Work

Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation

Year: 2023

Type: conference-paper

Abstract: Implicit discourse relation classification is a challenging task due to the absence of discourse connectives.To overcome this issue, we design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB.Specifically, our model jointly learns to generate discourse connectives between arguments and predict discourse relations based on the arguments and the generated connectives.To prevent our relation classifier from being misled by poor connectives generated at the early stage of training while alleviating the discrepancy between training and inference, we adopt Scheduled Sampling to the joint learning.We evaluate our method on three benchmarks, PDTB 2.0, PDTB 3.0, and PCC.Results show that our joint model significantly outperforms various baselines on three datasets, demonstrating its superiority for the task.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2023.acl-long.874

Authors: Wei Liu, Michael Strube

Institution: Heidelberg Institute for Theoretical Studies

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Klaus Tschira Stiftung

OpenAlex ID: W4385572205