# Great Service! Fine-grained Parsing of Implicit Arguments

arXiv: https://arxiv.org/abs/2106.02561 · HTML: https://mlsanity.com/paper/2106.02561/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Comprehensive Supersense Disambiguation of English Prepositions and Possessives](https://mlsanity.com/paper/1805.04905/) (2018)
- [A Transition-Based Directed Acyclic Graph Parser for UCCA](https://mlsanity.com/paper/1704.00552/) (2017)
- [Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution](https://mlsanity.com/paper/1905.10886/) (2019)
- [Implicit Argument Prediction with Event Knowledge](https://mlsanity.com/paper/1802.07226/) (2018)
- [SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA](https://mlsanity.com/paper/1903.02953/) (2019)
- [Content Differences in Syntactic and Semantic Representations](https://mlsanity.com/paper/1903.06494/) (2019)
- [Semantically Constrained Multilayer Annotation: The Case of Coreference](https://mlsanity.com/paper/1906.00663/) (2019)
- [Refining Implicit Argument Annotation for UCCA](https://mlsanity.com/paper/2005.12889/) (2020)

## Abstract (arXiv)

Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content. More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances. For example, in the web review "Great service!", the provider and consumer are implicit arguments of different types. We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies. Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset. We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models. This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.