The paper presents a Japanese discourse parsing system built on Segmented Discourse Representation Theory (SDRT) by translating existing English SDRT corpora with a large language model (LLM). Comparative experiments with prompt-based and fine-tuned LLMs show that Japanese and English parsers achieve similar performance, indicating cross‑lingual applicability of SDRT. Ablation studies reveal that Japanese discourse markers have little influence, while predictions rely mainly on contextual discourse relations rather than clause content, explaining the comparable results across typologically distinct languages.
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...
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.
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.
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.
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.
arXiv:2607. 18570v1 Announce Type: cross Abstract: Discourse relations provide document structure, critical to language understanding and enabling language model performance and ethicality.
The paper presents a retrieval‑augmented generation (RAG) system combined with prompt engineering for Japanese‑to‑Chinese translation, focusing on sentences with noun‑modifying clause constructions (NMCCs). It integrates linguistic analysis, embedding‑based retrieval, and prompt construction before generating translations with GPT‑4o, without altering the base model. Experiments on a 66‑sentence test set show that increasing the knowledge‑base size from 0 to 2,000 examples raises macro‑averaged BLEU scores from 24.28 to 29.96, indicating improved translation quality for NMCC‑rich sentences.
As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another. To explore this phenomenon, we created two corpora with multiple annotations of Czech texts, accompanied by annotators' explanations of their choices.
PragAlign is a reply‑assistance system that separates context reading from selective clarification, designed for multilingual settings. In evaluations with native Chinese and Japanese speakers, PragAlign achieved better rankings than baseline methods in Chinese and the highest top‑rank rate in Japanese, while both groups often chose the same top condition. The study highlights both shared and language‑specific judgment patterns that can inform culturally aware reply‑assistance tools.
The paper presents a corpus of 1,262 Classical Tamil verse‑commentary pairs and evaluates several neural representation learning models—including recurrent, Transformer, Siamese, mBART‑style encoder‑decoder, and decoder‑only language models—against a TF‑IDF baseline. Experiments reveal limited gains: token‑F1 scores range from 0.02 to 0.20, the encoder‑decoder continues to lower training loss even after validation loss rises, and the decoder‑only model only reproduces authentic word order in 95.5% of minimal‑pair tests but fails to generate held‑out commentary content. The authors release the extraction and evaluation protocol while noting that redistribution of the source commentaries requires permission.
arXiv:2609.36214v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematic...
arXiv:2609.39225v1 Announce Type: new Abstract: Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. Whil...