The paper presents a new dataset called the Les Misérables Dialogue temporal text network, created from the 1998 film adaptation of the novel. This network explicitly records speakers, addressees, other recipients, the content of each utterance, and its timing. Additionally, the authors generate multilayer network representations based on ten social dimensions, and provide a general methodology for constructing similar communication datasets from narrative sources.
arXiv:2606. 00012v1 Announce Type: cross Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations.
arXiv:2608. 02399v1 Announce Type: cross Abstract: Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag.
arXiv:2607. 02504v1 Announce Type: cross Abstract: Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \textbf{speaker recognition}, the task of accurately attributing each spoken utterance to its respective character.
arXiv:2608.20984v1 Announce Type: cross Abstract: Narratives are central to how social communication is framed, making their detection critical for understanding and analysing public discourse. Prior...
The paper explores how large language models can detect hidden narratives in social messages without training data. By feeding the models human-written narrative descriptions, performance improves markedly, while automatically generated descriptions or few-shot examples can hurt accuracy. Ensemble techniques, especially majority voting, further boost robustness, and larger models show the best results with less sensitivity to prompts.
The paper presents a detailed examination of narrative elements—agency, setting, and events—within the Dolma web-scale pretraining corpus. Using a framework of 11 interpretable dimensions, the authors hand‑annotated 400 passages, expanded this to a 25,000‑passage LLM‑labeled dataset, and trained NarraBERT models to predict narrative features across 13 million passages, producing the NarraDolma dataset. The study reveals that narrative structure is measurable at scale and that narrative qualities vary unevenly across different data sources, topics, and formats, highlighting gaps in current data curation practices.
Conversational DNA is a visual language and interactive atlas designed to explore human and AI dialogue by mapping speaker strands, communicative bases, and directed pairings. It visualizes speaker switching, response distance, and contribution length through adjustable helix geometry, and covers 151,489 episodes across eight corpora totaling 1.57 million source records. The system improves precision@5 on Molweni motif queries from 58.8% to 77.2% and demonstrates how annotation coverage affects perceived collection differences.
The paper introduces a method for forecasting conversational derailment by incorporating speech act information as an auxiliary signal to enhance pragmatic representations. This approach aims to reduce lexical noise and improve generalizability, especially in low-data and cross-domain scenarios. Experiments on three datasets demonstrate performance gains over existing methods.
Synthetic dialogue generation offers a way to study conversational dynamics in sensitive domains where real data are difficult to access, release, or annotate. The underlying abuse may occur online or offline: threats and coercion can appear directly in messages, while behaviours such as surveillance, isolation, stalking, and physical violence may be planned, disclosed, or referred to conversationally.
arXiv:2608. 11200v1 Announce Type: cross Abstract: Synthetic dialogue generation offers a way to study conversational dynamics in sensitive domains where real data are difficult to access, release, or annotate.
The paper introduces the character naming task, where a model must identify which visible character in a film bears a name mentioned in dialogue, using only the movie’s video and dialogue without external cast lists or actor photos. It highlights the difficulty of name resolution due to cinematic cues such as shot-reverse-shot structure, self-introduction, direct address, third-party reference, gaze, and temporal continuity. The authors provide a dataset and annotations, develop a video-and-dialogue model, compare it with large multimodal language models, and propose a progressive framework for building a character bank as the film unfolds.
arXiv:2609.36218v1 Announce Type: cross Abstract: Large language models are increasingly evaluated in specialized domains such as law, medicine, software engineering, and cybersecurity, yet film rema...