Joseph Renner


Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks
Joseph Renner | Pascal Denis | Remi Gilleron | Angèle Brunellière
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

The psychological plausibility of word embeddings has been studied through different tasks such as word similarity, semantic priming, and lexical entailment. Recent work on predicting category structure with word embeddings report low correlations with human ratings. (Heyman and Heyman, 2019) showed that static word embeddings fail at predicting typicality using cosine similarity between category and exemplar words, while (Misra et al., 2021)obtain equally modest results for various contextual language models (CLMs) using a Cloze task formulation over hand-crafted taxonomic sentences. In this work, we test a wider array of methods for probing CLMs for predicting typicality scores. Our experiments, using BERT (Devlin et al., 2018), show the importance of using the right type of CLM probes, as our best BERT-based typicality prediction methods improve on previous works. Second, our results highlight the importance of polysemy in this task, as our best results are obtained when contextualization is paired with a disambiguation mechanism as in (Chronis and Erk, 2020). Finally, additional experiments and analyses reveal that Information Content-based WordNet (Miller, 1995) similarities with disambiguation match the performance of the best BERT-based method, and in fact capture complementary information, and when combined with BERT allow for enhanced typicality predictions.


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Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)
Tatiana Anikina | Natalia Skachkova | Joseph Renner | Priyansh Trivedi
Proceedings of the CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

We describe three models submitted for the CODI-CRAC 2022 shared task. To perform identity anaphora resolution, we test several combinations of the incremental clustering approach based on the Workspace Coreference System (WCS) with other coreference models. The best result is achieved by adding the “cluster merging” version of the coref-hoi model, which brings up to 10.33% improvement1 over vanilla WCS clustering. Discourse deixis resolution is implemented as multi-task learning: we combine the learning objective of coref-hoi with anaphor type classification. We adapt the higher-order resolution model introduced in Joshi et al. (2019) for bridging resolution given gold mentions and anaphors.


An End-to-End Approach for Full Bridging Resolution
Joseph Renner | Priyansh Trivedi | Gaurav Maheshwari | Rémi Gilleron | Pascal Denis
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

In this article, we describe our submission to the CODI-CRAC 2021 Shared Task on Anaphora Resolution in Dialogues – Track BR (Gold). We demonstrate the performance of an end-to-end transformer-based higher-order coreference model finetuned for the task of full bridging. We find that while our approach is not effective at modeling the complexities of the task, it performs well on bridging resolution, suggesting a need for investigations into a robust anaphor identification model for future improvements.