# The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning

arXiv: https://arxiv.org/abs/2305.12737 · HTML: https://mlsanity.com/paper/2305.12737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://mlsanity.com/paper/2102.01454/) (2021)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Scaling Neural Machine Translation](https://mlsanity.com/paper/1806.00187/) (2018)
- [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://mlsanity.com/paper/2008.09335/) (2020)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex](https://mlsanity.com/paper/2301.12868/) (2023)
- [AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data](https://mlsanity.com/paper/2010.04806/) (2020)
- [Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation](https://mlsanity.com/paper/2302.08079/) (2023)
- [On Robustness of Neural Semantic Parsers](https://mlsanity.com/paper/2102.01563/) (2021)
- [Bootstrapping a Crosslingual Semantic Parser](https://mlsanity.com/paper/2004.02585/) (2020)
- [Phrase-level Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2106.11375/) (2021)
- [Merging Weak and Active Supervision for Semantic Parsing](https://mlsanity.com/paper/1911.12986/) (2019)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
- [Paraphrasing Techniques for Maritime QA system](https://mlsanity.com/paper/2203.10854/) (2022)
- [Active Learning for Multilingual Semantic Parser](https://mlsanity.com/paper/2301.12920/) (2023)
- [Learning Cross-lingual Distributed Logical Representations for Semantic Parsing](https://mlsanity.com/paper/1806.05461/) (2018)

## Built on by (most cited first, 3 of 3)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Models](https://mlsanity.com/paper/2406.10882/) (2024)
- [DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement](https://mlsanity.com/paper/2506.15583/) (2025)

## Abstract (arXiv)

Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem. Prior works propose to utilize the translations by either humans or machines to alleviate such issues. However, human translations are expensive, while machine translations are cheap but prone to error and bias. In this work, we propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set. Besides, we propose novel aggregated acquisition criteria that help our active learning method select utterances to be manually translated. Our experiments demonstrate that an ideal utterance selection can significantly reduce the error and bias in the translated data, resulting in higher parser accuracies than the parsers merely trained on the machine-translated data.