Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring Conversations (2024.findings-emnlp)
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| Challenge: | POSR is a task of breaking down conversations into segments and linking each segment to the relevant reference item. |
| Approach: | They propose a task that breaks down conversations into segments and links each segment to the relevant reference item. |
| Outcome: | The proposed method outperforms independent segmentation pipelines and large language models on joint metrics. |
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Data Collection and End-to-End Learning for Conversational AI (D19-2)
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| Challenge: | tutorial aims to familiarise research community with recent advances in statistical dialogue systems . focus of tutorial is on learning end-to-end from data and their relation to more common modular systems. |
| Approach: | This tutorial aims to familiarise the research community with the latest advances in statistical dialogue systems . the focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. |
| Outcome: | This tutorial aims to familiarise the research community with the recent advances in statistical dialogue systems for open-domain and task-based dialogue paradigms. |
Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations (2023.emnlp-industry)
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| Challenge: | Utilizing natural language processing in clinical conversations is effective to improve the efficiency of workflows for medical staff and patients. |
| Approach: | They propose a model for dialogue segmentation and topic categorization that integrates natural language processing techniques into a joint model. |
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KTH Tangrams: A Dataset for Research on Alignment and Conceptual Pacts in Task-Oriented Dialogue (L18-1)
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| Challenge: | Existing studies on instructor-manipulator dialogue use disparate but similar datasets . a recent study examined the alignment of referring expressions (RL) in situated dialogue . |
| Approach: | They propose to use a corpus of referring expressions in a relatively free dialogue with physical features generated in simulated situations to study alignment in referring language. |
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PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)
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Rahul Goel, Waleed Ammar, Aditya Gupta, Siddharth Vashishtha, Motoki Sano, Faiz Surani, Max Chang, HyunJeong Choe, David Greene, Chuan He, Rattima Nitisaroj, Anna Trukhina, Shachi Paul, Pararth Shah, Rushin Shah, Zhou Yu
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IntrEx: A Dataset for Modeling Engagement in Educational Conversations (2025.findings-emnlp)
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| Challenge: | IntrEx is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Approach: | They propose a large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Outcome: | The proposed dataset is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)
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| Challenge: | Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE. |
| Approach: | They propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. |
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Did You Get It? A Zero-Shot Approach to Locate Information Transfers in Conversations (2024.lrec-main)
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| Challenge: | Existing models do not provide an efficient way to locate information that enters the common ground. |
| Approach: | They propose a method based on segmentation of a conversation into themes followed by their summarization and obtain the location of information transfers by computing the distance between the theme summary and the different utterances produced by a speaker. |
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Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems (2021.naacl-main)
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| Challenge: | Existing goal-oriented dialogue datasets focus on identifying slots and values, but in reality, customer service agents follow multi-step procedures derived from explicit company policies. |
| Approach: | They propose to use a fully-labeled dataset to study customer service dialogue systems in real-world scenarios. |
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Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System (N19-2)
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Shazia Afzal, Tejas Dhamecha, Nirmal Mukhi, Renuka Sindhgatta, Smit Marvaniya, Matthew Ventura, Jessica Yarbro
| Challenge: | Dialog-based intelligent tutoring systems capture the effectiveness of expert human teacher-learner interactions by using natural language dialogue. |
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Comprehensive Multi-Modal Interactions for Referring Image Segmentation (2022.findings-acl)
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| Challenge: | Existing methods for RIS compute different forms of interactions sequentially or ignore intra-modal interactions. |
| Approach: | They propose a method which outputs a segmentation map corresponding to the natural language description. |
| Outcome: | The proposed method performs on four benchmark datasets and shows significant performance gains over the existing state-of-the-art methods. |