Challenge: Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks .
Approach: They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates.
Outcome: The proposed model achieves new SOTA on CSQA, QASC, and OBQA.

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Prompting Contrastive Explanations for Commonsense Reasoning Tasks (2021.findings-acl)

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Challenge: Large pretrained language models (PLMs) can achieve near-human performance on commonsense reasoning tasks, but provide little human-interpretable evidence of the underlying reasoning they use.
Approach: They propose to use large pretrained language models to generate evidence for commonsense reasoning NLP tasks . they use models to contrast alternative explanations based on key attribute(s) required to justify the correct answer .
Outcome: The proposed model improves performance on two commonsense reasoning benchmarks compared to previous non-contrastive alternatives.
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference (2021.acl-long)

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Challenge: Existing approaches in NLP focus on “WHY A” rather than contrastive “WHA NOT B” Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones.
Approach: They propose to generate contrastive explanations with counterfactual examples in NLI by identifying key phrases from input sentences and using them as key perturbations to generate counterfacts.
Outcome: The proposed framework improves on SNLI and ETPA models by 91.9%.
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)

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Challenge: Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC).
Approach: They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem.
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KE-GCL: Knowledge Enhanced Graph Contrastive Learning for Commonsense Question Answering (2022.findings-emnlp)

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Challenge: Existing models for commonsense question answering lack effective representations of knowledge graphs.
Approach: They propose a Knowledge Enhanced Graph Contrastive Learning model by incorporating contextual descriptions into QA pairs and adopting a graph contrastive learning scheme.
Outcome: The proposed model outperforms existing methods consistently on two benchmark datasets.
Data-Efficient Concept Extraction from Pre-trained Language Models for Commonsense Explanation Generation (2022.findings-emnlp)

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Challenge: Existing methods to extract concepts from pre-trained language models are not suitable for commonsense explanation generation.
Approach: They propose a method to extract the key explanation concept from pre-trained language models by fine-tuning it with 20% training data and using a metric to evaluate the retrieved concepts.
Outcome: The proposed method improves evaluation metrics over pre-trained language models and the existing models.
Generated Knowledge Prompting for Commonsense Reasoning (2022.acl-long)

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Challenge: Existing methods for commonsense reasoning rely on high-quality knowledge, but they are often dominated by large-scale pretrained models that are fine-tuned on a target benchmark.
Approach: They develop generated knowledge prompting which generates knowledge from a language model and provides it as additional input when answering a question.
Outcome: The proposed method improves state-of-the-art models on four commonsense reasoning tasks.
Contrastive Pre-training for Personalized Expert Finding (2023.findings-emnlp)

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Challenge: Existing approaches to expert finding are effective for a community question answering platform.
Approach: They propose a CQA-domain Contrastive Pre-training framework for Expert Finding which could learn more comprehensive question representations.
Outcome: The proposed framework could learn more comprehensive question representations on six real-world datasets.
Fixed That for You: Generating Contrastive Claims with Semantic Edits (N19-1)

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Challenge: Understanding contrastive opinions is a key component of argument generation.
Approach: They create a corpus of Reddit comment pairs and train neural models to edit the original claim and produce a new claim with a different view.
Outcome: The proposed model improves on a sequence-to-sequence baseline and compared to a human evaluation for fluency, coherence, and contrast.
Contrastive Explanations for Model Interpretability (2021.emnlp-main)

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Challenge: Existing methods for producing model explanations seek all causal factors at once, making them difficult to comprehend.
Approach: They propose a method to produce contrastive explanations in the latent space . they use attribution and token/span attribution to produce models that consider only contrastive reasoning .
Outcome: The proposed method allows model behavior to consider only contrastive reasoning . it also uncovers which aspects of the input are useful for and against particular decisions .
Let the CAT out of the bag: Contrastive Attributed explanations for Text (2022.emnlp-main)

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Challenge: XAI has seen an explosion of interest in explaining black box behavior . contrastive/counterfactual explanations have seen a surge of interest recently .
Approach: They propose a method which provides contrastive explanations for natural language text data with a novel twist by exploiting attribute classifiers.
Outcome: The proposed method outperforms state-of-the-art methods on four benchmark metrics.

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