  At the confluence of 6G, deep learning (DL), and natural language processing
(NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G
enabler by promising to minimize bandwidth consumption, transmission delay, and
power usage. Among text SemCom techniques, \textit{DeepSC} is a popular scheme
that leverages advancements in DL and NLP to reliably transmit semantic
information in low signal-to-noise ratio (SNR) regimes. To understand the
fundamental limits of such a transmission paradigm, our recently developed
theory \cite{Getu'23_Performance_Limits} predicted the performance limits of
DeepSC under radio frequency interference (RFI). Although these limits were
corroborated by simulations, trained deep networks can defy classical
statistical wisdom, and hence extensive computer experiments are needed to
validate our theory. Accordingly, this empirical work follows concerning the
training and testing of DeepSC using the proceedings of the European Parliament
(Europarl) dataset. Employing training, validation, and testing sets
\textit{tokenized and vectorized} from Europarl, we train the DeepSC
architecture in Keras 2.9 with TensorFlow 2.9 as a backend and test it under
Gaussian multi-interferer RFI received over Rayleigh fading channels.
Validating our theory, the testing results corroborate that DeepSC produces
semantically irrelevant sentences as the number of Gaussian RFI emitters gets
very large. Therefore, a fundamental 6G design paradigm for
\textit{interference-resistant and robust SemCom} (IR$^2$ SemCom) is needed.
