Papers with semantic task
AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task. |
| Approach: | They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context. |
| Outcome: | The proposed model improves ABSA on four public datasets with 1.13% improvement over baselines. |
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning (2023.eacl-main)
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| Challenge: | Recent advances apply artificial intelligence to predict clinical events or infer the probable diagnosis for clinical decision support. |
| Approach: | They propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning. |
| Outcome: | Experiments on clinical notes from the real-world MIMIC database show that the proposed model can achieve better performance than baselines and improve zero-shot prediction on unseen diagnoses. |
Strong and Light Baseline Models for Fact-Checking Joint Inference (2021.findings-acl)
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| Challenge: | Automated fact checking is rapidly gaining attention of the NLP and AI communities. |
| Approach: | They propose lightweight strong baselines for automated fact-checking systems . they propose to combine multiple pieces of evidence to verify a claim . |
| Outcome: | The proposed methods outperform heavier models on the leaderboard with blind TEST set. |
Multimodal Differential Network for Visual Question Generation (D18-1)
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| Challenge: | Current dialog systems show improvement in visual question answering but this does not translate to improved human-AI dialog. |
| Approach: | They propose to use a Multimodal Differential Network to generate natural questions from images using a multimodal differential network. |
| Outcome: | The proposed approach significantly improves over state-of-the-art benchmarks on the quantitative metrics. |
Assessing Word Importance Using Models Trained for Semantic Tasks (2023.findings-acl)
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| Challenge: | Many NLP tasks require to automatically identify the most significant words in a text. |
| Approach: | They propose to use attribution methods to explain the predictions of two NLP tasks to derive word significance from models trained to solve semantic tasks. |
| Outcome: | The proposed method is robust to the initial task and is able to identify important words in sentences without explicit word importance labeling in training. |
Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)
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| Challenge: | Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries . |
| Approach: | They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations. |
| Outcome: | The proposed framework is more efficient than existing methods. |