MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over
Time-Involved Document
- URL: http://arxiv.org/abs/2311.04816v1
- Date: Wed, 8 Nov 2023 16:41:37 GMT
- Title: MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over
Time-Involved Document
- Authors: Zheng Chu, Zekun Wang, Jiafeng Liang, Ming Liu, Bing Qin
- Abstract summary: MTGER is a novel framework for temporal reasoning over time-involved documents.
It explicitly models the temporal relationships among facts by multi-view temporal graphs.
We show that MTGER gives more consistent answers under question perturbations.
- Score: 26.26604509399347
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 Temporal 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.
Related papers
- TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph.
Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales.
We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
arXiv Detail & Related papers (2024-01-06T06:26:49Z) - Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning [3.3571415078869955]
This paper proposes an innovative reasoning approach that focuses on Learning Multi-graph Structure (LMS)
LMS incorporates an adaptive gate for merging entity representations both along and across timestamps effectively.
It also integrates timestamp semantics into graph attention calculations and time-aware decoders.
arXiv Detail & Related papers (2023-12-04T08:23:09Z) - Fusing Temporal Graphs into Transformers for Time-Sensitive Question
Answering [11.810810214824183]
Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents.
We apply existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents.
Experimental results show that our proposed approach for fusing temporal graphs into input text substantially enhances the temporal reasoning capabilities of Transformer models.
arXiv Detail & Related papers (2023-10-30T06:12:50Z) - Once Upon a $\textit{Time}$ in $\textit{Graph}$: Relative-Time
Pretraining for Complex Temporal Reasoning [96.03608822291136]
We make use of the underlying nature of time, and suggest creating a graph structure based on the relative placements of events along the time axis.
Inspired by the graph view, we propose RemeMo, which explicitly connects all temporally-scoped facts by modeling the time relations between any two sentences.
Experimental results show that RemeMo outperforms the baseline T5 on multiple temporal question answering datasets.
arXiv Detail & Related papers (2023-10-23T08:49:00Z) - Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic [84.59255070520673]
Large language models (LLMs) face a challenge when engaging in temporal reasoning.
We propose TempLogic, a novel framework designed specifically for temporal question-answering tasks.
arXiv Detail & Related papers (2023-05-24T10:57:53Z) - Generic Temporal Reasoning with Differential Analysis and Explanation [61.96034987217583]
We introduce a novel task named TODAY that bridges the gap with temporal differential analysis.
TODAY evaluates whether systems can correctly understand the effect of incremental changes.
We show that TODAY's supervision style and explanation annotations can be used in joint learning.
arXiv Detail & Related papers (2022-12-20T17:40:03Z) - Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF)
Our model avoids the influence of cumulative error and does not increase the time complexity.
Our model achieves state-of-the-art performance on many popular multivariate datasets.
arXiv Detail & Related papers (2022-05-16T07:53:42Z) - Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic
Representations [1.8262547855491458]
We introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER.
Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features.
We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing.
arXiv Detail & Related papers (2022-04-10T22:24:11Z) - Continuous-Time Sequential Recommendation with Temporal Graph
Collaborative Transformer [69.0621959845251]
We propose a new framework Temporal Graph Sequential Recommender (TGSRec) upon our defined continuous-time bi-partite graph.
TCT layer can simultaneously capture collaborative signals from both users and items, as well as considering temporal dynamics inside sequential patterns.
Empirical results on five datasets show that TGSRec significantly outperforms other baselines.
arXiv Detail & Related papers (2021-08-14T22:50:53Z) - Time-aware Graph Embedding: A temporal smoothness and task-oriented
approach [9.669206664225234]
This paper presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness.
At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding.
Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally-aware information.
arXiv Detail & Related papers (2020-07-22T02:20:25Z) - Software Engineering Event Modeling using Relative Time in Temporal
Knowledge Graphs [15.22542676866305]
We present a multi-relational temporal knowledge graph based on the daily interactions between artifacts in GitHub.
We introduce two new datasets for i) interpolated time-conditioned link prediction and ii) extrapolated time-conditioned link/time prediction queries.
Our experiments on these datasets highlight the potential of adapting knowledge graphs to answer broad software engineering questions.
arXiv Detail & Related papers (2020-07-02T16:28:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.