Papers with open-domain dialogue systems
Grounding in social media: An approach to building a chit-chat dialogue model (2022.naacl-srw)
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| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
| Approach: | They propose to use social media comments to improve the raw conversation ability of open-domain dialogue systems. |
| Outcome: | The proposed model improves the raw conversation ability of open-domain dialogue systems by mimicking human responses through casual interactions found on social media. |
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations (2022.emnlp-main)
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Chujie Zheng, Jinfeng Zhou, Yinhe Zheng, Libiao Peng, Zhen Guo, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Minlie Huang
| Challenge: | Existing methods for detecting dialogue contradictions are difficult due to contextualization nature of conversations. |
| Approach: | They propose a benchmark for Contradiction Detection in Chinese Conversations . they use automatic conversation generation to simulate common user behaviors . |
| Outcome: | The proposed benchmark simulated the user behaviors that trigger chatbots to make contradictions . the results show that the current state-of-the-art chatbot can be easily goaded into making contradictions. |
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. |
uBLEU: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems (2020.acl-srw)
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| Challenge: | Existing evaluation metrics for text generation tasks do not consider uncertain responses without writing additional reference responses by hand. |
| Approach: | They propose a human-aided, uncertainty-aware evaluation method for open-domain dialogue systems, BLEU. |
| Outcome: | The proposed method is comparable to existing methods on Twitter and improves state-of-the-art evaluation method RUBER. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue Systems (2021.findings-emnlp)
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| Challenge: | Existing studies on open-domain dialogue systems that allow free topics are challenging . however, non-English dialogue systems suffer from reproducing the performance of English dialogue systems . |
| Approach: | They propose to use English knowledge to improve the performance of open-domain dialogue systems . they construct a Korean-English T5 language model and develop a knowledge-grounded Korean dialogue model . |
| Outcome: | The proposed model improves even when only English knowledge is given . the model is built with a pre-trained language model and a knowledge-grounded Korean dialogue model . |
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
| Approach: | They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show . |
| Outcome: | The proposed method performs well on zero-shot experiments and is more robust to real-world data. |
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations (2022.acl-long)
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| Challenge: | Existing evaluation metrics for incoherent dialogues are insufficient to accurately reflect incohérence . despite the effectiveness of large pretrained language models, not everyone is into this type of work. |
| Approach: | They propose a Dialogue coherence Evaluation metric that uses Abstract Meaning Representation to apply semantic-level Manipulations for incoherent (negative) data generation. |
| Outcome: | The proposed evaluation metric achieves higher correlations with human judgments compared to baseline methods on dialog datasets by significant margins. |
Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)
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| Challenge: | Discourse relation identification is a challenging problem in open-domain dialogue systems . previous work relies on formal text but this data is not suitable for informal dialogue . |
| Approach: | They propose a method to automatically extract the implicit discourse relation argument pairs from dialogic turns and a pipeline to identify them. |
| Outcome: | The proposed pipeline extracts argument pairs from dialogic turns and improves it by performing feature ablation and incorporating dialogue features. |
Dialogue Language Model with Large-Scale Persona Data Engineering (2025.naacl-industry)
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| Challenge: | Existing persona-consistent dialogue models lack robustness due to limited scale and diversity of datasets. |
| Approach: | They propose an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialog dataset to enhance persona consistency. |
| Outcome: | The proposed model generates vast persona dialogue datasets and addresses invalid persona bias. |
RECAP: REwriting Conversations for Intent Understanding in Agentic Planning (2026.findings-eacl)
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| Challenge: | Understanding user intent is essential for effective conversational assistants . however, real-world dialogues are often ambiguous, underspecified, or dynamic . |
| Approach: | They propose a benchmark to evaluate intent rewriting in user-agent dialogues . they propose rewriters that reframe user-goal dialogues into concise representations of user goals . |
| Outcome: | The proposed benchmark outperforms baselines in terms of plan preference and fine-tuning two DPO-based rewriters yields additional utility gains. |
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022.emnlp-main)
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| Challenge: | Existing methods to augment dialogue datasets are labor-intensive and time-consuming . Currently, smoking is harmful for your health. |
| Approach: | They propose a data augmentation method to augment dialogue responses with different semantics by counterfactual inference. |
| Outcome: | The proposed method outperforms baselines on multiple downstream tasks. |
Adversarial DPO: Harnessing Harmful Data for Reducing Toxicity with Minimal Impact on Coherence and Evasiveness in Dialogue Agents (2024.findings-naacl)
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| Challenge: | Existing toxicity within large language models can negatively impact the user experience, causing performance degradation. |
| Approach: | They propose an adversarial DPO algorithm that improves direct preference optimization (DPO) by incorporating harmful data into the generative model. |
| Outcome: | The proposed training algorithm improves the model’s resilience against harmful conversations while minimizing performance degradation. |
StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing methods for generating responses following a desired style are lacking of parallel data for training. |
| Approach: | They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods . |
| Outcome: | The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets. |
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022.naacl-main)
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| Challenge: | Recent large-scale language models have produced human-like responses in open-domain dialogue systems. |
| Approach: | They propose a framework for imposing roles on open-domain dialogue systems . they use few-shot learning to build a Korean dialogue dataset from scratch . |
| Outcome: | The proposed framework meets role specifications while maintaining conversational abilities. |
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks (2021.findings-emnlp)
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| Challenge: | Existing methods to generate pre-trained language models with attributes are expensive and overfitted on small training sets. |
| Approach: | They propose a novel approach to control the generation of Transformer-based pre-trained language models using a new control attributes loss framework. |
| Outcome: | The proposed method is shown to perform well with very limited training samples. |
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
| Outcome: | The proposed model significantly boosts response quality of open-domain dialogue systems. |
REAM♯: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation (2021.findings-acl)
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| Challenge: | Existing evaluation metrics for open-domain dialogue systems are limited by the diversity of possible outcomings. |
| Approach: | They propose a method to augment a reference set to improve reliability . they propose BLEU to measure similarity between a predicted response and a small set of references . |
| Outcome: | The proposed model improves the reliability of reference-based metrics with augmented reference sets. |
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems (2023.acl-long)
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| Challenge: | evaluating commonsense in dialogue systems remains an open challenge . despite the success of open-domain dialogue systems, systems struggle to produce commonsensical responses as humans do. |
| Approach: | They propose an event commonsense evaluation metric empowered by commonsensence knowledge bases. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than baselines. |
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)
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| Challenge: | Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated. |
| Approach: | They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses. |
| Outcome: | The proposed model outperforms baseline models on a large-scale MSC dataset. |
Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue (2024.findings-naacl)
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| Challenge: | emergence of large language models (LLMs) improves capabilities of dialogue systems . but they lack communication skills, which make them more like information seeking tools . |
| Approach: | They propose to empower LLMs with communication skills through inner monologues . they use a benchmark to evaluate the dialogue generation ability of the model . |
| Outcome: | The proposed model outperforms the baselines in the evaluation of communication skills. |
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data (2020.emnlp-main)
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| Challenge: | Existing research has focused on training open-domain dialogue models using unpaired data. |
| Approach: | They propose a data-level distillation method for training open-domain dialogue models by utilizing unpaired data. |
| Outcome: | The proposed method produces high-quality dialogue pairs with diverse contents, and can improve competitive baselines. |
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions (2021.emnlp-main)
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| Challenge: | Recent advances on neural approaches to natural language processing have triggered a renaissance in end-to-end neural open-domain chatbots. |
| Approach: | They propose to use offline and online steps to evaluate the quality of clarifying questions in various open-domain dialogues to improve the quality and accuracy of the system response. |
| Outcome: | The proposed pipeline is suitable as a foundation for further research. |
Achieving Reliable Human Assessment of Open-Domain Dialogue Systems (2022.acl-long)
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| Challenge: | Evaluation of open-domain dialogue systems is challenging and unreliable . human evaluation of live conversations is highly reliable, but reliability cannot be assumed . |
| Approach: | They propose a method of open-domain dialogue evaluation that is highly reliable . they compare live conversations with models that avoid pre-created reference dialogues . |
| Outcome: | The proposed method is highly reliable while remaining feasible and low cost. |
CoMIF: Modeling of Complex Multiple Interaction Factors for Conversation Generation (2025.coling-main)
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| Challenge: | Existing methods for generating human-like dialogues lack implicit correlations among factors . different factors may alternately dominate token-level response generation during decoding . |
| Approach: | They propose a framework that can model complex multiple interaction factors to generate human-like conversations. |
| Outcome: | The proposed framework generates human-like conversations with multiple factors compared to state-of-the-art methods . et al. show that the proposed framework produces superior results over existing methods compared with the state- of-the art methods based on multiple datasets . |
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks (2020.acl-main)
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| Challenge: | Existing methods for training generative models with minimal corpus are difficult . fine-tuning distinguishes tasks from parameter perspective but ignores model-structure perspective . |
| Approach: | They propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. |
| Outcome: | The proposed method outperforms baselines on two datasets in task consistency, response quality, diversity and consistency. |
Multi-Source Probing for Open-Domain Conversational Understanding (2023.emnlp-main)
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| Challenge: | Existing models of open-domain dialogue comprehension have limited conversational understanding and response generation. |
| Approach: | They propose a multi-source probing method to probe dialogue comprehension abilities of open-domain dialogue models. |
| Outcome: | The proposed method aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner consistent with dialogue model pre-training to leverage model capabilities. |
Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems (2023.acl-long)
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| Challenge: | Existing evaluation methods are biased because of their subjectivity and inconsistent evaluation can misinform the performance of a chat-oriented open-domain dialogue system. |
| Approach: | They propose to use a human evaluation method to estimate the rates of manypasted macro ‘LN’ dialogue system behaviors to compare them with existing evaluation methods. |
| Outcome: | The proposed method is more suitable than alternative Likert-style or comparative approaches for dimensional evaluation of open-domain dialogue systems. |
DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation (2023.findings-acl)
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| Challenge: | Recent studies suggest that neural classifiers make overly confident predictions for examples from unseen distributions. |
| Approach: | They propose a new evaluation metric, DENSITY, which measures how likely a response would appear in the distribution of human conversations. |
| Outcome: | The proposed metric measures how likely a response would appear in the distribution of human conversations. |
DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment (2023.findings-emnlp)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Existing studies on dialogue quality assessment are uncapable of providing an end-to-end and human-epistemic assessment dataset . open-domain dialogue assessment is complicated and costly, but it can be done by recruiting human evaluators. |
| Approach: | They propose a large-scale dialogue quality assessment dataset for automatically assessing open-domain dialogue quality. |
| Outcome: | The proposed dataset is openly accessible at https://github.com/yukunZhao/Dialogue_quality_evaluation. |