Chaining Event Spans for Temporal Relation Grounding (2024)

Abstract: Accurately understanding temporal relations between events is a critical building block of diverse tasks, such as temporal reading comprehension (TRC) and relation extraction (TRE). For example in TRC, we need to understand the temporal semantic differences between the following two questions that are lexically near-identical: “What finished right before the decision?” or “What finished right after the decision?”. To discern the two questions, existing solutions have relied on answer overlaps as a proxy label to contrast similar and dissimilar questions. However, we claim that answer overlap can lead to unreliable results, due to spurious overlaps of two dissimilar questions with coincidentally identical answers. To address the issue, we propose a novel approach that elicits proper reasoning behaviors through a module for predicting time spans of events. We introduce the Timeline Reasoning Network (TRN) operating in a two-step inductive reasoning process: In the first step model initially answers each question with semantic and syntactic information. The next step chains multiple questions on the same event to predict a timeline, which is then used to ground the answers. Results on the TORQUE and TB-dense, TRC, and TRE tasks respectively, demonstrate that TRN outperforms previous methods by effectively resolving the spurious overlaps using the predicted timeline.

## introduction understanding temporal relations is a challenging yet underexplored area in natural language processing @xcite @xcite . this challenge persists despite the prevalence of large language models (llms) @xcite , whose training processes lack grounding in timeline evidence. one example task requiring such evidence is temporal reading comprehension (trc), which requires to distinguish the temporal semantic difference between "what finished right before the decision?" and "what finished right after the decision?". to distinguish the two questions, existing solution for trc @xcite relies on overlaps between related questions as a weak supervision to ground the semantics of temporal relations. for example, in figure 1 , if we let the question's target event as x, q1 "what had started before x" and q2 "what happened before x" have similar semantics "before". subsequently, the two share the overlapping answer "sent". on the other hand, the temporal semantics of q2 and q3 "what happened while x" are different. so q2 does not have any common answer with q3. by using answer overlaps as a proxy label, existing work proposes a contrastive objective. it aims to pull the temporal relations in q1 and q2 closer together while broadening the distinction between q2 and q3. this method performs comparably with or outperforms baselines requiring stronger but expensive human-annotations @xcite , as shown in subsection 4.4. however, as illustrated in figure 2 , we argue that contrasting the evidence from answer overlaps misguide timeline as point-wise manner, leading to "spurious overlap". questions q3 and q4 "what happened while x" and "what probably ended after x", are temporally distinct but share answers "taken" and "bearing". in such cases, the pointwise timeline may fail to properly reason about the temporal meanings of the two questions. the timeline mistakenly pulls q3 and q4 closer, making the model insufficient to differentiate the complex temporal questions. the point-wise representation misses the timeline's inherent span-based nature. ## related work we overview state-of-the-art works on temporal relation understanding and graph networks. temporal relation understanding temporal relation understanding remains a challenging task even for large language models (llms) @xcite . this includes task types such as tre and trc. tre tasks @xcite are to categorize the temporal order into pre-defined categories. matres @xcite groups the temporal relations into 4 categories: before/after/simultaneous/vague. tb-dense @xcite ) considers 2 more classes, includes and is included. our proposed approach can benefit these tasks as we discuss in section 5. meanwhile, our main task is the trc task torque @xcite , requiring a temporal ordering in question form to reflect the realworld diversity of temporal relations. previous approaches to the trc task include continuous pre-training @xcite and question decomposition methods @xcite . econet @xcite continually pre-trains the model to inject the knowledge of temporal orders. question decomposition approaches @xcite divide the question into the event part and temporal relation expression part to better capture the complex semantics. all of the above use contrastive methods to understand different temporal relations, either by contrasting relations with human annotations @xcite or annotated answers @xcite . however, the former can be costly or imprecise, while the latter may rely on spurious problems. our distinction is the best of the two: no costly human annotation while avoiding spurious overlaps using span-based inductive reasoning. graph networks graph networks @xcite learn features through message passing on graph structures. these networks have demonstrated their effectiveness in tasks requiring complex reasoning skills, such as numerical reasoning @xcite and logical reasoning @xcite . graph networks also have been applied to tre @xcite @xcite , though their effectiveness in trc has not been investigated. ## proposed method we formulate predicting answers for a query q as a binary classification for every word p in the given passage p , determining whether it is an answer event to q foot_2 . our approach is to solve the task with the two steps of inductive reasoning. the core of inductive reasoning is inferring the whole picture from individual evidence. to transform the conventional function used in reading comprehension into the inductive form, we modify the function to consider answers to multiple questions together. the conventional one is denoted as âi = f (q i , p ; θ), where we answer ( âi ) the i-th question (q i ) in the passage with model θ. for inductive reasoning, the function is modified as: the overview of our model is in figure 3 . we first extract each answer ( âi ) as individual evidence in the evidence extraction step (subsection 3.1), represented as the output squares in (a). the inductive reasoning is elicited in the evidence chaining step (subsection 3.2). we chain the related question-answers ( â * ) depicted as paths of blue and red, marked with a dark background, and utilize them in (b). ## discussion while we empirically validated the effectiveness of trn, its implication and generalizability can be further clarified by the following discussion questions: ## limitations despite the promising results, there are some limitations to our approach. one limitation is that since we rely on the predicted timeline to mitigate spurious overlaps, it still has a chance of error. knowledge distillation or meta-learning could be applied in the future to remove the potential error. another limitation is that our main target, temporal reading comprehension, while a more realistic setting, is not commonly encountered in current nlp tasks. however, we argue that this is an important area that needs more active research, especially considering applications of nlp models in real-world and real-time scenarios. moreover, while our primary focus has been on advancing methods for temporal understanding, it is important to highlight that our approach extends beyond this specific domain such as logical and causal reasoning. these domains share a common thread of requiring inductive reasoning skills, demonstrating the applicability of our proposed method. for potential risks, our approach does not pose any significant risks. however, we note that our work utilizes plms so biases may exist in the models due to the nature of their training data.