Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach

Abstract: Direct speech translation (ST) models often struggle with rare words. Incorrect translation of these words can have severe consequences, impacting translation quality and user trust. While rare word translation is inherently challenging for neural models due to sparse learning signals, real-world scenarios often allow access to translations of past recordings on similar topics. To leverage these valuable resources, we propose a retrieval-and-demonstration approach to enhance rare word translation accuracy in direct ST models. First, we adapt existing ST models to incorporate retrieved examples for rare word translation, which allows the model to benefit from prepended examples, similar to in-context learning. We then develop a cross-modal (speech-to-speech, speech-to-text, text-to-text) retriever to locate suitable examples. We demonstrate that standard ST models can be effectively adapted to leverage examples for rare word translation, improving rare word translation accuracy over the baseline by 17.6% with gold examples and 8.5% with retrieved examples. Moreover, our speech-to-speech retrieval approach outperforms other modalities and exhibits higher robustness to unseen speakers. Our code is publicly available (https://github.com/SiqiLii/Retrieve-and-Demonstration-ST).

• The paper's main contribution is a retrieval-and-demonstration framework that leverages prepended examples to markedly improve rare word translation accuracy.
• The methodology adapts ST models to in-context learning by retrieving cross-modal examples, achieving up to 17.6% accuracy improvement with gold examples.
• The findings reveal a trade-off between rare word accuracy and overall translation quality, inviting further research on robust and efficient example integration.

Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach

The paper "Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach" addresses the persistent issue of accurately translating rare words in direct speech translation (ST) models. The researchers propose a novel retrieval-and-demonstration framework that significantly enhances the translation accuracy for rare words by leveraging previously translated examples.

Background and Challenges

Traditional speech translation approaches have evolved from cascading automatic speech recognition (ASR) and machine translation (MT) systems to direct ST models. The latter offers benefits such as lower inference latency and reduced error propagation. Despite these advantages, translating rare words like personal names and technical terminologies remain challenging due to the insufficient learning signals derived from their sparse occurrences in training data.

To address this, the researchers propose utilizing external resources such as translations from past recordings on similar topics. This method mimics the approach taken by human translators who frequently refer to existing translations for consistency, especially with rare or specialized terminology.

Proposed Framework

The proposed framework involves two primary components: adaptation of ST models to use examples and retrieval of appropriate examples during inference.

1. Model Adaptation: The ST models are adapted to incorporate examples in a manner akin to in-context learning. Specifically, the models are trained to prepend examples to the input sequence, allowing the translation process to be influenced by these demonstrations.
2. Example Retrieval: The paper introduces a cross-modal retriever capable of fetching suitable examples across speech-to-speech (S→S), speech-to-text (S→T), and text-to-text (T→T) modalities. This retriever is based on the Dense Passage Retriever (DPR) architecture but is adapted to handle the unique challenges of speech data, such as variability in pronunciation and longer sequence lengths.

Empirical Findings

The proposed method is evaluated using the MuST-C dataset for English-to-German speech translation, with a particular focus on rare words. Key findings include:

• Adapting ST models to leverage prepended examples significantly improves rare word translation accuracy. When using gold examples, an improvement of 17.6% over the baseline was observed, whereas retrieved examples led to an 8.5% improvement.
• Among the retrieval modalities, speech-to-speech retrieval demonstrated higher effectiveness and robustness to unseen speakers as measured by top-1 retrieval accuracy.
• The overall translation quality, measured by BLEU and COMET scores, showed slight degradation when prepending examples, indicating a trade-off between general translation quality and rare word accuracy.

Implications and Future Directions

This research has significant practical and theoretical implications. Practically, it demonstrates a feasible method to enhance the translation accuracy of rare words in real-world applications, such as live translation of scientific talks or international conferences. Theoretically, it extends the capabilities of in-context learning from text-based models to speech, showing that even conventional encoder-decoder ST models can benefit from demonstrations at inference time.

Future work could explore improving the robustness of the ST models to irrelevant examples, potentially through enhanced training methodologies that include noisy or incorrect examples. Additionally, addressing the challenge of increased inference latency due to longer input sequences will be crucial for practical deployment. Further exploration of chunk-based examples and adaptation of existing ST encoders for retrieval tasks also presents promising avenues for research.

In summary, the retrieval-and-demonstration approach provides a targeted solution to the specific problem of rare word translation in direct ST, advancing the state of the art and offering valuable insights for both researchers and practitioners in the field.