Discourse Embellishment Using a Deep Encoder-Decoder Network

A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure

Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years. However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse. In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS pa…

DeDisCo 在 DISRPT 2025 共享任务中的系统：一种话语关系分类系统

DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification

本文提出 DeDisCo 系统，通过对比 mt5 编码器与 Qwen 解码器方法，并结合低资源语言数据增强和额外语言特征，在 DISRPT 2025 话语关系分类任务中取得 71.28 的宏准确率。

ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph tha…

Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel varia…

Deep Keyphrase Completion

Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval. Though previous studies have made substantial efforts for automated keyphrase extraction and generation, surprisingly, few studies have been made for \textit{keyphrase completion} (KPC). KPC aims to generate more keyphrases for document (e.g. scientific publication) taking advantage of document content along with a very limited number of known keyphrases, which can be applied to improve text indexing system…

我们能否通过使用大语言模型在RST语篇解析中取得显著成效？

Can we obtain significant success in RST discourse parsing by using Large Language Models?

本文探究大语言模型对RST语篇解析的作用，将自顶向下、自底向上解析策略转化为提示，用QLoRA微调Llama 2，70亿参数自底向上模型在三个基准上取得SOTA，且跨数据集泛化性良好。