# DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths
## 5 Experiments and Analysis
### Results on ChFinAnn  
**Baselines.** We examined the effectiveness of DEEDP and the following baselines on the ChFiAnn dataset: DCFEE [9] proposed a key event detection model for discovering event mentions and a strategy for argument completion. Two variants were developed for this model, i.e., DEFEE-O and DCFEE-M. DCFEE-O is a simple version that only produces one event record from a document, while DCFEE-M produces multiple ones. Doc2 EDAG is an end-to-end model proposed by [PERSON] et al. [8]. Doc2 EDAG combines different Transformers [14] to perform event and event arguments recognition and transfers the DEE as a table filling task. To verify the necessity of end-to-end, a greedy baseline of Doc2 EDAG, named GreedyDec, was proposed, which only applies recognized entity roles to fill one event table entry for a document. DE-PPN is an encoder-decoder model for DEE proposed by [PERSON] et al. [12]. DE-PPN obtains document-aware representations by using a document-level Transformer encoder, and then decodes using multi-granularity non-autoregressive decoders in parallel. Similar to Doc2 EDAG, there is also a single event version of DE-PPN, DE-PPN-1. [PERSON] [16] formulated the paradigm of DEE as a MRC  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Head Noun Match**} & \multicolumn{3}{c}{**Extract Match**} \\ \cline{2-7}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline GLACIER & 47.80 & 57.20 & 52.08 & - & - & - \\ TIER & 50.80 & 61.40 & 55.60 & - & - & - \\ Cohesion Extract & 57.80 & 59.40 & 58.59 & - & - & - \\ MGR & 56.44 & **62.77** & 59.44 & 52.03 & **56.81** & 54.32 \\ MMR _w/o contextualized embedding_ & 63.95 & 58.71 & 61.19 & 60.66 & 55.34 & 57.87 \\ DEEDP & **65.42** & 61.57 & **62.96** & **61.34** & 56.29 & **58.71** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall precision (P), recall (R) and F1 scores evaluated on the MUC-4 test set.  
task, and proposed a multi-turn and multi-granularity reader for DEE, which can help extract events from given documents directly without conducting SEE. In addition, there is a simple one-record decoding baseline of MMR, MMR-1. For fairness, we also present a simple decoding version of DEEDP, DEEDP-O, which produces only one event record with the recognized entity roles.  
**Main Results.** Table 3 presents the overall performance for DEEDP and the baselines on the benchmark ChiFiAnn. As we can see, the proposed method, DEEDP, consistently outperforms all baselines and improves the F1-score by 0.4, 3.7, 2.2, 1.4, and 0.5 over the SOTA method, DE-PPN, on EF, ER, EU, EO, and EP, respectively. These vast improvements benefit from the multi-granularity encoder and explicitly model syntactic features of the input sentences at the global-to-local feature aggregation stage. Because the multi-granularity encoder applies more encoders to model the interdependency between sentence-level and document-level features, and the strategy of explicitly modeling syntactic features helps to capture more sentence-level features of the input sentences, both of these strategies facilitate the DEE task. Additionally, the simple decoding baseline, DEEDP-O, achieves the best performance compared with other simple baselines, such as DCFEE-O, GreedyDec, DE-PPN-1, and MRR-1, which also demonstrates the advantages of our proposed strategies.