# A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition arXiv: https://arxiv.org/abs/2112.11740 ยท HTML: https://mlsanity.com/paper/2112.11740/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 49 references that are in the map. - First posted on arXiv 22 Dec 2021. ## Builds on (most cited first, 20 of 20) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017) - [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018) - [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018) - [Towards Automated ICD Coding Using Deep Learning](https://mlsanity.com/paper/1711.04075/) (2017) - [Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification](https://mlsanity.com/paper/1909.04176/) (2019) - [Asynchronous Bidirectional Decoding for Neural Machine Translation](https://mlsanity.com/paper/1801.05122/) (2018) - [Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/1807.05154/) (2018) - [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1704.00217/) (2017) - [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://mlsanity.com/paper/1609.06380/) (2016) - [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://mlsanity.com/paper/1811.01697/) (2018) - [From Discourse to Narrative: Knowledge Projection for Event Relation Extraction](https://mlsanity.com/paper/2106.08629/) (2021) - [Implicit Discourse Relation Classification via Multi-Task Neural Networks](https://mlsanity.com/paper/1603.02776/) (2016) - [Variational Neural Discourse Relation Recognizer](https://mlsanity.com/paper/1603.03876/) (2016) ## Built on by (most cited first, 18 of 18) - [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2305.03973/) (2023) - [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://mlsanity.com/paper/2301.02724/) (2023) - [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2210.07032/) (2022) - [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://mlsanity.com/paper/2306.06480/) (2023) - [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2402.15080/) (2024) - [A Survey of Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2203.02982/) (2022) - [Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation](https://mlsanity.com/paper/2309.01717/) (2023) - [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2211.13873/) (2022) - [Implicit Discourse Relation Classification For Nigerian Pidgin](https://mlsanity.com/paper/2406.18776/) (2024) - [Leveraging Hierarchical Prototypes as the Verbalizer for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2411.14880/) (2024) - [A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2408.08971/) (2024) - [Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations](https://mlsanity.com/paper/2306.10658/) (2023) - [Getting the Most out of Simile Recognition](https://mlsanity.com/paper/2211.05984/) (2022) - [Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2311.00367/) (2023) - [An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction](https://mlsanity.com/paper/2405.09735/) (2024) - [Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition](https://mlsanity.com/paper/2409.13716/) (2024) - [LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models](https://mlsanity.com/paper/2509.15089/) (2025) - [Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs](https://mlsanity.com/paper/2602.21763/) (2026) ## Abstract (arXiv) Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels independently, while ignoring the dependence between hierarchically structured labels. In this paper, we consider multi-level IDRR as a conditional label sequence generation task and propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we first design a label attentive encoder to learn the global representation of an input instance and its level-specific contexts, where the label dependence is integrated to obtain better label embeddings. Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level. We further develop a mutual learning enhanced training method to exploit the label dependence in a bottomup direction, which is captured by an auxiliary decoder introduced during training. Experimental results on the PDTB dataset show that our model achieves the state-of-the-art performance on multi-level IDRR. We will release our code at https://github.com/nlpersECJTU/LDSGM.