DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions (2023.acl-long)
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| Challenge: | Modern machine learning relies on datasets to develop and validate research ideas. |
| Approach: | They propose a dataset recommendation system that uses a training set and an evaluation set to help people find relevant datasets. |
| Outcome: | The proposed model finds more relevant search results than existing third-party search engines. |
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| Challenge: | Empowering machines to understand scientific literature is crucial for accelerating scientific discovery and advancing the AI for Science paradigm. |
| Approach: | They propose a systematic taxonomy that organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
| Outcome: | The proposed taxonomy organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
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 . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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| Challenge: | Symbolic meaning representations of natural language text have been studied since at least the 1960s . with the availability of large annotated corpora, the field has recently seen several new developments . |
| Approach: | They propose a framework for expressing meaning in natural language text using annotated corpora and a set of tools for machine learning. |
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Beyond Metadata: What Paper Authors Say About Corpora They Use (2021.findings-acl)
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| Challenge: | Currently, dataset retrieval relies almost exclusively on metadata provided by the publishers. |
| Approach: | They propose to use metadata to extract review statements from scientific publications . they argue that a crucial piece of information is missing to inform the examination of search results . |
| Outcome: | The proposed analysis is the first of its kind in the field of Natural Language Processing. |
SciNLI: A Corpus for Natural Language Inference on Scientific Text (2022.acl-long)
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| Challenge: | Existing Natural Language Inference (NLI) datasets are not related to scientific text. |
| Approach: | They propose a large dataset for NLI that captures the formality in scientific text and contains 107,412 sentence pairs extracted from scholarly papers on NLP and computational linguistics. |
| Outcome: | The proposed model achieves a Macro F1 score of only 78.18% and an accuracy of 78.23%. |
Annotation Artifacts in Natural Language Inference Data (N18-2)
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| Challenge: | Large-scale datasets for natural language inference are created by crowdsourcing annotations . authors show that success of natural language models to date has been overestimated . |
| Approach: | They propose a method for crowdsourcing annotations to generate 3 new sentences based on a sentence (premise) they show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI and 53% of MultiNLI . |
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
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SciREX: A Challenge Dataset for Document-Level Information Extraction (2020.acl-main)
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| Challenge: | Conventional datasets and methods for information extraction focus on within-sentence relations from general Newswire text. |
| Approach: | They propose a document-level IE dataset that integrates automatic and human annotations to annotate entities and document- level N-ary relation identification from scientific articles. |
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Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)
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Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu
| Challenge: | Existing surveys focus on LLMs' specific utility for data annotation and synthesis. |
| Approach: | They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations . |
| Outcome: | The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information. |