MTGER : Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document (2023)

Abstract: The facts and time in the document are intricately intertwined, making temporal reasoning over documents challenging. Previous work models time implicitly, making it difficult to handle such complex relationships. To address this issue, we propose MTGER, a novel Multi-view Temporal Graph Enhanced Reasoning framework for temporal reasoning over time-involved documents. Concretely, MTGER explicitly models the temporal relationships among facts by multi-view temporal graphs. On the one hand, the heterogeneous temporal graphs explicitly model the temporal and discourse relationships among facts; on the other hand, the multi-view mechanism captures both time-focused and fact-focused information, allowing the two views to complement each other through adaptive fusion. To further improve the implicit reasoning capability of the model, we design a self-supervised time-comparing objective. Extensive experimental results demonstrate the effectiveness of our method on the TimeQA and SituatedQA datasets. Furthermore, MTGER gives more consistent answers under question perturbations.

## introduction in the real world, many facts change over time, and these changes are archived in the document such as wikipedia. facts and time are intertwined in documents with complex relationships. thus temporal reasoning is required to find facts that occurred at a specific time. to investigate this problem, @xcite propose the timeqa dataset and @xcite propose the situatedqa dataset. for example, figure 1 illustrates a question involving implicit temporal reasoning. from the human perspective, to answer this question, we first need to find relevant facts in the document and obtain new facts based on existing facts (left of figure 1(d) ). we need to deduce the answer from figure 1 : an example of a time-sensitive question involving implicit reasoning, best viewed in color. (a) describes the question, answer and constraints (b) shows a time-involved document (c) depicts the timeline of events in the document (d) illustrates the reasoning process in the human perspective, including three steps. the existing facts according to the constraints of the question, including time constraints and relation constraints (right of figure 1(d) ). recent work @xcite @xcite @xcite directly uses pre-trained models or large-scale language models to answer time-sensitive questions, neglecting to explicitly model the time like human, resulting in lack of understanding of time. even the state-of-the-art qa models have a large gap compared with human performance (50.61 em vs. 87.7 em), indicating that there is still a long way to go in this research area. inspired by the human reasoning process depicted in figure 1 (d), we intend to explicitly model the temporal relationships among facts. to this end, we propose multi-view temporal graph enhanced reasoning framework (mtger) for temporal reasoning over time-involved documents. we construct a multi-view temporal graph to establish correspondence between facts and time and to explicitly model the temporal relationships between facts. the explicit and implicit temporal relations between facts in the heterogeneous graph enhance the temporal reasoning capability, and the crossparagraph interactions between facts alleviate the inadequate interaction. specifically, each heterogeneous temporal graph (htg) contains factual and temporal layers. nodes in the factual layer are events, and nodes in the temporal layer are the timestamps (or time intervals) corresponding to the events. different nodes are connected according to the discourse relations and relative temporal relations. in addition, we construct a time-focused htg and a fact-focused htg to capture information with different focuses, forming a multi-view temporal graph. we complement the two views through adaptive fusion to obtain more adequate information. at the decoder side, we use a question-guided fusion mechanism to dynamically select the temporal graph information that is more relevant to the question. finally, we feed the time-enhanced representation into the decoder to get the answer. furthermore, we introduce a self-supervised time-comparing objective to enhance the temporal reasoning capability. extensive experimental results demonstrate the effectiveness of our proposed method on the timeqa and situatedqa datasets, with a performance boost of up to 9% compared to the stateof-the-art qa model and giving more consistent answers when encountering input perturbations. the main contributions of our work can be summarized as follows: ## conclusion in this paper, we devise mtger, a novel temporal reasoning framework over documents. mtger explicitly models temporal relationships through multi-view temporal graphs. the heterogeneous temporal graphs model the temporal and discourse relationships among facts, and the multi-view mechanism performs information integration from the time-focused and fact-focused perspectives. furthermore, we design a self-supervised objective to enhance implicit reasoning and dynamically aggregate text and graph information through the question-guided fusion mechanism. extensive experimental results demonstrate that mtger achieves better performance than state-of-the-art methods and gives more consistent answers in the face of question perturbations on two documentlevel temporal reasoning benchmarks.