This paper describes the submission of the HITS team to the DISRPT 2025 shared task. The shared task includes three sub-tasks: (1) discourse unit segmentation across formalisms, (2) cross-lingual discourse connective identification, and (3) cross-formalism discourse relation classification. This paper presents our strategies for the DISRPT 2025 Shared Task. In Task 1, our approach involves fine-tuning through multilingual joint training on linguistically motivated language groups. We incorporated two key techniques to improve model performance: a weighted loss function to address the task’s significant class imbalance and Fast Gradient Method (FGM) adversarial training to boost the model’s robustness. In task 2, our approach involves building an ensemble of three encoder models whose embeddings are smartly fused together with a multi-head attention layer. We also add Part-Of-Speech tags and dependency relations present in the training file as linguistic features. A CRF layer is added after the classification layer to account for dependencies between adjacent labels. To account for label imbalance, we use focal loss and label smoothing. This ensures our model is robust and flexible enough to handle different languages. In task 3, we use two-stage fine-tuning framework designed to transfer the nuanced reasoning capabilities of a very large “teacher” model to a compact “student” model so that the smaller model can learn complex discourse relationships. The fine-tuning process follows a curriculum learning framework. In such a framework the model learns to perform increasingly harder tasks. In our case, the model first learns to look at the discourse units and then predict the label followed by looking at Chain-Of-Thought reasoning for harder examples. This way it can learn to internalise such reasoning and increase prediction accuracy on the harder samples.

Understanding temporal relations between events in a text is essential for determining its temporal structure. Recent advancements in large language models (LLMs) have spurred research on temporal relation extraction. However, LLMs perform poorly in zero-shot and few-shot settings, often underperforming smaller fine-tuned models. Despite these limitations, little attention has been given to improving LLMs in temporal structure extraction tasks. This study systematically examines LLMs’ ability to extract and infer discourse-level temporal relations, identifying factors influencing their reasoning and extraction capabilities, including input context, reasoning process and ensuring consistency. We propose a three-step framework to improve LLMs’ temporal relation extraction capabilities: context selection, prompts inspired by Allen’s interval algebra (Allen, 1983), and reflection-based consistency learning (Shinn et al., 2024). Our results show the effectiveness of our method in guiding LLMs towards structured processing of temporal structure in discourse.

HITS participated in the Discourse Segmentation (DS, Task 1) and Connective Detection (CD, Task 2) tasks at the DISRPT 2023. Task 1 focuses on segmenting the text into discourse units, while Task 2 aims to detect the discourse connectives. We deployed a framework based on different pre-trained models according to the target language for these two tasks.HITS also participated in the Relation Classification track (Task 3). The main task was recognizing the discourse relation between text spans from different languages. We designed a joint model for languages with a small corpus while separate models for large corpora. The adversarial training strategy is applied to enhance the robustness of relation classifiers.

Translating procedural text, like recipes, into a graphical representation can be important for visualizing the text, and can offer a machine-readable formalism for use in software. There are proposals for translating recipes into a flow graph representation, where each node represents an ingredient, action, location, or equipment, and each arc between the nodes denotes the steps of the recipe. However, these proposals have had performance problems with both named entity recognition and relationship extraction. To address these problems, we propose a novel framework comprising two modules to construct a flow graph from the input recipe. The first module identifies the named entities in the input recipe text using BERT, Bi-LSTM and CRF, and the second module uses BERT to predict the relationships between the entities. We evaluate our framework on the English recipe flow graph corpus. Our framework can predict the edge label and achieve the overall F1 score of 92.2, while the baseline F1 score is 43.3 without the edge label predicted.