Boosting Neural Language Inference via Cascaded Interactive Reasoning

• URL: http://arxiv.org/abs/2505.06607v1
• Date: Sat, 10 May 2025 11:37:15 GMT
• Title: Boosting Neural Language Inference via Cascaded Interactive Reasoning
• Authors: Min Li, Chun Yuan,
• Abstract summary: Natural Language Inference (NLI) focuses on ascertaining the logical relationship between a given premise and hypothesis.<n>This task presents significant challenges due to inherent linguistic features such as diverse phrasing, semantic complexity, and contextual nuances.<n>We introduce the Cascaded Interactive Reasoning Network (CIRN), a novel architecture designed for deeper semantic comprehension in NLI.
• Score: 38.125341836302525
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Natural Language Inference (NLI) focuses on ascertaining the logical relationship (entailment, contradiction, or neutral) between a given premise and hypothesis. This task presents significant challenges due to inherent linguistic features such as diverse phrasing, semantic complexity, and contextual nuances. While Pre-trained Language Models (PLMs) built upon the Transformer architecture have yielded substantial advancements in NLI, prevailing methods predominantly utilize representations from the terminal layer. This reliance on final-layer outputs may overlook valuable information encoded in intermediate layers, potentially limiting the capacity to model intricate semantic interactions effectively. Addressing this gap, we introduce the Cascaded Interactive Reasoning Network (CIRN), a novel architecture designed for deeper semantic comprehension in NLI. CIRN implements a hierarchical feature extraction strategy across multiple network depths, operating within an interactive space where cross-sentence information is continuously integrated. This mechanism aims to mimic a process of progressive reasoning, transitioning from surface-level feature matching to uncovering more profound logical and semantic connections between the premise and hypothesis. By systematically mining latent semantic relationships at various representational levels, CIRN facilitates a more thorough understanding of the input pair. Comprehensive evaluations conducted on several standard NLI benchmark datasets reveal consistent performance gains achieved by CIRN over competitive baseline approaches, demonstrating the efficacy of leveraging multi-level interactive features for complex relational reasoning.

Related papers

• Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension [12.563060744760651]
  Relation-R1 is the first unified relational comprehension framework.<n>It integrates cognitive chain-of-thought (CoT)-guided Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO)<n>Experiments on widely-used PSG and SWiG datasets demonstrate that Relation-R1 achieves state-of-the-art performance in both binary and textitN-ary relation understanding.
  arXiv  Detail & Related papers  (2025-04-20T14:50:49Z)
• Semantic Layered Embedding Diffusion in Large Language Models for Multi-Contextual Consistency [0.0]
  The Semantic Layered Embedding Diffusion (SLED) mechanism redefines the representation of hierarchical semantics within transformer-based architectures.<n>By introducing a multi-layered diffusion process grounded in spectral analysis, it achieves a complex balance between global and local semantic coherence.<n> Experimental results demonstrate significant improvements in perplexity and BLEU scores, emphasizing the mechanism's ability to adapt effectively across diverse domains.
  arXiv  Detail & Related papers  (2025-01-26T05:17:04Z)
• Effective Context Modeling Framework for Emotion Recognition in Conversations [2.7175580940471913]
  Emotion Recognition in Conversations (ERC) facilitates a deeper understanding of the emotions conveyed by speakers in each utterance within a conversation.<n>Recent Graph Neural Networks (GNNs) have demonstrated their strengths in capturing data relationships.<n>We propose ConxGNN, a novel GNN-based framework designed to capture contextual information in conversations.
  arXiv  Detail & Related papers  (2024-12-21T02:22:06Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition [4.7938839332508945]
  We propose a Prompt-based Logical Semantics Enhancement (PLSE) method for Implicit Discourse Relation Recognition (IDRR) Our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-11-01T08:38:08Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Guiding the PLMs with Semantic Anchors as Intermediate Supervision: Towards Interpretable Semantic Parsing [57.11806632758607]
  We propose to incorporate the current pretrained language models with a hierarchical decoder network. By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks. We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines.
  arXiv  Detail & Related papers  (2022-10-04T07:27:29Z)
• HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction [24.853265244512954]
  We propose a hierarchical contrastive learning Framework for DistantlySupervised relation extraction (HiCLRE) to reduce noisy sentences. Specifically, we propose a three-level hierarchical learning framework to interact with cross levels, generating the de-noising context-aware representations. Experiments demonstrate that HiCLRE significantly outperforms strong baselines in various mainstream DSRE datasets.
  arXiv  Detail & Related papers  (2022-02-27T12:48:26Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.