Quantifying Information of Tokens for Simple and Flexible Simultaneous Machine Translation (2023)

Abstract: Simultaneous Translation (ST) involves translating with only partial source inputs instead of the entire source inputs, a process that can potentially result in translation quality degradation. Previous approaches to balancing translation quality and latency have demonstrated that it is more efficient and effective to leverage an offline model with a reasonable policy. However, using an offline model also leads to a distribution shift since it is not trained with partial source inputs, and it can be improved by training an additional module that informs us when to translate. In this paper, we propose an Information Quantifier (IQ) that models source and target information to determine whether the offline model has sufficient information for translation, trained with oracle action sequences generated from the offline model. IQ, by quantifying information, helps in formulating a suitable policy for Simultaneous Translation that better generalizes and also allows us to control the trade-off between quality and latency naturally. Experiments on various language pairs show that our proposed model outperforms baselines.

## introduction simultaneous translation (st) @xcite is a setting that employs incremental translation as the source input is being received, unlike conventional machine translation (mt) @xcite which translates using full source sentences, providing a sufficient context for high-quality translation. despite its invaluable potential in numerous real-world scenarios, st poses a significant challenge as the translation model may not always have access to sufficient source context, particularly under low latency conditions. in the pursuit of achieving simultaneous translation (st), a multitude of methods have been proposed for the training of online models, employing either fixed policies (i.e., wait-k) @xcite @xcite , or adaptive policies @xcite @xcite @xcite . regardless, the training of a dedicated online model for st often requires calibration of diverse factors to control latency, such as the count of reading windows (i.e., k), and latency weight. this typically induces the training of multiple models, thereby incurring high computational costs. while it is possible to consider multiple latency regimes within a single model @xcite , it does not account for the correlation between different latency conditions @xcite . in recent research, @xcite showed the effectiveness of directly deploying an offline model with a suitable decision policy for st. their promising results demonstrate that we can attain superior performance without having to depend on online models that are trained using incomplete inputs. despite their promising results, it is apparent that employing the offline model directly will suffer from a distributional shift caused by the partial source sentences that were not encountered during the training time. one previous work @xcite has alleviated it by training a policy to predict optimal translation points, we empirically found that such an approach struggles to generalize effectively when faced with unseen source sentences. to this end, we propose information quantifier (iq) which models source and target information based on the given oracle action sequences. iq is capable of quantifying the information contained within the source/target sentences, thereby guiding read/write decisions across diverse latency offline decoding schauen sie nach mi@@ gu@@ el , bauern wie mi@@ gu@@ el . <@xmath0, farmers like mi@@ gu@@ el . <@xmath1, bauern wie figure 1 : example of oracle action sequences generation as suggested by ssmt @xcite . it assumes that write (w) is the right action to do when the decoder with partial source sentence (online decoding above) produces the same target token as the decoder with full source sentence (offline decoding above), and read (r) otherwise. through experiments across various language pairs, we demonstrate that iq, despite its straightforward usage, delivers notable performance improvement over a number of baselines. offline model with decision policy recent studies @xcite demonstrate the efficiency and effectiveness of applying predefined or learned policy to an offline model for simultaneous speech translation, as opposed to training online models. predefined policies such as wait-k @xcite , wait-k-stride-n @xcite , sp-n (shared prefix) @xcite , la-n (local agreement) policy @xcite can be applied to the offline model for st. additionally, @xcite incorporates a policy that takes into account the attention weights of the most recent source tokens. @xcite suggested learning a policy model separately using oracle action sequences. we follow the same process to generate oracle action sequences. however, instead of training a policy to directly predict the actions, we introduce information quantification for decision policy which subsequently enhances the generalization capabilities of the model. in contrast to previous methods that quantify information @xcite @xcite based on heuristic policies such as the wait-k policy or crossattention values within the online model learning framework, our approach strategically aligns information learning with the action sequences generated by the oracle policy, which is entirely independent of the translation learning pipeline. ## background offline and online decoding we denote the source tokens as @xmath2, . . . , x m ) ∈ x and the generated target tokens as @xmath3, . . . , y n ) ∈ y . offline decoding uses full-sentence inputs for training, with the greedy target token at a time step t defined as: as shown in figure 1 , @xcite ) finds a near-optimal oracle action sequence by defining the optimal segment. it is the point when the target token in offline decoding (i.e., generating with complete source inputs) and the target token in online decoding (i.e., generating with incomplete source inputs) are the same. we used the same process to get oracle action sequences, primarily owing to its straightforwardness and efficiency. however, it should be noted that our proposed method can be integrated with any other oracle action sequences such as @xcite . ## propose method in this section, we introduce information quantifier (iq), which quantifies the information in both source and target sentences to make the right read/write decisions. based on oracle read/write action sequences of training parallel corpus (e.g., @xcite ), we train iq with a novel training objective in the following subsections. ## analysis we conducted additional experiments and analyses to better understand how our method works and show improvements. all analyses are based on either the iwslt14 @xmath4. ## conclusion in this paper, we introduced a novel framework of training and inferencing with information quantifier (iq) for simultaneous translation (st) by using oracle action sequences. we demonstrated that iq exhibits high performance despite its simplicity and flexibility, being able to adapt to various latency regimes with a single model.