# Translating Pro-Drop Languages with Reconstruction Models

arXiv: https://arxiv.org/abs/1801.03257 · HTML: https://mlsanity.com/paper/1801.03257/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 14 of 14)

- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs](https://mlsanity.com/paper/2311.03127/) (2023)
- [BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2205.02471/) (2022)
- [A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models](https://mlsanity.com/paper/2305.01181/) (2023)
- [TranSmart: A Practical Interactive Machine Translation System](https://mlsanity.com/paper/2105.13072/) (2021)
- [Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling](https://mlsanity.com/paper/2307.08074/) (2023)
- [A Survey on Zero Pronoun Translation](https://mlsanity.com/paper/2305.10196/) (2023)
- [Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation](https://mlsanity.com/paper/2103.16189/) (2021)
- [A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech](https://mlsanity.com/paper/2106.03345/) (2021)
- [JP-TL-Bench: Anchored Pairwise LLM Evaluation for Bidirectional Japanese-English Translation](https://mlsanity.com/paper/2601.00223/) (2026)
- [Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases](https://mlsanity.com/paper/2403.04376/) (2024)
- [To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese](https://mlsanity.com/paper/2404.11315/) (2024)
- [How Much Do LLMs Know About Chinese Zero Pronouns?](https://mlsanity.com/paper/2605.31056/) (2026)
- [A Formal Limitation on Learning Human Language From Textual Corpora](https://mlsanity.com/paper/2608.28560/) (2026)

## Abstract (arXiv)

Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese-English and Japanese-English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs.