# The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing

arXiv: https://arxiv.org/abs/2305.15108 · HTML: https://mlsanity.com/paper/2305.15108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2010.03546/) (2020)
- [Modern Baselines for SPARQL Semantic Parsing](https://mlsanity.com/paper/2204.12793/) (2022)
- [XPrompt: Exploring the Extreme of Prompt Tuning](https://mlsanity.com/paper/2210.04457/) (2022)
- [The Power of Prompt Tuning for Low-Resource Semantic Parsing](https://mlsanity.com/paper/2110.08525/) (2021)
- [Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models](https://mlsanity.com/paper/2203.02652/) (2022)
- [PIZZA: A new benchmark for complex end-to-end task-oriented parsing](https://mlsanity.com/paper/2212.00265/) (2022)

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

- [InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations](https://mlsanity.com/paper/2511.02002/) (2025)

## Abstract (arXiv)

In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe that the query vocabulary is distinct from human vocabulary. Language Models (LMs) are pre-dominantly trained for human language tasks, and hence, if the query vocabulary is replaced with a vocabulary more attuned to the LM tokenizer, the performance of models may improve. We carry out carefully selected vocabulary substitutions on the queries and find absolute gains in the range of 17% on the GrailQA dataset.