Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
- URL: http://arxiv.org/abs/2402.14798v3
- Date: Mon, 12 Aug 2024 23:47:48 GMT
- Title: Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
- Authors: Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, Benjamin Van Durme,
- Abstract summary: We introduce a consistent and theoretically grounded approach to annotating decompositional entailment.
We find that our new dataset, RDTE, has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets.
We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality.
- Score: 51.967603572656266
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what valid compositional entailment is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. We find that our new dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality, illustrating the practical benefit of this advance for textual inference.
Related papers
- Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models [20.107727903240065]
We propose DefinitionEMB to re-construct isotropically distributed and semantics-related token embeddings for encoder-based language models.
Our experiments demonstrate the effectiveness of leveraging definitions from Wiktionary to re-construct such embeddings.
arXiv Detail & Related papers (2024-08-02T15:00:05Z) - Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning [16.408149489677154]
ChapTER is a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning.
We evaluate ChapTER on four transductive and three few-shot inductive TKGR benchmarks.
arXiv Detail & Related papers (2024-03-25T17:25:40Z) - A Logical Pattern Memory Pre-trained Model for Entailment Tree
Generation [23.375260036179252]
Generating coherent and credible explanations remains a significant challenge in the field of AI.
We propose the logical pattern memory pre-trained model (LMPM)
Our model produces more coherent and reasonable conclusions that closely align with the underlying premises.
arXiv Detail & Related papers (2024-03-11T03:45:09Z) - Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
We propose Mulan, a unified multi-modal causal structure learning method for root cause localization.
We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data.
We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
arXiv Detail & Related papers (2024-02-04T05:50:38Z) - How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored.
Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges.
We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
arXiv Detail & Related papers (2023-11-14T08:51:00Z) - 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) - MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning [63.50909998372667]
We propose MERIt, a MEta-path guided contrastive learning method for logical ReasonIng of text.
Two novel strategies serve as indispensable components of our method.
arXiv Detail & Related papers (2022-03-01T11:13:00Z) - LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with
Self-training [76.90793623822866]
We propose a unified framework for logical knowledge-conditioned text generation in the few-shot setting.
Our approach leverages self-training and samples pseudo logical forms based on content and structure consistency.
arXiv Detail & Related papers (2021-12-02T16:49:41Z) - 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)
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.