Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts. Despite significant advances in ED, existing methods typically follow a one model fits all types approach, which sees no differences between event types and often results in a quite skewed performance. Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been lit-tle exploration of this problem. This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events. On this foundation, we develop a new training mechanism for ED, which can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks. Finally, by highlighting many distinct characteristics of trigger-dependent and context-dependent types, our work may promote more research into this problem. Event detection (ED) is the first and a crucial step of event extraction, which aims to identify events of certain types in plain texts (Ahn, 2006; Nguyen and Grishman, 2015; Mitamura et al., 2017). Previous methods to ED typically adopt a one model fits all types approach, seeing no difference between event types and using a single model to address them all (Ji and Grishman, 2008; Li et al., 2013; Chen et al., 2015; Lin et al., 2020). However, such approaches produce quite skewed performance on different types. Tasking the ACE benchmark as an example, we note the state-of-the-art ED model (Wadden et al., 2019) can strike 90% in F1 for the type DIVORCE , yet only 50% for the type STARTPOSITION , and it is more surprising that the training set of DIVORCE is eight times smaller than that of START-POSITION . Finding the causes underlying S1: The couple divorced four years later. the skewed performance is crucial to the robustness of an ED model; however, this problem is still understudied in current research. In this study we take a fresh look at above problem and for the first time attribute the skewed performance to the contextual patterns of events . Let consider the two typical instances of DIVORCE and START-POSITION shown in Figure 1. Intuitively, they demonstrate distinct patterns: the DIVORCE event is more trigger-dependent , and the trigger word (i.e., divorced) is very indicative of the event's occurrence; by contrast, the STARTPOSITION event is more context-dependent  the event semantic is primarily expressed by contexts rather than the trigger become, which is a merely light verb. We hypothesize an ED model performs poorly on context-dependent types because capturing context semantics is challenging (Lu et al., 2019; Liu et al., 2020b). With the above intuitions, two questions rise: (i) Can we estimate an event's pattern quantitatively? (ii)) How to robustify an ED model by characterizing such patterns? To address the first question, we introduce a brandy new concept called trigger saliency attribution , which can explicitly quantify an event's contextual pattern. Figure 2 illustrates the key idea: to determine how much an event is trigger-dependent or context-dependent, we measure the trigger's contribution to expressing overall the event semantic. Specifically, we first assign each sentence a global event label that represents the overall event semantic. Then, inspired by the feature attribution method 4573 (Simonyan et al., 2014; Sundararajan et al., 2017), we regard each word as a feature and compute its contribution (i.e., saliency value) for predicting the global event label. Finally, by examining the ground-truth trigger's saliency value, we can tell how much an event depends on triggers or contexts: a higher value, for example, indicates that the trigger contributes more to the event, implying the event is more trigger-dependent. To answer the second question, we develop a new training mechanism based on trigger saliency attribution, which uses saliency as evidence to enhance learning. Our method is simple and straightforward  instead of using a single model to detect all event types, we group event types with similar patterns together (assessed by trigger saliency attribution) and develop separate models for each group. This strategy enables different models to capture distinct patterns  for example, the model for context-dependent type can focus on mining contextual information for learning. To further boost learning, we also propose two saliency-exploration strategy to augment the above framework, which can explicitly integrate saliency information into learning and produce improved performance particularly for context-dependent types (  6.2). To verify the effectiveness of our approach, we have conducted extensive experiments on two ED benchmarks (i.e., ACE 2005 (LDC, 2005) and MAVEN (Wang et al., 2020)). According to the results: (i) Our trigger saliency attribution method can capture the underlying pattern and well explain the skewed performance, obtaining Spearman's correlation coefficients of 0.72 and 0.61 with per-type F1 on ACE 2005 and MAVEN respectively; (ii) Our new training regime based on saliency demonstrates improved results on the two benchmarks. On ACE 2005, for example, it produces a 2% ab-solute gain in F1 over methods training different event types jointly. Finally, in ablation studies, we compare and highlight many significant characteristics (e.g., linguistic and lexical patterns) of trigger-dependent and context-dependent event types; our work may inspire future research into their patterns. To summarize, our contributions are three-fold:  We analyze the origins of an ED model's skewed performance and propose a new notion termed trigger saliency attribution, which can assess the underlying pattern of events. Our findings, as a seminal study, raises the possibility that the traditional one model fits S1: The couple divorced four years later. S2: He became the first minister to England. [Divorce] [Start-Pos] Step 1 High Contribution i / J w   Step 2 Step 1 Step 2 Low Contribution i / J w   Figure 2: Illustration of trigger saliency attribution, where the saliency value of a trigger can quantify its contribution to the overall event semantic. all types paradigm may need to be changed. We present a new ED training mechanism based on trigger saliency attribution that achieves promising results on two benchmarks, especially when dealing with context-dependent event types. We highlight several diverse patterns of trigger-dependent and context-dependent event types, and our findings may stimulate future research into their differences. Event Detection. ED is a critical subtask of event extraction that seeks to locate event instances in text, which has received a lot of attention from researchers. Traditional methods for ED typically use fine-grained features (Ahn, 2006; Ji and Grish-man, 2008; Liao and Grishman, 2010; Hong et al., 2011; Li et al., 2013), whereas newer methods rely on neural networks (Chen et al., 2015; Nguyen and Grishman, 2015; Feng et al., 2016; Nguyen and Nguyen, 2019; Liu et al., 2018a, 2019a,b), which have investigated the use of syntactic information (Liu et al., 2018b; Lai et al., 2020), document-level cues (Wadden et al., 2019; Lin et al., 2020; Du and Cardie, 2020; Liu et al., 2020b; Lai et al., 2021; Pouran Ben Veyseh et al., 2021; Li et al., 2021; Chen et al., 2021; Liu et al., 2021), and external supervision signals (Tong et al., 2020; Liu et al., 2020a) to boost learning. However, most methods recognize no distinction between event types and train a single model to identify all event types, resulting in rather skewed performance on different event types. Two seminal works (Lu et al., 2019; 4574 Liu et al., 2020b) have observed the comparatively poor performance on context-dependent texts and offered a better context-exploration strategy to improve training. Nonetheless, they are in a position to improve performance rather than investigate the root causes. Our approach, on the other hand, takes a fresh look at the issue and aims to define the underlying patterns of events for learning. Feature Attribution. The goal of feature attribution (FA) is to assess how important an input feature for model prediction, which has sparked a lot of interest in interpreting model decisions (Si-monyan et al., 2014; Sundararajan et al., 2017). Formally, suppose we have an input vector x = ( x 1 , x 2 , ..., x n )  R n and a function F : R n  [0, 1] representing a model. The attribution value of x , with respect to the output F ( x ), is defined as a vector AF ( x ) = ( a 1 , a 2 , ..., a n )  R n , where a i measures the contribution of x i to F ( x ). The existing FA methods are classified as gradient-based methods, which consider the gradient of the output to the input as the attribution value (Simonyan et al., 2014; Springenberg et al., 2015), and reference-based methods, which consider the difference between the model's output and some reference" output, in terms of the difference between the input and some reference" input, as the attribution value (Ribeiro et al., 2016; Sundararajan et al., 2017). FA have been used to interpret model predictions in applications including image classification (Si-monyan et al., 2014), machine translation (Ding et al., 2017), text classification (Chen et al., 2018), and others (Bastings and Filippova, 2020). To the best of our knowledge, this is the first work introducing FA to ED for quantifying the underlying event patterns. Integrated Gradient. Integrated Gradient (Sun-dararajan et al., 2017) is a specific (reference-based) FA method that views the feature attribution value as the accumulated gradient along the line between the model's input x and a reference input x (cid:48) , which denotes the lack of a feature 1 . Particularly, the attribution value of x i (i.e., the i th dimension of x ) with respect to an output F ( x ) is defined as: a i = ( x i  x (cid:48) i )  (cid:90) 1  =0  F ( x (cid:48) +   ( x  x (cid:48) )) x i d (1) where  F ( x ) x i indicates the gradient of F ( x ) to x i . In our approach, we prefer Integrated Gradient to 1 In text related tasks, x (cid:48) is usually set as a sequence of embedding vectors with all zero values (Wallace et al., 2019). other FA methods due to its computing efficiency and effectiveness in addressing a wide range of text based tasks (Sundararajan et al., 2017; Liu and Avci, 2019; Bastings and Filippova, 2020). Algorithm 1 provides an overview of our trigger saliency attribution method, which consists of three major steps: (i) sentence-level event classification, (ii) word-level saliency estimation, and (iii) type-level saliency estimation. Let s = [ w 1 , w 2 ,    , w N ] be a sentence of N words, and the ED task corresponds to predicting an event label sequence Y s = [ y 1 , y 2 ,    , y N ], where y i  T  { O } indicates the event label of w i , T is a set containing all pre-defined event types, and O is a null type denoting no-trigger words. Sentence-Level Event Classification. We start by giving s a sentence-level event label G s , which represents the overall event semantic. Let the label be G s = [ g 1 , g 2 , ..., g |T | ]  R |T | , where g i  { 0 , 1 } indicates whether a trigger of the i th event type is contained by s ( g i =1) or not ( g i =0). Following that, we construct a sentence-level event classifier and aim to learn a mapping from s to G s . Particularly, we devise a BERT based sentence classifier (Devlin et al., 2019) and adopt a multi-label binary cross-entropy loss for optimization: L ( G s ; X s ) =  1 |T | |T | (cid:88) i =1 g i  log( o si )+(1  g i )  log(1  o si ) (2) where X s is the input embedding of s in BERT, o s  R |T | indicates the logits vector computed by the classier, and o si denotes the i th element of o s . Word-Level Saliency Estimation. Based on the sentence-level classifier, we next use Integrated Gradient (Sundararajan et al., 2017) to calculate the contribution (i.e., saliency value) of each word 4575 [Divorce] [CLS] The couple divorced four years later 0 0 1 0 0 0 [Start-Pos] US minister Deep Transformer (BERT) O O O O O 0 O Input Saliency Embeddings Output 0 0 0 1 Deep Transformer (BERT) O O 0 O Input Saliency Embeddings Output [CLS] He become the USO Context Evidence Divorce, Hearing, Fine Start-Position, Pardon, Contextualized Trigger Saliency Attribution S2: He become the first minister to England. We then normalize  w i as a scalar value  w i with a sentence-wise normalization:  w i = e (cid:107)  wi (cid:107) 2 / (cid:88) N n =1 e (cid:107)  wn (cid:107) 2 (4) where (cid:107)(cid:107) denotes the L 2 norm. In actuality, we may not be concerned with a word's saliency to the general event semantic G s , but rather with a specific event type T  T . To this end, we replace G s with the one-hot representation of T in Equation (3) for evaluation. Finally, we represent the word-level saliency of w i with respect to the event type T by  ( T ) w i , and we suppose  ( T ) w i = 0 if the sentence does not describe any event of type T . Event Type Division. Based on type-level saliency estimation, we divide all event types into a trigger-dependent set T trigger = { T | SL( T )   } and a context-dependent set T context = { T | SL( T ) <  } . The threshold  is empirically determined as the median of all per-type trigger saliency values, implying that the event types are evenly divided into two sets 2 . Saliency-Enriched Event Detector. Following that, we create separate ED models for T trigger and T context . Each model is implemented using the BERT architecture (Devlin et al., 2019), and given a sentence s , it performs a word-by-word classification over BERT's output to generate a label sequence:  Y s = (  y 1 ,  y 2 ,    ,  y N ), with  y i being the predicted event label for w i . Based on the different characteristics of trigger-dependent and context-dependent types, we devise different saliency-exploration methods to boost learning. 4 Saliency Enhanced ED Based on trigger saliency attribution, we devise a new training paradigm for ED, which can distinguish event types with similar patterns for learning and achieves promising results. The overview is shown in Figure 3, and the technical details follow. to the prediction. We utilize the loss function as the desired model (Wallace et al., 2019), and calculate the saliency of w i , more accurately, its BERT representation x i  X s , regarding the loss by:  w i = ( x i  x (cid:48) i )  (cid:90) 1  =0  L ( G s ; X (cid:48) +   ( X s  X (cid:48) ))  x i d (3) where X (cid:48) is a sequence of all-zero vectors (serving as a reference input), and x (cid:48) i denotes the i th element in X (cid:48) . Type-Level Saliency Estimation. Based on the word-level saliency, we measure the type-level trigger saliency value (regarding an event type T ) as: SL( T ) = (cid:80) ( s,Y s ) (cid:80) w { w i | y i = T }  ( T ) w #of training examples of type T (5) where ( s, Y s ) ranges over each training instance; { w i | y i = T } is a set containing all of the triggers of type T in s . SL( T ) indicates how trigger-dependent or context-dependent an event type T is, and it has been shown to correlate strongly with the per-type model performance (  6.1). gers, we build a mechanism called word saliency embeddings (WSEs) in the model for T trigger to capture such regularities. Specifically, we first quantify each word's saliency value 3 as 0 or 1 based on  , i.e., the threshold we used previously for distinguishing event types, and then use a separate embedding vector to distinguish 0 and 1, similar to word embeddings. Such embeddings are incorporated into the model 4 to capture a regularity that words with high saliency values are more likely to be triggers. Note WSEs are also incorporated in the model for the T context , which on the other hand seeks to learn the opposite regularity that words with high saliency values may not be triggers. (ii) Saliency as Context Evidence. In the event detector for T context , we also devise a regime for interpreting salient information as context evidence for reasoning. Consider the previous example S2. Our method identifies the context words US minis-ter as the most salient words (with saliency values larger than  ) expressing the overall event semantic. Here we regard salient contexts as supplementary evidence and concatenate them with the sentence for learning, as shown in the bottom of Figure 3. Compared with WSEs, this method can additional capture the lexical semantics of the salient words, which has been shown to considerably aid in the recognition of context-dependent event types (  7). Model Ensemble. In the testing stage, we combine the results of two models to make a final prediction. If ambiguous cases occur, i.e., the two ED models predict different event types for the same word, we use the type with a higher probability as the result. We use cross-entropy loss for optimization. For example, the model for T trigger is trained by minimizing the following loss: L =  (cid:88) ( s,Y s ) (cid:88) ( w i ,y i )  ( s,Y s ) log P ( y i | w i ) (6) where ( s, Y s ) refers to each training instance; ( w i , y i 5 ) ranges over each pair of word and its ground-truth event label; P ( y i | w i ) denotes the conditional probability that the model predicts y i for w i . We use Adam (Kingma and Ba, 2015) with default hyper-parameters for parameter update. 3 To prevent label leaking, at the testing stage we use predicted labels rather than ground-truth labels for attribution. 4 Because combining external embeddings with BERT remains difficult, we alter the segmentation embeddings in BERT to WSEs, motivated by (Wu et al., 2019). 5 Note in the event detector for T trigger , we should consider y i as O for y i  T context . Datasets. We conduct experiments on ACE 2005 (LDC, 2005) and MAVEN (Wang et al., 2020). ACE 2005 defines 33 event types and contains 599 documents. We adopt a common split for evaluation following previous works (Li et al., 2013; Wadden et al., 2019). MAVEN is a newly released corpus defining 168 more fine-grained event types (Wang et al., 2020). Because the MAVEN test set is not publicly available and our study is concerned with per-type performance, we instead use the MAVEN development set for assessment and divide the original MAVEN training set as 9:1 for training and validating. Table 1 displays the comprehensive data statistics for the two datasets. Evaluation Metrics. We adopt the following metrics to evaluate our model: (i) Spearman's rank correlation coefficient, which can determine the statistical dependency between two ranked variable sequences. The metric is defined as  = 1  6 (cid:80) d 2 i n ( n 2  1) , where d i is the difference between the i th pair of ranked variables, and n is the sequence length. We use it to measure how well our trigger saliency attribution results correlate with per-type model performance. (ii) Precision (P), Recall (R) and (Mi-cro) F1, which are widely used to assess the overall performance of an ED model. (iii) Macro F1, the arithmetic mean of class-wise F1-scores, which will be low for models that only perform well on common types but badly on rare types. Implementations. In our trigger saliency attribution method, the sentence-level classifier is built on the BERT-base . The batch size is set to 20, and the learning rate is set to 1e-5. After 5 epochs, it achieves 74.8% in F1 on the ACE 2005 development set, matching the state-of-the-art performance (Liu et al., 2019c). As for the two ED models, we consider BERT-base architectures. The batch size is set to 20, chosen from [1, 5, 10, 20, 30]. The 4577 Dataset Setting Method ACE 05 MAVEN Static # of Training Instances 0.06 0.09 Trigger Variance 0.26 0.25 Dynamic Trigger Attention 0.12 0.14 Trigger Saliency (Ours) 0.72 0.61 Table 2: The Spearman's  correlation (   [-1, 1]) between per-type F1 and different criteria (high correlation is considered when  > 0.6). learning rate is set to 1e-5, chosen from a range from 1e-3 to 1e-6. The dimension of word saliency embeddings is empirically set to 100. To allow for further investigation, we have made our code publicly available at https://github.com/ jianliu-ml/SaliencyED . Table 2 shows the Spearman's rank correlation between per-type F1 and four criteria: 1) the number of training instances (regarding an event type); 2) trigger variance, defined as the ratio of the number of unique event triggers to the total number of event triggers (regarding an event type); 3) trigger attention value, which corresponds to the ground-truth trigger's attention value in the BERT model; 4) trigger saliency attribution (our method). We use a state-of-the-art ED model (Wadden et al., 2019) and perform a 5-run average on the development set to obtain the per-type F1 score. According to the results, our trigger saliency attribution approach correlates the best with model performance, yielding a score as high as 0.72 and 0.61 in Spearman's  correlation. This suggests that our method can well explain the skewed performance. Our other findings are interesting: (i) Surprisingly, the number of training examples shows a negligible correlation (  = 0.06 and 0.09) with per-type F1. This implies that simply collecting more training data may not be an effective way to improve an ED model. (ii) The trigger variance metric demonstrates a moderate association (  = 0.25 and 0,26), indicating that the diversity of event triggers is a factor influencing model performance. (iii) The trigger attention value also shows a poor association, which may be another proof that attention is not explainable (Jain and Wallace, 2019). and our trigger saliency attribution method. In addition to noting that our method adequately explains the per-type F1-score, we find that  = 0.25 may be a good threshold for distinguishing between trigger-dependent and context-dependent event types. To test the efficacy of our saliency enhanced ED model: 1) For ACE 2005, we compare our model with (i) DYGIE++ (Wadden et al., 2019), which uses a graph view to learn context features; (ii) TriggerQA (Du and Cardie, 2020), which uses a question answering formulation for the task; (iii) OneIE (Lin et al., 2020), which adopts cross-sentence features for the task. Because pre-processing has a significant impact on the results (Orr et al., 2018), to ensure a fair comparison, we only consider models using the same pre-processing steps as in (Wad-den et al., 2019). 2) For MAVEN, we use the BERT+CRF proposed in the original work (Wang et al., 2020) for comparison. As a baseline, we also construct a model called BERTEns, which ensembles two BERT models similar to ours but does not differentiate event types. We refer to our approach that merely separates event types for learning (with-out saliency-exploration strategies) as SaliencyED (SL), and our full approach as SaliencyED (Full). Table 3 displays performances of different models. The results have confirmed our approach's effectiveness. Particularly: (i) our full model achieves the best Micro F1 score (75.8% and 67.1%) on 4578 Method P (cid:78) R (cid:78) F1 (cid:78) F1 (cid:79) ACE DYGIE++ (2019) -73.6 65.7 TriggerQA (2020) 71.2 73.7 72.4 64.5 OneIE (2020) -75.2 66.6 BERTEns 71.5 73.1 72.3 65.4 SaliencyED (SL) 74.7 75.5 75.1 68.1 SaliencyED (Full) 75.4 76.2 75.8 68.8 MAV BERT+CRF (2020) 62.3 64.1 63.2 55.2 BERTEns 64.7 66.9 65.8 58.0 SaliencyED (SL) 64.9 68.2 66.5 59.2 SaliencyED (Full) 64.9 69.4 67.1 60.3 Table 3: Results on ACE 2005 and MAVEN (MVN). ACE 2005 and MAVEN without the use of sophisticated architectures or external resources, as DY-GIE++ and OneIE do. (ii) Impressively, with the identical architectures, our full model SaliencyED (Full) outperforms BERTEns by 2.8% and 1.7% in F1 on the two datasets, respectively; SaliencyED (SL), which only differentiates event types for training, outperforms BERTEns by 1.6% in F1. This emphasizes the significance of identifying event patterns for ED. (iii) Our method gives the best Macro F1 on two datasets, indicating that it performs well on both common and rare event types. Table 4 shows the performance breakdown for trigger-dependent (TD) and context-dependent (CD) types. According to the results, different models consistently produce good performance on TD types but low performance on CD types, implying that the patterns found by our trigger saliency attribution method are reasonable. When comparing SaliencyED (SL) and SaliencyED (Full), we see that the saliency-exploring method is more effective on CD types (+2.3% in F1) than on TD types (+0.3% in F1). This makes sense because detecting context-dependent events relies significantly on context reasoning, and our method can just use important contexts as evidence to improve learning. Ablation Study. We undertake an ablation study in Table 5 to investigate different model components, using the more challenging context-dependent (CD) types as an example. In the variant models, +WSE and +Evidence denote supplementing SaliencyED (SL) with word saliency embeddings and context evidence, respectively. +MaskAtt is an approach for calculating atten-TD Types CD Types Method F1 (cid:78) F1 (cid:79) F1 (cid:78) F1 (cid:79) ACE DYGIE++ (2019) 78.2 74.4 65.8 52.1 TriggerQA (2020) 80.1 76.3 65.2 53.2 OneIE (2020) 83.6 77.9 69.0 54.2 BERTEns 83.3 77.8 68.3 52.3 SaliencyED (SL) 86.2 82.0 70.0 56.9 SaliencyED (Full) 86.4 81.6 71.5 57.8 MAV BERT+CRF (2020) 67.5 67.1 49.2 38.1 BERTEns 70.3 70.0 51.5 38.1 SaliencyED (SL) 71.3 70.2 52.6 49.1 SaliencyED (Full) 71.6 70.8 53.5 50.4 Table 4: Results on trigger-dependent (TD) and context-dependent (CD) event types, where F1 (cid:78) and F1 (cid:79) indicate Micro and Macro F1 respectively. tion that masks the word itself, which can drive the model to focus more on contexts for learning; +Gold Argument is an oracle method that uses gold event arguments as evidence for learning. Based on the results, +Evidence outperforms +WSE and +MaskAtt, indicating its efficacy. Interestingly, +MaskAtt also boosts performance, implying that the contexts of CD events do carry important information for asserting the event. Finally, the superior performance of +Gold Arguments implies that find-ing indicative evidence (e.g., event arguments) is the key factor boosting learning on CD types. Impact of Event Type Division. We use our event type division method as a baseline and compare it to three other event type division strategies: 1) at random; 2) based on the amount of training instances; 3) based on development set performance. According to the results, the first two strategies decrease performance by 1.27% and 1.41% in Micro F1 on ACE, and 1.53% and 1.40% on MAVEN, which suggests that an inappropriate separation of event types impairs learning. The third strategy based on development performance improves learning (+0.8%/+1.1% on ACE/MAVEN), but it 4579 H@1 H@2 H@5 A cc u r a cy ( % ) 72 84 88 43 61 69 TD Type CD Type TD Types CD Types F 1 -S c o r e ( % ) 78 65 23 31 -- =55  =34 Original Adversarial Figure 5: Left: Top k accuracy (hit@k) when the most salient word appears to be an event trigger. is still inferior to our approach. An explanation is that the final model performance is the product of a combination of factors, and thus categorizing event types based on development set performance may not assure that event types with similar patterns are grouped together, resulting in inferior results. Distinctions in TD/CD Types. We use ACE 2005 as a case to highlight the distinct characteristics between TD and CD types. Figure 5 (Left) depicts the top k accuracy (hit@k) in the case where the most salient word in a sentence appears to be an event trigger; Figure 5 (Right) depicts the performance drop in an adversarial attack in which the gold event triggers are masked for sentence-level event type classification. The CD and TD types exhibit opposing behaviors: TD types display excellent H@k accuracy but a significant performance loss in adversarial attack, whereas CD types exhibit the opposite tendency. This implies that the CD and TD types respectively rely on triggers and contexts. Figure 6 shows a comparison of the number of event arguments for TD and CD types. Clearly, CD types have a larger number of event arguments than TD types. This is also another indication that CD types rely on contexts  they require more arguments to convey an event. Linguistic/Lexical Insights. Table 6 give typical TD and CD types on ACE 2005 (Please refer to Appendixes for the full set). Intuitively, the TD types appear to be finer-grained and concrete, 8 Most Trigger-Dependent (TD) Types: Divorce (0 . 434) , Hearing (0 . 355) , Fine (0 . 349) , Injure (0 . 308) , Be_Born (0 . 306) , Elect (0 . 305) , Sentence (0 . 304) , Die (0 . 304) 8 Most Context-Dependent (CD) Types: Start_Org (0 . 127) , Pardon (0 . 129) , Nominate (0 . 132) , Extradite (0 . 134) , Acquit (0 . 142) , Merge_Org (0 . 151) , Transfer_Money (0 . 155) , End_Org (0 . 156) Table 6: Typical TD and CD types on ACE 2005. whereas the CD types appear to be coarser-grained and abstract. For example, we may further subdivide a CD type TRANSFER _ MONEY into finer-grained ones like LOAN and PURCHASE . We provide linguistic/lexical insights by comparing the hierarchy levels of TD/CD types on WordNet (Miller, 1992). Accordingly, triggers of TD types are at the lower level of WordNet, with an average of 5.6 hypernyms; yet CD type triggers are at a higher level of WordNet, with 2.3 hypernyms. This find-ing supports our intuition that TD types are more concrete whereas CD types are more abstract. Case Visualization. Figure 7 depicts the saliency map of several cases. Accordingly, event triggers of TD types do usually have large saliency values. For example, case 2) is the instance of DIVORCE with the lowest trigger saliency value, which is still as high as 0.34. In contrast, event triggers of CD types typically have low saliency values. For example, case 4) and 6) show random instances of TRANSFER-MONEY and TRANSPORT , where the trigger saliency values are only 0 . 01 . In this study, we analyze the origins of an ED model's skewed performance and introduce a new notion called trigger saliency attribution to quantify the pattern of events. We devise a new training paradigm for ED that can distinguish between trigger-dependent and context-dependent types for 4580 learning, yielding promising results on two benchmarks. We also examine the differences between the two types extensively, and our work may promote future research on this problem. In the future, we would apply our method to other tasks (e.g., relation extraction) where contextual patterns matter. This work is supported by the National Natural Science Foundation of China (No.62106016). This work is also supported by Fundamental Research Funds for the Central Universities (No. 2021RC234), the National Key R&D Program of China (2019YFB1405200), and the Open Projects Program of National Laboratory of Pattern Recognition.
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