Learning Sentence-internal Temporal Relations
M. Lapata, A. Lascarides

TL;DR
This paper introduces a data-driven method for inferring internal sentence temporal relations by leveraging explicit temporal markers, demonstrating its effectiveness in temporal inference tasks and annotation creation.
Contribution
It presents a novel approach that uses temporal markers to train models for intra-sentential temporal relation inference, bypassing manual coding and aiding annotation.
Findings
Models trained on clauses with temporal markers perform well in pseudo-disambiguation tasks.
The approach can semi-automatically generate temporal annotations in existing corpora.
Probabilistic models with different features show varying effectiveness, validated against gold standards and humans.
Abstract
In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like after", which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
