Papers with dialog generation
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
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| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation (D19-1)
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| Challenge: | Current approaches to visual dialog learning involve an end-to-end framework that maps the multi-modal context to a deep vector and in order to decode a natural dialog response. |
| Approach: | They propose a framework that trains a RL policy for image guessing and a seq2seq model to improve dialog quality. |
| Outcome: | The proposed framework achieves state-of-the-art performance on a guessWhich task . it can be applied to a wide range of tasks including assisting blind people . |
SDialog: A Python Toolkit for End-to-End Agent Building, User Simulation, Dialog Generation, and Evaluation (2026.eacl-demo)
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Sergio Burdisso, Séverin Baroudi, Yanis Labrak, David Grünert, Pawel Cyrta, Yiyang Chen, Srikanth Madikeri, Esaú Villatoro-tello, Ricard Marxer, Petr Motlicek
| Challenge: | SDialog is an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents. |
| Approach: | They present an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents. |
| Outcome: | SDialog enables more controlled, transparent, and systematic research on conversational systems. |
Paraphrase Augmented Task-Oriented Dialog Generation (2020.acl-main)
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| Challenge: | Neural generative models can perform dialog generation tasks with a large data set, but lack of high-quality data and expensive data annotation process limit their application in real world settings. |
| Approach: | They propose to combine paraphrase and response generation models to improve dialog generation performance by annotating dialog states and dialog act labels. |
| Outcome: | The proposed framework outperforms existing methods significantly in dialog generation tasks, especially under low resource settings. |
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering (2023.findings-acl)
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Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo, Arash Eshghi, Ioannis Papaioannou, Verena Rieser, Ioannis Konstas
| Challenge: | Empirical studies of dialogue have shown that people use different kinds of context-dependent linguistic behavior to indicate grounding, including use of fragments, ellipsis and pronominal reference. |
| Approach: | They propose to use open-domain question answering systems as test-bed for task based dialog generation and compare open- and closed-book models to test their hypothesis. |
| Outcome: | The proposed model parrots user input while providing an unfaithful response. |
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. |
Towards Knowledge-Based Recommender Dialog System (D19-1)
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| Challenge: | Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems. |
| Approach: | They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences. |
| Outcome: | The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines. |
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework (2020.findings-emnlp)
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Mingbo Ma, Baigong Zheng, Kaibo Liu, Renjie Zheng, Hairong Liu, Kainan Peng, Kenneth Church, Liang Huang
| Challenge: | Text-to-speech synthesis (TTS) has seen rapid progress in recent years, but still suffers from latencies. |
| Approach: | They propose a neural incremental TTS approach that synthesizes speech in an online fashion, playing a segment of audio while generating the next. |
| Outcome: | Experiments on English and Chinese TTS show that the proposed approach achieves similar speech naturalness compared to full sentence TTS, but with a constant (1-2 words) latency. |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
| Approach: | They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history . |
| Outcome: | The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation. |
Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions (2020.emnlp-main)
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| Challenge: | Existing persona-grounded dialog models fail to capture simple implications of given persona descriptions. |
| Approach: | They propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to expanded and richer set of persona descriptions. |
| Outcome: | The proposed model outperforms baselines on the Persona-Chat dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation. |
GDPO: Learning to Directly Align Language Models with Diversity Using GFlowNets (2024.emnlp-main)
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| Challenge: | Reinforcement learning with human feedback (RLHF) and its offline variant Direct Preference Optimization (DPO) are two of the most important methods for language model (LM) alignment. |
| Approach: | They propose to use a diversity-seeking RL algorithm called GFlowNet-DPO in an offline preference alignment setting to optimize a model's behavior. |
| Outcome: | Empirical results show that the proposed algorithm generates far more diverse responses than the baseline methods and is still relatively aligned with human values in dialog generation and summarization tasks. |
Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation (2024.emnlp-main)
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| Challenge: | Existing methods for knowledge graph–grounded dialog generation fail to leverage the rich knowledge of pretrained language models. |
| Approach: | They propose a method for dialog generation that integrates dialog history with a knowledge graph. |
| Outcome: | The proposed method achieves state-of-the-art in knowledge graph–grounded dialog generation on OpenDialKG and KOMODIS datasets. |
Training Language Model to Critique for Better Refinement (2025.findings-acl)
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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang
| Challenge: | Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. |
| Approach: | They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses. |
| Outcome: | The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. |