On the Hallucination in Simultaneous Machine Translation (2024)

Abstract: It is widely known that hallucination is a critical issue in Simultaneous Machine Translation (SiMT) due to the absence of source-side information. While many efforts have been made to enhance performance for SiMT, few of them attempt to understand and analyze hallucination in SiMT.Therefore, we conduct a comprehensive analysis of hallucination in SiMT from two perspectives: understanding the distribution of hallucination words and the target-side context usage of them.Intensive experiments demonstrate some valuable findings and particularly show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT.

## introduction in neural machine translation, hallucination occurrences are not common due to its small quantity @xcite @xcite . but in simultaneous machine translation (simt), it has been found that hallucination is extremely severe, especially as latency increases indicating that hallucination is a critical issue in simt. currently, most prior works concentrate on how to enhance model performance for simt @xcite @xcite @xcite , however, only a few of them measure the hallucination phenomenon @xcite @xcite . to our best knowledge, there are no researches which systematically analyze hallucination in simt. therefore, we conduct a comprehensive analysis of hallucinations in simt. initially, we seek to empirically analyze these hallucination words from the perspective of their distribution. we collect all hallucination words together and understand their frequency distribution, and we find that these words are randomly distributed with a high entropy: their entropy is almost as high as that for all target words. in addition, to delve into the contextual aspects of hallucination @xcite , we consider their predictive distribution. we discover that their uncertainty is significantly higher than that of nonhallucination words. furthermore, we find that the simt model does not fit the training data well for hallucination words due to the essence of simt (i.e., the limited source context), which explains why making correct predictions for hallucination words is difficult. intuitively, since a simt model is defined on top of a limited source context, this may indirectly cause the model to focus more on the target context and lead to the emergence of hallucination words. to verify this intuition, we propose to analyze the usage of the target context for hallucination words for simt. specifically, following @xcite al. (2019 2023 ), we firstly employ a metric to measure how much targetcontext information is used by simt with respect to the source-context information. with the help of this metric, we find that hallucination is indeed significantly more severe when the simt model focuses more on target-side information. drawing upon this, we reduce the over-targetreliance effects by introducing noise into the target-side context. experimental results show that the proposed method achieves some modest improvements in terms of bleu and hallucination effect when the latency is relatively small. this discovery gives us some inspiration: more flexible control over the use of target-side information may be a promising approach to alleviate the issue of hallucination. our key contributions are as follows: ## experimental settings our analysis is based on the most widely used simt models and datasets. this section introduces these models and datasets as follows. simt models and datasets. simt models translate by reading partial source sentences. @xcite proposed widely used wait-k models for simt. it involves reading k words initially and then iteratively generating each word until the end of the sentence. we conducted experiments on it. we use two standard benchmarks from iwslt14 de↔en @xcite and must-c release v2.0 @xmath0. appendix a provides detailed settings. due to space limitation, we only present the experimental results for the @xmath1. the results for @xmath2, as shown in appendix d and c. hallucination metric. in simt, @xcite pioneers the definition of hallucination metrics based on word alignment a. a target word ŷt , is a hallucination if there is no alignment to any source word x j . this is formally represented as: conversely, a target word ŷt , is not a hallucination if there is alignment to any source word x j . the hallucination rate (hr) is defined as following: hallucination if it does not align with the current source: ## understanding hallucination words from distribution hallucination is severe in simt. we measure hr of wait-k models, illustrated in ## analysis of target context usage for hallucination words to verify the hypothesis that using more on target-side context leads to the emergence of hallucination, we propose to analyze the usage of target-side context. measure on target-side context usage. to explicitly measure target context usage, we adapt an interpretive approach that evaluates the relevance of both target and source words. it involves deactivating connections between the corresponding words and the network. we compute the relevance between the words in the source or target and the next word to be generated and determine the maximum absolute relevance as source or target relevance. it allows us to calculate the target-side relevance to source-side relevance 's ratio (tssr). to begin with, we assess the relevance of targetside words and source-side words to the next word to be generated. this evaluation is conducted by selectively deactivating the connection between x j or y j and the encoder or decoder network in a deterministic manner, following the approach described in @xcite . more formally, the relevance r(y i , x j ) or r(y i , y j ) in wait-k is directly determined through the dropout effect on x j or y j , as outlined below: the relevance of the source-side and target-side is determined by selecting the maximum absolute value of the word's relevance on the current sourceside and the current target-side. formally, this can be expressed as: finally, the ratio of target-side relevance to source-side relevance(tssr) is calculated. a larger tssr indicates a higher usage of target-side context in generating the next word y i . ## related work in nmt, previous works have delved into the phenomenon of hallucinations @xcite @xcite @xcite . specifically, voita et al. (2021) assessed the relative contributions of source and target context to predictions. weng et al. (2020); miao et al. (2021) argued that an important reason for hallucination is the model's excessive attention to partial translations in nmt. furthermore, guerreiro et al. (2023) conducted a comprehensive study of hallucinations in nmt. differing from these works focusing on nmt, this paper conducts a comprehensive analysis of hallucination in simt. ## conclusions this paper conducts the first comprehensive analysis of hallucinations in simt from two perspectives: understanding the hallucination words from both frequency and predictive distributions and their effects on the usage of target-context information. intensive experiments demonstrate some valuable findings: 1) the frequency distribution of hallucination words is with high entropy and their predictive distribution is with high uncertainty due to the difficulty in memorizing hallucination words during training. 2) hallucination words make use of more target-side context than source-side context, and it is possible to alleviate hallucination by decreasing the usage of target-side context.