arXiv:2605.28782v2 Announce Type: replace
Abstract: Discourse particles, such as well and kind of, are crucial components that enable LLMs to "speak" more like humans. They are used to convey emotion...
By Mariah Al Giptiah Binte Yusoff, Jakin Tan, Bocheng Chen, Guangliang Liu, Xi Chen
RAG‑RST is a retrieval‑augmented framework that supplies a formal discourse schema as in‑context demonstrations for Rhetorical Structure Theory parsing. By retrieving exemplar EDU pairs, it improves nuclearity F1 from 0.794 to 0.846 and relation F1 from 0.738 to 0.788 without any training. Two configurations—precomputed and dynamic—both outperform existing RST parsers, with the dynamic variant consistently strongest, and the approach shows that a formal linguistic schema can be provided to a foundation model as a demonstration rather than encoded in its parameters.
By Mohammadreza Sediqin, Shlomo Engelson Argamon
The paper introduces discourse dependency (DDP) as a continuous measure of translation difficulty based on how far back a segment must look to resolve references. DDP is computed from named entity re‑mentions and pronominal coreference, and is validated against gold coreference with high reliability. Applying DDP to recent WMT benchmarks reveals a bias toward low‑DDP segments, and experiments show that as DDP increases, no current context‑injection strategy matches human post‑editing quality.
By Ahrii Kim, Chanjun Park, Seong-heum Kim
The paper investigates how different representations of Korean constituency structure affect parsing performance. It compares three formats—Morpheme+XPOS, Eojeol+XPOS, and Eojeol+UPOS—derived from the Penn Korean Treebank, using gold segmentation and labels to evaluate transition-based parsers. Results show that fine-grained morphological and XPOS information yields the best parsing accuracy, while eojeol-based representations offer shorter transition sequences but lower performance when only UPOS is used.
By Jungyeul Park, KyungTae Lim, Zihao Huang, Eunkyul Leah Jo, Yige Chen, Chulwoo Park
The paper presents an end‑to‑end framework for extracting and clustering trilingual Sri Lankan parliamentary debates in Sinhala, Tamil, and English. Using LLM‑based text extraction, multilingual embeddings, and density‑based clustering, the authors recover 30 macro‑topics with a cluster purity of 0.673. The temporal patterns of these topics align with major national events such as the 2019 Easter attacks and the 2022 economic crisis, demonstrating the method’s effectiveness where traditional LDA fails.
By Himath Dhanapala, Haren Daishika, Himandhi Kuruppu, Sithija Seneviratne, Ashini Kavindya, Patalee Narasinghe, Sandeepa Weerasekara, Nisansa de Silva, Sandareka Wickramanayake
arXiv:2607. 18570v1 Announce Type: cross Abstract: Discourse relations provide document structure, critical to language understanding and enabling language model performance and ethicality.
By Abhidip Bhattacharyya, Shira Wein