Papers with natural language processing communities

4 papers
ConceptBert: Concept-Aware Representation for Visual Question Answering (2020.findings-emnlp)

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Challenge: Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities.
Approach: They propose an algorithm which learns a joint Concept-Vision-Language embedding for questions which require common sense knowledge from external structured content.
Outcome: The proposed model is based on the Outer Knowledge-VQA and VQA datasets.
Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)

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Challenge: Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs .
Approach: They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers.
Outcome: The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets.
Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency (2022.emnlp-main)

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Challenge: Abstractive summarization systems have a lack of a defined definition for the task . factual consistency is a key factor in summarizing, but there are still deficiencies . a new study shows that summarized summarisation models achieve improved performance .
Approach: They propose a filtered summarization dataset with improved factual consistency to address this problem . they argue that the dataset should become a valid benchmark for developing and evaluating summarizing systems .
Outcome: The proposed model improves on a popular summarization dataset with improved factual consistency.
A Survey of Automatic Personality Detection from Texts (2020.coling-main)

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Challenge: Personality profiling has long been used in psychology to predict life outcomes.
Approach: They present the trajectory of automatic personality detection from purely psychology approaches to the latest purely natural language processing approaches on large social media datasets.
Outcome: The proposed models have been compared with the most recent approaches on large social media datasets.

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