Traditional approaches to dialogue segmentation perform reasonably well on synthetic or written dialogues but suffer when dealing with spoken, noisy dialogs. In addition, such methods require careful tuning of hyperparameters. We propose to leverage a novel approach that is based on dialogue summaries. Experiments on different datasets showed that the new approach outperforms popular state-of-the-art algorithms in unsupervised topic segmentation and requires less setup.

The recent integration of chemistry with natural language processing (NLP) has advanced drug discovery. Molecule representation in language models (LMs) is crucial in enhancing chemical understanding. We propose Augmented Molecular Retrieval (AMORE), a flexible zero-shot framework for assessment of Chemistry LMs of different natures: trained solely on molecules for chemical tasks and on a combined corpus of natural language texts and string-based structures. The framework relies on molecule augmentations that preserve an underlying chemical, such as kekulization and cycle replacements. We evaluate encoder-only and generative LMs by calculating a metric based on the similarity score between distributed representations of molecules and their augmentations. Our experiments on ChEBI-20 and QM9 benchmarks show that these models exhibit significantly lower scores than graph-based molecular models trained without language modeling objectives. Additionally, our results on the molecule captioning task for cross-domain models, MolT5 and Text+Chem T5, demonstrate that the lower the representation-based evaluation metrics, the lower the classical text generation metrics like ROUGE and METEOR.

Understanding image advertisements is a challenging task, often requiring non-literal interpretation. We argue that standard image-based predictions are insufficient for symbolism prediction. Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR. The resulting system establishes a new state of the art in symbolism prediction.