| Challenge: | Existing work on conversation disentanglement relies heavily on human annotations, which is expensive to obtain in practice. |
| Approach: | They propose to train a conversation disentanglement model without referencing human annotations . they use a message-pair classifier and a session classifier to retrieve local relations . |
| Outcome: | The proposed method achieves competitive performance compared to previous methods on a large movie dialogue dataset. |
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Conversation Disentanglement with Bi-Level Contrastive Learning (2022.findings-emnlp)
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| Challenge: | Existing methods focus on pairwise utterance relations but pay inadequate attention to utterant-to-context relation modeling. |
| Approach: | They propose a general disentangle model based on bi-level contrastive learning that brings closer utterances in the same session while encouraging each utterrance to be near its clustered session prototypes in representation space. |
| Outcome: | The proposed model achieves state-of-the-art performance on both settings across public datasets. |
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking (N18-1)
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| Challenge: | Existing methods to disentangle interleaved conversations can lead to difficulties in following discussions and retrieving relevant information from simultaneous messages. |
| Approach: | They propose to leverage representation learning to separate intermingled messages into detached conversations by estimating conversation-level similarity between closely posted messages. |
| Outcome: | The proposed approach outperforms baselines in pairwise similarity estimation and conversation disentanglement. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
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| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection (2021.emnlp-main)
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| Challenge: | a zero-shot dialogue disentanglement solution is difficult due to the need for manual annotation. |
| Approach: | They propose a zero-shot dialogue disentanglement solution using a web dataset . they train a model on the data and fine-tune the model using labeled data . |
| Outcome: | The proposed model achieves a cluster F1 score of 25 without labeling data . it can be used to analyze discourses and to perform response selection . |
A Large-Scale Corpus for Conversation Disentanglement (P19-1)
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Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph J. Peper, Vignesh Athreya, Chulaka Gunasekara, Jatin Ganhotra, Siva Sankalp Patel, Lazaros C Polymenakos, Walter Lasecki
| Challenge: | a dataset of 77,563 messages manually annotated with reply-structure graphs disentangles conversations and defines internal conversation structure. |
| Approach: | They use a dataset of 77,563 messages manually annotated with reply-structure graphs to disentangle conversations and define internal conversation structure. |
| Outcome: | The new dataset is 16 times larger than all previous datasets combined and includes adjudication of annotation disagreements and context. |
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning (2020.coling-main)
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| Challenge: | Using a multi-task learning framework, we train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Approach: | They propose a multi-task learning framework to train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Outcome: | The proposed model outperforms individual tasks and delivers competitive performance. |
Online Conversation Disentanglement with Pointer Networks (2020.emnlp-main)
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| Challenge: | Existing methods for disentangling textual conversations rely on dataset specific features that hinder generalization and adaptability. |
| Approach: | They propose an end-to-end online framework for conversation disentanglement that embeds the whole utterance that comprises timestamp, speaker, and message text. |
| Outcome: | The proposed method performs state-of-the-art on the Ubuntu IRC dataset and on other social and organizational platforms. |
Dramatic Conversation Disentanglement (2023.findings-acl)
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| Challenge: | a new dataset is available for studying conversation disentanglement in movies and TV series . a recent study focused on IRC chatroom dialogues, but movies and television show provide a space for study . |
| Approach: | They propose a dataset for studying conversation disentanglement in movies and TV series . they operationalize a conversational thread and apply the best-performing model to 808 movies . |
| Outcome: | The proposed model disentangles 808 movies from 10,033 dialogue turns . the best-performing model is compared with previous models . |
Learning Disentangled Representations for Natural Language Definitions (2023.findings-eacl)
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| Challenge: | Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. |
| Approach: | They propose to use syntactic and semantic regularities in textual data to provide models with both structural biases and generative factors. |
| Outcome: | The proposed model outperforms baselines on several qualitative and quantitative benchmarks and improves the results in the downstream task of definition modeling. |
An Evaluation of Disentangled Representation Learning for Texts (2021.findings-acl)
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| Challenge: | Disentangled representations of texts encode information pertaining to different aspects of the text in separate vector embeddings. |
| Approach: | They propose to use a highly-structured natural language dataset to evaluate disentangled representations for texts. |
| Outcome: | The proposed models are well-suited for learning disentangled representations of texts on a synthetic natural language dataset. |