# Argument Linking: A Survey and Forecast

arXiv: https://arxiv.org/abs/2107.08523 · HTML: https://mlsanity.com/paper/2107.08523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 79 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [A Span Selection Model for Semantic Role Labeling](https://mlsanity.com/paper/1810.02245/) (2018)
- [Implicit Argument Prediction with Event Knowledge](https://mlsanity.com/paper/1802.07226/) (2018)
- [Decomposing and Recomposing Event Structure](https://mlsanity.com/paper/2103.10387/) (2021)
- [Implicit Argument Prediction as Reading Comprehension](https://mlsanity.com/paper/1811.03554/) (2018)
- [Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards](https://mlsanity.com/paper/2010.05567/) (2020)

## Built on by (most cited first, 2 of 2)

- [On Event Individuation for Document-Level Information Extraction](https://mlsanity.com/paper/2212.09702/) (2022)
- [FAMuS: Frames Across Multiple Sources](https://mlsanity.com/paper/2311.05601/) (2023)

## Abstract (arXiv)

Semantic role labeling (SRL) -- identifying the semantic relationships between a predicate and other constituents in the same sentence -- is a well-studied task in natural language understanding (NLU). However, many of these relationships are evident only at the level of the document, as a role for a predicate in one sentence may often be filled by an argument in a different one. This more general task, known as implicit semantic role labeling or argument linking, has received increased attention in recent years, as researchers have recognized its centrality to information extraction and NLU. This paper surveys the literature on argument linking and identifies several notable shortcomings of existing approaches that indicate the paths along which future research effort could most profitably be spent.