Paper:
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms
This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-ofthe-arts and achieves the best F1 score on ACE 2005 dataset.
012
013
014
015
016
017
018
019
020
021
022
023
024
025
026
027
028
029
030
031
032
033
034
035
036
037
038
039
040
041
042
043
044
045
046
047
048
049
062
063
064
065
066
067
068
069
070
071
072
073
074
075
076
077
078
079
080
081
082
083
084
085
086
087
088
089
090
091
092
093
094
095
096
097
098
099
This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-ofthe-arts and achieves the best F1 score on ACE 2005 dataset.
1 Introduction
In the ACE (Automatic Context Extraction) event extraction program, an event is represented as a structure comprising an event trigger and a set of arguments. This work tackles event detection (ED) task, which is a crucial part of event extraction (EE) and focuses on identifying event triggers and categorizing them. For instance, in the sentence “He died in the hospital”, an ED system is expected to detect a Die event along with the trigger word “died”. Besides, the task of EE also includes event argument extraction (AE), which involves event argument identification and role classification. In the above sentence, the arguments of the event include “He”(Role = Person) and “hospital”(Role = Place). However, this paper does not focus on AE and only tackles the former task.
According to the above definitions, event arguments seem to be not essentially necessary to ED. However, we argue that they are capable of providing significant clues for identifying and categorizing events. They are especially useful for ambiguous trigger words. For example, consider a sentence in ACE 2005 dataset:
Mohamad fired Anwar, his former protege, in 1998.
In this sentence, “fired” is the trigger word and the other bold words are event arguments. The correct type of the event triggered by “fired” in this case is End-Position . However, it might be easily misidentified as Attack because “fired” is a multivocal word. In this case, if we consider the phrase “former protege”, which serves as an argument (Role = Position) of the target event, we would have more confidence in predicting it as an End-Position event.
Unfortunately, most exiting methods performed event detection individually, where the annotated arguments in training set are totally ignored (Ji and Grishman, 2008; Gupta and Ji, 2009; Hong et al., 2011; Chen et al., 2015; Nguyen and Grishman, 2015; Liu et al., 2016a,b; Nguyen and Grishman, 2016). Although some joint learning based methods have been proposed, which tackled event detection and argument extraction simultaneously (Riedel et al., 2009; Li et al., 2013; Venugopal et al., 2014; Nguyen et al., 2016), these approaches usually only make remarkable improvements to AE, but insignificant to ED. Table 1 illustrates our observations. Li et al. (2013) and Nguyen et al. (2016) are state-of-the-art joint models in symbolic and embedding methods for event extraction, respectively. Compared with state-of-the-art pipeline systems, both join-
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
Methods ED AE
Symbolic Hong’s pipeline (2011) 68.3 48.3
Methods Li’s joint (2013) 67.5 52.7
Embedding Chen’s pipeline (2015) 69.1 53.5
Methods Nguyen’s joint (2016) 69.3 55.4
Table 1: Performances of pipeline and joint approaches on ACE 2005 dataset. The pipeline method in each group was the state-of-the-art system when the corresponding joint method was proposed.
t methods achieved remarkable improvements on AE (over 1.9 points), whereas achieved insignificant improvements on ED (less than 0.2 points). The symbolic joint method even performed worse (67.5 vs. 68.3) than pipeline system on ED.
We believe that this phenomenon may be caused by the following two reasons. On the one hand, since joint methods simultaneously solve ED and AE, methods following this paradigm usually combine the loss functions of these two tasks and are jointly trained under the supervision of annotated triggers and arguments. However, training corpus contains much more annotated arguments than triggers (about 9800 arguments and 5300 triggers in ACE 2005 dataset) because each trigger may be along with multiple event arguments. Thus, the unbalanced data may cause joint models to favor AE task. On the other hand, in implementation, joint models usually pre-predict several potential triggers and arguments first and then make global inference to select correct items. When pre-predicting potential triggers, almost all existing approaches do not leverage any argument information. In this way, ED does hardly benefit from the annotated arguments. By contrast, the component for pre-prediction of arguments always exploits the extracted trigger information. Thus, we argue that annotated arguments are actually used for AE, not for ED in existing joint methods, which is also the reason we call it an indirect way to use arguments for ED.
Contrast to joint methods, this paper proposes to exploit argument information explicitly for ED. We have analyzed that arguments are capable of providing significant clues to ED, which gives us an enlightenment that ar-
guments should be focused on when performing this task. Therefore, we propose a neural network based approach to detect events in texts. And in the proposed approach, we adopt a supervised attention mechanism to achieve this goal, where argument words are expected to acquire more attention than other words. The attention value of each word in a given sentence is calculated by an operation between the current word and the target trigger candidate. Specifically, in training procedure, we first construct gold attentions for each trigger candidate based on annotated arguments. Then, treating gold attentions as the supervision to train the attention mechanism, we learn attention and event detector jointly both in supervised manner. In testing procedure, we use the ED model with learned attention mechanisms to detect events.
In the experiment section, we systematically conduct comparisons on a widely used benchmark dataset ACE20051. In order to further demonstrate the effectiveness of our approach, we also use events from FrameNet (FN) (F. Baker et al., 1998) as extra training data, as the same as Liu et al. (2016a) to alleviate the data-sparseness problem for ED to augment the performance of the proposed approach. The experimental results demonstrate that the proposed approach is effective for ED task, and it outperforms state-of-the-art approaches with remarkable gains.
To sum up, our main contributions are: (1) we analyze the problem of joint models on the task of ED, and propose to use the annotated argument information explicitly for this task. (2) to achieve this goal, we introduce a supervised attention based ED model. Furthermore, we systematically investigate different attention strategies for the proposed model. (3) we improve the performance of ED and achieve the best performance on the widely used benchmark dataset ACE 2005.
2 Task Description
The ED task is a subtask of ACE event evaluations where an event is defined as a specific occurrence involving one or more participants. Event extraction task requires certain specified types of events, which are mentioned
1https://catalog.ldc.upenn.edu/LDC2006T06
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
in the source language data, be detected. We firstly introduce some ACE terminologies to facilitate the understanding of this task:
Entity: an object or a set of objects in one of the semantic categories of interests.
Entity mention: a reference to an entity (typically, a noun phrase).
Event trigger: the main word that most clearly expresses an event occurrence.
Event arguments: the mentions that are involved in an event (participants).
Event mention: a phrase or sentence within which an event is described, including the trigger and arguments.
The goal of ED is to identify event triggers and categorize their event types. For instance, in the sentence “He died in the hospital”, an ED system is expected to detect a Die event along with the trigger word “died”. The detection of event arguments “He”(Role = Person) and “hospital”(Role = Place) is not involved in the ED task. The 2005 ACE evaluation included 8 super types of events, with 33 subtypes. Following previous work, we treat these simply as 33 separate event types and ignore the hierarchical structure among them.
3 The Proposed Approach
Similar to existing work, we model ED as a multi-class classification task. In detail, given a sentence, we treat every token in that sentence as a trigger candidate, and our goal is to classify each of these candidates into one of 34 classes (33 event types plus an NA class).
In our approach, every word along with its context, which includes the contextual words and entities, constitute an event trigger candidate. Figure 1 describes the architecture of the proposed approach, which involves two components: (i) Context Representation Learning (CRL), which reveals the representation of both contextual words and entities via attention mechanisms; (ii) Event Detector (ED), which assigns an event type (including the NA type) to each candidate based on the learned contextual representations.
3.1 Context Representation Learning
In order to prepare for Context Representation Learning (CRL), we limit the context to a fixed length by trimming longer sen-
tences and padding shorter sentences with a special token when necessary. Let n be the fixed length and w0 be the current candidate trigger word, then its contextual words Cw is [w−n
2 , w−n 2 +1, ..., w−1, w1, ..., wn 2 −1, wn 2 ]2, and its contextual entities, which is the corresponding entity types (including an NA type) of Cw, is [e−n
2 , e−n 2 +1, ..., e−1, e1, ..., en 2 −1, en 2 ].
For convenience, we use w to denote the current word, [w1, w2, ..., wn] to denote the contextual words Cw and [e1, e2, ..., en] to denote the contextual entities Ce in figure 1. Note that, both w, Cw and Ce mentioned above are originally in symbolic representation. Before entering CRL component, we transform them into real-valued vector by looking up word embedding table and entity type embedding table. Then we calculate attention vectors for both contextual words and entities by performing operations between the current word w and its contexts. Finally, the contextual words representation cw and contextual entities representation ce are formed by the weighted sum of the corresponding embeddings of each word and entity in Cw and Ce, respectively. We will give the details in the fol-
2The current candidate trigger word w0 is not included in the context.
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
lowing subsections.
3.1.1 Word Embedding Table
Word embeddings learned from a large amount of unlabeled data have been shown to be able to capture the meaningful semantic regularities of words (Bengio et al., 2003; Erhan et al., 2010). This paper uses the learned word embeddings as the source of basic features. Specifically, we use the Skip-gram model (Mikolov et al., 2013) to learn word embeddings on the NYT corpus3.
3.1.2 Entity Type Embedding Table
The ACE 2005 corpus annotated not only events but also entities for each given sentence. Following existing work (Li et al., 2013; Chen et al., 2015; Nguyen and Grishman, 2015), we exploit the annotated entity information in our ED system. We randomly initialize embedding vector for each entity type (including the NA type) and update it in training procedure.
3.1.3 Representation Learning
In this subsection, we illustrate our proposed approach to learn representations of both contextual words and entities, which serve as inputs to the following event detector component. Recall that, we use the matrix Cw and Ce to denote contextual words and contextual entities, respectively.
As illustrated in figure 1, the CRL component needs three inputs: the current candidate trigger word w, the contextual words Cw and the contextual entities Ce. Then, two attention vectors, which reflect different aspects of the context, are calculated in the next step. The contextual word attention vector αw is computed based on the current word w and its contextual words Cw. We firstly transform each word wk (including w and every word in Cw) into a hidden representation wk by the following equation:
wk = f(wk Ww) (1)
where f(·) is a non-linear function such as the hyperbolic tangent, and Ww is the transformation matrix. Then, we use the hidden representations to compute the attention value for each
3https://catalog.ldc.upenn.edu/LDC2008T19
word in Cw:
αkw = exp(w wTk )∑ i exp(w w T i )
(2)
The contextual entity attention vector αe is calculated with a similar method to αw.
αke = exp(we eTk )∑ i exp(we e T i )
(3)
Note that, we do not use the entity information of the current candidate token to compute the attention vector. The reason is that only a small percentage of true event triggers are entities4. Therefore, the entity type of a candidate trigger is meaningless for ED. Instead, we use we, which is calculated by transforming w from the word space into the entity type space, as the attention source.
We combine αw and αe to obtain the final attention vector, α = αw+αe. Finally, the contextual words representation cw and the contextual entities representation ce are formed by weighted sum of Cw and Ce, respectively:
cw = Cwα T (4)
ce = Ceα T (5)
3.2 Event Detector
As illustrated in figure 1, we employ a threelayer (an input layer, a hidden layer and a softmax output layer) Artificial Neural Networks (ANNs) (Hagan et al., 1996) to model the ED task, which has been demonstrated very effective for event detection by Liu et al. (2016a).
3.2.1 Basic ED Model
Given a sentence, as illustrated in figure 1, we concatenate the embedding vectors of the context (including contextual words and entities) and the current candidate trigger to serve as the input to ED model. Then, for a given input sample x, ANN with parameter θ outputs a vector O, where the i-th value oi of O is the confident score for classifying x to the i-th event type. To obtain the conditional probability p(i|x, θ), we apply a softmax operation over all event types:
p(i|x, θ) = e oi∑m
k=1 e ok
(6)
4Only 10% of triggers in ACE 2005 are entities.
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
Given all of our (suppose T) training instances (x(i); y(i)), we can then define the negative loglikelihood loss function:
J(θ) = − T∑ i=1 log p(y(i)|x(i), θ) (7)
We train the model by using a simple optimization technique called stochastic gradient descent (SGD) over shuffled mini-batches with the Adadelta rule (Zeiler, 2012). Regularization is implemented by a dropout (Kim, 2014; Hinton et al., 2012) and L2 norm.
3.2.2 Supervised Attention
In this subsection, we introduce supervised attention to explicitly use annotated argument information to improve ED. Our basic idea is simple: argument words should acquire more attention than other words. To achieve this goal, we first construct vectors using annotated arguments as the gold attentions. Then, we employ them as supervision to train the attention mechanism.
Constructing Gold Attention Vectors
Our goal is to encourage argument words to obtain more attention than other words. To achieve this goal, we propose two strategies to construct gold attention vectors:
S1: only pay attention to argument
words. That is, all argument words in the given context obtain the same attention, whereas other words get no attention. For candidates without any annotated arguments in context, we force all entities to average the whole attention. Figure 2 illustrates the details, where α∗ is the final gold attention vector.
Figure 2: An example of S1 to construct gold attention vector. The word fired is the trigger candidate, and underline words are arguments of fired annotated in the corpus.
S2: pay attention to both arguments
and the words around them. The assumption is that, not only arguments are important
to ED, the words around them are also helpful. And the nearer a word is to arguments, the more attention it should obtain. Inspired by Mi et al. (2016), we use a gaussian distribution g(·) to model the attention distribution of words around arguments. In detail, given an instance, we first obtain the raw attention vector α in the same manner as S1 (see figure 2). Then, we create a new vector α ′ with all points initialized with zero, and for each αi = 1, we update α ′ by adding g(d, µ, σ) with window size w, where d is the distance from a word to the argument (positioned at i) and µ, σ are hyper-parameters of the gaussian distribution. Finally, similar to S1, we normalize α ′ to obtain the target attention vector α∗.
Jointly Training ED and Attention Given the gold attention α∗ (see subsection 3.2.2) and the machine attention α produced by our model (see subsection 3.1.3), we employ the square error as the loss function of attentions:
D(θ) = T∑ i=1 n∑ j=1 (α∗ij − αij)2 (8)
Combining equation 7 and equation 8, we define the joint loss function of our proposed model as follows:
J ′ (θ) = J(θ) + λD(θ) (9)
where λ is a hyper-parameter for trade-off between J and D. Similar to basic ED model, we minimize the loss function J ′ (θ) by using SGD over shuffled mini-batches with the Adadelta update rule.
4 Experiments
4.1 Dataset and Experimental Setup
Dataset
We conducted experiments on ACE 2005 dataset. For the purpose of comparison, we followed the evaluation of (Li et al., 2013; Chen et al., 2015; Liu et al., 2016b): randomly selected 30 articles from different genres as the development set, and subsequently conducted a blind test on a separate set of 40 ACE 2005 newswire documents. We used the remaining 529 articles as our training set.
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
Hyper-parameter Setting
Hyper-parameters are tuned on the development dataset. We set the dimension of word embeddings to 200, the dimension of entity type embeddings to 50, the size of hidden layer to 300, the output size of word transformation matrix Ww in equation 1 to 200, the batch size to 100, the hyper-parameter for the L2 norm to 10
−6 and the dropout rate to 0.6. In addition, we use the standard normal distribution to model attention distributions of words around arguments, which means that µ = 0.0, σ = 1.0, and the window size is set to 3 (see Subsection 3.2.2). The hyper-parameter λ in equation 9 is various for different attention strategies, we will give its setting in the next section.
4.2 Correctness of Our Assumption
In this section, we conduct experiments on ACE 2005 corpus to demonstrate the correctness of our assumption that argument information is crucial to ED. To achieve this goal, we design a series of systems for comparison.
ANN is the basic event detection model, in which the hyper-parameter λ is set to 0. This system does not employ argument information and computes attentions without supervision (see Subsection 3.1.3).
ANN-ENT assigns λ with 0, too. The difference is that it constructs the attention vector α by forcing all entities in the context to average the attention instead of computing it in the manner introduced in Subsection 3.1.3. Since all arguments are entities, this system is designed to investigate the effects of entities.
ANN-Gold1 uses the gold attentions constructed by strategy S1 in both training and testing procedure.
ANN-Gold2 is akin to ANN-Gold1, but uses the second strategy to construct its gold attentions.
Note that, in order to avoid the interference of attention mechanisms, the last two systems are designed to use argument information (via gold attentions) in both training and testing procedure.
Table 2 compares these systems on ACE 2005 corpus. From the table, we observe that systems with argument information (the last two systems) significantly outperform system-
s without argument information (the first two systems), which demonstrates that argument information is very useful for this task. Moreover, since all arguments are entities, for preciseness we also investigate that whether ANN-Gold1/2 on earth benefits from entities or arguments. Compared with ANN-ENT (revising that this system only uses entity information), ANN-Gold1/2 performs much better, which illustrates that entity information is not enough and further demonstrates that argument information is necessary for ED.
4.3 Results on ACE 2005 Corpus
In this section, we conduct experiments on ACE 2005 corpus to demonstrate the effectiveness of the proposed approach. Firstly, we introduce systems implemented in this work.
ANN-S1 uses gold attentions constructed by strategy S1 as supervision to learn attention. In our experiments, λ is set to 1.0.
ANN-S2 is akin to ANN-S1, but use strategy S2 to construct gold attentions and the hyper-parameter λ is set to 5.0.
These two systems both employ supervised attention mechanisms. For comparison, we use an unsupervised-attention system ANN as our baseline, which is introduced in Subsection 4.2. In addition, we select the following state-ofthe-art methods for comparison.
1). Li’s joint model (Li et al., 2013) extracts events based on structure prediction. It is the best structure-based system.
2). Liu’s PSL (Liu et al., 2016b) employs both latent local and global information for event detection. It is the best-reported featurebased system.
3). Liu’s FN-Based approach (Liu et al., 2016a) leverages the annotated corpus of FrameNet to alleviate data sparseness problem of ED based on the observation that frames in
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
Methods P R F1 Li’s joint model (2013) 73.7 62.3 67.5
Liu’s PSL (2016) 75.3 64.4 69.4 Liu’s FN-Based (2016) 77.6 65.2 70.7
Ngyuen’s joint (2016) 66.0 73.0 69.3
Skip-CNN (2016) N/A 71.3
ANN 73.2 57.9 64.6 ANN-S1† 81.4 62.4 70.8 ANN-S2† 78.0 66.3 71.7
Table 3: Experimental results on ACE 2005. The first group illustrates the performances of state-of-the-art approaches. The second group illustrates the performances of the proposed approach. † designates the systems that employ arguments information.
FN are analogous to events in ACE.
4). Ngyen’s joint model (Nguyen et al., 2016) employs a bi-directional RNN to jointly extract event triggers and arguments. It is the best-reported representation-based joint approach proposed on this task.
5). Skip-CNN (Nguyen and Grishman, 2016) introduces the non-consecutive convolution to capture non-consecutive k-grams for event detection. It is the best reported representation-based approach on this task.
Table 3 presents the experimental results on ACE 2005 corpus. From the table, we make the following observations:
1). ANN performs unexpectedly poorly, which indicates that unsupervised-attention mechanisms do not work well for ED. We believe the reason is that the training data of ACE 2005 corpus is insufficient to train a precise attention in an unsupervised manner, considering that data sparseness is an important issue of ED (Zhu et al., 2014; Liu et al., 2016a).
2). With argument information employed via supervised attention mechanisms, both ANN-S1 and ANN-S2 outperform ANN with remarkable gains, which illustrates the effectiveness of the proposed approach.
3). ANN-S2 outperforms ANN-S1, but the latter achieves higher precision. It is not difficult to understand. On the one hand, strategy S1 only focuses on argument words, which provides accurate information to identify event type, thus ANN-S1 could achieve higher precision. On the other hand, S2 focus-
es on both arguments and words around them, which provides more general but noised clues. Thus, ANN-S2 achieves higher recall with a little loss of precision.
4). Compared with state-of-the-art approaches, our method ANN-S2 achieves the best performance. We also perform a t-test (p 6 0.05), which indicates that our method significantly outperforms all of the compared methods. Furthermore, another noticeable advantage of our approach is that it achieves much higher precision than state-of-the-arts.
4.4 Augmentation with FrameNet
Recently, Liu et al. (2016a) used events automatically detected from FN as extra training data to alleviate the data-sparseness problem for event detection. To further demonstrate the effectiveness of the proposed approach, we also use the events from FN to augment the performance of our approach.
In this work, we use the events published by Liu et al. (2016a)5 as extra training data. However, their data can not be used in the proposed approach without further processing, because it lacks of both argument and entity information. Figure 3 shows several examples of this data.
Processing of Events from FN
Liu et al. (2016a) detected events from FrameNet based on the observation that frames in FN are analogous to events in ACE (lexical unit of a frame ↔ trigger of an event, frame elements of a frame ↔ arguments of an event). All events they published are also frames in FN. Thus, we treat frame elements annotated in FN corpus as event arguments. Since frames generally contain more frame elements than events, we only use core6 elements
5https://github.com/subacl/acl16 6FrameNet classifies frame elements into three
groups: core, peripheral and extra-thematic.
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
in this work. Moreover, to obtain entity information, we use RPI Joint Information Extraction System7 (Li et al., 2013, 2014; Li and Ji, 2014) to label ACE entity mentions.
Experimental Results
We use the events from FN as extra training data and keep the development and test datasets unchanged.Table 4 presents the experimental results.
Methods P R F1 ANN 73.2 57.9 64.6
ANN-S1 81.4 62.4 70.8 ANN-S2 78.0 66.3 71.7
ANN +FrameNet 75.1 59.2 66.2 ANN-S1 +FrameNet 80.1 63.6 70.9 ANN-S2 +FrameNet 76.8 67.5 71.9
Table 4: Experimental results on ACE 2005 corpus. “+FrameNet” designates the systems that are augmented by events from FrameNet.
From the results, we observe that: 1). With extra training data, ANN achieves significant improvements on F1 measure (66.2 vs. 64.6). This result, to some extent, demonstrates the correctness of our assumption that the data sparseness problem is the reason that causes unsupervised attention mechanisms to be ineffective to ED.
2). Augmented with external data, both ANN-S1 and ANN-S2 achieve higher recall with a little loss of precision. This is to be expected. On the one hand, more positive training samples consequently make higher recall. On the other hand, the extra event samples are automatically extracted from FN, thus false-positive samples are inevitable to be involved, which may result in hurting the precision. Anyhow, with events from FN, our approach achieves higher F1 score.
5 Related Work
Event detection is an increasingly hot and challenging research topic in NLP. Generally, existing approaches could roughly be divided into two groups.
The first kind of approach tackled this task under the supervision of annotated triggers and entities, but totally ignored anno-
7http://nlp.cs.rpi.edu/software/
tated arguments. The majority of existing work followed this paradigm, which includes feature-based methods and representationbased methods. Feature-based methods exploited a diverse set of strategies to convert classification clues (i.e., POS tags, dependency relations) into feature vectors (Ahn, 2006; Ji and Grishman, 2008; Patwardhan and Riloff, 2009; Gupta and Ji, 2009; Liao and Grishman, 2010; Hong et al., 2011; Liu et al., 2016b). Representation-based methods typically represent candidate event mentions by embeddings and feed them into neural networks (Chen et al., 2015; Nguyen and Grishman, 2015; Liu et al., 2016a; Nguyen and Grishman, 2016).
The second kind of approach, on the contrast, tackled event detection and argument extraction simultaneously, which is called joint approach (Riedel et al., 2009; Poon and Vanderwende, 2010; Li et al., 2013, 2014; Venugopal et al., 2014; Nguyen et al., 2016). Joint approach is proposed to capture internal and external dependencies of events, including trigger-trigger, argument-argument and trigger-argument dependencies. Theoretically, both ED and AE are expected to benefit from joint methods because triggers and arguments are jointly considered. However, in practice, existing joint methods usually only make remarkable improvements to AE, but insignificant to ED. Different from them, this work investigates the exploitation of argument information to improve the performance of ED.
6 Conclusions
In this work, we propose a novel approach to model argument information explicitly for ED via supervised attention mechanisms. Besides, we also investigate two strategies to construct gold attentions using the annotated arguments. To demonstrate the effectiveness of the proposed method, we systematically conduct a series of experiments on the widely used benchmark dataset ACE 2005. Moreover, we also use events from FN to augment the performance of the proposed approach. Experimental results show that our approach outperforms state-of-the-art methods, which demonstrates that the proposed approach is effective for event detection.
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899

Review:
- Strengths: This paper tries to use the information from arguments, which is usually ignored yet actually quite important, to improve the performance of event detection. The framework is clear and simple. With the help of the supervised attention mechanism, an important method that has been used in many tasks such as machine translation, the performance of their system outperforms the baseline significantly.  - Weaknesses:  The attention vector is simply the summation of two attention vectors of each part. Maybe the attention vector could be calculated in a more appropriate approach. For the supervised attention mechanism, two strategies are proposed. Both of them are quite straightforward. Some more complicated strategies can work better and can be tried.  - General Discussion:  Although there are some places that can be improved, this paper proposed a quite effective framework, and the performance is good. The experiment is solid. It can be considered to be accepted.