| Challenge: | Pre-trained language models are weak in understanding the main semantic meaning of a dialogue context. |
| Approach: | They propose a semantic-based framework that leverages explicit semantic knowledge to capture the core semantic information in dialogues during pre-training. |
| Outcome: | The proposed model is superior to existing models on chit-chats and task-oriented dialogues. |
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Semantic Representation for Dialogue Modeling (2021.acl-long)
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| Challenge: | Existing models for dialogue modeling lack ability to represent core semantics, such as ignoring important entities. |
| Approach: | They develop an algorithm to construct dialogue-level AMR graphs from sentence-level data and explore two ways to incorporate AMRs into dialogue modeling. |
| Outcome: | The proposed model is superior to existing models on dialogue understanding and response generation tasks. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
| Outcome: | The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks. |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
| Outcome: | The proposed model can be applied to various downstream dialogue tasks. |
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2020.acl-main)
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| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
| Approach: | They propose a dialogue generation pre-training framework that leverages bi-directional context and uni-directional characteristic of language generation. |
| Outcome: | The proposed framework is superior to existing models on three publicly available datasets. |
Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)
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| Challenge: | Existing work on dialogue meaning representations is limited in scalability for complex expressions. |
| Approach: | They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility . |
| Outcome: | The proposed representation can be easily extended to a task-oriented dialogue dataset. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (D19-56)
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| Challenge: | Statistical conversational systems are complex, timeintensive, expensive, and not easily transferable due to data scarcity. |
| Approach: | They propose a task-oriented dialogue model that operates on text input . they validate it on multi-domain task-orientated dialogues from a multi-word dataset . |
| Outcome: | The proposed model bypasses explicit policy and language generation modules on multi-domain task-oriented dialogues from the MultiWOZ dataset. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training (2024.emnlp-main)
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| Challenge: | Existing pre-training methods focus on exploiting textual knowledge, which limits scalability and versatility of resulting models. |
| Approach: | They propose a pre-training framework that integrates structural semantic knowledge via contrastive learning. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-training methods across multiple tasks. |