Papers with TSV

6 papers
WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks (2022.naacl-main)

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Challenge: Word-in-Context (WiC) task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-Wic SemEval shared task.
Approach: They propose to use lexical resources from word sense disambiguation and target sense verification to reduce the relationship between the two tasks.
Outcome: The proposed methods can be pairwise reduced to each other and therefore work in practice.
WiC-TSV-de: German Word-in-Context Target-Sense-Verification Dataset and Cross-Lingual Transfer Analysis (2022.lrec-1)

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Challenge: Target Sense Verification is a binary disambiguation task that requires a sense-inventory-independent model to be developed.
Approach: They propose a multi-domain dataset for German Target Sense Verification . they use a domain-independent instance to train and develop sense-inventory-independence models .
Outcome: The proposed model fails to solve the German target Sense Verification dataset . the multi-domain dataset contains domain-bound subsets from four different domains .
WikiHan: A New Comparative Dataset for Chinese Languages (2022.coling-1)

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Challenge: Currently, there are 1.3 billion speakers of Sinitic varieties, making the family one of the largest in terms of speaker count.
Approach: They have collected a single constituent and structured form of Chinese varieties for comparative linguistics and Chinese NLP.
Outcome: The proposed dataset contains 67,943 entries across 8 varieties and Middle Chinese . it achieves 54.11% accuracy and 17.69% error rate on a protoform reconstruction task .
The ACoLi Dictionary Graph (2020.lrec-1)

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Challenge: ACoLi Dictionary Graph is a collection of multilingual open source dictionaries available in two machine-readable formats.
Approach: They propose to map and harmonize ACoLi Dictionary Graph into a unified representation and a tabular data format to facilitate their use in NLP tasks.
Outcome: The ACoLi Dictionary Graph is a collection of multilingual open source dictionaries available in two machine-readable formats.
Game on Tree: Visual Hallucination Mitigation via Coarse-to-Fine View Tree and Game Theory (2024.emnlp-main)

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Challenge: Large vision-language models produce unfaithful visual hallucinations, also known as visual halluinations, which hinders their application in multimodal understanding and decision-making.
Approach: They propose a plug-and-play train-free decoding algorithm for mitigating visual hallucinations . they leverage visual information to construct a coarse-to-fine visual view tree .
Outcome: The proposed algorithm reduces visual hallucinations (VH) by leveraging visual information to construct a coarse-to-fine visual view tree (CFTree)
Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework (2024.lrec-main)

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Challenge: Large Language Models (LLMs) are becoming increasingly influential in reasoning tasks, but they lack trustworthiness and introspective self-awareness when subjected to complex reasoning tasks.
Approach: They propose a framework to explore LLMs’ trustworthiness, introspective self-awareness, and collaborative reasoning by using the Think-Solve-Verify framework.
Outcome: The proposed approach improves from 67.3% to 72.8% on the AQuA dataset and demonstrates the model’s ability to explain the given answers.

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