Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation.Over the last few decades, multiple efforts have been undertaken to investigate incorrect translations caused by the polysemous nature of words.Within this body of research, some studies have posited that models pick up semantic biases existing in the training data, thus producing translation errors.In this paper, we present DIBIMT, the first entirely manuallycurated evaluation benchmark which enables an extensive study of semantic biases in Machine Translation of nominal and verbal words in five different language combinations, namely, English and one or other of the following languages: Chinese, German, Italian, Russian and Spanish.Furthermore, we test state-of-the-art Machine Translation systems, both commercial and non-commercial ones, against our new test bed and provide a thorough statistical and linguistic analysis of the results.We release DIBIMT at https:// nlp.uniroma1.it/dibimtas a closed benchmark with a public leaderboard.

Niccolò Campolungo, Tommaso Pasini, Denis Emelin, Roberto Navigli. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably.However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets.In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information.At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query.Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automatically created datasets are of a higher quality than those generated by other competitors and lead a supervised model to achieve state-of-the-art performances in all multilingual Word Sense Disambiguation tasks. We make our datasets available for research purposes at https://github.com/SapienzaNLP/mulan.