A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Abstract
Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored.
In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the longdistance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document.
To address these issues, we propose a T wo- S tream
A bstract meaning R epresentation enhanced extraction model ( T SAR ).
T SAR encodes the document from different perspectives by a twostream encoding module, to utilize local and global information and lower the impact of distracting context.
Besides, T SAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs.
An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly.
Extensive experiments illustrate that T SAR outperforms previous state-of-the-art by a large margin, with 2 . 54 F1 and 5 .
13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction.
We release our code in https://github.com/
PKUnlp-icler/TSAR .
Introduction
Event Argument Extraction (EAE) aims at identifying the entities that serve as event arguments, and predicting the roles they play in the event, which is a key step for Event Extraction (EE).
It helps to transform the unstructured text into structured event knowledge that can be further utilized in recommendation systems (Li et al., 2020), dialogue systems (Zhang et al., 2020a), and so on.
Most previous studies assume that the events are only
Vehicle
Transport artifact origin importer destination … [1] A ship carrying half a million barrels of oil that was pumped in the [Table] [Table]
docked at a terminal owned by Venezuela last week, according to oil data research firm ClipperData.
[2] The shipment was sent to a facility located on Dutch Island of Curacao in Caribbean.
[3] The fact that Venezuela is importing American oil is raising eyebrows because Venezuela has 298 billion barrels of oil reserves, according to the Energy
Information Administration.
[4] That 's more than Saudi Arabia,
Russia or Iran and eight times
[Table]
States. expressed by a single sentence and hence focus on sentence-level extraction (Chen et al., 2015;
Liu et al., 2018; Zhou et al., 2021).
However, in real-life scenarios, the events are often described through a whole document consisting of multiple sentences (e.g., a news article or a financial report), which still remains under-explored.
Figure 1 illustrates an example of documentlevel EAE, in which a Transport event is triggered by shipment .
Different from sentence-level EAE, extracting arguments out of the entire document faces two critical challenges.
(1) Long-distance dependency among trigger and arguments.
The arguments are usually located in different sentences from the trigger word and their distance can be quite far away.
In Figure 1, while the trigger shipment is in Sentence 2 , the vehicle , origin , artifact , and importer arguments are located in Sentence 1 or 3 , which highly increases the extraction difficulty.
To accommodate the long-range extraction, not only intra-sentential but also inter-sentential semantics should be well modeled.
(2) Distracting context .
While a document naturally encompasses more context than a single sentence, some distracting context can mislead the argument extraction.
As shown in Figure 1, the origin argument U.S. can be identified more easily without Sentence 4 , which does not offer useful information for the event, but contains many place entities that can be distracting, like Saudi Arabia and Russia or Iran .
It remains challenging to pinpoint the useful context while discarding the distracting information.
Recently, Du and Cardie (2020a) use a taggingbased method, which fails to handle nested arguments.
Instead, span-based methods predict argument roles for candidate spans (Ebner et al., 2020;
Zhang et al., 2020b).
Some studies directly generate arguments based on sequence-to-sequence model (Li et al., 2021).
However, how to model long-distance dependency among trigger and arguments, and how to handle distracting context explicitly, remain largely under-explored.
In this paper, to tackle the aforementioned two problems, we propose a T
wo- S tream
A M Renhanced extraction model ( T SAR ).
In order to take advantage of the essential context in the document, and avoid being misled by distractions, we introduce a two-stream encoding module.
It consists of a global encoder that encodes global semantics with as much context as possible to gather adequate context information, and a local encoder that focuses on the most essential information and prudently takes in extra context.
In this way, T SAR can leverage complementary advantages of different encoding perspectives, and therefore make better use of the feasible context to benefit the extraction.
Besides, to model the long-distance dependency, we introduce an AMR-guided interaction module.
Abstract Meaning Representation (AMR, Banarescu et al., 2013) graph contains rich hierarchical semantic relations among different concepts, which makes it favorable for complex event extraction.
From such a linguistic-driven angle, we turn the linear structure of the document into both global and local graph structures, followed by a graph neural network to enhance the interactions, especially for those non-local elements.
Finally, as T SAR extracts arguments in span level, where the span boundary may be ambiguous, we introduce an auxiliary boundary loss to enhance span representation with calibrated boundary.
To summarize, our contributions are three-fold.
1) We propose a two-stream encoding module for document-level EAE, which encodes the document through two different perspectives to better utilize the context.
We introduce an AMR-guided interaction module to facilitate the semantic interactions within the document, so that long-distance dependency can be better captured.
Our experiments show that T SAR outperforms the previous start-of-the-art model by large margins, with 2 .
F1 and 5 .
13 F1 improvements on public RAMS and WikiEvents datasets respectively, especially on cross-sentence event arguments extraction.
Related Work 2. Sentence-level Event Extraction
Previous studies mainly focus on sentence-level event extraction.
Li et al.
(2014) and Judea and
Strube (2016) use handcrafted features to extract events from the sentence.
Chen et al.
(2015) firstly propose a neural pipeline model to extract events, while Nguyen et al.
(2016) utilize a joint model to mitigate error propagation.
To better model the interactions among words, Liu et al.
Yan et al.
; Ma et al.
(2020) make use of the dependency tree, and Wadden et al.
(2019) enumerates all possible spans and propagate information in the span graph.
Data augmentation is also considered (Yang et al., 2019).
Moreover, some works try to reformulate the event extraction task as other tasks.
For example, Du and Cardie (2020b) and
Zhou et al.
(2021) cast event extraction as question answering, and Xiangyu et al.
(2021) model it as a sequence-to-sequence task.
However, all of these models can only extract events from a single sentence.
Thus, they fail to handle the much more common cases, where event arguments usually spread over multiple sentences within the document.
2. Document-level Event Extraction
In order to extract events from a whole piece of article with multiple sentences, document-level event extraction has attracted more and more attention recently.
Yang and Mitchell (2016) utilize welldefined features to extract arguments across sentences, while most recent methods are based on neural networks.
Some studies first identify entities in the document, followed by assigning these entities as specific argument roles (Yang et al., 2018;
Zheng et al., 2019; Xu et al., 2021).
Differently, some studies try to jointly extract entities and argument roles simultaneously, which can be further divided into tagging-based and span-based methods.
Tagging-based methods directly conduct sequence labeling for each token in the document with BIO-schema (Du and Cardie, 2020a; Veyseh
Global Encoder x 1 x 2 y 1 y 2 z 1 z 2 z
xx
yy 2 zz 2 zx 1 x 2 y 1 y 2 z 1 z 2 z
3 xx
yy 2 zz 2 z… … … …
x 1 x 2 y 1 y 2 z 1 z 2 z
Compose
Decompose
Compose
Decompose
AMR-Enhanced
Global AMR Graph
Local AMR Graph
Local Encoder
Two-Stream Encoder Module
AMR-guided Interaction Module
Fusion Module and Boundary Loss Classification Module
Fusion
Start:
End: x 1 x 2 y 1 y 2 z
z
z
Boundary Loss
Compute
S1: [x 1 ][x 2 ]
S2:
[y 1 ]
[y 2 ]
S3:
[z 1 ][z 2 ] [z 3 ]
Document
S
S S
S S
S y
Trigger Rep.
Span Rep.
Event Type Rep.
Span Length Rep.
Trigger z 2 z 3 Candidate Argument
Prediction Vector
Compute
Extraction Loss
Final Loss et al., 2021) predict argument role for candidate text spans which usually have a maximum length limitation (Ebner et al., 2020; Zhang et al., 2020b).
Another line of studies reformulate the task as a sequence-to-sequence task (Du et al., 2021a,b; Li et al., 2021), or machine reading comprehension task (Wei et al., 2021).
As a span-based method, T SAR is different from prior methods that simply encode it as a long sentence.
Instead, T SAR introduces a two-stream encoding module and AMR-guided interactions module to model intra-sentential and inter-sentential semantics, along with an auxiliary boundary loss to enhance span boundary information.
Task Formulation
Following Ebner et al.
(2020), we formulate doclevel event argument extraction as follows.
We define that a document D consists of N sentences, and a sentence is comprised of a sequence of words, i.e., D = (cid:8) w 1 , w 2 , . . .
, w |D| (cid:9) , and SEN ( w i ) ∈ [1 , N ] refers to the sentence that w i belongs to.
We also define the event types set E and the corresponding argument roles set R e for each event type e ∈ E .
Then, given a document D and the trigger t ∈ D triggering the event type e ∈ E , the task aims to detect all ( r, s ) pairs for the event, where r ∈ R e is an argument role for the event type e , and s ⊆ D is a contiguous text span in the document.
Methodology
Figure 2 shows the overall architecture of our model T SAR .
The document is fed into the twostream encoding module, followed by the AMRguided interaction module to derive both global and local contextualized representations.
The information fusion module fuses these two-stream representations, and the classification module finally predicts argument roles for candidate spans.
4. Two-Stream Encoding Module
Although more context is provided by the document, it also inevitably introduces irrelevant and distracting information towards the event.
These noise signals can be harmful to the argument extraction as shown in Figure 1.
To capture useful information and filter distracting one, we propose a two-stream encoding module, consisting of a global He was then arrested in Iran and was reportedly tried for treason . and arrestname Iran country then tryreport he betray- :op :op :location :name :op :time :ARG :ARG :ARG1-0f :ARG :ARG0 AMR graph encoder that is aware of all context, and a local encoder that only prudently focuses on the most essential information.
Therefore, we can leverage their complementary advantages to make better use of the context information.
Specifically, the global and local encoders share the same Transformer-based pre-trained language model such as BERT.
By controlling the reception field of the words in the self-attention module , we can encode the document from different perspectives.
In the global encoder, the attention technique is the same as the traditional Transformer:
[Equation]
where Q , K , V refers to query, key, and value matrix, and d m is the model dimension.
However, in the local encoder, we introduce a mask matrix
M , such that tokens can only attend to the sentence itself and the sentence where the trigger locates, to avoid redundant distracting information:
[Equation]
−∞ , Otherwise where SEN ( w i ) is the sentence that the word w i belongs to, and t refers to the trigger of the event.
Hence, we encode the document with two different streams, a global encoder Encoder G and a local encoder Encoder L , finally deriving two representations, Z G and Z L :
[Equation]
4. AMR-Guided Interaction Module
One key challenge to extract arguments from the document is to capture the intra-sentential and inter-sentential features.
Therefore, we propose an AMR-guided interaction module that adopts Abstract Meaning Representation (AMR, Banarescu et al., 2013) graph to provide rich semantic structure to facilitate the interactions among concepts, which also offers logical meanings of the document from a linguistic-driven perspective to benefit the language understanding.
AMR semantic graph models the meaning representations of a sentence as a rooted, directed, labeled graph.
Concretely, with an AMR parser, a natural sentence can be parsed into an AMR graph G =
( V, E ) .
The node v = ( a, b ) ∈ V represents a concept that corresponds to the span ranging from w a to w b in the origin sentence, while the edge represents a specific AMR relation (detail in Appendix A).
Thus, AMR focuses on semantic relations rather than syntactic ones, which is more high-level and beneficial to event understanding, and the structures are more close to the event trigger-arguments structures.
For example, Figure 3 demonstrates how a sentence is parsed into an AMR semantic graph.
As event arguments play essential roles in the text, most of them would be involved, if not all, in the AMR graphs ( 90% and 88% arguments in RAMS and WikiEvents datasets). We use the state-of-the-art
AMR parser Fernandez Astudillo et al.
(2020), which achieves satisfactory results (up to 81 .
Smatch on AMR2.0 data) for downstream application. As the number of AMR relation types is large, which results in too many demanded parameters, we also follow Zhang and Ji (2021) to cluster the relation types into main categories. More details can be found in Appendix A. The AMR-guided interaction module is attached after the global and local encoders as shown in
Figure 2.
We use the AMR graphs as skeletons for information interactions, under a composition, interaction, and decomposition paradigm.
From the local perspective, we construct AMR graphs for each sentence in the document, and they are isolated from each other.
For initialization, the vector representation of node u = ( a u , b u ) is composed by averaging the local representations of its corresponding text span:
[Equation]
Similar to Zeng et al. (2020), we then use L -layer stacked Graph Convolution Network (Kipf and Welling, 2017) to model the interactions among different concept nodes through edges with different relation types.
Given node u at the l -th layer, the information interaction and aggregation operation is defined as follows:
[Equation]
where K denotes different relation types, N k ( u ) denotes the neighbors for u connected with k -th relation types and c u,k is a normalization constant.
Besides, W ( l ) k ∈ R d m × d m is a trainable parameter.
Finally, we concatenate vectors in all layers and derive the final node representation by h u = W 1 [ h 0 u ; h 1 u ; . . .
; h Lu ] ∈ R d m .
Then h u is decomposed into the local representations of corresponding words, followed by token-wise aggregation, where I ( · ) refers to the indication function:
[Equation]
From the global perspective, we first construct the global AMR graphs by fully connecting the root nodes of AMR graphs of different sentences, since the root nodes contain the core semantics according to the AMR core-semantic principle (Cai and Lam, 2019) 1 .
Then similar graph-based interaction methods are used to obtain the AMRenhanced global representations (cid:101) h Gi , but based on global AMR graphs instead.
In this way, the intersentential information can flow through the sentence boundaries, and therefore long-distance dependency can also be better captured.
4. Information Fusion Module
In the information fusion module, we fuse the global representations (cid:101) H G = (cid:104) (cid:101) h G 1 , (cid:101) h G 2 , . . .
, (cid:101) h G |D| (cid:105) and local representations (cid:101) H L
= (cid:104)(cid:101) h L 1 , (cid:101)
h L 2 , . .
, (cid:101)
h L |D| (cid:105) , to construct the final vector representations for the candidate spans.
In detail, we use a gated fusion to control how much information is incorporated from the twostream representations.
Given (cid:101) h
Gi and (cid:101) h
Li , we calculate the gate vector g i with trainable parameters
W 2 and W 3 , g i = sigmoid( W 2 (cid:101) h
Gi + W 3 (cid:101)
h
Li + b ) .
Then we derive the fused representations (cid:101) h i : (cid:101) h
i =
g i ⊙ (cid:101) h Gi + (
[Equation]
− g [Equation] ⊙ (cid:101) h
Li
For a candidate text span ranging from w i to w j , its fused representation consists of the start representation (cid:101) h starti , the end representation (cid:101) h endj and the average pooling of the hidden state of the span with W span ∈ R d m × (3 × d m ) : s i : j = W span (cid:34) (cid:101) h starti ; (cid:101) h endi ; j − i +j (cid:88) k
= i (cid:101)
h k (cid:35) where (cid:101) h starti =
W s (cid:101) h i and (cid:101) h endi =
W e (cid:101) h
i .
Since we extract arguments in span level, whose boundary may be ambiguous, we introduce an auxiliary boundary loss to enhance boundary information for the (cid:101) h starti and (cid:101) h endi .
In detail, we predict whether the word w i is the first or last word of a golden argument span with token-wise classifiers.
We use a linear transformation followed by a sigmoid function, to derive the probability of the word w i being the first or last word of a golden argument span, i.e., P si and P ei .
[Equation]
Finally, the boundary loss is defined as the following cross-entropy losses of detecting the start and end position.
[Equation]
where, y si and y ei denote the golden labels.
In this way, we introduce an explicit supervision signal to inject boundary information of the start and end representation of an span, which is shown to be necessary and important to the extraction in our exploring experiments.
4. Classification Module
In the classification module, we predict what argument role the candidate span plays, or it does not belong to any specific argument roles.
Besides the span representation s i : j , we also consider the trigger, event type, and the length of the span.
Specifically, we concatenate the following representations to obtain the final prediction vector
I i : j : 1) the trigger representation (cid:101) h t , and the span representation s i : j , with their absolute difference (cid:12)(cid:12)(cid:12)(cid:101) h t − s i : j (cid:12)(cid:12)(cid:12) , and element-wise multiplication, (cid:101) h t ⊙ s i : j ; 2) the embedding of the event type E type .
3) the embedding of the
[Table] [Table]
We then use the cross entropy L c as loss function:
[Equation]
where y i : j is the golden argument role, and P ( r i : j ) is derived by a feed-forward network based on I i : j .
Finally, we train the model in an end-to-end way with the final loss function L =
L c + λ L b with hyperparameter λ .
Experiments 5. Datasets
We evaluate our model on two public documentlevel event argument extraction datasets, RAMS v 1 .
(Ebner et al., 2020) and WikiEvents (Li et al., 2021).
RAMS contains 9 , 124 human-annotated examples, with 139 event types and 65 kinds of argument roles, and more than 21 k arguments.
WikiEvents is another human-annotated dataset, with 50 event types and 59 event argument roles, and more than 3 .
9 k events.
We follow the official train/dev/test split for RAMS and WikiEvents datasets, and use the evaluation script provided by Ebner et al.
(2020) to evaluate the performance.
The detailed data statistics of RAMS and WikiEvents datasets are shown in Table 1.
5. Experiment Setups and Metrics
In our implementation, we use BERT base (Devlin et al., 2019) and RoBERTa large (Liu et al., 2019) as our backbone encoder for T SAR , with global and local encoders sharing parameters.
Detailed hyperparameters are listed in Appendix B.
Following Zhang et al.
(2020b), we report the
Span F1 and Head F1 for RAMS dataset.
Span
F1 requires the predicted argument spans to fully [Table] match the golden ones, while Head F1 relaxes the constraint and evaluates solely on the head word of the argument span.
The head word of a span is defined as the word that has the smallest arc distance to the root in the dependency tree.
In addition, following Li et al.
(2021), we report the Head F1 and
Coref F1 scores for WikiEvents dataset.
The model is given full credit in Coref F1 if the extracted argument is coreferential with the golden argument as used by Ji and Grishman (2008).
5. Main Results
We compare T SAR with the following baselines.
BERT-CRF (Shi and Lin, 2019) is a tagging-based method, which adopts a BERT-based BIO-styled sequence labeling model.
Two-Step (Zhang et al., 2020b) is a span-based method, which first identifies the head word of possible argument span, and then extends to the full span.
BERTCRF TCD and Two-Step TCD refers to adopting
Type-Constraint Decoding mechanism as used in (Ebner et al., 2020).
3) FEAE (Wei et al., 2021), Frame-aware Event Argument Extraction, is a concurrent work based on question answering.
4) BERT-QA (Du and Cardie, 2020c) is also a
QA-based model.
BERT-QA and BERT-QA-Doc extract run on sentence-level and document-level, respectively.
5) BART-Gen (Li et al., 2021) formulate the task as a sequence-to-sequence task and uses BART large (Lewis et al., 2020) to generate corresponding arguments in a predefined format.
Table 2 illustrates the results in both dev and test set on RAMS dataset.
As is shown, among models based on BERT base , T SAR outperforms other previous methods.
For example, T SAR yields an improvement of 4 .
93 ∼ 7 .
13 Span F1 and [Table]
70 ∼ 6 .
Head F
compared with the previous method in the dev set, and up to 8 .
76 Span F1 in the test set.
Besides, among models based on large pre-trained language models, T SAR outperforms
BART-Gen by 2 . 54 Span F1 and 1 .
21 Head F1 2 .
These results suggest that encoding the document in a two-stream way, and introducing AMR graphs to facilitate interactions, is beneficial to capturing intra-sentential and inter-sentential features, and thus improves the performance.
Moreover, we follow Li et al.
(2021) to evaluate both argument identification and argument classification, and report the Head F1 and Coref F1.
Identification requires the model to correctly detect the argument span boundary, while classification has to further correctly predict its argument role.
As illustrated in Table 3, T SAR consistently outperforms others in both tasks.
Compared with BART-Gen,
T SAR improves up to 4 .
87 / 3 . 23 Head/Coref F1 for argument identification, and 5 . 13 / 3 .
68 Head/Coref
F1 for argument classification.
Similar results also appear among models based on BERT base , with 5 .
69 ∼ 36 .
37 and 11 .
95 ∼ 33 .
34 Head F improvement for identification and classification.
These results show that T SAR is superior to other methods in not only detecting the boundary of argument spans, but also predicting their roles.
Analysis 6. Cross-sentence Argument Extraction
Since there are multiple sentences in the document, some event arguments are located far away from the trigger, which highly increases the difficulty of [Table]
Most improvements by T SAR come from cross-sentence ( d ̸ = 0) arguments extraction.
[Table] extraction. To explore the effect of handling such cross-sentence arguments of our T SAR , we divide the event arguments in RAMS dataset into five bins according to the sentence distance between arguments and trigger, i.e., d = {− 2 , − 1 , 0 , 1 , 2 } .
We report the Span F1 on the RAMS dev set for different methods.
As shown in Table 4, the Span F1 for cross-sentence arguments ( d ̸ = 0 ) is much lower than local arguments ( d = 0 ), suggesting the huge challenge to capture long-distance dependency between triggers and cross-sentence arguments.
However, T SAR still surpasses other strong baselines.
In detail, T SAR base improves 0 .
4 and T SAR large improves 0 .
7 F1 compared with the previous stateof-the-art, respectively.
More importantly, when extracting cross-sentence arguments, T SAR base and
T SAR large yield an improvement of up to 2 . 3 and 2 .
7 on average.
The results support our claims that T SAR is good at capturing both intra-sentential and inter-sentential features, especially the longdistance between trigger and arguments.
6. Ablation Study
We conduct an ablation study to explore the effectiveness of different modules in T SAR .
Tableshow the results on RAMS datasets for T SAR large .
We also provide results for T SAR base , and those on
WikiEvents datasets in Appendix C.
Firstly, we remove the global or local encoder in
Category
Examples
Two-step
Wrong Span
Over-extract
Partial
Overlap
Wrong Role
It was Bush’s administration participant , not [Obama] participant ’s, that negotiated meet the 2009 agreement from Iraq by Dec. 31, 2011.
[280 victims] victim ,including women, children and old people victim …
The massacre die is considered as one of the worst mass killings committed in Syria …
The investigation found 100 people were linked to the transport disperseseparate and … It found the missile had been driven from [Russia] destinationorigin into an …
Richard, the man accused of punching injury a [69-year-old protester] victimvictim outside a Donald Trump rally in Asheville, NC on Monday, is vigorously …
The information minister alleged that oil smuggled smuggle into Turkey was bought by [the Turkish president ’s son] transporter , who owns an oil company …
TSAR
Errors
[Figure]
BART-Gen:
[Figure]
… The Islamic State in Iraq and Syria took credit for the Minnesota attack on Saturday reportedly carried out by Dahir Adan, a 22-yearold Somali American who worked at a private security firm.
Nine people were wounded in the stabbings . Adan was shot and ... is triggered by stabbings arguments T manages to extract the cross-sentence argument Minnesota far from the trigger, while other methods fail. the two-stream encoding module.
As shown in Table 5, the removal causes drop in performance, e.g., 3 . 04 and 1 .
71 Head F1 drop on the test set without global and local encoder.
It suggests the global and local encoders are complementary to each other, and both of them are necessary for T SAR .
Secondly, once we remove the AMR-guided interaction module, the Head F1 would decrease by 1 . 83 on the test set.
It shows the semantic structure provided by AMR graphs is helpful to the arguments extraction of the document.
Finally, the removal of boundary loss causes the boundary information lost in span representations, which also leads to 1 . 62 and 0 .
78 Head F1 decrease on dev and test set.
6. Case Study
In this section, we show a specific case of the extraction results among different methods.
As shown in Figure 5, stabbings triggers an Attack event with three arguments in color.
Since Nine people is located near the trigger, all the methods correctly predict it as the target .
However, extracting Minnesota and Dahir Adan asks for capturing long-distance dependency.
Although Two-Step and BART-Gen wrongly predict the place as Iraq and Syria , and
Two-Step even fails to extract the Attacker , T SAR manage to extract the cross-sentence arguments.
It can be attributed to that our AMR-enhanced module catches Minnesota is the place of attack that is highly related to the trigger stabbings in semantics.
6. Error Analysis
To further explore the errors made by different models and analyze the reasons in detail, we randomly choose 200 examples from the RAMS test set and compare the predictions with golden annotations manually.
We divide the errors into five categories, which is shown in Figure 4.
Wrong Span refers to assigning a specific role to a wrong span nonoverlapped with the golden one.
We find it is usually due to the negative words like not , and the coreference spans for the golden one.
Over-extract denotes the model predicts an argument role while it does not exist in the document.
Some extracted spans are the sub-strings of the golden spans ( Partial ), or have some overlaps with them ( Overlap ).
These two kinds of errors are usually attributed to the annotation inconsistency in the dataset, such as whether the adjective, quantifier, and article (e.g., a and the ) before the noun should belong to the golden argument.
Besides, the Partial error also usually occurs in cases where there is punctuation like a comma in the golden span as shown in Figure 4.
Finally, though the model succeeds to identify the golden span, it can still assign wrong argument role to the span ( Wrong Role ).
We compare the errors of Two-step TCD and T SAR base .
We observe T SAR decrease the number of errors fromto 233 , especially for Wrong Role and Over-extract , with 27 and 16 errors reduction, respectively.
Conclusion
It is challenging to extract event arguments from a whole document, owing to the long-distance dependency between trigger and arguments over sentences and the distracting context.
To tackle these problems, we propose T wo- S tream
A M Renhanced extraction model ( T SAR ).
T SAR uses two-stream encoders to encode the document from different perspectives, followed by an AMR-guided interaction module to facilitate the document-level semantic interactions.
An auxiliary boundary loss is introduced to enhance the boundary information for spans.
Experiments on RAMS and WikiEvents datasets demonstrate that T SAR outperform previous state-of-the-art methods by a large margin, with 2 . 51 and 5 .
13 F1 improvements respectively, especially for cross-sentence argument extraction. Acknowledgements
This paper is supported by the National
Key R&D Program of China under Grand
No.2018AAA0102003, the National Science
Foundation of China under Grant No. and 61876004.
-level
Xinya Du Cardie 2020c
Xinya
Cardie
2021a
GRIT
the Association for Computational Linguistics (EACL) .
Xinya Du, Alexander Rush, and Claire Cardie. 2021b.
Template filling with generative transformers.
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational
Linguistics (NAACL) .
Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020.
Multi-sentence argument linking.
In Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics (ACL) .
Ramón Fernandez Astudillo, Miguel Ballesteros, Tahira
Naseem, Austin Blodgett, and Radu Florian. 2020.
Transition-based parsing with stack-transformers.
In
Findings of the Association for Computational Linguistics (EMNLP) .
Proceedings of the 2021 Conference of the North
American Chapter of the Association for Computational Linguistics (NAACL) .
Zhunchen
Jointly multiple
Huu Nguyen, Kyunghyun Cho
Ralph Grish Yu Da Gan
Mufei Li
Guo
Lin
Chapter of the Association for Computational Linguistics (NAACL) .
Shun Zheng Wei Cao, Wei Xu, and Jiang Bian. 2019. Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) .
Yang Zhou, Yubo Chen, Jun Zhao, Yin Wu, Jiexin Xu, and JinLong Li.
What the role is vs. what plays the role: Semi-supervised event argument extraction via dual question answering.
In Thirty-Fifth AAAI
Conference on Artificial Intelligence (AAAI) . A Abstract Meaning Representation (AMR) Graph There are many AMR parsing approaches (Bevilacqua et al., 2021; Fernandez Astudillo et al., 2020; Wang et al., 2022; Chen et al., 2022).
To obtain
AMR semantic graphs with the align information between text spans and AMR nodes, we use the transition-based AMR parser proposed by Fernandez Astudillo et al.
(2020), which is a state-of-theart AMR parser and can achieve satisfactory results for downstream application (up to 81 .
3 Smatch on AMR2.0 data).
As the number of AMR relation types is large, which results in too many demanded parameters, we follow Zhang and Ji (2021) to cluster the relation types into main categories as shown in Table 6.
[Table]
B Hyperparameters Setting
We set the dropout rate to 0 .
1 , batch size to 8 , and train T SAR using Adam (Kingma and Ba, 2015) as optimizer with [-
-]3 e-
5 learning rate.
We train T SAR for 50 epochs for RAMS dataset and 100 epochs for WikiEvents dataset.
We search the boundary loss weight λ from { 0 . 05 , 0 . 1 , 0 . 2 } , and L from { 3 , 4 } , and select the best model using dev set.
Our code is based on Transformers (Wolf et al., 2020) and DGL libraries (Wang et al., 2019).
C Ablation Study
In the main body of the paper, we illustrate the results of the ablation study for T SAR large on RAMS dataset.
To thoroughly show the effect of different modules of T SAR , we also provide the results of the ablation study for T SAR base on RAMS dataset.
Table 7 shows the results on RAMS dataset, from which we can observe removing different modules would cause 1 .
34 ∼ 2 .
77 Span F1 on test set.
Besides, we do ablation study on WikiEvents.
As shown in Table 8, the Head F1 decreases by 0 .
70 ∼ 2 .
02 and 0 .
88 ∼ 2 .
96 for Arg Identification and Arg Classification sub-tasks respectively, once different modules are removed from
T SAR base .
Similar conclusions can be drawn from the results of T SAR large , which is shown in Table
[Table]
[Table]
The performance of identification and classification would decrease without any kind of module.
[Table]
The performance of identification and classification would decrease without any kind of module