Papers with open-domain dialogue
A Persona-Aware LLM-Enhanced Framework for Multi-Session Personalized Dialogue Generation (2025.findings-acl)
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| Challenge: | Existing personalized dialogue models focus on dialogue history and personality information, reducing the responses’ consistency. |
| Approach: | They propose a Persona-Aware LLM-enAnCEd(PALACE) framework that generates responses consistent with dialogue history and personality information across multiple sessions to engage users’ interest in the dialogue. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in automatic and human evaluation metrics on the MSC and DuLeMon datasets. |
Flow Matching for Conditional Text Generation in a Few Sampling Steps (2024.eacl-short)
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| Challenge: | Current diffusion models face multiple drawbacks including slow sampling, noise schedule sensitivity, and misalignment between training and sampling stages. |
| Approach: | They propose a method which leverages flow matching for conditional text generation. |
| Outcome: | The proposed method can generate text in a few steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter optimization of the noise schedule prevalent in diffusion models. |
Discovering Dialog Structure Graph for Coherent Dialog Generation (2021.acl-long)
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| Challenge: | Existing studies on dialog structure graphs from open-domain dialogs have limited number of dialog states and can be laborious and costly to annotate manually. |
| Approach: | They propose to use dialog structure graph as a model to discover hierarchical latent dialog states and their transitions from corpus to facilitate dialog management in a RL based dialog system. |
| Outcome: | The proposed model can discover meaningful dialog structure graph and significantly improve multi-turn coherence on two benchmark corpora. |
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models (2025.coling-main)
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| Challenge: | Existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsensense inferences during response generation. |
| Approach: | They propose to separate commonsense reasoning into explicit steps for generating, selecting, and integrating commonsensense into dialogue responses. |
| Outcome: | The proposed model infers commonsense knowledge from dialogue contexts to improve response quality and naturalness of dialogue interactions. |
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System (2021.emnlp-main)
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| Challenge: | Consistency Identification has been used for preventing inconsistent response generation, but few efforts have been made to task-oriented dialogue. |
| Approach: | They propose a dataset for Consistency Identification in task-oriented dialog system. |
| Outcome: | The proposed dataset is based on a single label and provides fine-grained labels to encourage model to know what inconsistent sources lead to it. |
OTTers: One-turn Topic Transitions for Open-Domain Dialogue (2021.acl-long)
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| Challenge: | a mixed-initiative dialogue system is often purely responsive, make abrupt transitions, or fail to take initiative. |
| Approach: | They propose a task to generate a "bridging" utterance connecting a new topic to the previous conversation turn. |
| Outcome: | The proposed task generates a "bridging" utterance connecting a new topic to the previous topic. |
OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue (2022.lrec-1)
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| Challenge: | Named entity recognition (NER), named entity linking and discourse modeling are crucial aspects of natural language understanding for open domain dialogue systems. |
| Approach: | They present an annotated multi-domain corpus for linking entities in open-domain dialogue . they use dialogue context and anaphora resolution to assess the effectiveness of the task . |
| Outcome: | The OpenEL corpus is an annotated multi-domain corpus for linking entities in open-domain dialogue . the system Flair + BLINK has the best performance with a 0.65 F1 score . |
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems (2020.coling-main)
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| Challenge: | Existing evaluation metrics are not designed to cope with this flexibility. |
| Approach: | They propose to group the qualities into three groups to obtain a single metric called USL-H. |
| Outcome: | The proposed metric achieves good correlations with human judgment and maintains its configurability towards different aspects and metrics. |
An Investigation of Suitability of Pre-Trained Language Models for Dialogue Generation – Avoiding Discrepancies (2021.findings-acl)
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| Challenge: | Pre-trained language models have been widely used in open-domain dialogue generation. |
| Approach: | They propose to use decoder-only architecture to achieve excellent performance for dialogue generation. |
| Outcome: | The proposed frameworks are based on transformer-ED, transformer-Dec, transformer MLM and transformer-AR. |
MRF-Chat: Improving Dialogue with Markov Random Fields (2021.emnlp-main)
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| Challenge: | Existing approaches to deep learning for open-domain dialogue include training end-to-end models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts and persona of the agent and the user, among others. |
| Approach: | They propose a probabilistic approach using Markov Random Fields to augment existing deep-learning methods for improved next utterance prediction. |
| Outcome: | The proposed approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents. |
Logic Unveils Truth, While Disguise Obscures It: Transition Logic Augmented Response Selection for Multi-Turn Dialogue (2023.findings-emnlp)
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| Challenge: | Existing methods of negative samples tend to yield false negatives due to one-to-many property in open-domain dialogue. |
| Approach: | They propose a sequential variational ladder auto-encoder to capture one-to-many transition pattern of multiple characteristics in open-domain dialogue. |
| Outcome: | The proposed approach improves the performance of a retrieval dialogue system on two benchmarks. |
SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation (2023.acl-long)
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| Challenge: | Existing work on large-scale corpora-based language models is limited and hard to generalize to all types of pre-trained language models. |
| Approach: | They propose a two-stage SimOAP strategy that over-samples and post-evaluates large-scale responses from existing models and selects a good response based on multiple evaluation metrics. |
| Outcome: | The proposed strategy outperforms baseline and automatic evaluation strategies in both automatic and human evaluations. |
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)
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Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
| Challenge: | Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks. |
| Approach: | They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue. |
| Outcome: | The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work. |