| Challenge: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
| Approach: | This tutorial reviews main approaches to joint modeling for both statistical and neural methods. |
| Outcome: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
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How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
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| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
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Joint Models for Answer Verification in Question Answering Systems (2021.acl-long)
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| Challenge: | Using a joint approach, we found that the model is more efficient than those developed in machine reading (MR) work. |
| Approach: | They propose a joint model for selecting correct answer sentences among the top k provided by answer sentence selection modules. |
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Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
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| Challenge: | This tutorial will provide a background on interpretation techniques for neural NLP models. |
| Approach: | This tutorial will provide a background on interpretation techniques for NLP models . it will examine saliency maps, input perturbations, adversarial attacks and influence functions . |
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Designing, Evaluating, and Learning from Humans Interacting with NLP Models (2023.emnlp-tutorial)
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| Challenge: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
| Approach: | They will provide a systematic overview of key considerations and effective approaches for studying human-NLP model interactions. |
| Outcome: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
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Deep Learning for Natural Language Inference (N19-5)
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
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Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)
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| Challenge: | Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection. |
| Approach: | They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks. |
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AutoML for NLP (2023.eacl-tutorials)
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| Challenge: | Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. |
| Approach: | This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process. |
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Measure and Improve Robustness in NLP Models: A Survey (2022.naacl-main)
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| Challenge: | Despite the performance gains, NLP models are still fragile and brittle to out-of-domain data, adversarial attacks, or small perturbation to the input. |
| Approach: | They propose a survey of how to define, measure and improve robustness in NLP by connecting multiple definitions of robustness and identifying failures. |
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Latent Structure Models for Natural Language Processing (P19-4)
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| Challenge: | Latent structure models are a powerful tool for compositional data modeling and pipelines. |
| Approach: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |
| Outcome: | This tutorial will cover recent advances in discrete latent structure models . it will discuss their motivation, potential, and limitations . |