Toward a Behavioural Translation Style Space: Simulating the Temporal Dynamics of Affect, Behaviour, and Cognition in Human Translation Production

• URL: http://arxiv.org/abs/2507.12208v1
• Date: Wed, 16 Jul 2025 13:10:10 GMT
• Title: Toward a Behavioural Translation Style Space: Simulating the Temporal Dynamics of Affect, Behaviour, and Cognition in Human Translation Production
• Authors: Michael Carl, Takanori Mizowaki, Aishvarya Ray, Masaru Yamada, Devi Sri Bandaru, Xinyue Ren,
• Abstract summary: The paper introduces a Behavioural Translation Style Space (BTSS) that describes possible behavioural translation patterns.<n>We posit that observable translation behaviour is fundamental when executing the physical act of translation but it is caused and shaped by higher-order cognitive processes and affective translation states.<n>The BTSS serves as the basis for a computational translation agent to simulate the temporal dynamics of affect, automatized behaviour and cognition during human translation production.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The paper introduces a Behavioural Translation Style Space (BTSS) that describes possible behavioural translation patterns. The suggested BTSS is organized as a hierarchical structure that entails various embedded processing layers. We posit that observable translation behaviour - i.e., eye and finger movements - is fundamental when executing the physical act of translation but it is caused and shaped by higher-order cognitive processes and affective translation states. We analyse records of keystrokes and gaze data as indicators of the hidden mental processing structure and organize the behavioural patterns as a multi-layered embedded BTSS. The BTSS serves as the basis for a computational translation agent to simulate the temporal dynamics of affect, automatized behaviour and cognition during human translation production.

Related papers

• A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language [15.929767234646631]
  Increase in data, size, or compute can lead to sudden learning of specific capabilities by a neural network. "emergence" is a phenomenon often called "emergence"
  arXiv  Detail & Related papers  (2024-08-22T17:44:22Z)
• Analysis of Argument Structure Constructions in a Deep Recurrent Language Model [0.0]
  We explore the representation and processing of Argument Structure Constructions (ASCs) in a recurrent neural language model. Our results show that sentence representations form distinct clusters corresponding to the four ASCs across all hidden layers. This indicates that even a relatively simple, brain-constrained recurrent neural network can effectively differentiate between various construction types.
  arXiv  Detail & Related papers  (2024-08-06T09:27:41Z)
• Investigating the Timescales of Language Processing with EEG and Language Models [0.0]
  This study explores the temporal dynamics of language processing by examining the alignment between word representations from a pre-trained language model and EEG data. Using a Temporal Response Function (TRF) model, we investigate how neural activity corresponds to model representations across different layers. Our analysis reveals patterns in TRFs from distinct layers, highlighting varying contributions to lexical and compositional processing.
  arXiv  Detail & Related papers  (2024-06-28T12:49:27Z)
• Temporal Dynamics of Emotion and Cognition in Human Translation: Integrating the Task Segment Framework and the HOF Taxonomy [0.0]
  The article develops a generative model of the human translating mind, grounded in empirical translation process data.<n>The article relates this embedded generative model to various theoretical frameworks, dual-process theories and Robinson's (2023) ideosomatic theory of translation.
  arXiv  Detail & Related papers  (2024-05-06T02:07:13Z)
• Injecting linguistic knowledge into BERT for Dialogue State Tracking [60.42231674887294]
  This paper proposes a method that extracts linguistic knowledge via an unsupervised framework. We then utilize this knowledge to augment BERT's performance and interpretability in Dialogue State Tracking (DST) tasks. We benchmark this framework on various DST tasks and observe a notable improvement in accuracy.
  arXiv  Detail & Related papers  (2023-11-27T08:38:42Z)
• Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks [10.880057430629126]
  Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation. In this work, we focus on a more general form of sentence disentanglement, targeting the localised modification and control of more general sentence semantic features. We introduce a flow-based invertible neural network (INN) mechanism integrated with a transformer-based language Autoencoder (AE) in order to deliver latent spaces with better separability properties.
  arXiv  Detail & Related papers  (2023-05-02T18:27:13Z)
• Contextualized Semantic Distance between Highly Overlapped Texts [85.1541170468617]
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence as neighboring words and use masked language modeling (MLM) to predict the distributions on their positions. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts.
  arXiv  Detail & Related papers  (2021-10-04T03:59:15Z)
• Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses [62.197912623223964]
  We show a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI. This suggests that the embedding captures some part of the brain's natural language representation structure.
  arXiv  Detail & Related papers  (2021-06-09T22:59:12Z)
• Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation [54.864148836486166]
  We propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer. Our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several state-of-the-art non-autoregressive models.
  arXiv  Detail & Related papers  (2021-01-22T04:12:17Z)
• On Long-Tailed Phenomena in Neural Machine Translation [50.65273145888896]
  State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens. We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation. We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy.
  arXiv  Detail & Related papers  (2020-10-10T07:00:57Z)
• Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation [59.38247587308604]
  We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T dataset. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models.
  arXiv  Detail & Related papers  (2020-03-30T21:35:09Z)

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.