Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews (N19-3)
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| Challenge: | Using Amazon reviews, we find that the answer to a question is only in 45% of cases. |
| Approach: | They combine Amazon reviews with consumer reviews and manually analyse 400 questions from four domains to find that reviews directly contain the answer to the question . they then compare QA systems that use reviews in addition to the questions to see if they can be useful for other question types. |
| Outcome: | The proposed system outperforms the chance baseline but not by a large margin. |
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| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
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On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction (2023.findings-eacl)
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| Challenge: | Existing methods for detecting helpful reviews focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. |
| Approach: | They propose to integrate reviewer's expertise and temporal dynamics to predict helpfulness for unreliable and cold-start reviews. |
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The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who (2023.findings-emnlp)
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| Challenge: | Automated fact-checking is often presented as an epistemic tool fact-seekers, social media consumers, and other stakeholders can use to fight misinformation. |
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Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers (D19-55)
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| Challenge: | Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns. |
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AnswerFact: Fact Checking in Product Question Answering (2020.emnlp-main)
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| Challenge: | a product-related community question answering platform is widely employed in many E-commerce sites . however, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information. |
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Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)
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| Challenge: | Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility. |
| Approach: | They present an argumentational argumentation model that annotates 878 Amazon reviews on headphones and uses it to evaluate argument quality. |
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Proceedings of the 2nd Workshop on Machine Reading for Question Answering (D19-58)
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| Challenge: | a workshop focuses on machine reading for question answering . despite recent progress, there is much to be desired about these datasets and systems . |
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
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I love pineapple on pizza != I hate pineapple on pizza: Stance-Aware Sentence Transformers for Opinion Mining (2024.emnlp-main)
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| Challenge: | Sentence transformers excel at grouping topically similar texts, but struggle to differentiate opposing viewpoints on the same topic. |
| Approach: | They propose to fine-tune sentence transformers with arguments for and against controversial claims to enhance their utility for social computing tasks. |
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Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
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| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
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