WordNet is a database that represents relations between words and concepts as an abstraction of the contexts in which words are used.Contextualized language models represent words in contexts but leave the underlying concepts implicit.In this paper, we investigate how different layers of a pre-trained language model shape the abstract lexical relationship toward the actual contextual concept.Can we define the amount of contextualized concept forming needed given the abstracted representation of a word?Specifically, we consider samples of words with different polysemy profiles shared across three languages, assuming that words with a different polysemy profile require a different degree of concept shaping by context.We conduct probing experiments to investigate the impact of prior polysemy profiles on the representation in different layers.We analyze how contextualized models can approximate meaning through context and examine crosslingual interference effects.

In this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge’ez. The corpora are used for conducting a bi-directional statistical machine translation experiments. The BLEU scores of the bi-directional Statistical Machine Translation (SMT) systems show a promising result. The morphological richness of the Ethiopian languages has a great impact on the performance of SMT specially when the targets are Ethiopian languages. Now we are working towards an optimal alignment for a bi-directional English-Ethiopian languages SMT.