# What can Neural Referential Form Selectors Learn?

arXiv: https://arxiv.org/abs/2108.06806 · HTML: https://mlsanity.com/paper/2108.06806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Designing and Interpreting Probes with Control Tasks](https://mlsanity.com/paper/1909.03368/) (2019)
- [What do Neural Machine Translation Models Learn about Morphology?](https://mlsanity.com/paper/1704.03471/) (2017)
- [Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information](https://mlsanity.com/paper/1808.08079/) (2018)
- [Neural data-to-text generation: A comparison between pipeline and end-to-end architectures](https://mlsanity.com/paper/1908.09022/) (2019)
- [Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction](https://mlsanity.com/paper/2004.08134/) (2020)
- [Probing for Bridging Inference in Transformer Language Models](https://mlsanity.com/paper/2104.09400/) (2021)
- [NeuralREG: An end-to-end approach to referring expression generation](https://mlsanity.com/paper/1805.08093/) (2018)
- [Referring Expression Generation Using Entity Profiles](https://mlsanity.com/paper/1909.01528/) (2019)

## Built on by (most cited first, 4 of 4)

- [Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems](https://mlsanity.com/paper/2203.08274/) (2022)
- [Models of reference production: How do they withstand the test of time?](https://mlsanity.com/paper/2307.14817/) (2023)
- [The Role of Explanatory Value in Natural Language Processing](https://mlsanity.com/paper/2209.06169/) (2022)
- [Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset](https://mlsanity.com/paper/2210.04828/) (2022)

## Abstract (arXiv)

Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency. We probed neural Referential Form Selection (RFS) models to find out to what extent the linguistic features influencing the RE form are learnt and captured by state-of-the-art RFS models. The results of 8 probing tasks show that all the defined features were learnt to some extent. The probing tasks pertaining to referential status and syntactic position exhibited the highest performance. The lowest performance was achieved by the probing models designed to predict discourse structure properties beyond the sentence level.