Quantifying Conceptual Drift in Polysemous Technical Jargon Across Domain-Specific AI Translation Pipelines

Introduction

The rise of cross-domain AI deployment necessitates robust translation systems capable of handling specialized, often idiosyncratic jargon. However, when technical concepts must be rendered intelligible across disparate knowledge bases (e.g., translating quantum computing schematics into venture capital reports), the inherent ambiguity of natural language exerts a powerful, often destructive, force. Polysemy—a single word having multiple related meanings—is amplified in technical registers where context is dense and unforgiving [1]. When a term like ‘coherence’ shifts from its quantum mechanical definition (maintaining phase relationships) to its project management equivalent (overall team synergy), the machine translation pipeline risks severe conceptual drift.

This paper addresses the critical research question: To what measurable extent does conceptual meaning diverge from literal technical equivalence when a single, polysemous term undergoes sequential machine translation across heterogeneous domain models? We hypothesize that the accumulation of minor contextual approximations leads to a multiplicative error in latent semantic representation, ultimately rendering high-fidelity translation impossible without domain anchoring.

Theoretical Framework: Modeling Conceptual Drift

We model the semantic state of a term $T$ at any point in the translation pipeline as a high-dimensional vector $\mathbf{v}_t \in \mathbb{R}^d$, where $d$ is the dimensionality of the embedding space. The translation process, mediated by the neural network layers, functions as a transformation matrix $M_i$ applied at stage $i$. If the initial state is $\mathbf{v}_{initial}$, the final state after $N$ stages is:

$$\mathbf{v}_{final} = M_N M_{N-1} \cdots M_2 M_1 \mathbf{v}_{initial}$$

The conceptual drift $\Delta S$ quantifies the angular separation between $\mathbf{v}_{initial}$ and $\mathbf{v}_{final}$. This shift is particularly pronounced when the subsequent translation stage $M_{i+1}$ possesses a strong bias towards its target domain, overriding the residual context from $M_i$ [2].

Drift Velocity and Semantic Distance

The velocity of drift, $\dot{S}$, can be approximated during the transition between two stages ($i$ and $i+1$) using the cosine similarity degradation, $\text{Sim}(\mathbf{v}_i, \mathbf{v}_{i+1})$. A rapid decay in this similarity indicates high drift velocity. We empirically found that for technical terms, the initial stages ($i=1$ to $i=k$) are characterized by high fidelity ($\text{Sim} > 0.95$), while the transition into generalized registers ($i>k$) exhibits a rapid descent towards functional equivalence rather than conceptual fidelity.

Table 1 presents the measured drift rates for three high-entropy terms across the simulation run.

Table 1: Measured Conceptual Drift Rates ($\text{Stage } i \rightarrow \text{Stage } i+1$)

Methodology: Pipeline Construction and Testing

Our experimental setup involved a tripartite translation pipeline. Stage 1 (Source Encoding) translated Domain A ($\text{Physics}$) into an intermediate vector representation. Stage 2 (Mediator) used a generalized, large-scale multilingual model intended for broad accessibility. Stage 3 (Target Decoding) rendered the concept into Domain B ($\text{Business}$). The input corpus contained 500 instances of texts requiring translation, focusing specifically on passages where the chosen term was central to the passage’s technical constraint.

To quantify the objective decay, we employed a custom drift metric $\Delta S$:

$$\Delta S = 1 - \frac{\mathbf{v}_{\text{target}} \cdot \mathbf{v}_{\text{ground\_truth}}}{\|\mathbf{v}_{\text{target}}\| \|\mathbf{v}_{\text{ground\_truth}}\|}$$

This formula measures the cosine distance between the translated embedding ($\mathbf{v}_{\text{target}}$) and a latent ground-truth vector ($\mathbf{v}_{\text{ground\_truth}}$) representing the original technical constraint.

Results and Observations

The quantitative analysis revealed a strong, non-linear relationship between pipeline depth and semantic error accumulation. As shown in Figure 1 (conceptualized here through data summary), the average $\Delta S$ remained below $0.15$ after Stage 2 for terms with low inherent polysemy ($\text{Low Entropy}$). However, for the high-entropy term ‘Coherence,’ the drift accelerated dramatically between Stage 2 and Stage 3. The initial technical integrity, represented by a cosine similarity of $0.98$ against the source context vector, collapsed to an average of $0.62$ in the business domain translation.

This suggests that Stage 2, while succeeding in producing syntactically valid output for both domains, acted as a powerful semantic filter biased toward the structure of Domain B, effectively erasing the specific constraints of Domain A. The shift was not gradual; rather, it appears as a phase transition in the semantic vector space.

Discussion and Implications

The findings underscore a fundamental limitation in sequential, lossy translation architectures when applied to highly specialized knowledge domains. The successful transmission of information is conflated with the successful transmission of conceptual constraint. When the constraints are encoded sparsely, as in advanced physics models, the generalized embedding space of Stage 2 cannot simultaneously encode both the specific technicality and the potential for abstraction.

If we treat the translation pipeline as a dynamical system, conceptual drift occurs when the eigenvalues of the transformation matrices are unevenly distributed across frequency bands corresponding to domain-specific information versus generalized noise. To mitigate this, we propose inserting a localized ‘Domain Anchor’ module between Stage 1 and Stage 2. This anchor acts as a constraint injection layer, forcing the intermediate representation to retain critical tensors related to the initial domain’s invariants before entering the generalized bottleneck [3].

Table 2 summarizes the effect of applying a Domain Anchor module versus no intervention.

Table 2: Effect of Domain Anchor Insertion on Conceptual Drift ($\Delta S$)

Conclusion

Conceptual drift is not merely a translation error; it is a predictable thermodynamic process within the computational system. By quantifying the semantic vector trajectory through sequential translation layers, we have successfully modeled the rate and mechanism of technical meaning degradation. The introduction of domain-specific anchors presents a viable, theoretically sound mitigation strategy against the erosion of conceptual fidelity during cross-domain semantic mapping. Future work will explore dynamic scaling factors for anchor strength based on the entropy load of the input text [4].