Uri Berger, Edoardo Ponti. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

This work reimplements a recent semantic bootstrapping child language acquisition (CLA) model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning representations, and acquires both syntax and word meanings simultaneously. The results show that the model mostly transfers to Hebrew, but that a number of factors, including the richer morphology in Hebrew, makes the learning slower and less robust. This suggests that a clear direction for future work is to enable the model to leverage the similarities between different word forms.

Abstract The task of image captioning has recently been gaining popularity, and with it the complex task of evaluating the quality of image captioning models. In this work, we present the first survey and taxonomy of over 70 different image captioning metrics and their usage in hundreds of papers, specifically designed to help users select the most suitable metric for their needs. We find that despite the diversity of proposed metrics, the vast majority of studies rely on only five popular metrics, which we show to be weakly correlated with human ratings. We hypothesize that combining a diverse set of metrics can enhance correlation with human ratings. As an initial step, we demonstrate that a linear regression-based ensemble method, which we call EnsembEval, trained on one human ratings dataset, achieves improved correlation across five additional datasets, showing there is a lot of room for improvement by leveraging a diverse set of metrics.1

Indoor occupant sensing enables many smart home applications, and various sensing systems have been explored. Based on their installation requirements, we consider two categories of sensors – on- and off-body – and we look into the combination of them for occupant sensing due to their spatial and temporal complementarity. We focus on an example modality pair of wearable IMU and structural vibration that demonstrate modality complementarity in prior work. However, current efforts are built upon the assumption that the knowledge of the signal segments from two modalities are known, which is challenged in a multiple occupants co-living scenario. Therefore, establishing accurate cross-modal signal segment associations is essential to ensure that a correct complementary relationship is learned.

No abstract available.

Uri Berger, Gabriel Stanovsky, Omri Abend, Lea Frermann. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.