You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations (2020.aacl-main)
Copied to clipboard
| Challenge: | Several studies have addressed explainable recommendations that produce natural language sentences . however, this task cannot explain detailed evidences for each hotel . |
| Approach: | They propose to decompose the process into two subtasks: Evidence Identification and Evidence Explanation. |
| Outcome: | The proposed model can explain evidences in recommending hotels given vague requests . it can find evidence sentences with respect to various vague requests and generate recommendation sentences . |
Similar Papers
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
| Approach: | They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. |
| Outcome: | The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures. |
On Evaluating Explanation Utility for Human-AI Decision Making in NLP (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a lack of evidence that explanations help people in situations they are introduced for is a problem in NLP . prior work on explainability has focused on overcoming technical challenges and used proxy evaluations. |
| Approach: | They propose to use existing metrics to evaluate the effectiveness of explanations in NLP . they argue that providing AI predictions does not cause decision makers to speed up work . |
| Outcome: | The proposed evaluations show that providing AI predictions does not cause decision makers to speed up their work without compromising performance. |
Multi-Domain Explainability of Preferences (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for generating concept-based explanations of preferences are poorly understood. |
| Approach: | They propose a method for generating local and global concept-based explanations of preferences across multiple domains using an LLM. |
| Outcome: | The proposed method outperforms baselines while also being explainable. |
Deep Natural Language Feature Learning for Interpretable Prediction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Using a small transformer language model, we can break down a complex task into a set of intermediary easier sub-tasks. |
| Approach: | They propose a method to break down a main task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. |
| Outcome: | The proposed method breaks down a complex task into a set of easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
Copied to clipboard
| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Towards Explainable NLP: A Generative Explanation Framework for Text Classification (P19-1)
Copied to clipboard
| Challenge: | Existing approaches for explainable machine learning systems focus on interpreting outputs or connections between inputs and outputs. |
| Approach: | They propose a generative explanation framework that learns to make classification decisions and generates fine-grained explanations at the same time. |
| Outcome: | The proposed framework surpasses all baselines on two datasets and generates concise explanations at the same time. |
ClozEx: A Task toward Generation of English Cloze Explanation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing tasks and datasets specifically designed for generating language learner explanations for cloze questions are lacking . clozing questions are used to assess language proficiency and enhance language learning . |
| Approach: | They propose a task ClozEx to generate explanations for cloze questions in LA . they use a curated dataset of clozing questions paired with explanations . |
| Outcome: | The proposed task generates fluent explanations for cloze questions in English as a second language learners. |
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing models that learn accurate representations of users and items are based on ratings, which oversimplify user preferences and item characteristics. |
| Approach: | They propose a novel, accurate, and explainable recommender model that integrates three key elements: BERT, multilayer perceptron, and maximum subarray problem to derive contextualized review features, model user-item interactions, and generate explanations. |
| Outcome: | The proposed model outperforms state-of-the-art models by an improvement gain of nearly 7% based on the human judges’ assessment . |
Retrieve to Explain: Evidence-driven Predictions for Explainable Drug Target Identification (2025.acl-long)
Copied to clipboard
| Challenge: | Existing language models lack the capability to quantitatively and faithfully compare answer plausibility in terms of supporting evidence. |
| Approach: | They propose a retrieval-based model that scores and ranks all possible answers to a research question based on evidence retrieved from a document corpus. |
| Outcome: | The proposed model scores and ranks all possible answers to a scientific question based on evidence retrieved from a document corpus . |