In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance. To this end, we propose to exploit sibling mentions for enhancing the mention representations. Specifically, we present two different metrics for sibling selection and employ an attentive graph neural network to aggregate information from sibling mentions. The proposed graph model is scalable in that unseen test mentions are allowed to be added as new nodes for inference. Exhaustive experiments demonstrate the effectiveness of our sibling learning strategy, where our model outperforms ten strong baselines. Moreover, our experiments indeed prove the superiority of sibling mentions in helping clarify the types for hard mentions. Fine-Grained Entity Typing (FGET) aims to assign one or more fine-grained types to an entity mention given its context. For instance, the mention Steve Jobs should be classified as Person and Entrepreneur under the context Steve Jobs cofounded Apple .... Many tasks have witnessed the importance of FGET, such as relation extraction (Jiang et al., 2020b; Chu et al., 2020; Jiang et al., 2020a; Cheng et al., 2021), entity linking (Onoe and Durrett, 2020), and other tasks (Jiang et al., 2020c; Zhang et al., 2020b; Liu et al., 2021b). It is challenging to learn effective representations for contextualized mentions 1 in many information extraction tasks (Gao et al., 2022), espeEqual Contribution  Corresponding Authors 1 To simplify the statement, in the rest of this paper, the term mention is referred to as the contextualized mention, i.e., a mention accompanied with its context. cially in FGET, since the representations are required to well distinguish fine-grained types with similar but different semantics. Noticeable efforts have been made to learn type-aware representations for mentions (Ren et al., 2016; Xin et al., 2018; Choi et al., 2018; Zhang et al., 2018; Lin and Ji, 2019; Abhishek et al., 2017; Xu and Barbosa, 2018; Ali et al., 2020; Chen et al., 2021) and sig-nificant progress has been achieved. However, as supported by our empirical experiments, existing SOTA models perform poorly on a certain number of  hard  mentions, leading to limited overall performance. The main reasons are the following challenges. First, the structure of some contexts surrounding the hard mentions are inherently too complex to extract informative features for identifying entity types. Second, the contexts of some hard mentions are ambiguous and thus it is insufficient to handle these mentions by learning from their contexts only. In this paper, we show that representation learning of such hard mentions can be well handled by learning informative knowledge from their sibling mentions . Sibling mentions refer to the mentions that potentially share the same or semantically similar types (e.g., country and nation ) with the target mention. We illustrate how sibling mentions assist classifying hard mentions in Figure 1. Intuitively, the context of the target mention Sharp is ambiguous and insufficient for inferring the ground-truth types (i.e., organization , company , and tech company ), since both a person and a company can sign a deal with Qualcomm. Fortunately, the sibling mentions provide rich information that works as an important supplement for the target mention Sharp . By aggregating the supplementary information from siblings, it is promising to learn effective representations with less ambiguity for hard target mentions. To utilize sibling mentions, we model FGET as a heterogeneous graph learning problem. The graph is composed of two kinds of nodes, namely the mentions and the types. Besides, there are three kinds of edges connecting the nodes as shown in the left part of Figure 1, which represent the sibling relationship between mentions, the hierarchical relationship between types, and the isLabel relationship between mentions and types, respectively. The sibling relationship is considered as the most important part in our graph. For detecting it, we propose two similarity metrics, based on which we design an effective sibling selection algorithm. Upon the constructed nodes and edges, we employ an attentive graph neural module to learn their representations. Particularly, the representations of mention nodes are enriched by aggregating the information from their sibling and type neighbors. It is also noteworthy that, during inference stage, our graph model is scalable to include the unseen test mentions as new nodes and connect them with their existing sibling mention nodes in the graph to derive reliable representations for predictions. Extensive experiments are conducted to verify the effectiveness of our model. Our experimental results demonstrate that our model outperforms several strong baselines on the standard test sets with a large margin. Moreover, our model is indeed able to well handle hard mentions with the help from sibling mentions. We summarize our contributions as follows:  We are the first to point out a bottleneck issue suffered by existing SOTA models, i.e., they perform poorly on a certain number of hard mentions, and we quantitatively analyze its influence on typing accuracy via measuring hard mentions by entropy. We are the first to exploit sibling information for mention representation learning in FGET. E m , E y and E m,y are obtained as follows: E m = { ( m i , m j ) | m i , m j  V m , isSib ( m i , m j ) = 1 } (1) E y = { ( y i , y j ) | y i , y j  V y , isA ( y i , y j ) = 1 } (2) 2077 E m,y = { ( m i , y j ) | m i  V m , y j  V y , isLabel ( m i , y j ) = 1 } (3) where isA ( y i , y j ) = 1 indicates y j is the parent or child type of y i in the type hierarchy 2 , and isLabel ( m i , y j ) = 1 means mention m i is labeled with the type y j in the training set. We design two effective metrics for sibling detection and propose a scalable graph model to take advantages of sibling mentions. Given a mention m and the type set Y , an FGET model needs to predict the correct types Y m ( Y m  Y ) for m based on its context. In this paper, mention representations are learned and refined with the help of sibling mentions and ground-truth types. To achieve it, we propose a heterogeneous graph model enhanced by sibling mentions for FGET, as illustrated in Figure 1. First, a mention-type graph G is constructed from training samples (Sec 3). Then, the features for mentions and types are learned by an attentive graph neural module upon G (Sec 4). During inference stage (Sec 5), we add test mentions into graph G by connecting them to their sibling mentions in the training set. By aggregating sibling information, the representations of test mentions are generated and used for type prediction. Consider graph G = ( V m , V y , E m , E y , E m,y ) , where V m and V y are the set of mention nodes and type nodes, respectively. E m is the set of edges between the target mentions and their sibling mentions, while E y is the set of edges between types. E m,y denotes the edges connecting the target mentions and the ground-truth types. Since type hierarchy and the ground-truth types of mentions are available in the training set, V m , V y , E y and E m,y can be easily derived. isSib ( m i , m j ) = 1 means m j is the sibling mention of m i , which will be discussed in Sec 3.2. The key to construct E m is to define isSib ( m i , m j ) , i.e., the criterion for judging whether m j is the sibling of m i . We design two metrics to detect the sibling relationships between mentions, named (unsupervised) word distribution-based and (super-vised) typing distribution-based metrics. Word distribution-based metric The basic as-sumption for this metric is that mentions sharing more contextual words tend to have more similar ground-truth types. We use TF-IDF to encode mentions as sparse feature vectors. Then the sibling similarity between any two mentions is measured by the cosine similarity of their vectors. Typing distribution-based metric In this metric, we first derive the prior score distributions over the type set Y for all the mentions in the dataset from an extra base model (Lin and Ji, 2019) trained on the same dataset. Then the sibling mentions are selected by their cosine similarities to the target mention based on the score distributions. Sibling mention selection Given one of the metrics above, we obtain the sibling mentions according to Algorithm 1. Note that for each target mention m i  V m , we first select a subset V (cid:48) m from V m and only calculate the similarities between m i and the mentions m j  V (cid:48) m . The contexts of mentions from V (cid:48) m share at least one word with that of the target mention and |V (cid:48) m | (cid:28) |V m | , which greatly reduces time complexity. Then, based on the similarity scores, we choose the top-K most similar mentions V (cid:48) m,K as the siblings for m i and let isSib ( m i , m j ) = 1 for each m j  V (cid:48) m,K . Be aware that, by definition, the sibling relationship is directed, i.e., isSib ( m j , m i ) = 1 does not ensure isSib ( m i , m j ) = 1 holds. 2 The edges between y i and its parent or child type y j are directed, as detailed in Eq.(5) Algorithm 1: Sibling mention selection Input : the set of mention nodes V m 1 for m i , m j  V m do 2 isSib ( m i , m j )  0 3 end 4 for m i  V m do 5 (cid:46) select a candidate set V (cid:48) m from V m 6 for m j  V (cid:48) m do 7 (cid:46) compute similarity sim ( m i , m j ) 8 end 9 (cid:46) select the topK similar mentions V (cid:48) m,K from V (cid:48) m 10 for m j  V (cid:48) m,K do 11 isSib ( m i , m j )  1 12 end 13 end 4 Graph-based Typing Model 4.1 Attentive Graph Neural Module We employs graph neural networks (GNNs) with L layers (Velickovic et al., 2018; Xu et al., 2019) to aggregate the information of sibling mentions and types for learning mention representations. At the first layer of G , the embedding of each type y i  Y (denoted by y (1) i  R d r ) is randomly initialized. In contrast, to capture the rich features from contexts, the initial embeddings for mentions are derived by a parameterized mention encoder g (  ) , i.e., m (1) i = g ( m i ;  M )  R d r (details in Sec 4.2). Given the initial mention and type embeddings (i.e., m (1) i and y (1) i ), the graph module iteratively updates them to obtain m ( l +1) i and y ( l +1) i . Update of y ( l +1) i In the l -th ( l = 1 , ..., L  1 ) layer, the updating formula for type embedding y ( l +1) i  R d r is: y ( l +1) i = f 0 (cid:32) (cid:88) y k Y yi  ( l ) i,k f 1 ( y ( l ) k ) + f 1 ( y ( l ) i ) (cid:33) , (4) where f 0 and f 1 are linear layers with ReLU activation. Y y i denotes the type neighbors for y i in graph G , which are the parent or child types of y i in the type hierarchy. ( l ) i,k is the attention weight from type y i to y k defined as  ( l ) i,k = (cid:40)  (cid:0) y ( l ) (cid:62) i W ( l ) 1 y ( l ) k (cid:1) , y k is a child type ;  (cid:0) y ( l ) (cid:62) k W ( l ) 1 y ( l ) i (cid:1) , y k is a parent type , (5) 2078 W ( l ) 1  R d r  d r is the weight matrix to model the parent-child relationship. Note that Eq.(4) does not involve mention embeddings and only focuses on learning the hierarchical structure of types. The interaction between types and mentions will be modeled by Eq.(6) during the update process of mention embeddings. (cid:33) , 4.4 Loss Function The loss over m i is computed as: (cid:96) i =  |Y| (cid:88) k =1 (cid:0)  ik log p i [ k ]+(1   ik ) log(1  p i [ k ]) (cid:1) (9) where  ik  { 0 , 1 } indicates whether y k is the ground-truth type of m i in the training set. The overall loss is the average over all the mentions, i.e., L = 1 |V m | (cid:80) i (cid:96) i . where M m i and Y m i are the sibling and type neighbors 3 of m i in graph G . ( l ) i,j and  ( l ) i,k are the attention weights from m i to mention m j and type y k in the l -th layer, respectively. Specifically,  ( l ) i,j =  (cid:0) m ( l ) (cid:62) i W ( l ) 2 m ( l ) j (cid:1) ,  ( l ) i,k =  (cid:0) m ( l ) (cid:62) i W ( l ) 3 y ( l ) k (cid:1) , (7) W ( l ) 2 , W ( l ) 3  R d r  d r are learnable parameters. f 2 , f 3 , f 4 are linear layers with ReLU activation. Here, we use the attention mechanism to distinguish informative neighbors. Besides, the update process of target mentions involves both the sibling and type neighbors, whose representations are also updated at the same. In this way, the learned representations for both mentions and types are more consistent and thus more reliable for prediction. The mention encoder uses the backbone from Lin and Ji (2019). Given a mention, we first encode the mention span and the surrounding context as the weighted sum of their ELMo (Peters et al., 2018) word representations respectively. Then, the uni-fied feature vector for the mention is derived by concatenating both representations. Given a mention m i , the predicted score distribution p i  R |Y| over the type set Y is computed as: p i =  (cid:0) Y ( L ) W 4 m ( L ) i + W 5 m ( L ) i (cid:1) , (8) where Y ( L ) = (cid:104) y ( L ) 1 , y ( L ) 2 , ..., y ( L ) |Y| (cid:105)  R |Y| d r , y ( L ) i and m ( L ) i are the type and mention embeddings in the L -th layer in GNN. W 4  R d r  d r and 3 We define that M m i contains m i itself, thus the selfconnections are taken into account during graph learning. W 5  R |Y| d r are learnable parameters. p i [ k ] (the k -th element in p i ) denotes the predicted probability for type y k . The representation m ( L ) i incorporates the information from ground-truth type neighbors (Eq.(6)). However, it is then used for predicting the ground-truth types in turn (Eq.(8)). The setting that Y m i contains all the ground-truth types will inevitably degenerate the model to just focus on the type neighbors while totally ignore the mention neighbors. To overcome this, each neighboring type in Y m i is randomly discarded with a certain probability  . In this way, the prediction of discarded type will force the model to learn from the sibling mentions rather than directly from type neighbors. In the following, we describe the prediction process for test mentions. Step 1 : Given a batch of n test mentions, we first obtain their sibling mentions. To be specific, for each test mention m t , we select a candidate set V (cid:48) m from the training mentions V m . Then, the cosine similarity is computed between m t and each m i in V (cid:48) m , based on which the top K mentions are selected as siblings (see Sec 3.2). Step 2 : We add the test mentions as nodes into the mention-type graph G , where the test mentions are connected to their sibling mentions selected at Step 1. Note that, in the new graph, test mentions have no type neighbors since their ground-truth types are not available. Besides, there is no edge between any two test mentions in the new graph. Step 3 : Following Eq.(6), the representations of test mentions { m t } are updated by aggregating the embeddings for their sibling mentions. Note that Y m t is empty, so no information from the ground-truth types are involved. Through layers of updates, the final representations { m ( L ) t } are obtained. Step 4 : Based on the mention embedding m ( L t and the type embeddings Y ( L ) , we predict the type score distribution for m t by Eq.(8). We conclude that, (1) our graph module is scalable to add arbitrary number of unseen test mentions as new nodes to the existing graph to derive their representations. By contrast, many popular graph settings (Kipf and Welling, 2017; Velickovic et al., 2018; Wang et al., 2019) fail to extend to new nodes. (2) Since the embeddings for sibling mentions have been well learnt during training, the only need is to compute the embeddings for test mentions for prediction, which are derived simultaneously during graph inference with high efficiency. We evaluate the proposed model on two widely-used datasets: OntoNotes and BBN. OntoNotes The original OntoNotes dataset is annotated by distant supervision (Gillick et al., 2014). The training, development and test samples in OntoNotes are about 251K, 2K and 9K, respectively. We also conduct experiments on the augmented version 4 (Choi et al., 2018) with 793K training samples 5 . The above two versions share the same test set and development set, as well as the same type set of size 89. BBN Different from OntoNotes, BBN is manually annotated (Weischedel and Brunstein, 2005). The training, development and test set contain about 84K, 2K and 14K samples respectively, and the type set contains 47 type in total. Our model is implemented based on the PyTorch Geometric package (Fey and Lenssen, 2019). In the main experiments (Sec 6.4), we obtain the sibling mentions according to the typing distribution-based metric described in Sec 3.2. We conduct hyperparameter search on the development set and the optimal settings are presented in Appendix A. Following the previous works (Ling and Weld, 2012; Ren et al., 2016; Chen et al., 2019), we report the performance in terms of strict accuracy (Acc), macro-average F1 score (Ma-F1) and micro-average F1 score (Mi-F1). To guarantee the relia-4 http://nlp.cs.washington.edu/entity_type 5 We use the open-sourced version, which is a subset of the dataset reported in Choi et al. (2018). We compare our proposed model with several state-of-the-art FGET models: (1) AFET (Ren et al., 2016); (2) AAA (Abhishek et al., 2017); (3) NFETC (Xu and Barbosa, 2018); (4) NEURAL (Shimaoka et al., 2017); (5) ACT (Zhang et al., 2018); (6) Lin and Ji (2019); (7) Chen et al. (2020); (8) LABELGCN (Xiong et al., 2019); (9) Choi et al. (2018); (10) Ali et al. (2020). Note that Lin and Ji (2019) is considered as an important baseline in our experiments and is marked with (cid:70) in Table 1-3, since we use it as the base model to derive the prior typing distributions for sibling selection (Sec 3.2). Table 1, 2 and 3 illustrate the experimental results on the original and the augmented OntoNotes, as well as the BBN dataset. Analysis The results demonstrate that learning from sibling mentions helps our model outperform most baselines across the benchmarks. The detailed analysis is presented as follows: (1) We select sibling mentions according to the typing distribution from Lin and Ji (2019). We observe that, after aggregating sibling information through the attentive graph neural module (Sec 4.1), our model significantly outperforms Lin and Ji (2019) on both the original OntoNotes and the BBN dataset. When trained on the augmented OntoNotes of the same size, our model increases the accuracy score by more than 5% over Lin and Ji (2019) (cid:70) . Compared with Lin and Ji (2019)  which utilizes the full 3M augmented OntoNotes for training, our model still maintains a comparable performance and even improves the accuracy score by about 2%. (2) Many previous works have demonstrated the effectiveness of modeling type hierarchy for entity typing (Ren et al., 2016; Xu and Barbosa, 2018; Xiong et al., 2019; Chen et al., 2020). As a comparison, our model also considers the hierarchical information of types and incorporates it in a natural way (Sec 4.1). From the results, we conclude that learning jointly from type hierarchy and sibling mentions can remarkably improve the typing performance. (3) The attention mechanism plays an important role in our graph module and some of the baselines (Ren et al., 2016; Abhishek et al., 2017; Xu and 2080 Barbosa, 2018). It not only helps identify the informative features from neighbors but also helps alleviate noise from the training data constructed by distant supervision (e.g., OntoNotes). The results reveal that our graph-based solution is more effective than the existing solutions. In Sec 3.2, we propose two similarity metrics to discover sibling relationships in graph G , and abbreviate them as: Word-based and Typing-based metrics. Here, we provide two additional metrics for more detailed analysis: the Gold typing-based and the Random-based metrics, which are two extreme variations of the typing-based metrics. Under the gold typing-based metric, the siblings are selected by the gold typing distribution, where each dimension is 0 or 1 according to the ground-truth types of the mention. In this way, candidate mentions that share more ground-truth types with the target mention will have larger cosine similarity and thus be chosen as the siblings with a higher probability. On the contrary, under the random-based metric, siblings are selected at random. Since the type set is large, the siblings are more likely to be irrelevant with the target mention and may contain much noise. Measuring sibling quality Intuitively, different similarity metrics will affect the quality of siblings. To quantify this effect, we measure the sibling quality for the test mentions V (cid:48) in the original OntoNotes and define the metrics as follows. For each mention m i  V (cid:48) , denote its ground-truth types as Y m i and sibling mentions in graph G (defined in Sec 3.1) as M m i . Further, for M m i , we denote their ground-truth types as YM i , i.e., YM i = (cid:91) m j M mi \{ m i } Y m j . Similar to the definitions of Precision, Recall and F1, we define Purity, Coverage and Quality to measure the sibling quality of V (cid:48) : Purity = 1 |V (cid:48) | (cid:88) m i V (cid:48) = |Y m i  YM i | |Y M i | Coverage = 1 |V (cid:48) | (cid:88) m i V (cid:48) |Y m i  YM i | |Y m i | Quality = 2  Coverage  Purity Coverage + Purity (11) Results The results are presented in Table 4. In general, the model performance is closely related to the sibling quality. Besides, the typing-based metric performs better than the word-based metric. This indicates that the the continuous type-level probability distribution is more reliable for sibling selection than the discrete word-level distribution. The scores from the gold typing-based and the random-based metrics reveal the upper bound and the lower bound of the scores for the typing-based metric. On the one hand, the quality of the siblings selected by the gold typing-based metric is much higher than those by other methods, with the Coverage up to 97 . 6% . Meanwhile, its corresponding model also outperforms the other three by a large margin. Note that the typing performance in this scenario is limited by the annotation 2081 Macro Micro Metrics Purity Coverage Quality P R F1 P R F1 Random-based 9.6 71.3 16.9 65.1 56.4 60.1 65.1 43.5 52.2 Word-based 12.2 75.6 21.0 82.7 69.7 75.3 82.1 60.4 69.7 Typing-based 13.1 82.3 22.5 83.3 71.4 76.5 83.3 61.9 71.0 Gold typing-based 21.8 97.6 35.7 96.1 80.0 86.9 94.9 69.2 80.0 Table 4: Comparison among different sibling selection metrics. Note that the sibling quality scores of Gold typing-based do not reach 1, since the sibling mentions are only selected from a subset V (cid:48) m (see Sec 3.2) for time efficiency, which are not guaranteed to have exactly the same ground-truth types with the target mention. quality to some extent. Since OntoNotes is annotated by distant-supervision, the scores for the gold typing-based metric could not reach higher due to the label noise of the siblings. On the other hand, a distinct drop of the scores is observed with the random-based metric. This is reasonable since the randomly selected siblings contain much noisy information, which is helpless and even harmful for typing of the target mention. It can be concluded from the above observations that there is still much room to improve the sibling quality as well as the typing performance of the sibling-enhanced model. The model performance is sensitive to the size of selected sibling mentions for a target mention in the graph G . Denote the sibling size as K , following the default hyper-parameter settings, we train our model on the original OntoNotes under different K  { 0 , 5 , 10 , 15 } using the typing-based sibling selection metric. The corresponding sibling quality and model performance are reported in Table 5. We observe that the best scores are obtained with the top 5 sibling mentions. When K = 0 , the graph only contains the self-connections from the target mentions to themselves. Without the additional information from siblings, the Macro F1 score decreases by 2 . 1% , which indicates the effectiveness of sibling mentions for improving the typing performance of our model. When K (cid:54) = 0 , the Coverage score goes up while the Purity and Quality scores go down as K ranges from 5 to 15 . Meanwhile, the typing performance decreases as K increases. It suggests that, for OntoNotes, a properly smaller sibling size is a trade-off choice for the model to use siblings with higher quality and thus achieve better typing performance. We randomly discard some type neighbors with a dropout probability  during training (Sec 4.5), which forces the model to learn from the sibling mentions other than the ground-truth types. Table 6 shows the results under different values of  on the original OntoNotes dataset. Generally, the model achieves better performance with larger  . This indicates discarding a large proportion of ground-truth types is beneficial for learning from sibling mentions. Besides, it also narrows the difference between training and test settings where the test mentions do not have ground-truth types as neighbors. The best performance is achieved when  equals around 0 . 7 . However, dropping all the type neighbors (i.e.,  = 1 ) will block the interaction between the type and mention representations in the graph, which may slightly damage the performance. To select sibling mentions, we first derive the prior typing distribution from the base model (Lin and Ji, 2019) as described in Sec 3.2. During experiments, we observe that the contextual information 2082 for some mentions are insufficient or too complex, which makes the base model confused on these mentions. Entropy measures the uncertainty of a probability distribution. Thus, we quantify the difficulty of mentions by the entropy of their corresponding prior typing distributions and define the mentions with the top500 highest entropy values as hard mentions , which account for about 5% of the whole mentions . Table 7 compares the performance of our model and the base model on both the whole mentions and the hard mentions from the test dataset of the original OntoNotes. we see that both models perform worse on the hard mentions than on the whole mentions. Besides, except for the superiority of our model regarding the Acc, Ma-F1 and Mi-F1 scores, it also achieves a lower entropy value than the base model especially on the hard mentions. This indicates the information from siblings makes the output type distributions more concentrated and therefore increases the confidence for model predictions. To further provide an intuitive understanding about how our model benefits from sibling mentions, we present an example in Table 8. As expected, the retrieved siblings based on the metric defined in Sec 3.2 share similar ground-truth types with the target mention. This verifies the effectiveness of our sibling selection algorithm. Moreover, we observe that the siblings even help predict the correct but out-of-gold-set types for the target mention in this case. Although the annotated types for the target mention [ GM officials ] only contains /person in the test set. The sibling mentions still provide a strong evidence for our model to also predict /per-son/title as a possible type for the target mention. FGET is an important task for the downstream NLP tasks and many efforts have been make in improving its performance (Zhang et al., 2020a; Liu et al., 2021a). Early works in FGET (Ling and Weld, 2012; Shimaoka et al., 2016) mainly focus Target mention : [ GM officials ] told workers late last week of the following moves: production of full-sized vans will be consolidated into a single plant in Flint, Mich. Ground-truth : /person Prediction from our model : /person, /person/title Sibling 1 : It's been a steadily improving relationship., says the [ president ]. Sibling 2 : Apart from those two actions, Mr.Sikes and the three other [ commissioners ] said they expect to reexamine how AT&T is regulated since competition has increased. Ground-truth : /person, /person/title Sibling 3 : HUD Secretary [ Jack Kemp ] backed an unsuccessful effort to strike such language last week, but received little support from the White House    Ground-truth : /person, /person/artist, /person/artist/actor, /person/artist/author, /person/political_figure Table 8: An example to illustrate the relationship between the target mention and the sibling mentions from the Original OntoNotes. on feature extraction for mentions, which do not consider label noise introduced by distant supervision (Gillick et al., 2014; Choi et al., 2018; Li et al., 2020). Recent years have witnessed an increasing number of researchers being dedicated to data denoising. A popular solution (Ren et al., 2016; Abhishek et al., 2017; Xu and Barbosa, 2018; Ali et al., 2020) is to design loss functions for the clean and noisy parts of the training data separately. Nevertheless, Zhang et al. (2020c) proposes an automatic relabeling framework to estimate the pseudo-truth label distribution of each sample, which treats the noisy and clean data uniformly. Besides, Chen et al. (2019) groups mentions of the same type into a compact cluster to improve the robustness of the model. Ali et al. (2020) refines noisy representations by corpus-level contextual clues. Onoe and Durrett (2019) introduces two additional models to delete the samples that are too noisy to be useful, and repair noisy labels for the retained examples. In addition, there are some notable work which tries to build FGET with limited resources (Qian et al., 2021). Modeling the type hierarchy is another important topic in FGET. Prior solutions (Shimaoka et al., 2017) introduce a one-hot matrix to encode the hierarchy. Xu and Barbosa (2018) proposes a hierarchy-aware loss function. Recently, graph-based methods have been proven to be powerful in many NLP tasks (Kipf and Welling, 2017; Liang et al., 2021; Xu et al., 2019; Liang et al., 2022). Using graphs 2083 to model the type hierarchy in FGET is a natural idea. Jin et al. (2019) models the potential type correlations for in-knowledge-base entities via hierarchical multi graph convolutional networks (GCNs). Further, Xiong et al. (2019) extends GCNs to a vast number of free-form types. Chen et al. (2020) designs a multi-level learning-to-rank loss to leverage hierarchical information. Recently, Onoe et al. (2021) models the mention and type representations in a box space instead of the traditional vector space. In this paper, we firstly point out that SOTA typing models suffer from a bottleneck issue, i.e., they perform poorly on a certain number of hard mentions, which leads to their limited overall performance. To this end, we propose to exploit sibling information for mention representation learning and define two metrics for detecting sibling relationship between mentions. Further, we model sibling learning as a graph learning problem. Our model is scalable in that, once trained, it can generate sibling-aware representations for previously unseen mentions ef-ficiently during inference stage. Extensive experiments show that the proposed model indeed handles hard mentions well and thereby yields better overall performance than SOTA baseline models. This work was partially supported by the National Natural Science Foundation of China (61876053, 62006062, 62176076), the Shenzhen Foundational Research Funding (JCYJ20200109113441941, JCYJ20210324115614039), Joint Lab of Lab of HITSZ and China Merchants Securities.
result objective method abstain objective abstain abstain abstain abstain abstain abstain other method abstain abstain abstain result abstain abstain abstain abstain abstain method abstain abstain method objective method abstain objective result objective abstain objective objective other objective abstain abstain abstain abstain abstain method abstain abstain abstain abstain abstain abstain abstain method abstain method abstain abstain abstain result objective abstain method abstain abstain method abstain abstain abstain other abstain other abstain abstain abstain abstain abstain other abstain abstain abstain other abstain method abstain abstain abstain abstain abstain abstain other method abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain method abstain abstain abstain abstain abstain abstain method abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain method method method other other abstain abstain abstain abstain result abstain abstain abstain abstain result abstain abstain result abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain abstain abstain abstain abstain abstain other abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain result abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain abstain result abstain abstain abstain result result abstain abstain abstain abstain result abstain abstain other other abstain other other other other other other other other other other other other other other other other other other other method objective abstain method abstain other