Source-linked AI summary
Joint Reasoning for Temporal and Causal Relations
Qiang Ning, Zhili Feng, Hao Wu, Dan Roth
TL;DR
Temporal and causal event relations are closely connected, yet they have received limited joint study. This paper combines their extraction with constrained conditional models and ILP constraints, finding statistically significant improvements in both relations. The authors also introduce jointly annotated data to support this evaluation.
Problem
Jointly extracting temporal and causal relations has received limited attention, despite their close interaction and the way one relation can determine the other.
Method
The paper uses a constrained conditional model with an integer linear programming objective to jointly infer temporal and causal relations under constraints from time and causality.
Results
The joint framework produces statistically significant improvements in extracting both temporal and causal relations.
Takeaways & Limitations
Joint inference provides a framework for combining temporal and causal evidence when extracting event relations from text.
Takeaways & Limitations
The framework assumes that a cause temporally precedes its effect, while other temporal interpretations involving includes or simultaneously are rarely present in the dataset.
Abstract
from arXiv · showhide
Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and causal relations are closely related and one relation even dictates the other one in many cases. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints inherently in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.
1 Introduction
Temporal and causal relations are closely linked: causal direction can determine temporal order, while temporal order can determine causal direction. The paper proposes joint ILP-based inference with constraints and reports improvements in extracting both relations.
- Motivation: Temporal and causal relations can dictate one another in event-relation extraction.A cause must precede its effect, and temporal precedence can identify which event is the cause.
- Approach: The paper proposes Temporal and Causal Reasoning, a joint constrained conditional model for combining temporal and causal extraction.The framework uses an integer linear programming objective to enforce declarative constraints during inference.
- Approach: The ILP constraints encode causal precedence, relation symmetry, and temporal transitivity.These constraints connect the temporal and causal components while reflecting properties of time and causality.
- Data and evaluation: A jointly annotated dataset was developed by augmenting EventCausality with dense temporal annotations.The dataset addresses the absence of jointly annotated data for temporal and causal relations.
- Results: The joint framework yields statistically significant improvements in extracting both temporal and causal relations.The paper also reports that incorporating explicit time expressions and high-precision knowledge-based rules improves temporal extraction on TimeBank-Dense.
2 Related Work
Prior work largely treated temporal and causal relations separately or made local decisions, while global methods enforce graph consistency. The paper positions joint reasoning as a way to capture their close interaction more completely.
- Temporal relation extraction: Early temporal relation systems classified graph edges locally, risking violations of symmetry and transitivity.Global approaches were introduced to enforce graph constraints, including multi-sieve graph construction and closure operations.
- Global reasoning: The paper adopts ILP because it supports joint temporal-causal inference and uses relations among other events to inform pairwise decisions.Global context can resolve cases where a direct temporal relation is difficult to determine.
- Causal relation extraction: Causal relation extraction in NLP has used lexical features, discourse relations, construction grammar, and corpus-based cause-effect association statistics.Prior causal work was comparatively sparse, and many approaches in other domains assumed ground-truth timestamps.
- Joint reasoning: Temporal and causal relations are closely related, but prior literature gave limited attention to capturing both aspects in one framework.The paper connects its approach to emerging annotation work that represents both relation types.
3 Temporal and Causal Reasoning
The TCR framework jointly infers temporal and causal relations by combining classifier scores with constrained reasoning. It uses temporal structure, linguistic rules, and causal-temporal dependencies within a unified constrained search.
- Temporal component: The temporal component maximizes classifier confidence over event-event and event-timex labels while restricting assignments to a constrained space.Timex-timex relations are imposed through equality constraints because normalized dates determine them directly.
- Temporal component: Temporal constraints enforce reverse-relation symmetry and transitivity over the reduced label set RT = {b, a, i, ii, s, v}.The reverse mappings include before/after, includes/is included, and self-reversing simultaneous and vague relations.
- Temporal component: The framework adopts reduction scheme 2, which absorbs low-frequency interval relations into temporally closest labels and affects the transitivity sets used in joint inference.The paper adopts this scheme to cover all 13 interval relations; its effects become more pronounced when relations are jointly inferred or domain distributions change.
- Temporal component: Linguistic rules are added as hard constraints, using the same CAEVO rule set for fair comparison.The rules target special syntactic or semantic patterns and are treated as high-precision prior knowledge.
- Joint framework: TCR combines temporal and causal relation scoring in a constrained conditional model with an integer linear programming objective.The joint framework extends temporal inference with causal scores and searches over mutually constrained temporal and causal labels.
- Full model with causal relations: Causal labels constrain the search so that causes precede effects temporally and reversed event pairs receive corresponding causal labels.The causal label set is {c, ¯c, null}, and the causal search space depends on the selected temporal labels.
4 Experiments
The experiments evaluate temporal reasoning on TB-Dense and joint temporal-causal reasoning on a newly annotated newswire dataset. The joint system improves both relation types over individual components, with statistically significant gains.
- 4.1 Temporal Performance on TB-Dense: The ablation study adds information sources incrementally through global ILP inference, and every successive system differs significantly from its predecessor.The sources include transitivity constraints, event-time pairs, CAEVO rules, and causal extraction.
- 4.1 Temporal Performance on TB-Dense: The proposed joint temporal system achieves the best F1 score among the evaluated ablation systems and improves recall substantially over state-of-the-art methods.The comparison is performed on the TB-Dense test set.
- 4.2.1 Data Preparation: The new dataset combines dense temporal and causal annotations by augmenting EventCausality with modified dense temporal annotations.EventCausality contains more than 20 causal links per document on average, while the new dataset targets parallel annotations in the newswire domain.
- 4.2.2 Results: The joint system improves temporal precision, recall, and F1, while causal accuracy rises from 70.5% to 77.3%.The causal accuracy evaluation uses gold causal pairs.
- 4.2.2 Results: Both improvements are statistically significant according to McNemar’s test with p<0.05.The joint system is compared with the temporal-only and causal-only baselines.
- 4.2.2 Results: Gold causal relations yield approximately 2% higher temporal precision and recall, while gold temporal relations raise causal accuracy to 91.9%.The gold-relation results indicate remaining room for improvement in the joint system.
5 Discussion
The joint framework improves both relations, but temporal gains are relatively small because causal links are sparse. Its temporal-precedence assumption also fails for future-oriented causal examples and other temporal interpretations are uncommon in the dataset.
- Temporal improvements are relatively small, although statistically significant, because newswire texts contain fewer causal links than temporal links.Many temporal links therefore lack corresponding causal links, diluting the gains from joint inference.
- For event pairs containing both temporal and causal links, joint inference synthesizes both signals and improves both relations.The comparison between Lines 5 and 3 in Table 4 is presented as a showcase of this effectiveness.
- Temporal and causal information are complementary because causality can encode broader prior knowledge while temporality often reflects local context.
- The system cannot handle cases where a cause follows its effect, as in future-oriented actions that anticipate later events.The authors connect these cases to projection into the future and suggest that event definitions need further investigation.
- The framework assumes that causes temporally precede effects, while alternative interpretations involving inclusion or simultaneity are rare in the dataset.The authors state that these alternatives could be incorporated into the framework.
6 Conclusion
The paper presents TCR, a joint framework for extracting temporal and causal relations, and introduces a jointly annotated dataset to evaluate it. The framework improves both components, supporting broader joint study of event relations.
- TCR uses constrained conditional models and integer linear programming to jointly extract temporal and causal relations between events.
- The authors develop a dataset with joint temporal and causal annotations to evaluate the proposed framework.
- The joint framework improves both temporal and causal extraction components.
- The authors hope the improvement encourages joint study of additional event properties, including sequencing, coreference, and parent-child relations.