Document-level Event Factuality Identification via Reinforced Multi-Granularity Hierarchical Attention Networks

1 Introduction

This paper focuses on Document-level Event Factuality Identification (DEFI), which predicts the factual nature of an event according to a document fromwhich the event is derived, instead of a single sentence. DEFI is a crucial and fundamental task for information extraction in NLP. Therefore, we need to understand the event and document comprehensively, and pay more attentions to the tokens and sentences that are more relevant and useful with regard to the event.

As exemplified by Figure 1, the document-level factuality of event E1 is "certain negative"/CT- inferred from the document, where sentences S3, S4, S5, S6, and S7 refer to E1, i.e., contain event mentions of E1. In S4, S5 and S6, the negative cues (e.g., "denied" (S4), "fake" (S5), and "false" (S6)) and sentimental words (e.g., "disgraceful" and "offensive" (S4)) express the negative position to E1. However, some other sentences evaluate E1 as "possible positive"/PS+ (S3), "certain positive"/CT+ (S7), or do not give the factuality explicitly (S1 and S2), which may offer irrelevant or even wrong information for E1. Moreover, S8 mentions another irrelevant CT+ event "Mike Pence won election as Governor of Indiana in 2012" instead of E1, which may cause E1 to be identified most relevant and useful tokens and sentences; 3) Comprehensive understanding, i.e., learning inherent and interactive semantics from events and documents effectively.

∗Corresponding Author

To solve these issues, we propose a novel end-to-end DEFI model, i.e., Reinforced Multi-Granularity Hierarchical Attention Network (RMHAN), which integrates both text selection and comprehensive understanding for events and documents. Our contributions can be summarized as follows:

• RMHAN, to our best knowledge, is the first end-to-end DEFI model that does not rely on other annotated information (except for the event and document);
• RMHAN can learn semantical information of events at different levels of granularity, including sentences, topics, events, and documents, and considers both sentence and document-level encoding hierarchically;
• RMHAN integrates policy networks for both token and sentence selection with hierarchical reinforcement learning (HRL), and is the first DEFI model using HRL;
• The empirical results on DLEF-v2 corpus (both English and Chinese sub-corpora) demonstrate that RMHAN is superior to other state-of-the-art baselines.

2 Approach

2.1 Overview

The overall architecture of RMHAN is shown in Figure 2, which is composed of three sub-networks: 1) A MultiGranularity Hierarchical Attention Network (MHAN) ϕc performing as the classification network, and produces rewards for policy networks; 2) A token selection policy network TSPNet/πt ; 3) A sentence selection policy network SS-PNet/πs .

Hence, RMHAN mainly enables these attributes to tackle the challenges in §1: 1) Reinforced, i.e., hierarchical reinforcement learning for both token and sentence selection; 2) Multi-Granularity, i.e., capturing semantics at different levels of granularity from texts; 3) Hierarchical, i.e., hierarchical structure for encoding the sentences and document; 4) Attention, i.e., employing various attention networks to model the document.

In terms of Attention, we consider Self-Attention (SA) [Vaswani et al., 2017], bi-directional Co-Attention (CA) [Lu et al., 2016; Xiong et al., 2017], and Vanilla Attention (VA) pooling. Formally, SA is defined as:

where MHA is Multi-Head Attention, and FFN is FeedForward Network. While CA is computed as:

To learn the vector representation of a matrix, VA is applied in our model:

where us is the parameter. Since RMHAN contains stacks of several sub-encoders defined in §2.2 and §2.3, we consider Residual Networks (RN) to avoid the degradation of deep networks:

where LN is a normalization layer, and FCr is a stack of fully connected layers.

In summary, the encoding procedures of RMHAN include: 1) selecting tokens from each sentence Si by TS-PNet; 2) encoding sentences w.r.t the current event by the sentence encoding layer in MHAN; 3) selecting sentences from the document D by SS-PNet; 4) encoding the document by the document encoding layer in MHAN.

Firstly, we get the embedding of each token tij in Si and E by the sum of word and position embedding [Vaswani et al., 2017], i.e., S(0) i and E(0) , and feed them into the following layers in MHAN.

2.2 Sentence Encoding Layer

This layer learns representations of each sentence Si, mainly containing a stack of sentence encoders, a token selection policy network, and a VA pooling layer, as defined below.

Sentence Encoders (S-Enc)

Concretely, an S-Enc consists of the following components:

Intra-Sentence Sub-Encoder (IS-SubEnc). Based on self-attention, IS-SubEnc learns semantic information within sentences, i.e.,

Topic Sub-Encoder (T-SubEnc). The first sentences in the document D are selected as topics for further encoding. The main motivation is that topics usually contain the main semantics of documents and more related information w.r.t the current event E than other sentences, e.g., tense of events, event triggers and arguments. Therefore, topics can offer beneficial supplementary semantics for the event. Just as illustrated in Figure 1, S1 involves several event arguments, e.g., named entities "Mike Pence" and "New York Times" that hold different document-level factuality of E1. Therefore, we integrate the topic into other sentences by CA to learn interactive semantics as below (where i > 0):

Event Sub-Encoder (E-SubEnc). According to the definition of DEFI, the event E is given explicitly, and contains the fundamental information of the current event. For example, in Figure 1, E1 mentions the event trigger "preparing" and arguments "Mike Pence", "presidential election", and also the time stamp "2020". Therefore, we integrate events into D to guide the model to capture event-related semantics, and E-SubEnc is defined as

S-Enc is followed by residual networks to control the output: S(4) i = RN(S(3) i ). After selecting tokens by TSPNet, {S0, S1, . . . , SI-1} are fed into S-Enc in MHAN to obtain {S(4) 0 ,S(4) 1 , . . . , S(4) I-1 }. Then we get vector h(0) i of each sentence Si by VA, i.e., h(0) i = VA(S(4) i ). The representation of D contains vectors of {Si}, i.e., D(0) = {h(0) 0 , h(0) 1 , . . . , h(0) I-1 }, which is fed into document encoding layer in §2.3.

Token Selection Policy Network (TS-PNet)

Before encoding sentences, TS-PNet/πt is used to select tokens for each sentence Si. Concretely, state, action and reward are:

State stj of each token tij consists of two vectors: 1) word embedding tij ∈ S(0) i that is the input for ϕc and πt ; 2) vij ∈ S(4) i computed by another S-Enc in πt . Then we can get

where ⊕ is the concatenation operator.

Action atj ∈ {0, 1} ∼ πt represents whether token tij is selected (1) or not (0), whose distribution is computed as:

where FCt (·) is a stack of fully connected layers with tanh as the activation function, and σ is the logistic function.

Reward rtj is used to guide πt for token selection. To reduce the variance, we compute rtj by the vector representation of the sentence rather than those of each token [Zhang et al., 2018; Wang et al., 2019]:

where pθc (·) is the output layer of MHAN, y is the annotated label of the event E based on D. The first term is a delay reward of Si produced by MHAN, and we use VA pooling on S(4) i to get the vector for Si, then feed it to the softmax of MHAN to compute the probability according to y. J ∗ and J are the number of the selected and total tokens.

2.3 Document Encoding Layer

This layer aims to learn the vector representation for the document D. Firstly, it captures inter-sentence knowledge by a stack of document encoders.

Document Encoders (D-Enc)

Each D-Enc contains the components below.

Intra-Document Sub-Encoder (ID-SubEnc). It learns intra-document information among the sentences, and updates the representations of D as

Similar to S-Enc, we also consider the residual network to control the output of D-Enc, i.e., D(2) = RN(D(1) ). After selecting sentence by SS-PNet, D(0) is fed into document encoding layer to compute D(2) , on which we apply VA pooling to obtain the final vector representation hE of D w.r.t. E: hE = VA(D(2) ).

Sentence Selection Policy Network (SS-PNet)

Before encoding the document D, SS-PNet/πs selects sentences from D w.r.t E, and state, action and reward are:

State ssi of each sentence Si is made up with two vectors: 1) s(0) i computed by VA on S(0) i that is the input for πs ; 2) h(2) i ∈ D(2) computed by a separate D-Enc in SS-PNet. We can obtain the state of each sentence Si as

Action asi ∈ {0, 1} ∼ πs denotes whether the sentence Si is selected (1) or not (0), and πs is calculated as:

where FCs (·) is a stack of fully connected layers.

Reward rs i is to guide πs to select sentences [Zhang et al., 2018; Wang et al., 2019], and is computed as:

where the first term is a delay reward of sentence Si provided by MHAN. After SS-PNet completes all the actions, we feed each sentence whose vector representation is h(2) i into the softmax of MHAN to compute the probability according to the annotated label y of E, and I ∗ and I are the numbers of the selected and total sentences, respectively.

2.4 Model Output and Optimization

Finally, hE, which is the output of D-Enc in MHAN, is fed into the softmax layer to compute the distribution of the event factuality, i.e.,

Specifically, the total parameters Θ of RMHAN mainly include two sets, i.e., θc of MHAN, θtp and θsp of TS-PNet and SS-PNet. For the optimization of policy networks, we update θtp and θsp by REINFORCE algorithm [Williams, 1992] and policy gradient [Sutton et al., 1999] to maximize the expected reward, and policy networks πt and πs are optimized by the gradients:

where Rtj = rtj - b(˜ rt ) and Rs i = rs i - b(˜ rs ) estimates rewards of token selection rtj and sentence selection rs j , which can minimize the variance of the individual weight changes of rewards over time [Williams, 1992], i.e., we sample trajectories ˜ rt0 , ˜ rt1 , . . . , ˜ rtm1 and ˜ rs 0 , ˜ rs 1 , . . . , ˜ rsm2 over tokens and sentences according to πt and πs , and baseline values of rewards b(˜ rt ) and b(˜ rs ) are approximated by the average of all the previous rewards.

For the optimization of the classification network MHAN, we update the parameter set θc by back propagation algorithm, and the objective function is defined as:

where yn is the annotated label of the event E w.r.t D, and N is the number of samples.

3 Experimentation

In this section, firstly, we introduce the experimental settings, including corpus, event factuality, implementation details, and baselines. Then, we give the performance of our proposed model and the detailed analysis of experiments.

3.1 Corpus

Derived from DLEF [Qian et al., 2019], DLEF-v2 corpus, whose statistics are presented in Table 1, is employed as the benchmark dataset to evaluate our models. Their main differences lie in:

• Size. Their Chinese sub-corpora are comparable in size. But DLEF contains more Chinese documents than English ones (4649 vs. 1727). To overcome the problem of the small size of English sub-corpus, DLEF-v2 annotates more English documents compared with DLEF;
• Annotation. DLEF annotates not only event triggers and their document- and sentence-level factuality, but also speculative and negative cues. While DLEF-v2 further annotates a document-level event E according to event triggers for each document;
• Task. Previous work relied on annotated information. But RMHAN only utilizes documents, document-level events and their factuality for training, and does not rely on any other annotated information.

3.2 Event Factuality Values

Event factuality values are composed of modality and polarity [Saur´ı and Pustejovsky, 2012]. Modality conveys the certainty degrees of events, including certain (CT), probable (PR), and possible (PS), while polarity expresses whether the event happened, containing positive (+) and negative (-). In this paper, we use the factuality values in Table 2 [Qian et al., 2019], where underspecified (U/u) means unknown or uncommitted, which is a default value for both modality and polarity. PR and PS are merged into PS due to the similar semantics on modality. PSu and U+/- are not applicable (NA) [Saur´ı, 2008; Saur´ı and Pustejovsky, 2012]. No events can be annotated as CTu in DLEF-v2 though applicable. Therefore, there are five applicable factuality values in DLEF-v2 corpus, i.e., Uu, CT-, PS-, PS+, CT+.

3.3 Implementation Details

We focus on the performance of CT-, PS+ and CT+, since these events occupy 98.15%/98.75% in English/Chinese subcorpus. Similar to [Qian et al., 2019; Cao et al., 2021], we do not consider PS- and Uu due to their small proportions. 10-fold cross validation are performed on English and Chinese sub-corpus. Word embeddings are pre-trained by GloVe [Pennington et al., 2014]. To reduce the variance, we employ warm start, i.e., firstly train RMHAN without discarding any tokens or sentences using Adam, then consider both token and sentence selection and continue to optimize with SGD.

3.4 Baselines

For fair comparison with our model, we implement several baselines:

• SEFI model: 1) SGCN [Veyseh et al., 2019] is an Sentence-level model with Graph Convolutional Network, and employing voting for DEFI;
• DEFI models: 2) LSTM-A [Qian et al., 2019] adopts multi-layer LSTM with attention; 3) ULGN [Cao et al., 2021] designs graph networks relying on event triggers, speculative and negative cues;
• Target-specific classification models: 4) TEND-C [Duan et al., 2018] and TEND-T [Duan et al., 2019] encodes document relying on targets (i.e., events in this paper). TEND-C utilizes LSTM, while TEND-T employs attention; 5) RLSTM [Wang et al., 2019] is a reinforced LSTM network with token and sentence selection for aspect sentiment classification;

• Pre-trained model: 6) BERT-B [Devlin et al., 2019] is the base version of BERT;
• The variants of RMHANmodel: 7) MHAN is the supervised classification network in RMHAN, i.e., does not consider policy networks with reinforcement learning; 8) RMLAN concatenates all the sentences into a single one as input and only considering token selection; 9) RMHAN-SP has simpler policy networks compared with RMHAN, i.e., does not consider S-Enc or D-Enc, and only employs fully connected layers in TS-PNet and SS-PNet.

Specially, the parameter size of BERT-B is at the same order of magnitude as RMHAN for fair comparison and realworld application. Large versions (e.g., BERT-Large and RoBERTa-Large) have much more parameters than BERTB and RMHAN. Therefore, it is not a fair comparison for BERT-Large (or RoBERTa-Large, etc.) with RMHAN, since our model is task-specific. Although RMHAN has more layers, it has lower dimensions of hidden states and fewer parameters than BERT-Base and RoBERTa-Base.

3.5 Overall Results

Table 3 presents the results of RMHAN and several baselines on end-to-end DEFI task. RMHAN significantly outperforms other models (t-test: p<0.05 for RMHAN vs. RMHAN-SP, p<0.01 for RMHAN vs. others), proving the effectiveness of both text selection and comprehensive understanding integrated, whose attributes can be summarized as follows:

R/Reinforced. RMHAN is superior to the models without reinforcement learning, because policy networks can select the most relevant and meaningful tokens (e.g., event arguments, speculative and negative cues) and sentences with regard to the event. Moreover, RMHAN is better than RMHAN-SP, demonstrating the separate sentence and document encoders in policy networks TS-PNet and SS-PNet can learn useful semantics, and are beneficial to both token and sentence selection.

H/Hierarchical. RMHAN gets higher performance than the models concatenating all the texts into a single sentence as the input, e.g., BERT-B and RMLAN. Since sentences may hold different factuality values w.r.t. the events, the hierarchical structured model can extract meaningful semantics from them, especially from the core sentences of documents. Due to the hierarchical framework, RMHAN can select the most useful sentences by sentence selection policy network as well.

A/Attention. RMHAN obtains better results than those models with simpler structures, e.g, TEND-C, TEND-T and RLSTM. Based on attention, RMHAN can not only learn contextual information within and among sentences, but also integrate topics and events into documents to capture eventrelated semantics. Moreover, Table 3 also demonstrates that attention is better at modelling documents than LSTM on DEFI, e.g., MHAN/TEND-T vs TEND-C.

The performance of CT- and PS+ is lower on English subcorpus than that of Chinese one, indicating it is more difficult to identify speculation and negation for events in English documents due to fewer CT- and PS+ samples. In addition, the low results of other baselines are due to their limitations, e.g., based on LSTM, TEND-C is much simpler. SGCN, LSTM-A and ULGN rely on annotated information. To make them comparable with our end-to-end model, we use raw texts as the input, i.e., first detecting event triggers

Event (E1): Mike Pence is preparing for the presidential election run in 2020. Document-Level Event Factuality: CT(S1) On Sunday Mike Pence responded to a New York Times report that he was forming a shadow campaign as if President Trump were not involved . (S2) The report said Pence had not only kept a full political calendar but had created his own power base . (S3) Hence , it is possible that Pence is getting ready for 2020 election . (S4) But Mike Pence on Sunday denied that he is preparing for a presidential election run in 2020 , saying the suggestion is disgraceful and offensive . (S5) He called the article " fake news " and said his entire team was focused on advancing Trump 's agenda and seeing him re-elected in 2020 . (S6) " The allegations in this article are categorically false and represent just the latest attempt by the media to divide this Administration , " Pence said . (S7) A New York Times spokeswoman claimed that , " We are confident in the accuracy of our reporting and will let the story speak for itself . " (S8) Mike Pence was well known in Republican circles , and won election as Governor of Indiana in 2012 .

Event (E2): A US military aircraft was lost off the Australian coast. Document-Level Event Factuality: CT+ / PS+ (S9) A search and rescue operation is being conducted after a US military aircraft was lost off the Australian coast . (S10) Rescue teams recovered 23 people of the aircraft , but 3 service members are still missing and it is thought that they may have died . (S11) Australia 's Daily Telegraph reported that the accident probably happened as the aircraft was trying to land on the USS Ronald Reagan . Event (E3): Angela Merkel resigns before elections due. Document-Level Event Factuality: PS+ / CT(S12) Angela Merkel does not want to complete her full term as German chancellor and is planning to resign before elections due . (S13) In last year 's elections , Mrs Merkel 's party colleagues denied all speculation she may leave office early . (S14) Suggestions that Mrs Merkel wants to succeed Ban Ki moon as UN Secretary General was denied by Mrs Merkel 's spokesman .

(F1=83.19%/79.65% for English/Chinese sub-corpus), speculative and negative cues (F1=68.80%/75.42%), then identifying event factuality. Hence, their low results are mainly due to cascade errors. The low performance of SGCN shows that voting is less effective, because DEFI depends on the main semantics of documents, rather than simply aggregating or counting factuality values of sentences, since it does not conform to the mechanism of document understanding. TEND-T is superior to LSTM-A and TEND-C, mainly attributed to the hierarchical architecture and attention. Compared with most models (except for RMHAN-SP and RMHAN), BERT-B gets better results because of multi-head attention layers.

3.6 Ablation Study

To further evaluate the M/Multi-Granularity of RMHAN, i.e., the contributions of each component, we launch ablation study and present the results in Table 4, which manifests all the sub-encoders and policy networks are beneficial to RMHAN on DEFI. To be specific:

Sentence Encoder. Both topic and event sub-encoders are more important than intra-sentence sub-encoder. Topics and events usually contain event-related information, e.g., event triggers and arguments. Hence, T-SubEnc and E-SubEnc can capture correlative semantics about events and integrate them into the document. Specially, events, which can be seen as "weak" topics in most documents, usually contain less information and improve lower performance than topics. Meanwhile, IS-SubEnc is also useful, since it is mainly employed to learn information within sentences, including speculative and negative information.

Document Encoder. The removal of ID-SubEnc also leads to lower performance, especially on CT- and PS+. Actually, ID-SubEnc can learn interactive information among sentences, including those with speculative and negative semantics, and the ones that summarize the main idea of the document. In addition, ID-SubEnc, whose main function is learning inter-sentence knowledge, is also effective on deciding which sentences contribute to correct results.

Policy Networks. We can conclude that sentence selection with SS-PNet is more important and meaningful for end-toend DEFI task than token selection with TS-PNet. We argue that even if we do not consider token selection, those irrelevant or useless words are assigned lower attention weights by vanilla attention pooling, and hence have fewer effects on the semantics of sentences and documents. Compared to tokens, sentences contain more information, and performance will drop if SS-PNet is not considered, especially when sentences hold different factuality of the event, and irrelevant and noisy sentences can interfere with event-related and core ones that are crucial to the results of DEFI.

3.7 Case Study

To illustrate which tokens and sentences are more relevant and meaningful for end-to-end DEFI, we provide a qualitative analysis of RMHAN on event E1 in Figure 1, whose attention weights are visualized in Figure 3.

In general, selected tokens and sentences with higher attention weights contribute to the correct identification of the document-level factuality of E1. For token selection, there are mainly two types of tokens that are selected and assigned higher weights, i.e., 1) event arguments in topic and event sentences, including entities (e.g., "Mike Pence"); 2) negative words (e.g., "denied" in S4, "fake" in S5, and "false" in S6) in selected sentences that contain event mentions and can determine the factuality of E1. These results verifies the capability of S-Enc and TS-PNet in the sentence encoding layer.

While for sentence selection, those sentences containing event mentions and negative information are selected and have higher weights, i.e., S4, S5, and S6. It is worth noting that S1 is also selected, which can prove the usefulness of T-SubEnc. These four sentences summarize the semantics of the document w.r.t. E1, manifesting the validity of SS-PNet. Finally, the vector representation of E1 is also based on selected sentences encoded by document encoding layer, and the correct result is also attributed to ID-SubEnc in D-Enc.

3.8 Error Analysis

Since the performance of CT- and PS+ is lower than that of CT+, errors mainly derive from the wrong identification of speculative and negative information. To illustrate the production of errors, we list two error cases in Figure 4. Event E2 is annotated as CT+ according to sentence S9, but it is predicted as PS+ mistakenly, mainly attributed to the speculative information in S10 and S11. Actually, speculative cue "may" in S10 and "probably" in S11 cannot affect E2 grammatically. But RMHAN does not filter S10 and S11.

While event E3 is PS+ according to sentence S12. However, E3 gets a wrong predicted value CT- mainly due to sentence S13 and S14. On the timeline, S13 is before the current document, and cannot negate document-level factuality of E3, while S14 denies another event "Mrs Merkel wants to succeed Ban Ki moon as UN Secretary General" rather than E3. But RMHAN does not filter S13 and S14, assigning them considerable attention weights mistakenly.

These two examples illustrate DEFI models need to judge whether speculative and negative information can effect the event or not.

4 RelatedWork

4.1 Event Factuality Identification

Event Factuality Identification (EFI) is primarily concerned with Sentence-level Event Factuality Identification (SEFI) and Document-level Event Factuality Identification (DEFI), and most previous studies considered SEFI, e.g., rule-based models [Saur´ı and Pustejovsky, 2012], traditional machine learning models [de Marneffe et al., 2012; Lee et al., 2015; Qian et al., 2015], or hybrid approaches of them [Qian et al., 2015]. Recently, neural networks has been applied to SFEI, e.g., learning plain lexical and semantic information [He et al., 2017; Sheng et al., 2019], considering dependency trees [Rudinger et al., 2018; Qian et al., 2018a; Qian et al., 2018b; Cao et al., 2020; Zhang et al., 2020] or graph based network [Veyseh et al., 2019; Le and Nguyen, 2021]. Specially, [Qian et al., 2018a] designed a generative adversarial network to produce more syntactic information.

Compared with SEFI, DEFI is still in its preliminary stage. Based on DLEF corpus [Qian et al., 2019], [Qian et al., 2019; Huang et al., 2019] employed multi-layer LSTM with attention pooling to learn features from dependency paths and sentences. [Cao et al., 2021] extracted local and global information for events with a graph convolution network. However, the main limitations of them are that they depended heavily on annotated information, e.g., event triggers, speculative and negative cues, and did not discard any noisy texts.

4.2 Reinforcement Learning

Reinforcement Learning (RL) is effective on optimization for non-differentiable objectives, and obtains satisfactory performance in classification tasks in NLP [Zhang et al., 2018; Fei et al., 2019; Ye et al., 2020]. Hierarchical Reinforcement Learning (HRL) usually integrates two-level networks to capture different level information, e.g., [Wang et al., 2019] incorporated clause and word selection for document-level aspect sentiment classification. [Xiao et al., 2020] proposed a hybrid two-step framework for summarization switching between copying and rewriting sentences. [Wan et al., 2020] built a hierarchical two-level policies for structured encoding on link predictions.

This paper studies end-to-end DEFI task for the first time, and designs RMHAN model with HRL that can select relevant and meaningful tokens and sentences, and learn different levels of features from them hierarchically for events.

5 Conclusion

This paper proposes a novel multi-granularity model for endto-end DEFI, i.e., RMHAN, which can not only learn contextual information from tokens and sentences with multigranularity encoders hierarchically, but also select the most useful tokens and sentences with hierarchical reinforcement learning. Experimental results on DLEF-v2 corpus show that RMHAN is superior to several state-of-the-arts on DEFI. In the future work, we plan to launch more fine-grained end-toend DEFI task, e.g., identifying factuality of several events separately in one document and evidential sentences of them, and exploring cross-document task.

A Resources

For more details of the resources and reproducibility of this paper, please refer to https://github.com/qz011/rmhan.

Acknowledgments

The authors would like to thank the three anonymous reviewers for their comments on this paper. This work was supported by national Natural Science Foundation of China (NSFC) via Grant Nos. 62006167 and 61836007, and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).