EMNLP/CoNLL 2021 showcases the compelling research of Stanford AI Lab
Introduction:
Welcome to the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) and CoNLL 2021! This prestigious conference showcases the latest research and advancements in the field of natural language processing. Hosted by Stanford University, this event brings together leading researchers, scientists, and practitioners from around the world.
At EMNLP 2021, you can expect a wide range of presentations, papers, and videos covering various topics such as language generation, named entity disambiguation, natural language inference, grammatical error correction, and much more. The conference aims to foster collaboration, knowledge sharing, and innovation in the field of natural language processing.
Join us at EMNLP 2021 to learn about groundbreaking research, explore cutting-edge technologies, and connect with experts in the field. We look forward to an exciting and enriching conference experience.
Full Article: EMNLP/CoNLL 2021 showcases the compelling research of Stanford AI Lab
The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) is set to take place next week, in conjunction with CoNLL 2021. This highly anticipated event will showcase cutting-edge research and advancements in the field of natural language processing. Stanford’s Stanford Artificial Intelligence Laboratory (SAIL) is proud to present their work at the conference. Below, you will find a list of accepted papers, along with links to the corresponding papers, videos, and blogs.
Calibrate your listeners! Robust communication-based training for pragmatic speakers
One of the accepted papers, titled “Calibrate your listeners! Robust communication-based training for pragmatic speakers,” explores the concept of language generation and pragmatics. The authors, Rose E. Wang, Julia White, Jesse Mu, and Noah D. Goodman, delve into the importance of calibration in communication-based training, particularly for pragmatic speakers. For more information on this research, you can access the paper and watch the corresponding video.
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, and Christopher Ré have authored a paper on cross-domain data integration for named entity disambiguation in biomedical text. This research addresses the challenges of dealing with rare entities in biomedical text and how data integration can enhance the disambiguation process. The paper and video related to this study are available for further exploration.
ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts
Yuta Koreeda and Christopher D. Manning present “ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts.” This paper introduces a dataset specifically designed for natural language inference tasks in the context of legal contracts. The authors provide valuable insights into the dataset and its applications in the legal domain. You can access the paper and visit the website for more details.
The Emergence of the Shape Bias Results from Communicative Efficiency
Eva Portelance, Michael C. Frank, Dan Jurafsky, Alessandro Sordoni, and Romain Laroche examine the emergence of the shape bias in their paper titled “The Emergence of the Shape Bias Results from Communicative Efficiency.” This research focuses on the role of emergent communication, shape bias, and language learning through multi-agent reinforcement learning. For a deeper understanding of this study, the paper and associated website are available.
LM-Critic: Language Models for Unsupervised Grammatical Error Correction
Michihiro Yasunaga, Jure Leskovec, and Percy Liang present their work on LM-Critic, which involves language models for unsupervised grammatical error correction. This paper explores the use of language models in automatically correcting grammatical errors without relying on labeled data. To learn more about this research, you can access the paper, read the blog post, or visit the dedicated website.
Sensitivity as a Complexity Measure for Sequence Classification Tasks
In the paper “Sensitivity as a complexity measure for sequence classification tasks,” Michael Hahn, Dan Jurafsky, and Richard Futrell investigate the concept of sensitivity as a measure of computational complexity for sequence classification tasks. This research touches upon decision boundaries and their relationship to computational complexity. The paper provides valuable insights into this area of study.
Distributionally Robust Multilingual Machine Translation
Chunting Zhou, Daniel Levy, Marjan Ghazvininejad, Xian Li, and Graham Neubig present their work on distributionally robust multilingual machine translation. This research focuses on developing machine translation models that are robust to distribution shifts. The authors explore the concept of cross-lingual transfer and its implications in machine translation.
Learning from Limited Labels for Long Legal Dialogue
Jenny Hong, Derek Chong, and Christopher D. Manning explore the challenges of learning from limited labels for long legal dialogue. This research falls within the domain of legal natural language processing and information extraction, particularly in the context of weak supervision. The authors provide valuable insights into this area of study.
Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser
Yuta Koreeda and Christopher D. Manning delve into the logical structure of visually structured documents with their research on the multimodal transition parser. This study focuses on legal preprocessing and examines the challenges and opportunities presented by visually structured documents within legal contexts. The paper and associated website provide further details on this research.
These are just a few of the accepted papers that will be presented at EMNLP/CoNLL 2021. We hope to see you at the conference to explore these ground-breaking studies and more. Stay tuned for updates and exciting developments in the field of natural language processing!
Summary: EMNLP/CoNLL 2021 showcases the compelling research of Stanford AI Lab
The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) will be held next week in conjunction with CoNLL 2021. This event will showcase the latest research and work from SAIL at Stanford University. The conference will feature a diverse range of topics, including language generation, named entity disambiguation, natural language inference, emergent communication, grammatical error correction, and more. Attendees can access the conference papers, videos, and blogs to learn more about the innovative work happening in the field. Don’t miss out on this exciting opportunity to stay up to date with the advancements in natural language processing!
Frequently Asked Questions:
Q1: What is Artificial Intelligence (AI)?
A1: Artificial Intelligence (AI) refers to the development of computer systems that have the ability to perform tasks that would typically require human intelligence. These tasks include problem-solving, decision-making, speech recognition, and learning.
Q2: How is Artificial Intelligence used in our daily lives?
A2: Artificial Intelligence has become an integral part of our daily lives, with various applications such as smart assistants (e.g., Siri, Alexa), recommendation systems (e.g., Netflix, Amazon), autonomous vehicles, fraud detection systems, and medical diagnosis tools. AI enhances efficiency and convenience by automating tasks, providing personalized recommendations, and improving overall accuracy in decision-making processes.
Q3: What are the different types of Artificial Intelligence?
A3: There are mainly two types of Artificial Intelligence: Narrow AI (also known as Weak AI) and General AI (also known as Strong AI). Narrow AI is designed to perform specific tasks and is the most common type currently in use; examples include voice assistants and image recognition systems. General AI, on the other hand, possesses human-like intelligence and can understand, learn, and apply knowledge across multiple domains. However, General AI is still largely theoretical and not yet fully developed.
Q4: What are the ethical considerations surrounding Artificial Intelligence?
A4: As AI continues to advance, ethical considerations become increasingly important. Some key concerns include job displacement due to automation, privacy issues related to data collection and usage, biases present in AI algorithms, and the potential for AI systems to be misused or manipulated. Therefore, it is crucial for developers, policymakers, and society at large to consider these ethical aspects and develop responsible AI frameworks.
Q5: Can Artificial Intelligence replace humans in the future?
A5: While Artificial Intelligence has the potential to automate certain tasks, it is unlikely to completely replace humans in all domains. AI excels in repetitive, data-driven tasks but still falls short in areas that require creativity, empathy, and complex decision-making based on nuances and contextual understanding. Additionally, human oversight is crucial to ensure that AI systems operate ethically and responsibly. Therefore, rather than replacement, it is more likely that AI will augment human capabilities and lead to a symbiotic relationship between humans and machines.
