DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
Authors: Xinglin Lyu, Tang wei, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min zhang
Accepted at: acl25

DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation

Xinglin Lyu*, Wei Tang*, Yuang Li*, Xiaofeng Zhao*, Ming Zhu*, Junhui Li*, Yunfei Lu*, Min Zhang*, Daimeng Wei*, Hao Yang*, Min Zhang*
*Huawei Translation Services Center, Beijing, China
†Huawei Consumer Business Group, Beijing, China
‡School of Computer Science and Technology, Soochow University, Suzhou, China
{lvxinglin1,tangwei133,zhangming186,yanghao30}@huawei.com
{lijunhui,minzhang}@suda.edu.cn

Abstract

Document-level context is crucial for handling discourse challenges in text-to-text document-level machine translation (MT). Despite the increased discourse challenges introduced by noise from automatic speech recognition (ASR), the integration of document-level context in speech translation (ST) remains insufficiently explored. In this paper, we develop DoCIA, an online framework that enhances ST performance by incorporating document-level context. DoCIA decomposes the ST pipeline into four stages. Document-level context is integrated into the ASR refinement, MT, and MT refinement stages through auxiliary LLM (large language model)-based modules. Furthermore, DoCIA leverages document-level information in a multi-level manner while minimizing computational overhead. Additionally, a simple yet effective determination mechanism is introduced to prevent hallucinations from excessive refinement, ensuring the reliability of the final results. Experimental results show that DoCIA significantly outperforms traditional ST baselines in both sentence and discourse metrics across four LLMs, demonstrating its effectiveness in improving ST performance.1

1 Introduction

Speech translation (ST) involves translating spoken language into written text in a different language. Despite significant progress in recent years (Zhang et al., 2019a; Sperber and Paulik, 2020; Ye et al., 2021; Fang et al., 2022; Lei et al., 2023), incorporating document-level context into ST remains a major challenge due to the cross-modal nature of the task. This paper shifts the focus to document-level context2 and examines how it can enhance machine translation (MT) when combined with automatic speech recognition (ASR) in the cascaded ST systems.

In a traditional cascaded ST system, as shown in Figure 1 (a), the ASR and MT models operate independently at the segment level. This leads to significant discourse-level issues due to the absence of inter-sentence context. These challenges become even more pronounced in ST, where ASR errors—such as misrecognizing entity pronouns or handling disfluencies—further complicate the translation process. Incorporating document-level context offers two key advantages: first, it can potentially correct ASR transcription errors by providing a broader understanding of the context; second, when integrated into the MT model, it helps address discourse phenomena such as entity inconsistencies, coreference resolution, and long-range dependencies (Sennrich et al., 2016; Zhang et al., 2018; Bao et al., 2021, 2023; Lyu et al., 2024). To fully leverage document-level context, we introduce DoCIA—Document-level Context Incorporation Agent—an online framework specifically designed to improve ST performance by incorporating document-level context.

![The traditional cascade-based ST system (top) and our proposed DoCIA for ST (bottom). Differently, DoCIA introduces two refinement stages and is LLM-based and context-aware when translating i-th audio segment in a speech.](page_1012_370_377_246.png)

Figure 1: The traditional cascade-based ST system (top) and our proposed DoCIA for ST (bottom). Differently, DoCIA introduces two refinement stages and is LLM-based and context-aware when translating \( i \)-th audio segment in a speech.

1 Corresponding author: Hao Yang.
2 Code is available at https://github.com/xllyu-nlp/DoCIA
3 Also referred to as inter-segment context in ST.
End-to-end ST systems, which directly translate source-language speech into target-language text, can reduce the propagation of ASR errors. However, these systems suffer from limited interpretability and the challenge of scarce parallel ST data, making it expensive to develop a reliable and effective end-to-end solution. In contrast, the cascading approach—especially with the emergence of powerful large language models (LLMs) (OpenAI, 2023; Google, 2023; Dubey et al., 2024)—provides a more efficient and flexible alternative. The cascading model enables modular optimization in ST, allowing LLMs to be used to enhance performance at various stages of the process. As shown in Figure 1 (b), DoCIA takes full advantage of the scalability and flexibility inherent in the cascading approach by breaking the ST process into four key stages: ASR, ASR refinement, MT, and MT refinement. Document-level context is incorporated during the latter three stages (ASR refinement, MT, and MT refinement), improving both transcription and translation through LLM-based agents. Crucially, the document-level context is updated online as each segment is processed, ensuring that the context remains current and relevant throughout the translation process.

In addition, we employ two techniques — a multi-level context integration strategy and a refinement determination mechanism — to enhance the performance of DoCIA. First, while document-level context can be beneficial, it often includes redundant information, with only a small portion being relevant to discourse issues (Kang et al., 2020). Using all available context indiscriminately can even degrade ST performance and increase computational overhead. To address this, we propose a multi-level context integration strategy that retains the advantages of document-level context while reducing redundancy. Second, our two refinement stages are designed to resolve inter-segment inconsistencies using document-level context. In most cases, minimal adjustments are sufficient to address discourse-related issues, as extensive changes may introduce errors such as hallucinations or semantic distortions. To minimize these risks, we introduce a determination mechanism that ensures the refined text remains consistent with the original semantics, improving the output without introducing undesirable changes.

Overall, the main contributions of this paper are summarized as follows:

• We extend cascaded ST to four stages and introduce DoCIA, an online agent that enhances ST by progressively incorporating document-level context at each text-to-text stage.

• We propose two techniques to enhance DoCIA: a multi-level document context integration strategy that selectively incorporates context, and a simple determination mechanism to prevent hallucinations during refinement.

• We validate DoCIA across five ST directions using four LLMs, including both closed- and open-source models, highlighting the importance of document-level context in ST.

2 DoCIA: Document-level Context Incorporation Agent

We propose DoCIA, an online agent designed to enhance speech translation (ST) by effectively leveraging document-level context. DoCIA operates in a cascaded four-stage process: ASR, ASR refinement, translation, and translation refinement. Document-level context is incorporated during the ASR refinement, translation, and translation refinement stages (Section 2.1). To optimize context utilization, we introduce a multi-level integration strategy, splitting the context into short- and long-memory components (Section 2.2). To prevent hallucinations during refinement, we also propose an effective determination mechanism (Section 2.3).

2.1 Overview of DoCIA

Given a speech \( \mathcal{A} = \{a_1, a_2, \cdots, a_N\} \), which consists of \( N \) audio segments, DoCIA translates these segments sequentially. The overview of DoCIA is illustrated in Figure 2. To explain the translation process, let us consider the \( i \)-th audio segment \( a_i \), as an example. The translation process in DoCIA involves four key stages, which produce the following outputs for \( a_i \): the draft ASR result \( \bar{s}_i \), the refined ASR result \( s_i \), the draft translation \( \bar{t}_i \), and the final refined translation \( t_i \).

ASR Stage. First, DoCIA generates the draft transcription \( \bar{s}_i \) of \( a_i \) using an ASR model:

\[
\bar{s}_i = \text{ASR}(a_i).
\]

Here, ASR refers to the ASR model. Note that in this stage we obtain draft transcription at the segment level.
Figure 2: The overall illustration of DoCIA when translating i-th audio segment in a speech. The blue, purple and red lines denote the context retrieving, refinement determining and context updating processes, respectively.

ASR Refinement Stage. In this stage, DoCIA aims to correct errors in the draft transcription \( \bar{s}_i \) and enhance its quality by incorporating document-level ASR context, denoted as \( C_{asr} = (s_1, \cdots, s_{i-1}) \). DoCIA uses an LLM to obtain the refined transcription \( s_i \) via:

\[
s_i = \argmax p(s_i|\bar{s}_i, inst_{ar}, C_{asr}, \theta_{llm}),
\]

where \( inst_{ar} \) represents the instruction for the context-aware ASR refinement task.\footnote{Details of the instructions used in DoCIA can be found in Appendix A.} \( C_{asr} \subseteq C_{asr} \) is the selected document-level context, which is determined using the strategy described in Section 2.2. The parameter \( \theta_{llm} \) refers to the parameters of the LLM.

Translation Stage. In this stage, DoCIA similarly uses the LLM to translate the transcription \( s_i \) while incorporating both the source-side and target-side document-level context \( C_{asr} \) and \( T_{tr} = (t_1, \cdots, t_{i-1}) \), resulting in the draft translation \( \bar{t}_i \) for the audio segment \( a_i \). This process is expressed as follows:

\[
\bar{t}_i = \argmax p(\bar{t}_i|s_i, inst_{mt}, C_{tr}, \theta_{llm}),
\]

where \( inst_{mt} \) represents the instruction for the context-aware translation task. The document-level context for translation, \( C_{tr} \), combines the source-side context \( C_{asr} \) and the target-side context \( T_{tr} \) (also referred to as inter-segment translation history). \( T_{tr} \) contains the corresponding refined translations of the segments in \( C_{asr} \).

Translation Refinement Stage. In this stage, we further leverage document-level context to improve the translation through a translation refinement process. Unlike the initial translation stage, where the focus is on generating a translation, the goal here is to enhance the word choice in the draft translation and ensure better cohesion and coherence with the preceding translation history. This process mimics the correction preferences typically applied by human translators. DoCIA again uses the LLM to perform the refinement. Given the draft translation \( \bar{t}_i \) for the i-th audio segment \( a_i \), DoCIA refines it by incorporating document-level context. The refinement process is expressed as follows:

\[
t_i = \argmax p(t_i|s_i, inst_{tr}, \bar{t}_i, C_{tr}, \theta_{llm}),
\]

where \( inst_{tr} \) denotes the instruction for the context-aware translation refinement task, \( C_{tr} \) is the document-level context, the same as used in Eq. 3. The result, \( t_i \), is the final, refined translation of \( a_i \).

Once the process of \( a_i \Rightarrow \bar{t}_i \) is finished, all document-level context used in various stages are immediately updated online and will be incorporated into the process of \( a_{i+1} \Rightarrow \bar{t}_{i+1} \):

\[
C_{asr} \Rightarrow C_{asr} = \{s_1, \cdots, s_{i-1}, \underline{s_i}\}
\]
\[
T_{tr} \Rightarrow T_{tr} = \{t_1, \cdots, t_{i-1}, \underline{t_i}\}
\]

2.2 Multi-Level Context Integration

The translation of different source sentences requires varying amounts of context (Kang et al., 2020), and the most relevant context for a given segment should be both dynamic and limited in scope. Therefore, using all preceding transcripts and translations as \( C_{asr} \) and \( T_{tr} \) may be less effective when translating the i-th segment, \( a_i \). To address this limitation, we propose a multi-level context, which
consists of two components: a short-term context and a long-term context. The multi-level context has a fixed window size \( L = m + n \), where \( m \) and \( n \) represent the number of segments included in the short-term and long-term contexts, respectively.

Short-Memory Context. Related studies (Zhang et al., 2018; Maruf et al., 2019) have shown that adjacent sentences are effective in addressing intersentence issues during translation. Hence, we define the short-memory context as the \( m \) preceding transcript segments of \( a_i \) along with their corresponding translations. Specifically, when translating \( a_i \), the short-memory context consists of the following: the \( m \) preceding transcript segments \( C_{asr}^s = \{ s_{i-m}, \cdots, s_{i-1} \} \) and their corresponding translations \( T_{tr}^s = \{ t_{i-m}, \cdots, t_{i-1} \} \).

Long-Memory Context. Some clues for alleviating inter-segment issues may lie in a longer memory window (i.e., a window size greater than \( m \)), which makes relying solely on the short-memory context insufficient. To address this, we propose incorporating a long-memory context consisting of \( n \) transcript segments and the corresponding translations. More specifically, the transcripts and translation in long-memory context are retrieved all preceding segments, except those already included in the short-memory context:

\[
C_{asr}^l = f(q_i, \mathcal{C}, n),
\]

where \( \mathcal{C} = \{ s_1, \cdots, s_{i-m} \} \) represents the set of transcripts preceding the short-memory context and \( f \) is a retrieval function. Given a query \( q_i \), \( f \) returns the top \( n \) matching transcript segments from \( \mathcal{C} \), forming \( C_{asr}^l \). During ASR refinement, \( q_i \) is set to \( \bar{s}_i \) while during translation and translation refinement, \( q_i \) is set to \( s_i \). Once we obtain \( C_{asr}^l \), we can easily retrieve the corresponding translations \( T_{tr}^l \) from \( \mathcal{T}_{tr} \). In the strategy, we use BM25 (Lü, 2024) as the retrieval function \( f \). Finally, the document-level context used in Eq. 2,3 and 4 combines long- and short-memory context:

\[
C_{asr} = C_{asr}^l + C_{asr}^s,
\]
\[
T_{tr} = T_{tr}^l + T_{tr}^s.
\]

2.3 Refinement Determination Mechanism

To enhance both the overall quality of transcriptions and translations, DoCIA incorporates two context-aware refinement processes. These processes aim to leverage document-level context, improving the coherence and cohesion between segments. Given that inter-segment issues are typically sparse, the refinement process generally focuses on making minor adjustments to the source input. However, excessive refinement could introduce errors that distort the original meaning, leading to hallucinations (Xu et al., 2024b). To address this, we introduce a refinement determination mechanism. Specifically, we define a refinement threshold: if the percentage of modifications in the refined output exceeds this threshold, the refinement is discarded, and the original input is retained as the final output:

\[
R = \begin{cases}
O & \text{if } g(O, I) \geq \lambda, \\
I & \text{if } g(O, I) < \lambda,
\end{cases}
\]

where \( I \) denotes the original input (i.e., the draft text (\( \bar{s}_i \) or \( t_i \)), \( O \) is the refined output, and \( R \) is the final output. \( \lambda \) denotes the threshold of modification. We use the normalized *indel similarity* between \( I \) and \( O \) as \( g \):

\[
g(O, I) = 1 - \frac{d(O, I)}{|I| + |O|},
\]

where \( d(\cdot) \) is *Levenshtein edit distance* function, \( |\cdot| \) denotes segment length.

For simplicity, we use the same threshold \( \lambda \) for both ASR and translation refinement.

3 Experimentation

In this section, we validate the effectiveness of DoCIA on five ST translation tasks.

3.1 Experimental Settings

Datasets. We conduct our experiments on the MuST-C v1.0 test sets (Di Gangi et al., 2019), which are extracted from TED talks and consist of document-level and sentence/segment-level parallel corpora. In our study, we focus on five language pairs: English (En) \( \Rightarrow \) {German (De), Italian(It), Portuguese (Pt), Romanian (Ro), Russian (Ru)}. Each test set contains approximately 2.5K segments drawn from 27 talks (documents).

Metrics. We evaluate translation quality using two COMET-based metrics. For segment-level evaluation, we use s-COMET with the wmt22-comet-da model (Rei et al., 2020). For document-level evaluation, we use d-COMET with
<table>
  <tr>
    <th rowspan="2">System</th>
    <th colspan="4">En ⇒ De</th>
    <th colspan="4">En ⇒ It</th>
    <th colspan="4">En ⇒ Pt</th>
    <th colspan="4">En ⇒ Ru</th>
    <th colspan="4">En ⇒ Ro</th>
    <th colspan="4">Average</th>
  </tr>
  <tr>
    <th>s-Comet</th><th>d-Comet</th><th>s-Comet</th><th>d-Comet</th>
    <th>s-Comet</th><th>d-Comet</th><th>s-Comet</th><th>d-Comet</th>
    <th>s-Comet</th><th>d-Comet</th><th>s-Comet</th><th>d-Comet</th>
    <th>s-Comet</th><th>d-Comet</th><th>s-Comet</th><th>d-Comet</th>
    <th>s-Comet</th><th>d-Comet</th><th>s-Comet</th><th>d-Comet</th>
    <th>s-Comet</th><th>d-Comet</th><th>s-Comet</th><th>d-Comet</th>
  </tr>
  <tr>
    <td>ASR-SMT</td><td>78.01</td><td>5.680</td><td>79.67</td><td>5.619</td><td>80.57</td><td>5.438</td><td>76.36</td><td>5.168</td><td>79.07</td><td>5.372</td><td>78.73</td><td>5.455</td>
  </tr>
  <tr>
    <td>ASR-DMT</td><td>77.88</td><td>5.712</td><td>79.79</td><td>5.651</td><td>80.69</td><td>5.477</td><td>76.99</td><td>5.211</td><td>79.01</td><td>5.401</td><td>78.87</td><td>5.490</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a</sub></td><td>78.11</td><td>5.764</td><td>80.03</td><td>5.703</td><td>81.45</td><td>5.519</td><td>77.16</td><td>5.288</td><td>79.69</td><td>5.473</td><td>79.29</td><td>5.549</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m</sub></td><td>78.50</td><td>5.801</td><td>80.53</td><td>5.792</td><td>81.99</td><td>5.621</td><td>78.03</td><td>5.401</td><td>80.39</td><td>5.599</td><td>79.89</td><td>5.643</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m-p</sub></td><td><b>79.15</b></td><td><b>5.912</b></td><td><b>80.88</b></td><td><b>5.909</b></td><td><b>81.751</b></td><td><b>5.757</b></td><td><b>78.39</b></td><td><b>5.556</b></td><td><b>80.54</b></td><td><b>5.734</b></td><td><b>80.15</b></td><td><b>5.774</b></td>
  </tr>
  <tr>
    <td>LLaMA-3.1-8B</td><td colspan="12"></td>
  </tr>
  <tr>
    <td>ASR-SMT</td><td>81.11</td><td>5.997</td><td>82.01</td><td>5.811</td><td>82.03</td><td>5.626</td><td>80.26</td><td>5.686</td><td>83.28</td><td>5.808</td><td>81.73</td><td>5.785</td>
  </tr>
  <tr>
    <td>ASR-DMT</td><td>81.54</td><td>6.143</td><td>82.36</td><td>5.976</td><td>82.85</td><td>5.745</td><td>80.99</td><td>5.867</td><td>83.15</td><td>5.979</td><td>82.17</td><td>5.942</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a</sub></td><td>81.64</td><td>6.098</td><td>82.55</td><td>5.948</td><td>82.53</td><td>5.740</td><td>81.26</td><td>5.803</td><td>83.82</td><td>5.935</td><td>82.36</td><td>5.905</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m</sub></td><td><b>82.69</b></td><td>6.155</td><td><b>83.85</b></td><td>6.132</td><td>83.87</td><td>5.893</td><td><b>82.73</b></td><td>6.034</td><td>84.64</td><td>6.131</td><td>83.57</td><td>6.069</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m-p</sub></td><td>82.63</td><td><b>6.373</b></td><td>83.66</td><td><b>6.264</b></td><td><b>83.99</b></td><td><b>6.037</b></td><td>82.69†</td><td><b>6.168</b></td><td><b>85.32</b></td><td><b>6.365</b></td><td><b>83.66</b></td><td><b>6.241</b></td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td><td colspan="12"></td>
  </tr>
  <tr>
    <td>ASR-SMT</td><td>82.01</td><td>6.001</td><td>83.14</td><td>5.683</td><td>82.56</td><td>5.671</td><td>82.21</td><td>5.827</td><td>84.25</td><td>5.940</td><td>82.83</td><td>5.824</td>
  </tr>
  <tr>
    <td>ASR-DMT</td><td>82.42</td><td>6.108</td><td>83.52</td><td>5.833</td><td>83.32</td><td>5.943</td><td>82.80</td><td>5.948</td><td>84.82</td><td>6.018</td><td>83.37</td><td>5.970</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a</sub></td><td>82.99</td><td>6.174</td><td>85.70</td><td>6.006</td><td>84.03</td><td>5.804</td><td>83.77†</td><td>5.923†</td><td>85.89†</td><td>6.082†</td><td>83.68†</td><td>6.006†</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m</sub></td><td><b>83.75</b></td><td>6.366</td><td>84.54</td><td>6.233</td><td><b>84.57</b></td><td>6.024</td><td>84.10</td><td>6.215</td><td>85.46</td><td>6.213</td><td>84.48</td><td>6.210</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m-p</sub></td><td>83.64†</td><td><b>6.444</b></td><td><b>84.76</b></td><td><b>6.387</b></td><td>84.51†</td><td><b>6.297</b></td><td><b>84.32</b></td><td><b>6.286</b></td><td><b>86.34</b></td><td><b>6.424</b></td><td><b>84.71</b></td><td><b>6.368</b></td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td colspan="12"></td>
  </tr>
  <tr>
    <td>ASR-SMT</td><td>81.51</td><td>5.974</td><td>81.74</td><td>5.732</td><td>82.40</td><td>5.658</td><td>79.21</td><td>5.566</td><td>82.91</td><td>5.644</td><td>81.55</td><td>5.715</td>
  </tr>
  <tr>
    <td>ASR-DMT</td><td>81.68</td><td>5.977</td><td>81.93</td><td>5.760</td><td>82.53</td><td>5.687</td><td>79.50</td><td>5.611</td><td>83.30</td><td>5.687</td><td>81.78</td><td>5.744</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a</sub></td><td>81.70</td><td>5.961</td><td>82.30</td><td>5.705</td><td>82.63</td><td>5.634</td><td>79.57</td><td>5.651</td><td>83.77†</td><td>5.601†</td><td>81.99†</td><td>5.710†</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m</sub></td><td>82.93</td><td>6.126</td><td>83.18</td><td>5.838</td><td>83.60</td><td>5.763</td><td>81.71</td><td>5.804</td><td>84.68</td><td>5.891</td><td>83.22</td><td>5.884</td>
  </tr>
  <tr>
    <td>DoCIA<sub>a-m-p</sub></td><td><b>82.95</b></td><td><b>6.192</b></td><td><b>83.39</b></td><td><b>5.997</b></td><td><b>83.90</b></td><td><b>5.797</b></td><td><b>81.97</b></td><td><b>5.841</b></td><td><b>85.01</b></td><td><b>6.033</b></td><td><b>83.45</b></td><td><b>5.973</b></td>
  </tr>
</table>

Table 1: s-Comet and d-Comet scores on five ST directions when using various LLMs. The column of Average refers to the averaged performance across all translation directions. The top score in each block is highlighted in bold font. Darker colors indicate greater improvements. † indicates that DoCIA<sub>a-m-p</sub> achieves significantly higher s-Comet scores than ASR-SMT/ASR-DMT with a p-value < 0.01.

the wmt21-comet-qe-mqm model (Vernikos et al., 2022), which incorporates document-level context to assess improvements across segments.

Models and Hyperparameters. DoCIA is built upon four LLMs: two closed-source models, GPT-4o-mini and GPT-3.5-turbo (OpenAI, 2023), and two open-source models, LLaMA-3.1-8B and LLaMA-3.1-70B (Dubey et al., 2024), and run inference of open-source models with 8× Ascend 910B NPUs. For all experiments, we use Whisper-medium (Radford et al., 2023) to generate draft ASR results. During inference, we set do_sample to true to enable sampling, allowing the LLMs to generate more diverse outputs. A discussion on the impact of different ASR models is provided in Appendix C. We set the context window size \( L \) as 6, with \( m = n = 3 \). The refinement threshold \( \lambda \) is set to 0.7. Further model and hyperparameter selection details are discussed in Appendix C and D.

Comparison System. We implement the following two systems for comparison: 1) ASR-SMT, which performs segment-level translation directly on the draft ASR output; 2) ASR-DMT, which performs context-aware translation directly on the draft ASR output, using all preceding ASR segments to incorporate document-level context. To better analyze the impact of document-level context at different stages, we define three configurations of DoCIA: 1) DoCIA<sub>a</sub>, which only the context-aware ASR refinement stage; 2) DoCIA<sub>a-m</sub>, which integrates both context-aware ASR refinement and MT; and 3) DoCIA<sub>a-m-p</sub>, which in all three text-to-text stages, leverages document-level information.

3.2 Main Results

We report our main results in Table 1. Additionally, we report the ASR refinement results in Appendix B. From them, we have the following observations:

DoCIA gains a great improvement over baseline systems. DoCIA delivers substantial gains over both ASR-SMT and ASR-DMT, particularly in d-Comet scores, highlighting its effectiveness in handling document-level context. For example, with the LLaMA-3.1-8B model, the configuration DoCIA<sub>a-m-p</sub> (which fully integrates document-level context) achieves an average s-Comet score of 80.15 and a d-Comet score of 5.774. This outperforms both ASR-SMT and ASR-DMT, with improvements of +1.42 in s-Comet and +0.319 in d-Comet over ASR-SMT. Similarly, with the GPT-4o-mini model, DoCIA<sub>a-m-p</sub> shows even
more pronounced improvements, surpassing ASR-SMT by +1.88 in s-Comet and +0.544 in d-Comet. This demonstrates the effectiveness of incorporating document-level context in ST.

Better base model brings more significant improvement. DoCIA yields more substantial improvements when applied to a better base model such as LLaMA-3.1-70B and GPT-4o-mini. For instance, with LLaMA-3.1-8B, DoCIA results in improvements of +1.42 in s-Comet and +0.319 in d-Comet on average, compared to ASR-SMT. While using GPT-4o-mini as the base model, DoCIA achieves even larger gains, with improvements of +1.93 in s-Comet and +0.466 in d-Comet. This may suggest that more powerful LLMs can better utilize document-level context within the DoCIA framework, resulting in improved speech translation quality and enhanced context.

Document-level context boosts performance more when combined with other stages than using alone. When the document-level context is integrated into the ASR refinement phase alone (i.e., DoCIA_a), the improvements in s-Comet and d-Comet scores are relatively small but still noticeable. For example, with LLaMA-3.1-8B, DoCIA_a shows a modest improvement of +0.56 in s-Comet and +0.094 in d-Comet on average compared to ASR-SMT. However, the performance boost becomes much more substantial when combined with additional stages. For example, compared to DoCIA_a, which solely incorporates document-level context during ASR refinement, DoCIA_a-m bring a + 1.12 s-Comet and + 0.164 d-Comet gains. This demonstrates that the multi-stage integration approach effectively unlocks the potential of document-level context, enabling comprehensive optimization of ST.

3.3 Ablation Study

In this section, we conduct an ablation study to evaluate the contributions and impacts of individual components within DoCIA (i.e., DoCIA_{a-m,p}), including the multi-level context integration and the refinement determination. As shown in Table 2, the comparison shows that the refinement determination (w/o R.D.) primarily affects s-Comet, while the multi-level context integration influences d-Comet more. For instance, removing the refinement determination module leads to a 0.98 drop in s-Comet and 0.145 in d-Comet for En⇒De translation using the GPT-4o-mini model. While disabling the long-memory context in multi-level context integration (w/o L.C.) causes a decrease of 0.63 in s-Comet and 0.243 in d-Comet. This suggests that the two components are complementary, highlighting the necessity of their combined use. Furthermore, we observe that long-memory context has a more substantial effect on performance than short-term context, underscoring the importance of leveraging long-range dependency.

<table>
  <tr>
    <th rowspan="2">System</th>
    <th colspan="2">En ⇒ De</th>
    <th colspan="2">En ⇒ Ru</th>
  </tr>
  <tr>
    <th>s-Comet</th>
    <th>d-Comet</th>
    <th>s-Comet</th>
    <th>d-Comet</th>
  </tr>
  <tr>
    <td colspan="5" style="text-align:center;">LLaMA-3.1-8B</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td>79.15</td>
    <td>5.912</td>
    <td>78.39</td>
    <td>5.556</td>
  </tr>
  <tr>
    <td>w/o R.D.</td>
    <td>78.33</td>
    <td>5.812</td>
    <td>77.50</td>
    <td>5.431</td>
  </tr>
  <tr>
    <td>w/o S.C.</td>
    <td>78.63</td>
    <td>5.792</td>
    <td>77.81</td>
    <td>5.331</td>
  </tr>
  <tr>
    <td>w/o L.C.</td>
    <td>78.41</td>
    <td>5.761</td>
    <td>77.88</td>
    <td>5.311</td>
  </tr>
  <tr>
    <td colspan="5" style="text-align:center;">LLaMA-3.1-70B</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td>82.62</td>
    <td>6.373</td>
    <td>82.69</td>
    <td>6.168</td>
  </tr>
  <tr>
    <td>w/o R.D.</td>
    <td>81.97</td>
    <td>6.299</td>
    <td>81.81</td>
    <td>6.037</td>
  </tr>
  <tr>
    <td>w/o S.C.</td>
    <td>82.23</td>
    <td>6.198</td>
    <td>82.11</td>
    <td>5.901</td>
  </tr>
  <tr>
    <td>w/o L.C.</td>
    <td>82.35</td>
    <td>6.211</td>
    <td>82.19</td>
    <td>5.863</td>
  </tr>
  <tr>
    <td colspan="5" style="text-align:center;">GPT-4o-mini</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td>83.64</td>
    <td>6.444</td>
    <td>84.32</td>
    <td>6.286</td>
  </tr>
  <tr>
    <td>w/o R.D.</td>
    <td>82.66</td>
    <td>6.299</td>
    <td>83.11</td>
    <td>6.116</td>
  </tr>
  <tr>
    <td>w/o S.C.</td>
    <td>83.11</td>
    <td>6.231</td>
    <td>83.88</td>
    <td>6.061</td>
  </tr>
  <tr>
    <td>w/o L.C.</td>
    <td>83.01</td>
    <td>6.201</td>
    <td>83.77</td>
    <td>6.011</td>
  </tr>
  <tr>
    <td colspan="5" style="text-align:center;">GPT-3.5-turbo</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td>82.95</td>
    <td>6.192</td>
    <td>81.97</td>
    <td>5.841</td>
  </tr>
  <tr>
    <td>w/o R.D.</td>
    <td>82.19</td>
    <td>6.104</td>
    <td>81.01</td>
    <td>5.806</td>
  </tr>
  <tr>
    <td>w/o S.C.</td>
    <td>82.46</td>
    <td>6.037</td>
    <td>81.23</td>
    <td>5.711</td>
  </tr>
  <tr>
    <td>w/o L.C.</td>
    <td>82.51</td>
    <td>6.072</td>
    <td>81.35</td>
    <td>5.694</td>
  </tr>
</table>

Table 2: Ablation study for refinement determination (R.D.) and multi-level context integration. w/o S.C. disables short-memory context, using only the top \( L \) matching segments from the long-memory context. w/o L.C. disables long-memory context and uses the \( L \) preceding segments from short-memory context instead.

4 Discussion and Analysis

In this section, we use the En ⇒ De and En ⇒ Ru tasks, with LLaMA-3.1-8B and GPT-4o-mini, as representative examples to explore how DoCIA (i.e., DoCIA_{a-m,p} in Table 1) enhances ST performance.

4.1 Multi-Dimension Evaluation via GPT-4o

In this section, we extend the evaluation by using GPT-4o to assess various discourse phenomena. Specifically, we follow Sun et al. (2024) and ask GPT-4o to evaluate the inter-sentence fluency, lexical cohesion errors (LE), and grammatical cohesion errors (GE) in the given translations, using reference translations for comparison. As shown
<table>
  <tr>
    <th rowspan="2">System</th>
    <th colspan="4">En ⇒ De</th>
    <th colspan="4">En ⇒ Ru</th>
  </tr>
  <tr>
    <th>Fluency</th>
    <th>LE↓</th>
    <th>GE↓</th>
    <th>Fluency</th>
    <th>LE↓</th>
    <th>GE↓</th>
  </tr>
  <tr>
    <td colspan="8">LLaMA-3.1-8B</td>
  </tr>
  <tr>
    <td>ASR-SMT</td>
    <td>3.01</td>
    <td>5.21</td>
    <td>4.28</td>
    <td>2.89</td>
    <td>6.11</td>
    <td>4.75</td>
  </tr>
  <tr>
    <td>ASR-DMT</td>
    <td>3.11</td>
    <td>4.32</td>
    <td>3.63</td>
    <td>3.12</td>
    <td>5.28</td>
    <td>4.63</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td><b>3.76</b></td>
    <td><b>1.98</b></td>
    <td><b>1.42</b></td>
    <td><b>3.71</b></td>
    <td><b>3.32</b></td>
    <td><b>2.29</b></td>
  </tr>
  <tr>
    <td colspan="8">GPT-4o-mini</td>
  </tr>
  <tr>
    <td>ASR-SMT</td>
    <td>4.35</td>
    <td>3.21</td>
    <td>2.28</td>
    <td>4.01</td>
    <td>3.78</td>
    <td>2.75</td>
  </tr>
  <tr>
    <td>ASR-DMT</td>
    <td>4.47</td>
    <td>2.01</td>
    <td>1.77</td>
    <td>4.24</td>
    <td>2.61</td>
    <td>1.63</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td><b>5.16</b></td>
    <td><b>1.01</b></td>
    <td><b>0.79</b></td>
    <td><b>4.98</b></td>
    <td><b>1.33</b></td>
    <td><b>0.82</b></td>
  </tr>
</table>

Table 3: Evaluation results on test set by GPT-4o.

<table>
  <tr>
    <th rowspan="2">System</th>
    <th colspan="2">En ⇒ De</th>
    <th colspan="2">En ⇒ Ru</th>
  </tr>
  <tr>
    <th>s-Comet</th>
    <th>d-Comet</th>
    <th>s-Comet</th>
    <th>d-Comet</th>
  </tr>
  <tr>
    <td colspan="5">LLaMA-3.1-8B</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td>79.15</td>
    <td>5.912</td>
    <td>78.39</td>
    <td>5.556</td>
  </tr>
  <tr>
    <td>w/ offline</td>
    <td>78.24</td>
    <td>5.783</td>
    <td>77.30</td>
    <td>5.342</td>
  </tr>
  <tr>
    <td colspan="5">GPT-4o-mini</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td>83.64</td>
    <td>6.444</td>
    <td>84.32</td>
    <td>6.286</td>
  </tr>
  <tr>
    <td>w/ offline</td>
    <td>82.81</td>
    <td>6.252</td>
    <td>83.01</td>
    <td>6.095</td>
  </tr>
</table>

Table 4: Performance comparison between online and offline DoCIA on test set.

<table>
  <tr>
    <th rowspan="2">System</th>
    <th colspan="2">En ⇒ De</th>
    <th colspan="2">En ⇒ Ru</th>
  </tr>
  <tr>
    <th>DA</th>
    <th>CE↓</th>
    <th>CTE↓</th>
    <th>DA</th>
    <th>CE↓</th>
    <th>CTE↓</th>
  </tr>
  <tr>
    <td colspan="8">LLaMA-3.1-8B</td>
  </tr>
  <tr>
    <td>ASR-SMT</td>
    <td>89.7</td>
    <td>13.0</td>
    <td>16.0</td>
    <td>76.7</td>
    <td>16.3</td>
    <td>20.3</td>
  </tr>
  <tr>
    <td>ASR-DMT</td>
    <td>90.1</td>
    <td>9.5</td>
    <td>14.0</td>
    <td>78.0</td>
    <td>11.5</td>
    <td>17.3</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td><b>92.3</b></td>
    <td><b>5.5</b></td>
    <td><b>7.5</b></td>
    <td><b>80.7</b></td>
    <td><b>8.5</b></td>
    <td><b>11.5</b></td>
  </tr>
  <tr>
    <td colspan="8">GPT-4o-mini</td>
  </tr>
  <tr>
    <td>ASR-SMT</td>
    <td>92.5</td>
    <td>8.0</td>
    <td>12.0</td>
    <td>81.3</td>
    <td>12.3</td>
    <td>15.0</td>
  </tr>
  <tr>
    <td>ASR-DMT</td>
    <td>92.8</td>
    <td>7.3</td>
    <td>13.0</td>
    <td>82.6</td>
    <td>11.1</td>
    <td>12.3</td>
  </tr>
  <tr>
    <td>DoCIA</td>
    <td><b>94.7</b></td>
    <td><b>3.3</b></td>
    <td><b>6.0</b></td>
    <td><b>85.0</b></td>
    <td><b>7.3</b></td>
    <td><b>9.5</b></td>
  </tr>
</table>

Table 5: Results of human evaluation on the test set.

in Table 3, ASR-DMT outperforms ASR-SMT, demonstrating that integrating inter-segment context significantly reduces lexical and grammatical cohesion errors while improving overall fluency. Notably, DoCIA achieves the best performance on all translation tasks across all three metrics, further highlighting its effectiveness in leveraging inter-segment context.

4.2 Effect of Online/Offline Setting

In DoCIA, the document context is updated in real-time during the translation process, following an online setting. This means the system continuously updates the context based on the latest translation or ASR outputs, leading to more accurate and coherent translations. In contrast, we also compare this with an offline setting, denoted as offline DoCIA, which does not update the context during translation. In this case, the system uses only the initial segment-level translation or ASR results, without any real-time updates to the context. Specifically, this corresponds to replacing Eq. 5 and Eq. 6 with initial context: \( c_{asr} = \{ \hat{s}_1, \cdots, \hat{s}_i \} \) and \( \mathcal{T}_{tr} = \{ \hat{t}_1, \cdots, \hat{t}_i \} \), respectively. As shown in Table 4, the offline DoCIA shows a significant drop in performance compared to online DoCIA. For example, in the En⇒Ru task using the LLaMA-3.1-8B model, offline DoCIA results in a -1.09 decrease in s-Comet score and a -0.214 decrease in d-Comet score. This suggests that DoCIA’s performance is highly sensitive to the quality of the context, with real-time updates leading to more accurate and effective context, which in turn significantly improves speech translation quality.

4.3 Human Evaluation

We use the Direct Assessment (DA) (Graham et al., 2017) to evaluate the translation quality of DoCIA and its counterparts. Here, human evaluators compare machine translations with human-produced references in the same language and assign a score from 1 to 100. For each translation direction, we randomly select 4 talks, totaling 312 audio segments, and have two professional translators score the translations from DoCIA, ASR-SMT, and ASR-DMT. Additionally, we report the average counts (per talk) of coherence errors (CE) and content translation errors (CTE) annotated by evaluators. The results, presented in Table 5, show that DoCIA outperforms the others with higher DA scores and fewer CE and CTE scores, providing strong evidence of its effectiveness. For more details of human evaluation, refer to Appendix E.

4.4 Effect of Context Window

In this section, we examine the impact of the context window from two perspectives: 1) varying the context window size \( L \), and 2) exploring different combinations of \( m \) and \( n \) while keeping \( L \) fixed. As shown in Figure 3, increasing the context window size \( L \) generally improves performance across all metrics. However, the gains start to diminish when \( L \) exceeds 6. Figure 4 illustrates the effects of different \( m \) and \( n \) combinations. Similar to the trends observed in Section 3.3, we find that reducing the short-memory context (i.e., smaller \( m \)) has a more significant impact on s-Comet, while decreasing the long-memory context (i.e., smaller \( n \)) affects the d-Comet score more. This further re-
Figure 3: Performance comparison when setting different context window size \( L \).

Figure 4: Performance comparison when setting different combinations of \( m \) and \( n \).

inforces the complementary nature of short- and long-memory contexts in DoCIA.

5 Related Work

LLM-based Autonomous Agents. LLM-based autonomous agents have recently demonstrated impressive capabilities across a variety of natural language processing tasks. For long-context comprehension and processing, researchers such as Park et al. (2023), Wang et al. (2023a), and Lee et al. (2024) have developed specialized memory and retrieval mechanisms. In efforts to improve output quality, Xu et al. (2024a), Wang et al. (2024), and Feng et al. (2025) have employed prompting techniques that allow LLMs to self-assess and refine their results. Additionally, Li et al. (2023), Liang et al. (2023), Li et al. (2024a), Wu et al. (2024b) and Wang et al. (2025) boost LLM performance on specific tasks through multi-agent collaboration.

Speech-to-Text Translation. Existing studies on ST can be roughly categorized into two groups: cascade-based and end-to-end approaches. The cascade-based system (Zhang et al., 2019a; Sperber and Paulik, 2020; Lam et al., 2021) separates ASR and text translation stages, which doesn’t require parallel audio-translation data and can fully leverage ASR and text translation corpus for ST. While the end-to-end system combines these stages and is trained on parallel audio-translation data using strategies such as multi-task learning (Ye et al., 2021), contrastive learning (Ye et al., 2022; Zhang et al., 2022a; Ouyang et al., 2023), sequence mixup (Fang et al., 2022; Yin et al., 2023; Zhang et al., 2023; Zhou et al., 2023), knowledge distillation (Tang et al., 2021; Lei et al., 2023), regularization (Han et al., 2023; Gao et al., 2024), pre-training (Wang et al., 2020; Alinejad and Sarkar, 2020; Tang et al., 2022; Zhang et al., 2022b), and data augmentation (Pino et al., 2019, 2020; Lam et al., 2022). Recently, with the rise of LLMs, some research has explored combining speech encoders with LLMs for end-to-end ST (Wu et al., 2023; Chen et al., 2024). However, few studies explore the effect of document-level information in ST. For example, Tian et al. (2025) enhance ST by incorporating audio context from the two preceding sentences. Similarly, Dou et al. (2025) leverage document-level context during the refinement stage of ST.

Document-Level Text Translation. Document-level context has already been widely considered in text translation studies whether based on the lightweight neural machine translation models (Jean et al., 2017; Wang et al., 2017; Voita et al., 2018; Maruf et al., 2019; Kang et al., 2020; Bao et al., 2021; Sun et al., 2022; Bao et al., 2023) or powerful LLMs (Wang et al., 2023b; Wu and Hu, 2023; Wu et al., 2024a; Li et al., 2024b; Koneru et al., 2024; Lyu et al., 2024). These studies primarily focus on efficiently leveraging document-level
context to address inter-sentence translation issues. For example, Lyu et al. (2024) enable LLMs to discriminatively model and utilize both inter- and intra-sentence context, making them more effective at context-aware translation. Similarly, Wu et al. (2024a) investigate effective tuning methods that allow LLMs to better leverage the benefits of document-level context. Despite the effectiveness of document-level context in text translation, it remains underexplored in ST.

6 Conclusion

Inspired by the success of incorporating document-level context in text-to-text MT, we propose DoCIA, an online LLM-based agent designed to improve ST performance by integrating document-level context. DoCIA breaks the whole ST process into four stages, producing the final translation in a cascading manner. Additionally, we introduce a multi-level context integration strategy and a refinement determination mechanism to enhance DoCIA’s ability to utilize inter-segment context while minimizing hallucinations during refinement. Experimental results across five ST tasks, using four different LLMs, demonstrate that DoCIA effectively addresses discourse issues from both the ASR and MT stages, leading to significant improvements in overall ST quality.

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive feedback. This work was supported by the National Natural Science Foundation of China (Grant No. 62036004, 62261160648).

Limitations

In this paper, we propose a document-level context incorporation agent for ST, focusing primarily on its effectiveness in improving ST performance rather than optimizing inference speed. The inference requires multiple calls to LLMs during translation, which results in longer inference latency. Additionally, due to computational resource constraints, DoCIA currently only considers context from the text modality and does not include audio modality information. In the future, we plan to incorporate context from the audio modality to further enhance ST performance.

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A Prompt Templates in DoCIA

This section presents the prompt templates used in each stage of DoCIA. The prompt templates for ASR Refinement, machine translation and translation refinement are shown in Figure 5, 6 and 7, respectively. To ensure accuracy and proper formatting, we instruct the LLM to generate outputs in JSON format.

B Performance of ASR Refinement

<table>
  <tr>
    <th>Refining Model</th>
    <th>WER↓</th>
    <th>ERA</th>
    <th>BERTScore</th>
  </tr>
  <tr>
    <td>LLaMA-3.1-8B</td>
    <td>19.16</td>
    <td>55.81</td>
    <td>88.75</td>
  </tr>
  <tr>
    <td>LLaMA-3.1-70B</td>
    <td>18.66</td>
    <td>56.45</td>
    <td>88.97</td>
  </tr>
  <tr>
    <td>GPT-4o-mini</td>
    <td>16.01</td>
    <td><b>57.12</b></td>
    <td><b>89.21</b></td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td>
    <td>18.96</td>
    <td>56.87</td>
    <td>89.05</td>
  </tr>
  <tr>
    <td>Draft ASR</td>
    <td><b>14.71</b></td>
    <td>55.78</td>
    <td>88.50</td>
  </tr>
</table>

Table 6: Performance comparison of ASR refinement when using various LLMs.

In this section, we evaluate the performance of ASR refinement in addition to the main translation performance. Apart from Word Error Rate (WER),\footnote{In this paper, we retain the punctuation from the ASR results and report the case-sensitive WER.} we compare BERTScore (Zhang et al., 2019b) and Entity Recognition Accuracy (ERA) to assess how well the models utilize context to improve semantic accuracy and correct entity recognition errors. ERA is evaluated using GPT-4o. Specifically, we first use GPT-4o to extract entities from both the refined and non-refined ASR outputs (draft ASR), as well as from the reference ASR. ERA is calculated by comparing the extracted entities to the reference.

As shown in Table 6, although WER increases after refinement, both ERA and BERTScore show improvements. This indicates that leveraging document-level context significantly enhances entity recognition and semantic accuracy.

Additionally, we present a case study in Figure 8, where DoCIA corrects an ASR error. In this case, "DigiNotar" is misrecognized as "TAR" in draft ASR, but DoCIA successfully corrects the error by considering the inter-segment context, which include the proper entity "DigiNotar".

C Effect of ASR Model

<table>
  <tr>
    <th rowspan="2">System</th>
    <th colspan="2">En ⇒ De</th>
    <th colspan="2">En ⇒ Ru</th>
  </tr>
  <tr>
    <th>s-Comet</th>
    <th>d-Comet</th>
    <th>s-Comet</th>
    <th>d-Comet</th>
  </tr>
  <tr>
    <td colspan="5"><b>LLaMA-3.1-8B</b></td>
  </tr>
  <tr>
    <td>ASR-SMT</td>
    <td>78.01</td>
    <td>5.680</td>
    <td>76.36</td>
    <td>5.168</td>
  </tr>
  <tr>
    <td>ASR-DMT</td>
    <td>77.88</td>
    <td>5.712</td>
    <td>76.99</td>
    <td>5.211</td>
  </tr>
  <tr>
    <td>DoCIA (w/ WM)</td>
    <td><b>79.15</b></td>
    <td>5.912</td>
    <td><b>78.39</b></td>
    <td>5.556</td>
  </tr>
  <tr>
    <td>w/ WS, WER=14.89</td>
    <td>78.99</td>
    <td>5.901</td>
    <td>78.45</td>
    <td><b>5.562</b></td>
  </tr>
  <tr>
    <td>w/ WL, WER=14.41</td>
    <td>79.11</td>
    <td><b>5.935</b></td>
    <td><b>78.61</b></td>
    <td>5.551</td>
  </tr>
  <tr>
    <td colspan="5"><b>GPT-4o-mini</b></td>
  </tr>
  <tr>
    <td>ASR-SMT</td>
    <td>82.01</td>
    <td>6.001</td>
    <td>82.21</td>
    <td>5.827</td>
  </tr>
  <tr>
    <td>ASR-DMT</td>
    <td>82.42</td>
    <td>6.108</td>
    <td>82.80</td>
    <td>5.948</td>
  </tr>
  <tr>
    <td>DoCIA (w/ WM)</td>
    <td><b>83.64</b></td>
    <td>6.444</td>
    <td><b>84.32</b></td>
    <td>6.256</td>
  </tr>
  <tr>
    <td>w/ WS, WER=14.89</td>
    <td>83.43</td>
    <td>6.401</td>
    <td>84.34</td>
    <td>6.275</td>
  </tr>
  <tr>
    <td>w/ WL, WER=14.41</td>
    <td><b>83.82</b></td>
    <td><b>6.478</b></td>
    <td><b>84.71</b></td>
    <td><b>6.299</b></td>
  </tr>
</table>

Table 7: Performance comparison when using various ASR models. WS, WM and WL denote the Whisper-Small, Whisper-Medium and Whisper-Large models, respectively.

In our experiments, DoCIA uses the Whisper-Medium ASR model to generate segment-level transcriptions. We now investigate the effect of using ASR models of different sizes on the final translation performance. Table 7 presents a comparison of translation results across different ASR models. It shows that larger ASR models tend to achieve better ASR performance (i.e., lower WER), leading to modest improvements in translation quality. For instance, using the Whisper-Large yields a +0.39 improvement in the s-COMET score for the En⇒Ru task compared to the Whisper-Medium, when DoCIA uses the GPT-4o-mini translation model.

D Effect of Hyperparameter \( \lambda \) in Refinement Determination

To prevent hallucinations in both the transcription and translation refinement processes, we introduce a refinement determination mechanism. In this
Context-aware ASR Refinement Prompt Template

You are an expert in automatic speech recognition refinement. Given an automatic speech recognition sentence in <SRC-LANG>, please check it based on its preceding automatic speech recognition sentences. Correct the capitalization, add punctuation, and eliminate incoherences such as fillers, false starts, repetitions, corrections, hesitations, and interjections. Maintain the original meaning and structure of the sentence and make it more coherent with the preceding ASR sentence. Provide your output in the following JSON format:
{'Output': <Refined ASR sentence>}

### Preceding ASR sentences:
<Preceding ASR sentence>

### Draft current ASR sentence:
<Draft current ASR sentence>

### Your output:

Figure 5: Prompt template for ASR Refinement in DoCIA.

Context-aware Translation Prompt Template

You are a professional translator from <SRC-LANG> to <TGT-LANG>. Given a current source sentence, please translate it to <TGT-LANG> based on its preceding source sentence and translation history. The translation of the current sentence should be more coherent with its preceding translations and have better lexical cohesion. Provide your translation in the following JSON format:
{'Output': <Translation>}

### Preceding source sentences:
<Preceding source sentences>

### Preceding translation history:
<Preceding translation history>

### Current source sentence:
<Current source sentence>

### Your output:

Figure 6: Prompt template for context-aware translation in DoCIA.

section, we investigate the impact of the determination threshold, \( \lambda \), and explore the effect of using BERTScore to compute the similarity between \( I \) (input) and \( O \) (output) by replacing Eq. 11 with BERTScore. The results, presented in Table 8, show that both excessively high and low threshold values negatively affect performance. Additionally, using BERTScore in the refinement determination process leads to significant performance improvements. This suggests that the determination mechanism is not highly sensitive to the choice of similarity function.

E  Details of Human Evaluation

Recruitment and Criterion. We recruit evaluators who are professional translators with a mini-
Context-aware Translation Refinement Prompt Template

You are a professional <SRC-LANG> to <TGT-LANG> translation post-editor. Given a current source sentence and its draft translation, please refine the draft translation based on its preceding source sentence and translation history. The refined translation should have the same semantics as the current source sentence be more coherent and have better lexical cohesion with its preceding translation history. Provide the refined translation in the following JSON format:
{'Output': <Refined Translation>}

### Preceding source sentences:
<Preceding source sentences>

### Preceding translation history:
<Preceding translation history>

### Current source sentence:
<Current source sentence>

### Draft translation:
<Draft translation>

### Your output:

Figure 7: Prompt template for context-aware translation refinement in DoCIA.

<table>
  <tr>
    <th colspan="2">Inter-Segment Context</th>
    <th colspan="4">System</th>
    <th colspan="2">En ⇒ De</th>
    <th colspan="2">En ⇒ Ru</th>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">s-Comet</td>
    <td colspan="2">d-Comet</td>
    <td colspan="2">s-Comet</td>
    <td colspan="2">d-Comet</td>
  </tr>
  <tr>
    <td colspan="2">And then we look at cases like what happened in <b>DigiNotar</b>. This is a prime example of what happens when governments attack against their own citizens.</td>
    <td colspan="2">LLaMA-3, 1-8B</td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
  </tr>
  <tr>
    <td colspan="2">ASR Result of Current Segment</td>
    <td colspan="2">DoCIA (λ = 0.7) <b>79.15</b></td>
    <td colspan="2"><b>5.912</b></td>
    <td colspan="2">78.39</td>
    <td colspan="2"><b>5.556</b></td>
  </tr>
  <tr>
    <td colspan="2">Did you know <b>TAR</b> is a certificate authority from the Netherlands? Or actually it was?</td>
    <td colspan="2">λ = 0.0</td>
    <td colspan="2">78.24</td>
    <td colspan="2">5.735</td>
    <td colspan="2">77.56</td>
    <td colspan="2">5.244</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 0.5</td>
    <td colspan="2">78.54</td>
    <td colspan="2">5.733</td>
    <td colspan="2">77.81</td>
    <td colspan="2">5.533</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 0.9</td>
    <td colspan="2">78.21</td>
    <td colspan="2">5.712</td>
    <td colspan="2">77.31</td>
    <td colspan="2">5.432</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 1.0</td>
    <td colspan="2">78.33</td>
    <td colspan="2">5.812</td>
    <td colspan="2">77.50</td>
    <td colspan="2">5.431</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">w/ BS (λ = 0.7)</td>
    <td colspan="2">78.81</td>
    <td colspan="2">5.865</td>
    <td colspan="2"><b>78.45</b></td>
    <td colspan="2">5.511</td>
  </tr>
  <tr>
    <td colspan="2">Refined ASR Result by DoCIA</td>
    <td colspan="2">GPT-40-mini</td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
    <td colspan="2"></td>
  </tr>
  <tr>
    <td colspan="2">DigiNotar is a certificate authority from the Netherlands. Or actually, it was.</td>
    <td colspan="2">DoCIA (λ = 0.7) <b>83.64</b></td>
    <td colspan="2"><b>6.444</b></td>
    <td colspan="2"><b>84.32</b></td>
    <td colspan="2"><b>6.286</b></td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 0.0</td>
    <td colspan="2">82.79</td>
    <td colspan="2">6.259</td>
    <td colspan="2">83.33</td>
    <td colspan="2">6.199</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 0.5</td>
    <td colspan="2">83.31</td>
    <td colspan="2">6.387</td>
    <td colspan="2">83.83</td>
    <td colspan="2">6.218</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 0.9</td>
    <td colspan="2">82.98</td>
    <td colspan="2">6.253</td>
    <td colspan="2">83.45</td>
    <td colspan="2">6.166</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">λ = 1.0</td>
    <td colspan="2">82.66</td>
    <td colspan="2">6.299</td>
    <td colspan="2">83.11</td>
    <td colspan="2">6.116</td>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2">w/ BS (λ = 0.7)</td>
    <td colspan="2"><b>83.75</b></td>
    <td colspan="2">6.395</td>
    <td colspan="2">84.12</td>
    <td colspan="2">6.201</td>
  </tr>
</table>

Table 8: Performance comparison when setting different \( \lambda \). When setting \( \lambda = 1.0 \) (or \( \lambda = 0.0 \)), we always take the original (or refined) text as the final output.

<table>
  <tr>
    <th colspan="2">Inter-Segment Context</th>
    <th colspan="2">Reference ASR Result</th>
  </tr>
  <tr>
    <td colspan="2"></td>
    <td colspan="2"><i>DigiNotar</i> is a certificate authority from the Netherlands—or actually, it was.</td>
  </tr>
</table>

Figure 8: A case study for context-aware ASR refinement. ASR result is from Whisper-Medium.

mum of five years of experience. Given a reference ASR output, its translations from various systems, and the human-produced reference translation, evaluators are tasked with assigning a score on a scale from 0 to 100. The detailed scoring criterion as follows:

• 0–20: The translation is completely incorrect and unclear, with only a few words or phrases being correct. It is totally unreadable and difficult to understand.

• 21–40: The translation has very little semantic similarity to the source sentence, with key information missing or incorrect. It has numerous unnatural and unfluent expressions and grammatical errors.

• 41–60: The translation can express part of the
key semantics but has many non-key semantic errors. It lacks fluency and idiomaticity.

• 61–80: The translation can express the key semantics but has some non-key information errors and significant grammatical errors. It lacks idiomaticity.

• 81–100: The translation can express the semantics of the source sentence with only a few non-key information errors and minor grammatical errors. It is fluent and idiomatic.

Coherence Error and Content Error. We manually count the average number of coherence errors (CE) and content translation errors (CTE) for evaluation terms. Specifically, CE involves two types of errors, including inter-sentential consistency errors, such as inconsistent translations of the same entity across sentences, and inter-sentential logical errors, such as improper translation or usage of transition words and conjunctions. CTE includes three error types: mistranslation, under- and over-translation.

Metareview score: 3
Metareview: This paper introduces a document-aware framework for improving speech translation through two key refinements: ASR refinement and MT refinement, extending the standard ASR→MT pipeline into four stages. By incorporating document-level context and leveraging LLMs, DoCIA enhances both recognition and translation quality. The framework includes mechanisms for context retrieval and hallucination filtering. Evaluations across language pairs, including human assessments and COMET scores, demonstrate significant performance gains over baselines, with ablation studies highlighting the contributions of each module.
Summary of reasons to publish: - well-written paper
- performance gains in speech translation
- good ablations that outline the contributions of each component (if taking into account the additional results presented during the discussion).