# Modeling Target-side Inflection in Placeholder Translation

arXiv: https://arxiv.org/abs/2107.00334 · HTML: https://mlsanity.com/paper/2107.00334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Guided Open Vocabulary Image Captioning with Constrained Beam Search](https://mlsanity.com/paper/1612.00576/) (2016)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Addressing the Rare Word Problem in Neural Machine Translation](https://mlsanity.com/paper/1410.8206/) (2014)
- [SYSTRAN's Pure Neural Machine Translation Systems](https://mlsanity.com/paper/1610.05540/) (2016)
- [Continuous Learning in Neural Machine Translation using Bilingual Dictionaries](https://mlsanity.com/paper/2102.06558/) (2021)
- [Modeling Target-Side Inflection in Neural Machine Translation](https://mlsanity.com/paper/1707.06012/) (2017)
- [Neural Name Translation Improves Neural Machine Translation](https://mlsanity.com/paper/1607.01856/) (2016)
- [Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation](https://mlsanity.com/paper/1704.04521/) (2017)
- [Improved English to Russian Translation by Neural Suffix Prediction](https://mlsanity.com/paper/1801.03615/) (2018)

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

- [DICTDIS: Dictionary Constrained Disambiguation for Improved NMT](https://mlsanity.com/paper/2210.06996/) (2022)

## Abstract (arXiv)

Placeholder translation systems enable the users to specify how a specific phrase is translated in the output sentence. The system is trained to output special placeholder tokens, and the user-specified term is injected into the output through the context-free replacement of the placeholder token. However, this approach could result in ungrammatical sentences because it is often the case that the specified term needs to be inflected according to the context of the output, which is unknown before the translation. To address this problem, we propose a novel method of placeholder translation that can inflect specified terms according to the grammatical construction of the output sentence. We extend the sequence-to-sequence architecture with a character-level decoder that takes the lemma of a user-specified term and the words generated from the word-level decoder to output the correct inflected form of the lemma. We evaluate our approach with a Japanese-to-English translation task in the scientific writing domain, and show that our model can incorporate specified terms in the correct form more successfully than other comparable models.