liu et al., 2021) and modular learning-based) are colored in yellow and green, respectively. For simplicity, we only draw the labels of entity type classification in the modular learning-based approach (Zhang et al., 2022a). Left: the original representation of entities, where the entities of the different entity types from the source domain and the target domain are mixed. Right: the refined representations after applying MoCL, where entities of the different entity types from the source domain and target domain are separated.
In this paper, we propose a momentum contrastive learning-based model-agnostic framework named MoCL for cross-domain NER. To guide the learning processing of momentum contrastive learning, we first design two approaches to generate keys‡ required by contrastive learning
‡Here we denote keys are refined sentences of the originally given sentence.
and name them Entity Bridge (EB) and Label Bridge (LB) since they work as bridges to enable knowledge transfer from the data-resource source domain to the data-limited target domain. Then based on the generated keys, as shown in Figure 2 (a), MoCL would explicitly pull closer entity representations belonging to the same entity type. Besides, it would simultaneously push away entity representations belonging to different entity types. Thus, as shown in Figure 2 (b), the distances between entities of different entity types become larger while the distances between entities of the same entity type are reduced, resulting in discriminative representations. To summarize, we make the following contributions:
• To the best of our knowledge, we are the first to utilize contrastive learning to refine the original chaotic representations in cross-domain NER. A model-agnostic framework MoCL is proposed and we respectively combine it with two distinct models and two different pre-training language models to explore its generalization ability.
• In order to guide the process of contrastive learning, we explore two methods to generate keys, namely Entity Bridge (EB) and Label Bridge (LB). With the combination of both bridges, MoCL could capture the relations of entities at different granularities, which have been shown effective for NER (Ma et al., 2022a; Chen et al., 2021a).
• Experimental results show the effectiveness of MoCL and the visualization analysis shows it could provide better separation among different entity types in the embedding space.

heading: 2 Model
text: This paper proposes a contrastive learning-based framework MoCL for cross-domain NER, which facilitates the ability to discriminate entities with different entity types. MoCL mainly consists of two modules: the Base Cross-NER model and the Contrastive Learning framework. We first introduce the Base cross-domain NER Model (Section 2.1) and then describe the Contrastive Learning Framework (Section 2.2). Finally, we present the training procedure (Section 2.3). The whole architecture of MoCL is shown in Figure 3.

heading: 2.1 Base cross-domain NER Model
text: The Base cross-domain NER model involves a Base cross-domain NER Encoder (Section 2.1.1) and an Output Layer (Section 2.1.2), is constructed to perform the task of cross-domain NER. The base cross-domain NER Model can be implemented by different existing cross-NER approaches (Liu et al., 2021; Zhang et al., 2022a).
For the cross-domain NER, there are a large set of annotated sentences S = (S1, S2, . . . , SNs) from a source domain and a set of limited sentences T = (T1, T2, . . . , TNt) from a target domain, where Di denotes the ith sentence of the domain D, and the lengths of the number of sentences are Ns and Nt respectively. Given two sentences Si = (wSi1, w S i2, . . . , w S im) and Ti = (wTi1, w T i2, . . . , w T in), one from each domain side, here l (m for source domain and n for the target domain, respectively) denote the sentence length (i.e., the total number of words). Each sentence can be constructed as “[CLS]Di[SEP ]”, where [CLS] and [SEP] denote two special symbols (Kenton and Toutanova, 2019). Then, we feed them into the Base cross-domain NER Encoder, which can be implemented by a pre-trained model like BERT to respectively obtain their hidden representations, de-
noted as zSi = (h S iCLS , h S i1, h S i2, . . . , h S im, h S iSEP ) and zTi = (h T iCLS , h T i1, h T i2, . . . , h T in, h T iSEP ).

heading: 2.1.2 Output Layer
text: Sequentially,(hSi1, h S i2, . . . , h S im) and (hSi1, h S i2, . . . , h S in) are delivered to an output layer to obtain the types of entities. Then the probability that the jth word in ith sentence of domain D be categorized to the kth entity type typek, denoted by p(typek|hDij ), can be computed by Softmax function:
p(typek|wDij ) = exp{wDk hDij + bDk }∑cD g=1 exp{wDg hDij + bDg } . (1)
where cD, wDg and b D g denotes the number of entity types, the weight and bias parameters in the domain D (source or target), respectively. We then utilize cross-entropy loss to train on the corresponding sentence (S or T ) as follows:
LDtask = − ND∑
i=1
1 |Di| l∑
j=1
cD∑
k=1
yj,klog(p(typek|wDij )
(2)
where yj,k denotes the kth element in yi, which is an one-hot label indicating the entity type of wDij . In terms of the source domain, the training loss is LStask. When it comes to the target domain, the training loss is LTtask.

heading: 2.2 The Contrastive Learning Framework
text: The Contrastive Learning Framework mainly contains three components: 1) the Keys Generators (Section 2.2.1); 2) the Keys Encoder (Section 2.2.2), and 3) a Contrastive Learning Module (Section 2.2.3). The Keys Generators (i.e., Positive Keys Generator and Negative Keys Generator) can be implemented by our proposed three bridges. The Contrastive Learning module is designed to allow the model to distinguish entities with respect to their entity types based on the output from the two encoders (i.e., the Base cross-domain NER Encoder, the Key Encoder).

heading: 2.2.1 Keys Generator
text: Motivated by the power of contrastive learning to learn discriminative representations in computer vision, we consider applying it in cross-domain NER. In computer vision, the typical ways to construct keys and queries are such that the query is an original image, and its positive keys are obtained by
applying operations like revolving or cutting the same image. In contrast, negative keys are other images (He et al., 2020). However, vanilla momentum contrastive learning is not directly applicable in cross-domain NER. If directly taking the typical data augmentation in cross-NER, some information may be closed in the immediate keys. For instance, given the sentence “He worked in Consortium”, the generated positive key may be “The workplace of him is Consortium” while the generated negative key may be “The workplace of him is not Consortium”. In this way, the model mainly learns “Consortium is a location” instead of “Consortium” belongs to the entity type ORG. In fact, the typical way to construct keys and queries neglects the fine-grained entity-type information.
To address this limitation, we explore three different key generation strategies, which can make better use of the fine-grained entity-type information. The detailed key generation strategies are illustrated in Figure 4.
• Entity Bridge. Given a sentence, an intuitive way to generate keys is based on entities. In particular, we first use the entities and their entity types from the whole training set to construct a dictionary and we did not use additional dictionaries or knowledge bases. Given a sentence, we would ergodic all entities in a sentence and modify them one by one. In particular, based on one given entity, and its entity type, we would randomly select another entity from the constructed dictionary to replace the entity. For example, suppose there are five entities (entity types are shown in the form of ()) in the training set: XXX (person), YYY (person), and ZZZ (song), AAA (location), BBB (song). the dictionary would
be person: XXX, YYY, location: AAA, song: BBB. Then given the sentence A= "XXX and YYY are running". If we want to modify XXX, we would randomly select another entity type except person from the dictionary (e.g., song and location). Suppose the selected entity type is song. Then we would randomly select one entity from entities whose entity type is song (e.g., ZZZ and BBB). Suppose the selected entity is BBB. Then the generated negative key would be BBB is running. Similarly, as shown in Figure 4 (a), we can replace “Moldavia” in the original sentence from the source domain with “America”, where the “America” is a different entity with the same entity type country as “Moldavia”. Similarly, we can replace “Wallachia” with “Chicago”, “Algeria” with “India”, from which we can get a positive Key “He also collected in America, Chicago, and (in 1913) India.”. Besides, a negative entity could be generated by replacing “Moldavia” with “Westenra”, an entity belonging to another entity type award, which is randomly sampled from the dataset. Finally, we can obtain a negative Key “He also collected in Westenra, HeadtoHeart, and (in 1913) Hero.”.
• Label Bridge. In order to leverage the label information of text, which has been shown effective in the cross-domain NER task (Hu et al., 2022), we propose a key generation strategy called Label Bridge. As shown in Figure 4 (b), to get a positive key, we replace the entity “Moldavia” in the original sentence with its entity type country, and we can produce a negative key by replacing “Moldavia” with another entity type song, a different entity type randomly sampled
from the dataset.
• The combination. In order to simultaneously utilize the entity and label information, we adopt either the entity bridge or the label bridge randomly with equal likelihood, which has been shown in Figure 4 (c). In terms of the positive key, we can replace “Algeria” with an entity “India”, “Moldavia” with its entity type country, and “Wallachia” with its entity type location. When it comes to the negative key, it can be generated by replacing “Moldavia” with another entity type song and replacing “Wallachia” with another entity HeadtoHeart.
After applying one of the above strategies, given original sentences Si and Ti mentioned in Section 2.1.1, a new positive key possi for Si and a new positive key posTi for Ti will be generated, respectively. Meanwhile, we generate N‡ different negative keys negSi1 and negi2S for Si and two different negative keys negTi1 and neg T i2 for Ti to make better use of mutual information (Oord et al., 2018).

heading: 2.2.2 Keys Encoder
text: By leveraging one of the above three key generation strategies, we can obtain a total of six keys for each original sentence. Each key X can be constructed as “[CLS]X[SEP ]”, where [CLS] and [SEP] denote two special symbols (Kenton and Toutanova, 2019). Then, we feed them into the keys Encoder, which can be implemented by a pre-trained model like BERT to respectively obtain the corresponding sentence representations, denoted as hSi+CLS of possi , h T i+CLS of pos T i , h S i1−CLS of neg S i1, h T i1−CLS of negTi1,h S i2−CLS of neg S i1, h T i2−CLS of neg T i1.

heading: 2.2.3 the Contrastive Learning Module
text: Based on the generated keys, we apply contrastive learning to cross-domain NER by minimizing the distance between representations of entities with the same type and maximizing the distance between representations of entities belonging to different types in order to improve the applicability of the model in the target domain.
Given the above sentence representations, we can calculate the contrast loss for each original sentence and its sampled sentences by:
‡According to preliminary experiment results, which is described in Section 4.2 A3, we set N to 2.
LDcon = − ND∑
i=1
1 |Di| ∗log s(q, k
+)
s(q, k+) + ∑2
j=1 s(q, k −)
(3)
s(q, k+) = s(hDiCLS , h D i+CLS)/τ (4)
s(q, k−) = s(hDiCLS , h D ij−CLS)/τ (5)
Here s denotes the function to calculate the similarity score by applying the dot product operation between two given embeddings, while τ is a scalar temperature parameter (Wang and Isola, 2020). Based on the similarity score, models could minimize the distance between positive keys and maximize the distance between the negative keys, achieving alignments among entities.

heading: 2.3 Model training
text: Following (He et al., 2020), we utilize momentum update to maintain the stability and to keep the consistency of representations between the Base cross-domain NER Encoder and the keys Encoder. In particular, by having the weights of the networks slowly track the learned networks, which means the keys encoder updates slowly, this can greatly improve the stability during training. Momentum updates can be formulated as:
θ ← mθ + (1−m)θ′ (6)
where m is a momentum coefficient, which is a relatively large number between 0 and 1, and θ and θ′ is the parameter of the Base cross-domain NER Encoder and the keys Encoder respectively.
Eventually, we attempt to minimize the combined loss to train our model by:
L = LStask ++LTtask + γ(LScon + LTcon) (7)
where γ is a weight coefficient.

heading: 3 Experimental Setups

heading: 3.1 Datasets & Evaluation Metrics
text: We use two datasets for experiments, including one domain Social Media of the dataset Twitter (Lu et al., 2018), and five domains in the dataset CrossNER (Liu et al., 2021). We take Social Media as the source domain and five domains in CrossNER as target domains (Liu et al., 2021). Table 2 shows detailed statistics of each domain and their corresponding entity types are shown in Table 3.
Following (Liu et al., 2021) and (Zhang et al., 2022a), we use F1-score to evaluate the performance of models. In particular, an entity is considered to be correct only if its range and entity type are both correct.

heading: 3.2 Experimental Settings
text: We combine MoCL with two pre-training language models, including BERT‡ (Kenton and Toutanova, 2019) (i.e., the basic setting) and the domainadaptive pre-training language model (i.e., the DAPT setting) of each target domain (Liu et al., 2021). Following (Liu et al., 2021) and (Zhang et al., 2022a), for the basic setting, we initialize the textual representation by BERT and set the dimension to 768. While for the DAPT setting, following (Liu et al., 2021) we use BERT and unlabeled domain-specific corpus to train a domain-adaptive pre-training language model for each domain‡. As for two competitive baseline models BERT-JF and MTD, we respectively follow the same settings from the implementation of (Liu et al., 2021)‡ and (Zhang et al., 2022a)‡ for a fair comparison.‡. In order to get the keys required by contrastive learning, we first utilize the training set in each domain to construct dictionaries of each entity type. Then given the sentence from the source domain or target domain, we apply one of the three key generation strategies to generate keys based on the constructed dictionaries. Moreover, we set τ = 0.07 (Eq. 4/5), m = 0.999 (Eq. 8). While γ is tuned from 0.1, 0.2, 0.3, 0.5, 0.7, 0.9 1.0 in different settings and finally is set to 0.7 (Politics), 1 (Science, Music), 0.1 (Literature, AI) under the basic setting, 0.1 (Politics, AI, Music), 0.7 (Science), 0.3 (Literature) under
‡https://huggingface.co/bert-base-cased ‡We will release the checkpoints of all domain-adaptive pre-training language models to facilitate further research. ‡https://github.com/zliucr/CrossNER ‡https://github.com/AIRobotZhang/MTD ‡We are highly grateful for their public codes, our code will be publicly available via GitHub.
the DAPT setting. We implement our model with the PyTorch framework and conduct experiments at Tesla P100 and V100.

heading: 3.3 Baseline Models
text: Our baselines are:
• BiLSTM-CRF, which combines BiLSTM and CRF to train the model (Lample et al., 2016).
• LM-NER, which integrates cross-domain language models (Jia and Zhang, 2020).
• BERT-PF, which firstly utilizes the source domain data and then uses the target domain data (Liu et al., 2021).
• BERT-JF, which simultaneously utilizes both the source and target domain data (Liu et al., 2021).
• Style-NER, a method that applies data augmentation (Chen et al., 2021b).
• MultiCell-LM, a method utilizes a separate cell state to model each entity type for domain adaptation (Jia and Zhang, 2020).
• MTD, a modular learning-based method that splits cross-domain NER into two sub-tasks (Zhang et al., 2022a).

heading: 4 EXPERIMENTAL RESULTS

heading: 4.1 Overall Performance
text: According to Table 4, we observe that: (1) MTDMoCL achieves better performance than no alignment work BERT-JF with 8-11% improvements,
showing the effectiveness of contrastive learning. (2) MTD-MoCL achieves the state-of-the-art performance and beats MTD (a representative model of the modular learning-based approaches). Moreover, the performance of MTD-MoCL is relatively high when the source domain is Twitter, whose size is smaller than conll2003. This demonstrates that MoCL could help methods achieve better performance by refining the original representations, especially in the low-source setting.

heading: 4.2 Analysis
text: A1: The effectiveness of incorporating MoCL with different base cross-domain NER models. As shown in Tables 4, BERT-JF-MoCL also achieves better performance than BERT-JF (a representative model of the compositional labelingbased approaches). This shows that MoCL can not only benefit compositional labeling-based methods but also modular learning-based methods. A2: The effectiveness of incorporating MoCL with different pre-training models. we incorporate MoCL with MTD with a domain-adaptive pre-training model (Liu et al., 2021). As shown in Table 4, both MTD-MoCL and BERT-JF-MoCL respectively outperform MTD and BERT-JF from across all domains with a noticeable margin, which shows the great generalization ability of MoCL.
less than 2, when N increases, the results are better. On the other hand, when N is large than 2, when N increases, the results are worse. As such, we set N to 2.
‡Here Ours (entity) means MTD-MoCL with the Entity Bridge, Ours (label) means MTD-MoCL with the Label Bridge, while Ours (both) means MTD-MoCL with the combination of both bridges.
Music, when γ increases, the results of are better. We also conduct experiments to evaluate the influence of γ with different bridges under the DAPT setting and the results are shown in Figure 6. Similar to Figure 6, the γ = 0 means MTD, which still can be improved regardless of what kind of bridge is applied. Moreover, we observe that during the DAPT setting, the influence of contrastive learning is smaller than in the basic setting (e.g. the basic between the best model and worst is smaller than that in Figure 6). We think after fine-tuning BERT on the large domain-specific corpus, models may learn some discriminative representations. However, compared with applying contrastive learning, training a domain-adaptive pre-training language model is inefficient (e.g., it takes almost 30 hours to train a model for each domain). A6: Visualization analysis. We conduct visualization analysis to explore the effects of MoCL on the representation of entities. As shown in Figure 7:(1) On one hand, we observe that the original entity representations of the same entity types under the sequence-labeling framework or the modularlearning-based framework disperse sparsely, which is consistent with the observation of (Kenton and Toutanova, 2019). After applying our proposed bridges and contrastive learning, MoCL tries to force the entities belonging to the same entity type to collapse into essentially a close cluster. (2) On the other hand, we observe that the original entity representations of similar entity types under the sequence-labeling framework or the modularlearning-based framework are prone to mix with each other, thus making them hard to be distinguished by the prediction model. In contrast, the entity representations produced by MoCL are clearly separated, which is much more discriminative.

heading: 5 Related Work
text: Due to the capability of extracting useful information and benefiting many NLP applications (e.g., information retrieval (Fetahu et al., 2021; Guo et al., 2009) and question answering (Longpre et al., 2021)), NER appeals to many researchers (Jiang et al., 2021; Feng et al., 2018; Kim et al., 2015; Lee et al., 2018; Qu et al., 2016; Rodriguez et al., 2018; Wang et al., 2018; Zhang et al., 2021b; Yang et al., 2017; Yang and Katiyar, 2020; Fei et al., 2021). Recently, to reduce the huge cost of annotating data, researchers start to explore crossdomain NER methods. According to whether the
labeled data of the target domain are used or not, these methods can be classified into unsupervised (Jia et al., 2019; Peng et al., 2021; Chen et al., 2022; Yang et al., 2022; Liu et al., 2022; Ma et al., 2022b; Zhang et al., 2021a) and supervised (Wang et al., 2020; Lin and Lu, 2018; Houlsby et al., 2019; Zheng et al., 2022). This paper focuses on the latter and according to the tagging scheme, supervised cross-domain NER methods can be classified into compositional labeling-based (Wang et al., 2020) and modular learning-based (Zhang et al., 2022a). Compared with previous studies, we attempt to improve both kinds of methods from the perspective of representation. In particular, a model-agnostic framework MoCL is introduced to refine the original chaotic representations by contrastive learning, motivated by its success in computer vision (Radford et al., 2021; Grill et al., 2020; Caron et al., 2020; Chen and He, 2021; Choi et al., 2022; Zhang et al., 2022b; Giorgi et al., 2021; Xu et al., 2022).

heading: 6 Conclusion
text: This paper explores utilizing contrastive learning to gain discriminative entity representations in the field of cross-domain named entity recognition. To guide contrastive learning at the entity level, we explored two bridges to capture different relations of entities at different granularities. Additionally, our framework is model-agnostic, so we respectively integrate it into two existing cross-NER baselines and two different pre-training language models to evaluate its generalization ability. The experimental results show that MoCL could help models learn discriminative representations and it has good generalization ability. In terms of the limitation, currently, we mainly evaluate MoCL under the single-source cross-domain setting. We plan to further extend it to multi-source cross-domain settings. Moreover, the interaction between named entity recognition and relation extraction can be considered to improve performance in the future.
Limitations
We propose a sequence-level contrastive learningbased model-agnostic framework MoCL to enhance entity type classification in cross-domain named entity recognition (NER). In the future, we would like to combine the different granularities of contrastive learning (i.e., token-level and sequencelevel) to learn generalized representation for further improving the capability of MoCL. In addition, due
to the hierarchical structure of entity types between the source domain and the target domain, it would also be beneficial to adopt Non-Euclidean space to represent words for better learning the relative hierarchical relationship between entities.

heading: Acknowledgement
text: This work has been supported by the National Natural Science Foundation of China (62076100), Fundamental Research Funds for the Central Universities, SCUT (x2rjD2220050), the Science and Technology Planning Project of Guangdong Province (2020B0101100002), CAAIHuawei MindSpore Open Fund, CCF-Zhipu AI Large Model Fund.
Ethical Considerations
Our research aims to benefit the efforts in delivering domain-adaption technology to data-limited settings. As a key task in NLP, named entity recognition has broad applications, e.g., machine translation, question answering, and potentially protecting endangered languages. Compared with many previous studies, we stress the importance of diversity in the sense that our experiments cover seven domains, including five lower-resource domains from the CrossNER dataset. Hoping that our work can contribute to extending modern NLP techniques to the lower-resource named entity recognition setting. The three datasets we use are both publicly available. To our best knowledge, the data do not contain any sensitive information and have no foreseeable risk.

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
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heading: 3 A2. Did you discuss any potential risks of your work?
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heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
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7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 7 Did you use or create scientific artifacts? Left blank.
B1. Did you cite the creators of artifacts you used? No response.
B2. Did you discuss the license or terms for use and / or distribution of any artifacts? No response.
B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? No response.
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? No response.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? No response.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. No response.
C 3 Did you run computational experiments? Please refer to Section 3.2 to see more details.

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Please refer to Section 3.2 to see more details.
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Please refer to Section 3.2 to see more details.

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Please refer to Section 3.2 to see more details.

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Please refer to Section 3.2 to see more details.
D 7 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.
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D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

heading: Future Work
text: Moreover, the interaction between named entity recognition and relation extraction can be considered to improve performance in the future. Limitations
We propose a sequence-level contrastive learningbased model-agnostic framework MoCL to enhance entity type classification in cross-domain named entity recognition (NER). In the future, we would like to combine the different granularities of contrastive learning (i. e. , token-level and sequencelevel) to learn generalized representation for further improving the capability of MoCL. In addition, due
to the hierarchical structure of entity types between the source domain and the target domain, it would also be beneficial to adopt Non-Euclidean space to represent words for better learning the relative hierarchical relationship between entities