# Adverbs, Surprisingly

arXiv: https://arxiv.org/abs/2305.19650 · HTML: https://mlsanity.com/paper/2305.19650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 68 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://mlsanity.com/paper/1907.04380/) (2019)
- [Structured Sentiment Analysis as Dependency Graph Parsing](https://mlsanity.com/paper/2105.14504/) (2021)
- [A task in a suit and a tie: paraphrase generation with semantic augmentation](https://mlsanity.com/paper/1811.00119/) (2018)
- [Question Answering as Global Reasoning over Semantic Abstractions](https://mlsanity.com/paper/1906.03672/) (2019)
- [Construction Grammar Provides Unique Insight into Neural Language Models](https://mlsanity.com/paper/2302.02178/) (2023)
- [Morphological Word Embeddings](https://mlsanity.com/paper/1907.02423/) (2019)
- [Representation biases in sentence transformers](https://mlsanity.com/paper/2301.13039/) (2023)
- [Unsupervised Semantic Frame Induction using Triclustering](https://mlsanity.com/paper/1805.04715/) (2018)

## Built on by (most cited first, 1 of 1)

- [Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion](https://mlsanity.com/paper/2403.20015/) (2024)

## Abstract (arXiv)

This paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing word meaning, as in FrameNet, provides a promising approach to adverb analysis, given its ability to describe ambiguity, semantic roles, and null instantiation.