Measuring User's Mental Models of Speech Translation in Human-AI Collaboration

• URL: http://arxiv.org/abs/2606.24644v1
• Date: Tue, 23 Jun 2026 14:40:36 GMT
• Title: Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
• Abstract summary: This paper studies users' mental models of speech translation systems through a new framework based on cross-lingual question answering.<n>By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong.
• Score: 23.64422798744437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users' mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and how this mental model evolves. Users develop stronger mental models with practice, especially when they have some knowledge of the source language, primarily by relying on surface-level error cues. Moreover, providing speech transcriptions can help users develop better mental models. Our results show the promise of cross-lingual question answering as a downstream task for studying MT mental models and advancing our understanding of human-AI collaboration.

Related papers

• Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States [72.77158276658636]
  Mind2Dialogue is a framework for simulating users' mental states and turning them into privileged supervision for human-aware training.<n>We first propose a psychology-guided simulator that preserves personal characteristics while updating mental states to generate coherent conversations.<n>Our privileged distillation then trains models on the Oracle's well-informed responses to assist users without direct access to their mental states at deployment.
  arXiv  Detail & Related papers  (2026-09-14T17:55:58Z)
• Learning to Learn from Language Feedback with Social Meta-Learning [17.85279270632852]
  Large language models (LLMs) often struggle to learn from corrective feedback within a conversational context.<n>We draw inspiration from social meta-learning in humans - the process of learning how to learn from others.<n>We train LLMs to solicit and learn from language feedback in simulated pedagogical dialogues.
  arXiv  Detail & Related papers  (2026-02-18T14:22:13Z)
• Align to the Pivot: Dual Alignment with Self-Feedback for Multilingual Math Reasoning [71.4175109189942]
  We present Pivot-Aligned Self-Feedback Multilingual Reasoning (PASMR)<n>This approach designates the model's primary language as the pivot language.<n>It establishes a cross-lingual self-feedback mechanism without relying on external correct answers or reward models.
  arXiv  Detail & Related papers  (2026-01-25T03:20:00Z)
• Analyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation [5.878901309908815]
  We study cross-lingual knowledge transfer in neural models and develop methods to improve robustness and generalization in multilingual settings.<n>We examine the role of language diversity during training and show that increasing translation coverage improves generalization and reduces off-target behavior.
  arXiv  Detail & Related papers  (2026-01-07T15:51:54Z)
• Can Language Models Learn to Listen? [96.01685069483025]
  We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Our approach autoregressively predicts a response of a listener: a sequence of listener facial gestures, quantized using a VQ-VAE. We show that our generated listener motion is fluent and reflective of language semantics through quantitative metrics and a qualitative user study.
  arXiv  Detail & Related papers  (2023-08-21T17:59:02Z)
• Accessible Instruction-Following Agent [0.0]
  We introduce UVLN, a novel machine-translation instructional augmented framework for cross-lingual vision-language navigation. We extend the standard VLN training objectives to a multilingual setting via a cross-lingual language encoder. Experiments over Room Across Room dataset prove the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-05-08T23:57:26Z)
• Computational Language Acquisition with Theory of Mind [84.2267302901888]
  We build language-learning agents equipped with Theory of Mind (ToM) and measure its effects on the learning process. We find that training speakers with a highly weighted ToM listener component leads to performance gains in our image referential game setting.
  arXiv  Detail & Related papers  (2023-03-02T18:59:46Z)
• Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study [1.3999481573773072]
  We show that there are linguistic phenomena specific of Twitter data that pose a challenge in translation of emotions in different languages. We also assess the capacity of commonly used methods for evaluating the performance of an MT system with respect to the preservation of emotion in the source text.
  arXiv  Detail & Related papers  (2021-06-20T16:12:48Z)
• Improving Cross-Lingual Reading Comprehension with Self-Training [62.73937175625953]
  Current state-of-the-art models even surpass human performance on several benchmarks. Previous works have revealed the abilities of pre-trained multilingual models for zero-shot cross-lingual reading comprehension. This paper further utilized unlabeled data to improve the performance.
  arXiv  Detail & Related papers  (2021-05-08T08:04:30Z)
• Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale [52.663117551150954]
  A few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models.
  arXiv  Detail & Related papers  (2020-10-26T13:57:20Z)
• AttViz: Online exploration of self-attention for transparent neural language modeling [7.574392147428978]
  We propose AttViz, an online toolkit for exploration of self-attention---real values associated with individual text tokens. We show how existing deep learning pipelines can produce outputs suitable for AttViz, offering novel visualizations of the attention heads and their aggregations with minimal effort, online.
  arXiv  Detail & Related papers  (2020-05-12T12:21:40Z)
• It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information [90.35685796083563]
  Cross-mutual information (XMI) is an asymmetric information-theoretic metric of machine translation difficulty. XMI exploits the probabilistic nature of most neural machine translation models. We present the first systematic and controlled study of cross-lingual translation difficulties using modern neural translation systems.
  arXiv  Detail & Related papers  (2020-05-05T17:38:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.