| Challenge: | Existing industrial PA products require software developers to build new skills via IDE tools. |
| Approach: | They propose a software that automatically develops a natural language understanding engine and implements the action without the need of coding. |
| Outcome: | The proposed system performs well on both benchmark and in-house datasets. |
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| Challenge: | Existing systems require developers to manually generate and annotate a large number of utterances. |
| Approach: | They propose a system that guides ordinary software developers to build a high quality NLU engine from scratch. |
| Outcome: | The proposed system shows that iterative pruning of incorrect utterances reduces human workload and cognitive load. |
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. |
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Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)
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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan
| Challenge: | Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs. |
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Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (P19-1)
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| Challenge: | a majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. |
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Recipes for Building an Open-Domain Chatbot (2021.eacl-main)
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Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston
| Challenge: | Existing work shows that scaling models in the number of parameters and the size of the data they are trained on gives improved results, but other factors are important. |
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Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback (2020.findings-emnlp)
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| Challenge: | Currently, user feedback contains extraneous sequences hindering their usefulness as a training sample. |
| Approach: | They propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation and fools the discriminator which distinguishes feedback from natural responses. |
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Textinator: an Internationalized Tool for Annotation and Human Evaluation in Natural Language Processing and Generation (2022.lrec-1)
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| Challenge: | Large-scale pretrained language models have brought substantial advances to the natural language processing field. |
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Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| 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. |
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Learning API Functionality from In-Context Demonstrations for Tool-based Agents (2025.findings-emnlp)
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| Challenge: | Documentation is often missing, outdated, privatized, or inconsistent in tool-based agents. |
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LLM-driven Instruction Following: Progresses and Concerns (2023.emnlp-tutorial)
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| Challenge: | a tutorial on task instruction is aimed at researchers and practitioners interested in NLP generalization . labeled examples are unlikely to be available in large numbers or do not exist . |
| Approach: | This tutorial will examine the progress of natural language processing (NLP) using labeled examples. authors propose that task instructions act as a novel resource for supervision. |
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