Deep neural network models are especially susceptible to noise in annotated labels. In the real world, annotated data typically contains noise caused by a variety of factors such as task difficulty, annotator experience, and annotator bias. Label quality is critical for label validation tasks; however, correcting for noise by collecting more data is often costly. In this paper, we propose a contrastive meta-learning framework (CML) to address the challenges introduced by noisy annotated data, specifically in the context of natural language processing. CML combines contrastive and meta learning to improve the quality of text feature representations. Meta-learning is also used to generate confidence scores to assess label quality. We demonstrate that a model built on CML-filtered data outperforms a model built on clean data. Furthermore, we perform experiments on de- identified commercial voice assistant datasets and demonstrate that our model outperforms several SOTA approaches.

CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost

**Organizers:** :Shervin Malmasi, Eugene Agichtein, Surya Kallumadi, Nicola Ueffing, Oleg Rokhlenko, Ido
Guy
 **Description:** NLP and IR have been closely linked to e-commerce applications since the early days of the fields. This close relationship
between the two is evidenced by early publications as well as the growing number of recent publications at the intersection
of the two areas. Today, NLP and IR play a significant role in e-commerce tasks, including product search, recommender
systems, product question answering, sentiment analysis, product description and review summarization, chatbots and
shopping assistants, and customer review processing, among many other tasks being investigated by researchers in the
field. These methods play a key part in today’s online retail and shopping landscape, and continue to evolve and further
enhance the customer experience. The ECNLP workshop aims to provide a venue for the dissemination of NLP/IR
research related to e-commerce and online shopping, bringing together researchers from both academia and industry.
 
**Please visit our [website](https://sites.google.com/view/ecnlp)**

(ECNLP) The Fifth Workshop on e-Commerce and NLP

workshop paper

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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CML: A Contrastive Meta Learning Method to Estimate Human Label Confidence Scores and Reduce Data Collection Cost

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