user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
We propose a new framework for entity and event extraction based on generative adversarial imitation learning -- an inverse reinforcement learning method using generative adversarial network (GAN).
We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse.
We utilize discriminators to estimate proper rewards according to the difference between the labels committed by ground-truth (expert) and the extractor (agent).
Experiments also demonstrate that the proposed framework outperforms state-of-the-art methods.
Event extraction (EE) is a crucial information extraction (IE) task that focuses on extracting structured information (i.e., a structure of event trigger and arguments, "what is happening", and "who or what is involved ") from unstructured texts.
In most recent five years, many event extraction approaches have brought forth encouraging results by retrieving additional related text documents BID18 , introducing rich features of multiple categories [Li et al., 2013 BID26 , incorporating relevant information within or beyond context BID23 , Judea and Strube, 2016 BID24 BID7 and adopting neural network frameworks BID4 , Nguyen and Grishman, 2015 BID8 , Nguyen et al., 2016 BID8 , Nguyen and Grishman, 2018 BID17 , Huang et al., 2018 BID13 BID27 .There are still challenging cases: for example, in the following sentences: "Masih's alleged comments of blasphemy are punishable by death under Pakistan Penal Code" and "Scott is charged with first-degree homicide for the death of an infant.
", the word death can trigger an Execute event in the former sentence and a Die event in the latter one.
With similar local information (word embeddings) or contextual features (both sentences include legal events), supervised models pursue the probability distribution which resembles that in the training set (in ACE2005 data, we have overwhelmingly more Die annotation on death than Execute), and will label both as Die event, causing error in the former instance.
Such mistake is due to the lack of a mechanism that explicitly deals with wrong and confusing labels.
Many multi-classification approaches utilize cross-entropy loss, which aims at boosting the probability of the correct labels.
Many approaches -including AdaBoost which focuses weights on difficult cases -usually treat wrong labels equally and merely inhibits them indirectly.
Models are trained to capture features and weights to pursue correct labels, but will become vulnerable and unable to avoid mistakes when facing ambiguous instances, where the probabilities of the confusing and wrong labels are not sufficiently "suppressed".
Therefore, exploring information from wrong labels is a key to make the models robust.
In this paper, we propose a dynamic mechanism -inverse reinforcement learning -to directly assess correct and wrong labels on instances in entity and event extraction.
We assign explicit scores on cases -or rewards in terms of Reinforcement Learning (RL).
We adopt discriminators from generative adversarial networks (GAN) to estimate the reward values.
Discriminators ensures the highest reward for ground-truth (expert) and the extractor attempts to imitate the expert by pursuing highest rewards.
For challenging cases, if the extractor continues selecting wrong labels, the GAN keeps expanding the margins between rewards for ground-truth labels and (wrong) extractor labels and eventually deviates the extractor from wrong labels.
The main contributions of this paper can be summarized as follows: • We apply reinforcement learning framework to event extraction tasks, and the proposed framework is an end-to-end and pipelined approach that extracts entities and event triggers and determines the argument roles for detected entities.• With inverse reinforcement learning propelled by GAN, we demonstrate that a dynamic reward function ensures more optimal performance in a complicated RL task.
We follow the schema of Automatic Content Extraction (ACE) 1 to detect the following elements from unstructured natural language text:• Entity: word or phrase that describes a real world object such as a person ("Masih" as PER in Figure 1 ).
ACE schema defines 7 types of entities.• Event Trigger: the word that most clearly expresses an event (interaction or change of status).
ACE schema defines 33 types of events such as Sentence ("punishable" in Figure 1 ) and Execute ("death").• Event argument: an entity that serves as a participant or attribute with a specific role in an event mention, e.g., a PER "Masih" serves as a Defendant in a Sentence event triggered by "punishable".
For broader readers who might not be familiar with reinforcement learning, we briefly introduce by their counterparts or equivalent concepts in supervised models with the RL terms in the parentheses: our goal is to train an extractor (agent A) to label entities, event triggers and argument roles (actions a) in text (environment e); to commit correct labels, the extractor consumes features (state s) and follow the ground truth (expert E); a reward R will be issued to the extractor according to whether it is different from the ground truth P l a c e N / A P e r s o n A g e n t P l a c e N / A P e r s o n A g e n tFigure 1: Our framework includes a reward estimator based on GAN to issue dynamic rewards with regard to the labels (actions) committed by event extractor (agent).
The reward estimator is trained upon the difference between the labels from ground truth (expert) and extractor (agent).
If the extractor repeatedly misses Execute label for "death", the penalty (negative reward values) is strengthened; if the extractor make surprising mistakes: label "death" as Person or label Person "Masih" as Place role in Sentence event, the penalty is also strong.
For cases where extractor is correct, simpler cases such as Sentence on "death" will take a smaller gain while difficult cases Execute on "death" will be awarded with larger reward values.and how serious the difference is -as shown in Figure 1 , a repeated mistake is definitely more serious -and the extractor improves the extraction model (policy π) by pursuing maximized rewards.
Our framework can be briefly described as follows: given a sentence, our extractor scans the sentence and determines the boundaries and types of entities and event triggers using Q-Learning (Section 3.1); meanwhile, the extractor determines the relations between triggers and entities -argument roles with policy gradient (Section 3.2).
During the training epochs, GANs estimate rewards which stimulate the extractor to pursue the most optimal joint model (Section 4).
The entity and trigger detection is often modeled as a sequence labeling problem, where longterm dependency is a core nature; and reinforcement learning is a well-suited method [Maes et al., 2007 BID15 are also good candidates for context embeddings.
From RL perspective, our extractor (agent A) is exploring the environment, or unstructured natural language sentences when going through the sequences and committing labels (actions a) for the tokens.
When the extractor arrives at tth token in the sentence, it observes information from the environment and its previous action a t−1 as its current state s t ; the extractor commits a current action a t and moves to the next token, it has a new state s t+1 .
The information from the environment is token's context embedding v t , which is usually acquired from Bi-LSTM BID12 outputs; previous action a t−1 may impose some constraint for current action a t , e.g., I-ORG does not follow B-PER 2 .
With the aforementioned notations, we have DISPLAYFORM0 To determine the current action a t , we generate a series of Q-tables with DISPLAYFORM1 where f sl (·) denotes a function that determine the Q-values using the current state as well as previous states and actions.
Then we achievê DISPLAYFORM2 Equation 2 and 3 suggest that an RNN-based framework which consumes current input and previous inputs and outputs can be adopted, and we use a unidirectional LSTM as BID1 .
We have a full pipeline as illustrated in Figure 2 .2.
In this work, we use BIO, e.g., "B-Meet" indicates the token is beginning of Meet trigger, "I-ORG" means that the token is inside an organization phrase, and "O" denotes null. , with fixed rewards r = ±5 for correct/wrong labels and discount factor λ = 0.01.
Score for wrong label is penalized while correct one is reinforced.
For each label (action a t ) with regard to s t , a reward r t = r(s t , a t ) is assigned to the extractor (agent).
We use Q-learning to pursue the most optimal sequence labeling model (policy π) by maximizing the expected value of the sum of future rewards E(R t ), where R t represents the sum of discounted future rewards r t + γr t+1 + γ 2 r t+2 + . . .
with a discount factor γ, which determines the influence between current and next states.
We utilize Bellman Equation to update the Q-value with regard to the current assigned label to approximate an optimal model (policy π * ).
DISPLAYFORM3 As illustrated in FIG0 , when the extractor assigns a wrong label on the "death" token because the Q-value of Die ranks first, Equation 4 will penalize the Q-value with regard to the wrong label; while in later epochs, if the extractor commits a correct label of Execute, the Q-value will be boosted and make the decision reinforced.
We minimize the loss in terms of mean squared error between the original and updated Q-values notated as Q sl (s t , a t ): DISPLAYFORM4 and apply back propagation to optimize the parameters in the neural network.
After the extractor determines the entities and triggers, it takes pairs of one trigger and one entity (argument candidate) to determine whether the latter serves a role in the event triggered by the former.
In this task, for each pair of trigger and argument candidate, our extractor observes the context embeddings of trigger and argument candidate -v ttr and v tar respectively, as well as the output of another Bi-LSTM consuming the sequence of context embeddings between trigger and argument candidates in the state; the state also includes a representation (onehot vector) of the entity type of the argument candidate a tar , and the event type of the trigger a tar also determine the available argument role labels, e.g., an Attack event never has Adjudicator arguments as Sentence events.
With these notations we have: DISPLAYFORM0 where the footnote tr denotes the trigger, ar denotes argument candidate, and f ss denotes the sub-sentence Bi-LSTM for the context embeddings between trigger and argument.
We have another ranking table for argument roles: DISPLAYFORM1 where f tr,ar represents a mapping function whose output sizes is determined by the trigger event type a ttr .
e.g., Attack event has 5 -Attacker, Target, Instrument, Place and Not-a-role labels and the mapping function for Attack event contains a fully-connected layer with output size of 5.
And we determine the role witĥ a tr,ar = arg max atr,ar Q tr,ar (s tr,ar , a tr,ar ).We assign a reward r(s tr,ar , a tr,ar ) to the extractor, and since there is one step in determining the argument role label, the expected values of R = r(s tr,ar , a tr,ar ).We utilize another RL algorithm -Policy Gradient BID19 to pursue the most optimal argument role labeling performance.
We have probability distribution of argument role labels that are from the softmax output of Q-values: P (a tr,ar |s tr,ar ) = softmax(Q tr,ar (s tr,ar , a tr,ar )).To update the parameters, we minimize loss function DISPLAYFORM2 From Equation 10 and FIG1 we acknowledge that, when the extractor commits a correct label (Agent for the GPE entity "Pakistan"), the reward encourages P (a tr,ar |s tr,ar ) to increase; and when the extractor is wrong (e.g., Place for "Pakistan"), the reward will be negative, leading to a decreased P (a tr,ar |s tr,ar ).
Here we have a brief clarification on different choices of RL algorithms in the two tasks.
In the sequence labeling task, we do not take policy gradient approach due to high variance of E(R t ), i.e., the sum of future rewards R t should be negative when the extractor chooses a wrong label, but an ill-set reward and discount factor γ assignment or estimation may give a positive R t (often with a small value) and still push up the probability of the wrong action, which is not desired.
There are some variance reduction approaches to constrain the R t but they still need additional estimation and bad estimation will introduce new risk.
Q-learning only requires rewards on current actions r t , which are relatively easy to constrain.
In the argument role labeling task, determination on each trigger-entity pair consists of only one single step and R t is exactly the current reward r, then policy gradient approach performs correctly if we ensure negative rewards for wrong actions and positive for correct ones.
However, this one-step property impacts the Q-learning approach: without new positive values from further steps, a small positive reward on current correct label may make the updated Q-value smaller than those wrong ones.
So far in our paper, the reward values demonstrated in the examples are fixed, we have DISPLAYFORM0 and typically we have c 1 > c 2 .
This strategy makes RL-based approach no difference from classification approaches with cross-entropy in terms of "treating wrong labels equally" as discussed in introductory section.
Moreover, recent RL approaches on relation extraction BID25 BID9 ] adopt a fixed setting of reward values with regard to different phases of entity and relation detection based on empirical tuning, which requires additional tuning work when switching to another data set or schema.
In event extraction task, entity, event and argument role labels yield to a complex structure with variant difficulties.
Errors should be evaluated case by case, and from epoch to epoch.
In the earlier epochs, when parameters in the neural networks are slightly optimized, all errors are tolerable, e.g., in sequence labeling, extractor within the first 2 or 3 iterations usually labels most tokens with O labels.
As the epoch number increases, the extractor is expected to output more correct labels, however, if the extractor makes repeated mistakes -e.g., the extractor persistently labels"death" as O in the example sentence "... are punishable by death ..." during multiple epochs -or is stuck in difficult cases -e.g.
, whether FAC (facility) token "bridges" serves as a Place or Target role in an Attack event triggered by "bombed " in sentence "U.S. aircraft bombed Iraqi tanks holding bridges...
"-a mechanism is required to assess these challenges and to correct them with salient and dynamic rewards.
We describe the training approach as a process of extractor (agent A) imitating the ground-truth (expert E), and during the process, a mechanism ensures that the highest reward values are issued to correct labels (actions a), including the ones from both expert E and a. DISPLAYFORM1 This mechanism is Inverse Reinforcement Learning BID0 , which estimates the reward first in an RL framework.
Equation 12 reveals a scenario of adversary between ground truth and extractor and Generative Adversarial Imitation Learning (GAIL) BID11 , which is based on GAN BID10 , fits such adversarial nature.
In the original GAN, a generator generates (fake) data and attempts to confuse a discriminator D which is trained to distinguish fake data from real data.
In our proposed GAIL framework, the extractor (agent A) substitutes the generator and commits labels to the discriminator D; the discriminator D, now serves as reward estimator, aims to issue largest rewards to labels (actions) from the ground-truth (expert E) or identical ones from the extractor but provide lower rewards for other/wrong labels.
Rewards R(s, a) and the output of D are now equivalent and we ensure: DISPLAYFORM2 where s, a E and a A are input of the discriminator.
In the sequence labeling task, s consists of the context embedding of current token v t and a one-hot vector that represents the previous action a t−1 according to Equation 1, in the argument role labeling task, s comes from the representations of all elements mentioned in Equation 6 ; a E is a one-hot vector of ground-truth label (expert, or "real data") while a A denotes the counterpart from the extractor (agent, or "generator").
The concatenated s and a E is the input for "real data" channel while s and a A build the input for "generator" channel of the discriminator.
In our framework, due to different dimensions in two tasks and event types, we have 34 discriminators (1 for sequence labeling, 33 for event argument role labeling with regard to 33 event types).
Every discriminator consists of 2 fully-connected layers with a sigmoid output.
The original output of D denotes a probability which is bounded in [0, 1], and we use linear transformation to shift and expand it: Figure 5: An illustrative example of the GAN structure in sequence labeling scenario (argument role labeling scenario has the identical frameworks except vector dimensions).
As introduced in Section 4, the "real data" in the original GAN is replaced by feature/state representation (Equation 1, or Equation 6 for argument role labeling scenario) and ground-truth labels (expert actions) in our framework, while the "generator data" consists of features and extractor's attempt labels (agent actions).
The discriminator serves as the reward estimator and a linear transform is utilized to extend the D's original output of probability range [0, 1].
DISPLAYFORM3 e.g., in our experiments, we set α = 20 and β = 0.5 and make R(s, a) ∈ [−10, 10].To pursue Equation 13, we minimize the loss function and optimize the parameters in the neural network: DISPLAYFORM4 During the training process, after we feed neural network mentioned in Section 3.1 and 3.2 with a mini-batch of data, we collect the features (or states s), corresponding extractor labels (agent actions a A ) and ground-truth (expert actions a E ) to update the discriminators according to Equation 15; then we feed features and extractor labels into the discriminators to acquire reward values and train the extractor -or the generator from GAN's perspective.
Since the discriminators are continuously optimized, if the extractor (generator) makes repeated mistakes or makes surprising ones (e.g., considering a PER as a Place), the margin of rewards between correct and wrong labels expands and outputs reward with larger absolute values.
Hence, in sequence labeling task, the updated Q-values are updated with a more discriminative difference, and, similarly, in argument role labeling task, the P (a|s) also increases or decreases more significantly with a larger absolute reward values.
Figure 5 illustrates how we utilize GAN for reward estimation.
In case where discriminators are not sufficiently optimized (e.g., in early epochs) and may output undesired values -e.g., negative for correct actions, we impose a hard margiñ R(s, a) = max(0.1, R(s, a)) when a is correct, min(−0.1, R(s, a)) otherwise (16) to ensure that correct actions will always take positive reward values and wrong ones take negative.
In training phase, the extractor selects labels according to the rankings of Q-values in Equation 3 and 8 and GANs will issue rewards to update the Q-tables and policy probabilities; and we also adopt -greedy strategy: we set a probability threshold ∈ [0, 1) and uniformly sample a number ρ ∈ [0, 1] before the extractor commits a label for an instance: a = arg max a Q(s, a), if ρ ≥ Randomly pick up an action, if othersWith this strategy, the extractor is able to explore all possible labels (including correct and wrong ones), and acquires rewards with regard to all labels to update the neural networks with richer information.
Moreover, after one step of -greedy exploration, we also force the extractor to commit ground-truth labels and issue it with expert (highest) rewards, and update the parameters accordingly.
This additional step is inspired by BID14 Bansal, 2017, 2018] , which combines cross-entropy loss from supervised models with RL loss functions 3 .
Such combination can simultaneously and explicitly encourage correct labels and penalize wrong labels and greatly improve the efficiency of pursuing optimal models.
To evaluate the performance with our proposed approach, we utilize ACE2005 documents excluding informal documents from cts (Conversational Telephone Speech) and un (UseNet) and we have 5, 272 triggers and 9, 612 arguments.
We follow training (529 documents with 14, 180 sentences), validation (30 documents with 863 sentences) and test (40 documents with 672 sentences) splits and adopt the same criteria of the evaluation to align with [Nguyen et al., 2016 BID17 :• An entity (named entities and nominals) is correct if its entity type and offsets find a match in the ground truth.• A trigger is correct if its event type and offsets find a match in the ground truth.• An argument is correctly labeled if its event type, offsets and role find a match in the ground truth.
We use ELMo embeddings 4 BID15 .
Because ELMo is delivered with builtin Bi-LSTMs, we treat ELMo embedding as context embeddings in Figure 2 and 4.
We use GAIL-ELMo in the tables to denote the setting.
Moreover, in order to disentangle the contribution from ELMo embeddings, we also present the performance in a non-ELMo setting (denoted as GAIL-W2V) which utilizes the following embedding techniques to represent tokens in the input sentence.• Token surface embeddings: for each unique token in the training set, we have a lookup dictionary for embeddings which is randomly initialized and updated in the training phase.• Character-based embeddings: each character also has a randomly initialized embedding, and will be fed into a token-level Bi-LSTM network, the final output of this network will enrich the information of token.• POS embeddings: We apply Part-of-Speech (POS) tagging on the sentences using Stanford CoreNLP tool BID21 .
The POS tags of the tokens also have a trainable look-up dictionary (embeddings).• Pre-trained embeddings: We also acquire embeddings trained from a large and publicly available corpus.
These embeddings preserve semantic information of the tokens and they are not updated in the training phase.
We concatenate these embeddings and feed them into the Bi-LSTM networks as demonstrated in Figure 2 and 4.
To relieve over-fitting issues, we utilize dropout strategy on the input data during the training phase.
We intentionally set "UNK" (unknown) masks, which hold entries in the look-up dictionaries of tokens, POS tags and characters.
We randomly mask known tokens, POS tags and characters in the training sentences with "UNK" mask.
We also set an all-0 vector on Word2Vec embeddings of randomly selected tokens.
We tune the parameters according to the F1 score of argument role labeling.
For Qlearning, we set a discount factor γ = 0.01.
For all RL tasks, we set exploration threshold = 0.1.
We set all hidden layer sizes (including the ones on discriminators) and LSTM (for subsentence Bi-LSTM) cell memory sizes as 128.
The dropout rate is 0.2.
When optimizing the parameters in the neural networks, we use SGD with Momentum and the learning rates start from 0.02 (sequence labeling), 0.005 (argument labeling) and 0.001 (discriminators), then the learning rate will decay every 5 epochs with exponential of 0.9; all momentum values are set as 0.9.For the non-ELMo setting, we set 100 dimensions for token embeddings, 20 for PoS embeddings, and 20 for character embeddings.
For pre-trained embeddings, we train a 100-dimension Word2Vec [Mikolov et al., 2013] model from English Wikipedia articles (January 1st, 2017), with all tokens preserved and a context window of 5 from both left and right.
We also implemented an RL framework with fixed rewards of ±5 as baseline with identical parameters as above.
For sequence labeling (entity and event trigger detection task), we also set an additional reward value of −50 for B-I errors, namely an I-label does not follow B-label with the same tag name (e.g., I-GPE follows B-PER).
We use RL-W2V and RL-ELMo to denote these fixed-reward settings.
We compare the performance of entity extraction (including named entities and nominal mentions) with the following state-of-the-art and high-performing approaches:• JointIE [Li et al., 2014] : a joint approach that extracts entities, relations, events and argument roles using structured prediction with rich local and global linguistic features.• JointEntityEvent BID23 : an approach that simultaneously extracts entities and arguments with document context.
• Tree-LSTM [Miwa and Bansal, 2016] : a Tree-LSTM based approach that extracts entities and relations.
JointIE [Li et al., 2014] 85.2 76.9 80.8 JointEntityEvent BID23 83.5 80.2 81.8 Tree-LSTM [Miwa and Bansal, 2016] 82.9 83.9 83.4 KBLSTM BID24 85 BID24 • KBLSTM BID24 : an LSTM-CRF hybrid model that applies knowledge base information on sequence labeling.
From Table 1 we can conclude that our proposed method outperforms the other approaches, especially with an impressively high performance of recall.
CRF-based models are applied on sequence labeling tasks because CRF can consider the label on previous token to avoid mistakes such as appending an I-GPE to a B-PER, but it neglects the information from the later tokens.
Our proposed approach avoids the aforementioned mistakes by issuing strong penalties (negative reward with large absolute value); and the Q-values in our sequence labeling sub-framework also considers rewards for the later tokens, which significantly enhances our prediction performance.
For event extraction performance with system-predicted entities as argument candidates, besides [Li et al., 2014] and BID23 we compare 5 our performance with:• dbRNN BID17 : an LSTM framework incorporating the dependency graph (dependency-bridge) information to detect event triggers and argument roles.
TAB6 demonstrates that the performance of our proposed framework is better than state-of-the-art approaches except lower F1 score on argument identification against BID17 .
BID17 utilizes Stanford CoreNLP to detect the noun phrases and take the detected phrases as argument candidates, while our argument candidates come from system predicted entities and some entities may be missed.
However, BID17 's approach misses entity type information, which cause many errors in argument role labeling task, whereas our argument candidates hold entity types, and our final role labeling performance is better than BID17 .Our framework is also flexible to consume ground-truth (gold) annotation of entities as argument candidates.
And we demonstrate the performance comparison with the following state-of-the-art approaches on the same setting besides BID17 :• JointIE-GT [Li et al., 2013] : similar to [Li et al., 2014] , the only difference is that this approach detects arguments based on ground-truth entities.5.
Some high-performing event approaches such as [Nguyen and Grishman, 2018, Hong et al., 2018] have no argument role detection, thus they are not included for the sake of fair comparison.
Nguyen et al., 2016] , an RNN-based approach which integrates local lexical features.
For this setting, we keep the identical parameters (including both trained and preset ones) and network structures which we used to report our performance in Table 1 and 2, and we substitute system-predicted entity types and offsets with ground-truth counterparts.
TAB7 demonstrates that, without any further deliberate tuning, our proposed approach can still provide better performance.
We notice that some recent approaches [Liu et al., 2018] consolidate argument role labels with same names from different event types (e.g., Adjudicator in Trial-Hearing, Charge-Indict, Sue, Convict, etc.), for argument role labeling they only deal with 37 categories while our setting consists of 143 categories (with a hierarchical routine of 33 event types and 3-7 roles for each type).
The strategy of consolidation can boost the scores and our early exploration with similar strategy reaches an argument role labeling F1 score of 61.6 with gold entity annotation, however, the appropriateness with regard to ACE schema definition still concerns us.
For example, the argument role Agent appear in Injure, Die, Transport, Start-Org, Nominate, Elect, Arrest-Jail and Release-Parole events, the definition of each Agent in these types includes criminals, business people, law enforcement officers and organizations which have little overlap and it is meaningless and ridiculous to consider these roles within one single label.
Moreover, when analyzing errors from this setting, we encounter errors such as Attacker in Meet events or Destination in Mary events, which completely violate ACE schema.
Hence, for the sake of solid comparison, we do not include this setting, though we still appreciate and honor any work and attempt to pursue higher performance.
The statistical results in Table 1 , 2 and 3 demonstrate that dynamic rewards outperforms the settings with fixed rewards.
As presented in Section 4, fixed reward setting resembles classification methods with cross-entropy loss, which treat errors equally and do not incorporate much information from errors, hence the performance is similar to some earlier approaches but does not outperform state-of-the-art.
For instances with ambiguity, our dynamic reward function can provide more salient margins between correct and wrong labels: e.g., "... they sentenced him to death ...", with the identical parameter set as aforementioned, reward for the wrong Die label is −5.74 while correct Execute label gains 6.53.
For simpler cases, e.g., "... submitted his resignation ...", we have flatter rewards as 2.74 for End-Position, −1.33 for None or −1.67 for Meet, which are sufficient to commit correct labels.
Scores in Table 1 , 2 and 3 prove that non-ELMo settings already outperform state-of-the-art, which confirms the advantage and contribution of our GAIL framework.
Moreover, in spite of insignificant drop in fixed reward setting, we agree that ELMo is a good replacement 6 for a combination of word, character and PoS embeddings.
The only shortcoming according to our empirical practice is that ELMo takes huge amount of GPU memory and the training procedure is slow (even we do not update the pre-trained parameters during our training phase).
Losses of scores are mainly missed trigger words and arguments.
For example, the Meet trigger "pow-wow " is missed because it is rarely used to describe a formal political meeting; and there is no token with similar surface form -which can be recovered using character embedding or character information in ELMo setting -in the training data.
We observe some special erroneous cases due to fully biased annotation.
In the sentence "Bombers have also hit targets ...", the entity "bombers" is mistakenly classified as the Attacker argument of the Attack event triggered by the word "hit".
Here the "bombers" refers to aircraft and is considered as a VEH (Vehicle) entity, and should be an Instrument in the Attack event, while "bombers" entities in the training data are annotated as Person (who detonates bombs), which are never Instrument.
This is an ambiguous case, however, it does not compromise our claim on the merit of our proposed framework against ambiguous errors, because our proposed framework still requires a mixture of different labels to acknowledge ambiguity.
One of the recent event extraction approaches mentioned in the introductory section BID13 utilizes GAN in event extraction.
The GAN in the cited work outputs generated features to regulate the event model from features leading to errors, while our approach directly assess the mistakes to explore levels of difficulty in labels.
Moreover, our approach also covers argument role labeling, while the cited paper does not.
RL-based methods have been recently applied to a few information extraction tasks such as relation extraction; and both relation frameworks from BID9 BID25 apply RL on entity relation detection with a series of predefined rewards.
We are aware that the term imitation learning is slightly different from inverse reinforcement learning.
Techniques of imitation learning BID5 BID16 BID3 attempt to map the states to expert actions by following demonstration, which resembles supervised learning, while inverse reinforcement learning BID0 BID20 BID28 BID11 BID2 estimates the rewards first and apply the rewards to RL.
BID22 is an imitation learning application on bio-medical event extraction, and there is no reward estimator used.
We humbly recognize our work as inverse reinforcement learning approach although "GAIL" is named after imitation learning.
In this paper, we propose an end-to-end entity and event extraction framework based on inverse reinforcement learning.
Experiments have demonstrated that the performance benefits from dynamic reward values estimated from discriminators in GAN, and we also demonstrate the performance of recent embedding work in the experiments.
In the future, besides releasing the source code, we also plan to further visualize the reward values and attempt to interpret these rewards so that researchers and event extraction system developers are able to better understand and explore the algorithm and remaining challenges.
Our future work also includes using cutting edge approaches such as BERT BID6 , and exploring joint model in order to alleviate impact from upstream errors in current pipelined framework.

assistant: text: We use dynamic rewards to train event extractors.