Source-linked AI summary
Can neural machine translation do simultaneous translation?
Kyunghyun Cho, Masha Esipova
TL;DR
The paper asks whether attention-based neural machine translation can support simultaneous translation, where output begins before the full source sentence is received. It introduces simultaneous greedy decoding for an existing neural translation model, jointly handling segmentation and translation, and finds that the approach can trade translation delay against quality. The work is presented as an initial step toward a full neural simultaneous translation system.
Problem
Simultaneous translation must generate target symbols before the full source sentence arrives while balancing translation quality and delay.
Method
Simultaneous greedy decoding jointly performs segmentation and translation with an existing neural machine translation model, conditioning each segment on preceding segments.
Results
The approach can use a neural machine translation model trained for consecutive translation and provides a controllable trade-off between translation delay and quality.
Takeaways & Limitations
The findings support using existing neural machine translation models for simultaneous translation and motivate further development of full neural simultaneous systems.
Takeaways & Limitations
The waiting criteria are manually designed and do not exploit the recurrent networks’ hidden-state information; a trainable criterion is expected to improve delay and quality.
Abstract
from arXiv · showhide
We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segment is strongly conditioned on all the previous segments. This paper presents a first step toward building a full simultaneous translation system based on neural machine translation.
1 Introduction
The paper addresses simultaneous translation with neural machine translation by introducing a decoding algorithm that jointly segments and translates incoming source text. It evaluates this approach as a way to trade translation quality against delay while preserving conditioning on preceding segments.
- Simultaneous translation must balance translation quality with delay because target symbols are generated before the full source sentence arrives.
- Conventional approaches separate source segmentation from phrase translation, with limited information exchange between the two stages.
- Simultaneous greedy decoding jointly performs segmentation and translation using conditional probabilities from an existing neural machine translation model.
- Each translated segment is fully conditioned on all preceding segments through the recurrent network’s hidden states.
- Evaluation covers En-Cs, En-De, and En-Ru, and the algorithm provides a way to control the trade-off between translation quality and delay.
- The work is presented as a first step toward a full neural simultaneous translation system, with future work targeting trainable waiting criteria and dedicated learning algorithms.
2 Attention-based Neural Translation
The paper describes attention-based neural translation as an encoder-decoder model with an attention mechanism, then provides the setup for simultaneous greedy decoding. The model encodes source context, updates the decoder state, and computes the next target-word distribution.
- Attention-based neural translation combines an encoder, decoder, and attention mechanism.
- The encoder reads a source sentence and returns a set of context vectors for the decoder.
- The encoder may use a unidirectional recurrent network, bidirectional recurrent network, or tree-based recursive network.
- At each decoder step, attention computes a time-dependent vector from the context vectors, which updates the decoder’s recurrent state.
- The simultaneous greedy decoding algorithm requires δ, s0, an input pipe, and an output pipe, and initializes the decoder state from the context representation.
3 Simultaneous Greedy Decoding
Simultaneous greedy decoding adapts a trained neural machine translation model to incremental input by jointly deciding when to wait and when to emit target symbols. Its parameters and waiting criteria expose a controllable quality–delay trade-off, with worst-case computational cost bounded relative to consecutive greedy decoding.
- Algorithm: Simultaneous greedy decoding uses a trained neural machine translation model to translate before the full source sentence arrives.The algorithm is introduced as a decoding procedure rather than a new model or training algorithm.
- Delay in Translation: The step size δ and initial read size s0 control the trade-off between translation delay and quality.Delay is defined from the amount of source context required for each decoded target symbol; full and word-by-word translation provide reference cases.
- Algorithm: The algorithm reads an initial source prefix, then incrementally adds δ symbols while maintaining current and additional context vectors.Its state tracks received source words, committed target words, context sets, and the decoder hidden state.
- Computational Complexity: The method has twice the worst-case complexity of consecutive greedy decoding, but its practical cost decreases as the waiting criterion is satisfied more often.The paper also leaves multi-symbol, less-greedy decoding for future work because it would greatly increase per-step computation.
- Translation Core: At each step, it either waits for more source context or commits the most likely target symbol based on the available context.After the full source is read, it performs ordinary greedy decoding; otherwise, a waiting criterion controls the decision.
- Waiting Criteria Λ: Wait-If-Worse compares prediction confidence across source prefixes, whereas Wait-If-Diff waits when the most likely target symbol changes.The latter distinguishes a changed best prediction from a merely reduced probability for the same prediction.
4 Related Work
Earlier simultaneous translation systems commonly separated source segmentation from phrase translation, often using speech recognition and external translation systems. The paper situates its approach as a neural alternative in which segmentation and translation are integrated, while Figure 1 compares quality and delay across strategies.
- Prior Approaches: Prior simultaneous translation work largely focused on speech translation, transcribing incoming speech before segmenting it into translation units.Segmentation commonly relied on acoustic or linguistic cues such as silence or punctuation.
- Trainable Policies: Trainable segmentation policies extend this setup by querying an underlying translation system and learning when to commit intermediate translations.One cited approach also predicts final verbs for verb-final languages using a separate verb-conditioned language model.
- Comparison: Figure 1 plots BLEU against delay τ for Wait-If-Worse, Wait-If-Diff, and consecutive greedy or beam-search decoding in both translation directions.Lower delay and higher BLEU are preferred, with dashed lines linking matching δ and s0 settings across directions.
5 Experimental Settings
The experiments evaluate simultaneous greedy decoding across three English-centered language pairs and both directions using neural translation models trained on WMT’15 data. They vary decoding parameters, report BLEU and delay, and compare against consecutive decoding while documenting model-quality limitations.
- Tasks and Corpora: The evaluation covers En-Cs, En-De, and En-Ru in both directions, using 12.12m, 4.15m, and 2.32m sentence pairs respectively.The corpora were drawn from WMT’15 parallel data.
- Tasks and Corpora: Training and evaluation use BPE-segmented sentences limited to 50 subword symbols, with newstest-2013 for validation and newstest-2015 for testing.The validation set supports early stopping and development evaluation, while the test set checks model training quality.
- Translation Models: The study trains six separate neural translation models, one for each language-pair direction.The models use recurrent encoder–decoder architectures with attention and are trained until validation performance stops improving.
- Translation Models: The trained models do not reach state-of-the-art translation quality because they omit ensemble techniques and monolingual data.Within those constraints, the paper reports that they translate comparably to earlier models such as Firat et al. (2016).
- Decoding Parameters: Experiments vary δ ∈ {1,2,3} and s0 ∈ {2,3,4,5,6,7}, reporting both BLEU and delay τ.Consecutive greedy and beam-search decoding are included as quality references, with beam width 5.
6 Quantitative Analysis
The experiments show a quality–delay trade-off across simultaneous translation settings, with substantial variation across language pairs and waiting criteria. Wait-If-Worse generally favors quality and lower variance at the cost of delay, while Wait-If-Diff covers a broader range of trade-offs.
- Wait-If-Worse tends to achieve better translation quality but substantially higher delay than Wait-If-Diff.
- The quality–delay trade-off appears across criteria, but its strength and direction vary substantially among German, Czech, and Russian.
- Russian-to-English translation does not improve translation quality over consecutive translation, but generally has lower delay than English-to-Russian translation.
- Translation to English generally reduces delay relative to translation from English, except for Czech with Wait-If-Worse.
- Wait-If-Diff spans a wider range of delay and quality, whereas Wait-If-Worse produces more delayed translation with less quality variance using the same decoding parameters.
- Wait-If-Worse is more sensitive to s0, Wait-If-Diff is more sensitive to δ, and delay patterns remain invariant to the criterion.
7 Qualitative Analysis
Qualitative analyses illustrate how the waiting criteria shape simultaneous translations and expose characteristic errors. Wait-If-Worse is more conservative, while Wait-If-Diff can produce phrase repetition and premature grammatical commitments.
- The quality-to-delay ratio Q2D combines BLEU with average corpus delay to select models favoring high quality and low delay.
- Across English-to-target examples, Wait-If-Worse is more conservative than Wait-If-Diff.
- In English-to-Russian examples, Wait-If-Worse is much more conservative than the other criterion.
- Wait-If-Diff can repeat translated words or phrases, including “также эффект” twice and “не означает” three times.
- The authors conjecture that repetition reflects Wait-If-Diff failing to account for decreased prediction confidence as more source context arrives.
- Premature adjective commitments in English-to-Russian translation can cause agreement errors, such as translating singular “photography” as plural “фотографии”.
- In Russian-to-English translation, Wait-If-Worse remains more conservative, while Wait-If-Diff more often produces repeated phrases; similar behaviors appear across other language pairs.
8 Discussion and Future Research
The proposed approach shows that an existing neural machine translation model can support simultaneous translation while allowing users to trade translation delay against quality. However, its manually designed waiting criteria and training mismatch motivate trainable waiting and simultaneous-specific learning methods.
- Discussion: An existing neural machine translation model trained without simultaneous translation can serve as the basis for simultaneous machine translation.
- Discussion: The simultaneous greedy decoding algorithm uses adjustable parameters to smoothly control the trade-off between translation delay and quality.The parameters are the initial source-symbol count s0 and step size δ.
- Discussion: The trade-off depends heavily on the waiting criterion, with Wait-If-Worse sensitive to s0 and Wait-If-Diff sensitive to δ.The paper reports markedly opposite behaviors for the two criteria and associates their differing sensitivities with qualitative differences.
- Future Research: The manually designed waiting criteria do not exploit information in recurrent-network hidden representations, motivating trainable criteria.The authors expect a hidden-state-based trainable criterion to improve both delay and quality.
- Future Research: The tested translation models were not trained for simultaneous translation, creating a mismatch between decoder context during training and testing.The paper calls for a training procedure that addresses this mismatch and is evaluated.