| Challenge: | Recent development of spoken dialog systems aims at allowing a natural input style. |
| Approach: | They investigate how crowdsourced data can be assessed with respect to its naturalness and usefulness by using a word based language model to identify valid data. |
| Outcome: | The proposed methods show that valid data can be identified with the help of a word based language model. |
Similar Papers
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? (2021.acl-long)
Copied to clipboard
| Challenge: | Despite the importance of datasets for natural language understanding, there has been little attention on crowdsourcing methods for collecting datasets. |
| Approach: | They compare the effectiveness of crowdsourcing methods for boosting NLU example difficulty with training crowdworkers instead of expert judgments. |
| Outcome: | The proposed method is ineffective for boosting NLU example difficulty, but it is not effective for training crowdworkers and qualifying workers based on expert judgments. |
Cross-Task Generalization via Natural Language Crowdsourcing Instructions (2022.acl-long)
Copied to clipboard
| Challenge: | Despite the success of supervised learning, models often struggle with generalization across tasks. |
| Approach: | They propose to use crowdsourcing instructions to build a model that learns a new task by understanding the human-readable instructions that define it. |
| Outcome: | The proposed model can learn from seen tasks and generalize to unseen tasks given its natural crowdsourcing instructions. |
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (L18-1)
Copied to clipboard
| Challenge: | Various approaches for script knowledge extraction and processing have been proposed in recent years. |
| Approach: | They propose a dataset to evaluate natural language understanding approaches based on commonsense knowledge. |
| Outcome: | The proposed dataset provides test cases for the broader natural language understanding community. |
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have examined the quality of labeled data in non-English languages. |
| Approach: | They annotate how datasets are created, input text and label sources, tools used to build them and what they study. |
| Outcome: | The results show that language-proficient NLP researchers' estimated availability correlates with dataset availability. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection (2021.emnlp-tutorials)
Copied to clipboard
| Challenge: | Developing a theory of crowdsourcing for practical language problems remains an open challenge . |
| Approach: | This tutorial exposes NLP researchers to data collection crowdsourcing methods and principles through case studies. |
| Outcome: | This tutorial exposes NLP researchers to various data collection crowdsourcing methods and practices through case studies. |
A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
Copied to clipboard
| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
| Outcome: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)
Copied to clipboard
| Challenge: | AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models. |
| Approach: | They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results . |
| Outcome: | The proposed model achieves state-of-the-art on two public benchmarks and is easy to use and use. |
Crowdsourcing Natural Language Data at Scale: A Hands-On Tutorial (2021.naacl-tutorials)
Copied to clipboard
| Challenge: | a tutorial on crowdsourcing for efficient data annotation will introduce crowdsourcing and provide an overview of the technology. |
| Approach: | This tutorial will introduce users to efficient data annotation via crowdsourcing marketplaces. |
| Outcome: | This tutorial will introduce users to the use of crowdsourcing for data annotation. |
Does Putting a Linguist in the Loop Improve NLU Data Collection? (2021.findings-emnlp)
Copied to clipboard
Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman
| Challenge: | Many datasets for training and evaluating natural language understanding (NLU) models contain systematic artifacts that are identified only after data collection is complete. |
| Approach: | They propose to have linguists identify artifacts and gaps in the data and communicate with non-expert crowdworkers to adjust task instructions and incentives. |
| Outcome: | The proposed protocol does not increase accuracy on out-of-domain test sets, and adds a chatroom does not. |