SELEXINI

Despite great enthusiasm for deep learning in NLP, concern is rising about its limitations. First, neural models are often blackboxes, and their behavior is hard to interpret. Second, benchmark-based evaluation overlooks biases, questioning the robustness and coverage of the resulting generalisations, yielding a landscape of overall diversity. The goal of the SELEXINI project is to address these issues by developing **weakly supervised methods to induce semantic lexicons** from raw corpora, which will then be **seamlessly integrated with semantic text processing models**. Lexical units are seen as useful abstractions that allow representing complex phenomena (e.g. polysemy, similarity, multiword units) associated with interpretable labels, avoiding the overhead and opaqueness of contextualized embeddings (one vector per occurrence). Moreover, our lexicon will combine continuous data (embeddings, clusters) and symbolic data (labels). We will model single and multiword units, their senses, and their semantic frames (arguments, roles). Hence, we propose a new "by-construction" view on interpretability, which can be seen as an alternative to methods trying to dissect complex neural models. For extrinsic evaluation of interpretability and diversity, the induced lexicon will be integrated into standard deep learning models in downstream tasks requiring semantic information: machine reading comprehension and multiword expressions identification. We will develop an experimental protocol to assess the lexicon-corpus complementarity on diverse linguistic phenomena, and to assess the lexicon's usefulness for non-expert end users requiring interpretable results. We expect that this original approach will increase both the interpretability of models and the coverage of diverse phenomena (e.g. rare/unseen items in training data).