Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies have revealed a number of pathologies of neural machine translation systems. |
| Approach: | They propose to use maximum a posteriori decoding to identify the highest-scoring translation, i.e. the mode problem, to validate the model and its training algorithm. |
| Outcome: | The proposed model reproduces the statistical data well, but the beam search strays from the statistics. |
Similar Papers
Quality-Aware Decoding for Neural Machine Translation (2022.naacl-main)
Copied to clipboard
Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, Andre Martins
| Challenge: | Despite advances in machine translation quality estimation and evaluation, decoding is mostly oblivious to this. |
| Approach: | They propose to use a decoding framework that is quality-aware for neural machine translation . they compare various methods like N-best reranking and minimum Bayes risk decoding . |
| Outcome: | The proposed quality-aware decoding outperforms MAP-based decoding on four datasets and two model classes. |
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words. |
| Approach: | They propose to use minimum bayes risk decoding instead of beam search to investigate the effects of beam decoding on unbiased samples. |
| Outcome: | The proposed method improves on a number of previously reported biases and failure cases of beam search on unbiased samples. |
Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve beam search quality are inadequate in many ways . a new approximation to the beam search curse has been proposed . |
| Approach: | They propose an approximation to minimum Bayes risk decoding that would solve the beam search curse. |
| Outcome: | The proposed approximation has no equivalent to the beam search curse. |
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space (2022.emnlp-main)
Copied to clipboard
| Challenge: | Current evaluation of neural machine translation systems is limited by one best hypothesis and search errors brought by heuristic decoding algorithms. |
| Approach: | They propose a new evaluation protocol which defines model errors with model’s ranking capability over hypothesis space and Monte Carlo sampling evaluation to tackle the problem of exponentially large space. |
| Outcome: | The proposed evaluation protocol is consistent with what is currently used in the field and is consistent to what is being proposed. |
Revisiting Negation in Neural Machine Translation (2021.tacl-1)
Copied to clipboard
| Challenge: | Negation is an important linguistic phenomenon in machine translation, as errors in translating negation may change the meaning of source sentences completely. |
| Approach: | They evaluate the translation of negation in English–German (EN–DE) and English– Chinese (EN-ZH) . they find that NMT models can distinguish negation and non-negation tokens very well and encode a lot of information about negation . |
| Outcome: | The accuracy of manual evaluation in ENDE, DEEN, ENZH, and ZHEN is 95.7%, 94.8%, 93.4%, and 91.7% respectively. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
Copied to clipboard
Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
On Search Strategies for Document-Level Neural Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Document-level neural machine translation models produce a more consistent output across a document . however, the exact decoding strategy is often not described and not mentioned at all. |
| Approach: | They propose to use standard automatic metrics and specific linguistic phenomena to compare different decoding schemes. |
| Outcome: | The proposed decoding strategies perform similar to each other on three standard document-level translation benchmarks. |
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |
On NMT Search Errors and Model Errors: Cat Got Your Tongue? (D19-1)
Copied to clipboard
| Challenge: | We show that at the root of the problem of empty translations lies an inherent bias towards shorter translations. |
| Approach: | They propose an exact inference procedure for neural sequence models based on beam search and depth-first search. |
| Outcome: | The proposed procedure finds that beam search fails to find the best model scores . the results show that the model often prefers an empty translation . |
Dynamic Oracle for Neural Machine Translation in Decoding Phase (L18-1)
Copied to clipboard
| Challenge: | Existing methods to improve NMT performance but there is a discrepancy between training and inference when decoding. |
| Approach: | They propose to use Scheduled Sampling to reduce the discrepancy between training and inference in NMT when decoding to mitigate the discrépancy. |
| Outcome: | The proposed methods improve translation quality over standard NMT system. |