Paul Roit, Aviv Slobodkin, Eran Hirsch, Arie Cattan, Ayal Klein, Valentina Pyatkin, Ido Dagan. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Various works suggest the appeal of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios.Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides a shallow and partial representation.More recently, an appealing trend introduces semi-structured natural-language structures as an intermediate meaning-capturing representation, often in the form of questions and answers.In this work, we further promote this line of research by considering three prior QA-based semantic representations.These cover verbal, nominalized and discourse-based predications, regarded here as jointly providing a comprehensive representation of textual informationtermed QASem.To facilitate this perspective, we investigate how to best utilize pre-trained sequence-to-sequence language models, which seem particularly promising for generating representations that consist of natural language expressions (questions and answers).In particular, we examine and analyze input and output linearization strategies, as well as data augmentation and multitask learning for a scarce training data setup.Consequently, we release the first unified QASem parsing tool, easily applicable for downstream tasks that can benefit from an explicit semi-structured account of information units in text.

Multi-text applications, such as multidocument summarization, are typically required to model redundancies across related texts.Current methods confronting consolidation struggle to fuse overlapping information.In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation.We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents.Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments.We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset.Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.

Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, Ido Dagan. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, Ido Dagan. Proceedings of the 28th International Conference on Computational Linguistics. 2020.

Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding.However, annotating discourse relations typically requires expert annotators.Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs.This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source widecoverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers.Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.