  This paper introduces PyDCI, a new implementation of Distributional
Correspondence Indexing (DCI) written in Python. DCI is a transfer learning
method for cross-domain and cross-lingual text classification for which we had
provided an implementation (here called JaDCI) built on top of JaTeCS, a Java
framework for text classification. PyDCI is a stand-alone version of DCI that
exploits scikit-learn and the SciPy stack. We here report on new experiments
that we have carried out in order to test PyDCI, and in which we use as
baselines new high-performing methods that have appeared after DCI was
originally proposed. These experiments show that, thanks to a few subtle ways
in which we have improved DCI, PyDCI outperforms both JaDCI and the
above-mentioned high-performing methods, and delivers the best known results on
the two popular benchmarks on which we had tested DCI, i.e.,
MultiDomainSentiment (a.k.a. MDS -- for cross-domain adaptation) and
Webis-CLS-10 (for cross-lingual adaptation). PyDCI, together with the code
allowing to replicate our experiments, is available at
https://github.com/AlexMoreo/pydci .
