Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzing linguistic abnormalities.In this work, we explore the potential of Large Language Models (LLMs) as health assistants for AD diagnosis from patient-generated text using in-context learning (ICL), where tasks are defined through a few input-output examples.Empirical results reveal that conventional ICL methods, such as similarity-based selection, perform poorly for AD diagnosis, likely due to the inherent complexity of this task.To address this, we introduce Delta-KNN, a novel demonstration selection strategy that enhances ICL performance.Our method leverages a delta score to assess the relative gains of each training example, coupled with a KNN-based retriever that dynamically selects optimal "representatives" for a given input.Experiments on two AD detection datasets across three opensource LLMs demonstrate that Delta-KNN consistently outperforms existing ICL baselines.Notably, when using the Llama-3.1 model, our approach achieves new state-of-the-art results, surpassing even supervised classifiers. 1

We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and images.CEMTM builds on fine-tuned large vision language models (LVLMs) to obtain contextualized embeddings, and employs a distributional attention mechanism to weight token-level contributions to topic inference.A reconstruction objective aligns topic-based representations with the document embedding, encouraging semantic consistency across modalities.Unlike existing approaches, CEMTM can process multiple images per document without repeated encoding and maintains interpretability through explicit word-topic and documenttopic distributions.Extensive experiments on six multimodal benchmarks show that CEMTM consistently outperforms unimodal and multimodal baselines, achieving a remarkable average LLM score of 2.61 (1-3 scale).Further analysis shows its effectiveness in downstream few-shot retrieval and its ability to capture visually grounded semantics in complex domains such as scientific articles 1 .

Despite the challenges posed by data sparsity in discourse parsing for dialogues, unsupervised methods have been underexplored.Leveraging recent advances in Large Language Models (LLMs), in this paper we investigate an unsupervised coherence-based method to build discourse structures for multi-party dialogues using open-source LLMs fine-tuned on conversational data.Specifically, we propose two algorithms that extract dialogue structures by identifying their most coherent sub-dialogues: DS-DP employs a dynamic programming strategy, while DS-FLOW applies a greedy approach.Evaluation on the STAC corpus demonstrates a micro-F 1 score of 58.1%, surpassing prior unsupervised methods.Furthermore, on a cleaned subset of the Molweni corpus, the proposed method achieves a micro-F 1 score of 74.7%, highlighting its effectiveness across different corpora.

Discourse analysis studies the sentence organization within a document, aiming to reveal its underlying structural information.Existing works on dialogue discourse parsing mostly use encoder-only models and sophisticated decoding strategies to extract structures.Despite recent advances in Large Language Models (LLMs), applying directly these models on discourse parsing is challenging.To fully leverage the rich semantic and discourse knowledge in LLMs, we propose to transform discourse parsing into a generation task using a text-to-text paradigm.Our approach is intuitive and requires no modification of the LLM architecture.Experimental results on STAC and Molweni datasets show that a sequence-tosequence model such as T0 can perform reasonably well.Notably, our improved transitionbased sequence-to-sequence system achieves new state-of-the-art performance on Molweni.Furthermore, our systems can generate richer discourse structures such as graphs, whereas previous methods are mostly limited to trees. 1

This paper describes the continuation of a project that aims at establishing an interoperable annotation schema for quantification phenomena as part of the ISO suite of standards for semantic annotation, known as the Semantic Annotation Framework. After a break, caused by the Covid-19 pandemic, the project was relaunched in early 2022 with a second working draft of an annotation scheme, which is discussed in this paper. Keywords: semantic annotation, quantification, interoperability, annotation schema, ISO standard

Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others.This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity.We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction.We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling.We experiment on the DAIC and DailyDialog corpora -both contain dialogs in English -and show important improvements over state-ofthe-art on depression detection (at best 70.6% F 1 ), which demonstrates the correlation of depression with emotion and dialog organization and the power of MTL to leverage information from different sources.

The main aim of this paper is to provide a characterization of the response space for questions using a taxonomy grounded in a dialogical formal semantics.As a starting point we take the typology for responses in the form of questions provided in (Łupkowski and Ginzburg, 2016).This work develops a wide coverage taxonomy for question/question sequences observable in corpora including the BNC, CHILDES, and BEE, as well as formal modelling of all the postulated classes.Our aim is to extend this work to cover all responses to questions.We present the extended typology of responses to questions based on a corpus studies of BNC, BEE and Maptask with include 506, 262, and 467 question/response pairs respectively.We compare the data for English with data from Polish using the Spokes corpus (205 question/response pairs).We discuss annotation reliability and disagreement analysis.We sketch how each class can be formalized using a dialogical semantics appropriate for dialogue management.