Explanation Generation

61 papers with code • 5 benchmarks • 9 datasets

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Use these libraries to find Explanation Generation models and implementations
2 papers

Most implemented papers

Explainable Automated Fact-Checking for Public Health Claims

neemakot/Health-Fact-Checking EMNLP 2020

We present the first study of explainable fact-checking for claims which require specific expertise.

AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations

mainuliitkgp/ar-bert 26 Aug 2021

We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models.

TE2Rules: Explaining Tree Ensembles using Rules

linkedin/TE2Rules 29 Jun 2022

Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic.

Explaining Patterns in Data with Language Models via Interpretable Autoprompting

csinva/imodelsX 4 Oct 2022

Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks.

Explaining black box text modules in natural language with language models

csinva/imodelsX 17 May 2023

Here, we ask whether we can automatically obtain natural language explanations for black box text modules.

Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation

mdda/worldtree_corpus WS 2019

The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions.

QED: A Framework and Dataset for Explanations in Question Answering

google-research-datasets/QED 8 Sep 2020

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust.

Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

nehasrikn/elaborative-simplification Findings (ACL) 2021

Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions.

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

JamesHujy/ELV NeurIPS 2020

In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training.