Cross-Document Event Coreference Resolution (CD-ECR) is a task of grouping event mentions across multiple documents that refer to the same real-world events. In contrast to within-document event mentions, which are linked by rich, coherent contexts, cross-document event mentions lack such contexts, making it challenging for the model to establish a connection between two event mentions in different documents. To address this issue, we propose a novel mechanism of enhancing discourse coherence to boost CD-ECR. Specifically, we introduce a new task, ECD-CoE (Event-oriented Cross-Document Coherence Enhancement), which selects coherent sentences that form a coherent text for two cross-document event mentions. We then use this coherent text to represent the event mentions and resolve coreferent events. Experimental results on both the ECB+ and GVC datasets indicate that our proposed method outperforms several state-of-the-art baselines.

Event temporal relation extraction is to recognize the temporal relations of two events, which is an important task in natural language processing. Previous work only focused on intra-document information, neglecting the introduction of external knowledge. To address this issue, we propose a Chinese event Temporal relation extraction model on Large language model (LLM) predictive information and Prompt (CTLP). Specifically, we first introduce the large language model ChatGPT to provide inferred information for event pairs to enrich the representation of event information, and then use the prompt method to classify the temporal relations. Moreover, with the enriched semantic information provided by LLM, we perform hierarchical mining of single temporal relations and formulate reasonable hierarchical classification criteria. The experimental results on the Chinese ACE2005-extended dataset show that our model CTLP outperforms the SOTA baselines.

Event coreference resolution (ECR) aims to group event mentions referring to the same realworld event into clusters.Most previous studies adopt the "encoding first, then scoring" framework, making the coreference judgment rely on event encoding.Furthermore, current methods struggle to leverage human-summarized ECR rules, e.g., coreferential events should have the same event type, to guide the model.To address these two issues, we propose a prompt-based approach, CorefPrompt, to transform ECR into a cloze-style MLM (masked language model) task.This allows for simultaneous event modeling and coreference discrimination within a single template, with a fully shared context.In addition, we introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility, to explicitly demonstrate the reasoning process of ECR, which helps the model make final predictions.Experimental results show that our method CorefPrompt 1 performs well in a state-of-the-art (SOTA) benchmark.

Cross-document event coreference resolution (CD-ECR) is a task of clustering event mentions across multiple documents that refer to the same real-world events.Previous studies usually model the CD-ECR task as a pairwise similarity comparison problem by using different event mention features, and consider the highly similar event mention pairs in the same cluster as coreferent.In general, most of them only consider the local context of event mentions and ignore their implicit global information, thus failing to capture the interactions of long-distance event mentions.To address the above issue, we regard discourse structure as global information to further improve CD-ECR.First, we use a discourse rhetorical structure constructor to construct tree structures to represent documents.Then, we obtain shortest dependency paths from the tree structures to represent interactions between event mention pairs.Finally, we feed the above information to a multi-layer perceptron to capture the similarities of event mention pairs for resolving coreferent events.Experimental results on the ECB+ dataset show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-theart baselines.

Extracting event temporal relations is an important task of natural language processing. It is even more challenging to extract the temporal relations of events without using annotated auxiliary information, which is time-consuming and expensive. Therefore, we propose an event temporal relation extraction model ETEMA for Chinese text based on BERT and multi-dimensional attention mechanism, using contextual information interaction and tensor matching methods. Specifically, ETEMA uses BERT to mine the semantic information of event sentences, and uses attention and semantic information to interactively combine event information with its contextual information. Experimental results on ACE2005-extended, a corpus of Chinese temporal relations, show that the proposed model ETEMA achieves optimal performance without using any auxiliary annotated information.

Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT). Moreover, DCT can act as a hub to semantically connect the other events and timexes in a document. Unfortunately, previous work cannot benefit from such critical information. To address the above issues, we propose a unified DCT-centered Temporal Relation Extraction model DTRE to identify the relations among events, timexes and DCT. Specifically, sentence-style DCT representation is introduced to address the first issue and unify event expressions, timexes and DCT. Then, a DCT-aware graph is applied to obtain their contextual structural representations. Furthermore, a DCT-anchoring multi-task learning framework is proposed to jointly predict three types of temporal relations in a batch. Finally, we apply a DCT-guided global inference to further enhance the global consistency among different relations. Experimental results on three datasets show that our DTRE outperforms several SOTA baselines on E-E, E-T and E-D significantly.

Event coreference resolution (ECR) aims to cluster event mentions that refer to the same real-world events.Deep learning methods have achieved SOTA results on the ECR task.However, due to the encoding length limitation, previous methods either adopt classical pairwise models based on sentence-level context or split each document into multiple chunks and encode them separately.They failed to capture the interactions and contextual cues among those long-distance event mentions.Besides, highlevel information, such as event topics, is rarely considered to enhance representation learning for ECR.To address the above two issues, we first apply a Longformer-based encoder to obtain the document-level embeddings and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context.In addition, we propose an event topic generator to infer the latent topic-level representations.Finally, using the above event embeddings, we employ a multiple tensor matching method to capture their interactions at the document, sentence, and topic levels.Experimental results on the KBP 2017 dataset show that our model 1 outperforms the SOTA baselines.

Most previous work focused on extracting event temporal relations that the events appear in the same sentence or in two adjacent sentences, failing to address those nonadjacent-sentence event relations, which limits the development of tem-poral relation extraction at document-level and its real-world application. In this paper, we propose a novel Document-level event Temporal Relation Extraction (DTRE) model which can incorporate effective global cues with local cues. In particular, we select both the contextual sentences strongly related to the events and the temporal words in the context as global cues, which can provide additional semantic cues to extract those nonadjacent-sentence event temporal relations. Moreover, we further encode the events and their neighbor words as local cues to extract those intra-sentence relations and enhance the event representation. Experimental results on the English dataset show that our proposed DTRE outperforms several state-of-the-art baselines, especially for handling those nonadjacent-sentence temporal relations.

In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentencelevel and topic-level representations.In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations.Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topiclevel relevance using multi-slice tensors.Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.

In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Fol-lowing RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB.

The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1.