Papers with dialogue agents
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)
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| Challenge: | Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust. |
| Approach: | They propose to disentangle persona-based dialogue generation into two sub-tasks by using a BERT-over-BERT model. |
| Outcome: | The proposed model outperforms baselines in response quality and persona consistency under different limited data settings. |
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
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| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
| Approach: | They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives. |
| Outcome: | The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives. |
Decontextualization: Making Sentences Stand-Alone (2021.tacl-1)
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| Challenge: | Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. |
| Approach: | They define a problem of sentence decontextualization by rewriting a sentence to be interpretable out of context while preserving its meaning. |
| Outcome: | The proposed method can be used in question answering and document understanding tasks. |
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)
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| Challenge: | Empathy is a multi-dimensional concept consisting of cognitive and affective aspects. |
| Approach: | They propose two new in-context example selection methods that utilize emotion and situational information. |
| Outcome: | The proposed method is effective in measuring the degree of human empathy. |
Human-like informative conversations: Better acknowledgements using conditional mutual information (2021.naacl-main)
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| Challenge: | Existing chatbots generate responses that are non-specific w.r.t. one of the contexts, typically the conversational history. |
| Approach: | They propose to build a dialogue agent that can weave new factual content into conversations as naturally as humans. |
| Outcome: | The proposed method trades off pmi for pcmi_h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time. |
Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys (2024.findings-eacl)
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| Challenge: | integrating cultural dimensions with dialogue encoding features can enhance the predictive accuracy and quality of dialogue agents. |
| Approach: | They propose to incorporate cultural dimensions into dialogue encoding features to enhance the predictive accuracy of dialogue agents. |
| Outcome: | The proposed model improves the accuracy and quality of dialogue predictions by incorporating cultural dimensions with dialogue encoding features. |
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020.emnlp-main)
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| Challenge: | Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent. |
| Approach: | They propose to encode personas into dialogue embeddings and a persona-conditioned dialogue dataset to improve persona consistency. |
| Outcome: | The proposed approach can enforce dialogue agents to refrain from contradictions and improve consistency of existing models. |
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)
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| Challenge: | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios. |
| Approach: | They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses. |
| Outcome: | The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses. |
Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU (2021.emnlp-main)
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| Challenge: | Recent work attempts to apply incremental processing to NLUs but this is computationally expensive and does not scale efficiently for long sequences. |
| Approach: | They propose to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. |
| Outcome: | The proposed model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restart-incrementality, at the cost of part of the non-incremental quality. |
How Robust are Fact Checking Systems on Colloquial Claims? (2021.naacl-main)
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| Challenge: | Existing fact checking systems that perform well on colloquial claims significantly degenerate on collotic claims with the same semantics. |
| Approach: | They propose to transfer the styles of claims from FEVER into colloquialism to investigate fact checking systems on colloqual claims. |
| Outcome: | The proposed system significantly degenerates on colloquial claims with the same semantics. |
Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on DSTC7 and DSTF8* with competitive results on UbuntuV2 . |
Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition (2024.findings-naacl)
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| Challenge: | Existing frameworks for fast and accurate intent classification for task-oriented dialogue systems do not provide a clear definition of the true intent. |
| Approach: | They propose to augment the framework for out-of-scope detection by disambiguating between a small number of likely intents. |
| Outcome: | The proposed framework generates small clarification questions and is capable of out-of-scope detection. |
Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue (2026.eacl-long)
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| Challenge: | Prior research on meta-reviewing has treated this as a summarization problem over review reports . prior research demonstrated that decision-makers can be effectively assisted in such scenarios via dialogue agents. |
| Approach: | They propose to use large-scale large-language models to generate synthetic data for meta-reviewing . they then use these data to train dialogue agents tailored for meta review . |
| Outcome: | The proposed method outperforms *off-the-shelf* dialogue agents in meta-reviewing scenarios. |
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes (2021.emnlp-main)
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| Challenge: | Empathy is a complex cognitive ability based on the reasoning of others’ affective states. |
| Approach: | They propose a method to infer emotion cause words from utterances without a word-level label and a novel method to make dialogue models focus on targeted words in the input during generation. |
| Outcome: | The proposed method improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of automatic and human evaluation. |
What makes a good conversation? How controllable attributes affect human judgments (N19-1)
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| Challenge: | Existing work on dialogue models for conversational quality is incompletely understanding the relationship between quality and individual attributes. |
| Approach: | They propose to use conditional training and weighted decoding to control four attributes for chit-chat dialogue: repetition, specificity, response-relatedness and question-asking. |
| Outcome: | The proposed methods improve human quality judgments by controlling combinations of these variables. |
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)
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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam
| Challenge: | X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research . |
| Approach: | They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source . |
| Outcome: | The proposed toolkit accelerates the post-editing of a new language dataset after translation. |
Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (2023.findings-acl)
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| Challenge: | Existing approaches to personalize dialogue agents rely on explicit persona descriptions during inference, which severely limits their application in real-world scenarios. |
| Approach: | They propose a method that learns to predict persona information based on the dialogue history to personalize dialogue agents without relying on explicit persona descriptions during inference. |
| Outcome: | The proposed method improves the consistency and engagingness of generated responses when conditioning on the predicted profile of the dialogue agent. |
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots (D19-1)
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| Challenge: | Existing models for personalized dialogues rank responses according to their semantic relevance with the given context. |
| Approach: | They propose a dually interactive matching network (DIM) for presenting personalities of dialogue agents in retrieval-based chatbots. |
| Outcome: | The proposed model outperforms the existing model by 14.5% and 27.7% on a PERSONA-CHAT dataset. |
Clipping Loops for Sample-Efficient Dialogue Policy Optimisation (2021.naacl-main)
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| Challenge: | In previous work, a large number of human dialogues are required to train dialogue agents. |
| Approach: | They propose loop-clipping policy optimisation to eliminate useless responses by clipping loops from dialogue history and clipping advantage to distinguish useless actions from others. |
| Outcome: | The proposed method achieves 80% success rate on a Cambridge restaurant dialogue system using 260 training dialogues compared to baseline of 2160 dialogues. |
X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents (2025.acl-long)
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| Challenge: | Traditional Turing test limits each participant to one message at a time and requires constant human participation. |
| Approach: | They propose to enhance the original Turing test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
| Outcome: | The proposed test improves the original test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems (2020.findings-emnlp)
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| Challenge: | Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress through using reinforcement learning methods. |
| Approach: | They propose a dialogue action decoder and a simulator-free adversarial learning method to improve dialogue agent performance without using reinforcement learning. |
| Outcome: | The proposed methods achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. |
LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback (2024.naacl-long)
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| Challenge: | Existing dialogue systems do not utilize quality dimensions specifically designed for dialogue evaluation to guide the response generation during training. |
| Approach: | They propose a two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training. |
| Outcome: | The proposed framework generates and utilizes conversation evaluation as explicit feedback during training. |
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)
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| Challenge: | Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming. |
| Approach: | They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents. |
| Outcome: | The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system . |
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)
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Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo
| Challenge: | a human-like chatbot requires commonsense reasoning to comprehend and respond to information . however, identifying and aggregating key evidence within a single hop is a challenge . a knowledge distillation framework is proposed that leverages LLMs as unreliable teachers . |
| Approach: | They propose a framework that leverages large language models as unreliable teachers to facilitate multi-hop reasoning over a dialogue context. |
| Outcome: | The proposed framework leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. |
Textual Time Travel: A Temporally Informed Approach to Theory of Mind (2021.findings-emnlp)
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| Challenge: | Existing models that attribute mental states to oneself and others perform poorly on false belief tasks where beliefs differ from reality. |
| Approach: | They propose a temporally informed approach for improving the theory of mind capability of memory-augmented neural models by integrating priors about entities’ minds and tracking their mental states over time through an extended passage. |
| Outcome: | The proposed model improves performance on false belief tasks where beliefs differ from reality, especially when the dataset contains distracting sentences. |
Dialogue Natural Language Inference (P19-1)
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| Challenge: | Consistency is a long standing issue faced by dialogue models. |
| Approach: | They propose to frame the consistency of dialogue agents as natural language inference and create a new natural language dataset called Dialogue NLI. |
| Outcome: | The proposed model can improve the consistency of a dialogue model with human evaluation and automatic metrics on a suite of evaluation sets designed to measure the model’s consistency. |
SQLWOZ: A Realistic Task-Oriented Dialogue Dataset with SQL-Based Dialogue State Representation for Complex User Requirements (2025.emnlp-main)
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| Challenge: | Existing TOD datasets present simplified interactions with simple slot-value style constraints and preferences. |
| Approach: | They propose a novel TOD dataset that captures complex user requirements using SQL statements. |
| Outcome: | The proposed dataset captures complex, real-world user requirements. |
AirDialogue: An Environment for Goal-Oriented Dialogue Research (D18-1)
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| Challenge: | Recent advances in dialogue generation have inspired a number of studies on dialogue systems . however, current datasets are limited in size and the environment for training agents is relatively unsophisticated. |
| Approach: | They propose to use a context-generator to generate travel and flight restrictions to train agents. |
| Outcome: | The proposed model achieves a score of 0.17 while humans can reach 0.91 . the proposed model is based on a large dataset that contains 301,427 goal-oriented conversations . |
A Dataset of Argumentative Dialogues on Scientific Papers (2023.acl-long)
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| Challenge: | Recent advances in question-answering models have made them a great asset in accessing the content of scientific papers. |
| Approach: | They propose to use a dataset of 41 argumentative dialogues between scientists on 20 NLP papers to improve and evaluate their question-answering models. |
| Outcome: | The proposed dataset includes both exploratory and argumentative questions and answers in a dialogue discourse on a scientific paper. |
An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning (2025.coling-main)
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| Challenge: | Existing models for dialogue policy training consider one-step dialogues, leading to inaccurate simulations. |
| Approach: | They propose a framework for dialogue policy learning that trains an agent to select dialogue actions via deep reinforcement learning. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three dialogue datasets . it uses model-based reinforcement learning with automatically constructed causal chains . |
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue (2022.findings-emnlp)
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| Challenge: | Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. |
| Approach: | They propose a modular model for incorporating knowledge into conversational agents that generates a knowledge sequence and then attends to its own generated knowledge sequence. |
| Outcome: | The proposed model hallucinates less in knowledge-grounded dialogue tasks and has advantages in terms of interpretability and modularity. |
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (P19-1)
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| Challenge: | EmpatheticDialogues dataset provides a benchmark for empathetic dialogue generation . human evaluators perceive dialogue models as more epathetic . |
| Approach: | They propose a benchmark for empathetic dialogue generation from a dataset of 25k conversations grounded in emotional situations. |
| Outcome: | The proposed benchmarks show that existing models are perceived to be more empathetic by human evaluators compared to models trained on large-scale Internet conversations. |
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment (P19-1)
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| Challenge: | Existing approaches to generate informative responses based on external knowledge are limited to singleround settings. |
| Approach: | They propose a framework for multi-turn conversations with two dialogue agents . they propose to evaluate dialogues on informativeness and coherence . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches significantly on the publicly available dataset. |
Profile Consistency Identification for Open-domain Dialogue Agents (2020.emnlp-main)
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| Challenge: | Existing studies on improving attribute consistency focus on incorporating attribute information in responses, but few efforts have identified the consistency relations between response and attribute profile. |
| Approach: | They propose a key-value structure information enriched BERT model to identify the profile consistency . they propose to incorporate attribute information into the generated responses . |
| Outcome: | The proposed model improves over strong baselines on downstream tasks. |
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources. |
| Approach: | They propose a method for training large language models by enabling "self-talk" they propose supervised fine-tuning of LLMs to improve quality of dialogues . |
| Outcome: | The proposed method generates training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. |
Large Language Models Meet Harry Potter: A Dataset for Aligning Dialogue Agents with Characters (2023.findings-emnlp)
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| Challenge: | Existing models that can create open-domain dialogue agents lack character representation and annotations. |
| Approach: | They propose a dataset to study character alignment and character representation . it includes all dialogue sessions from the Harry Potter series and includes annotations . |
| Outcome: | The proposed dataset can be used as a universal benchmark for character-driven LLMs. |
ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization (2023.emnlp-main)
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| Challenge: | Recent development of large language models (LLMs) have boosted interest on dialogue agents . however, research on these tasks is limited by the insufficiency of public datasets . stance detection and debate summarization are key for engaging argumentative dialogues - but are not available for non-English languages. |
| Approach: | They propose to use ORCHID to benchmark stance detection and debate summarization in Chinese debates. |
| Outcome: | The proposed task is based on 1,218 real-world debates conducted in Chinese on 476 unique topics. |
Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems (2023.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models enable them to follow freeform instructions, including imitating generic or specific demographic personas in conversations. |
| Approach: | They propose to investigate persona biases by experimenting with UNIVERSALPERSONA, a model that incorporates both generic and specific personas. |
| Outcome: | The proposed model systematically measures persona biases in harmful expression and harmful agreement. |
Fora: A corpus and framework for the study of facilitated dialogue (2024.acl-long)
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| Challenge: | a new study of facilitated dialogues focuses on the sharing of personal experience . social media is a popular method of civic engagement but lacks the tools to analyze it . |
| Approach: | They compile 262 facilitated conversations hosted with partner organizations . they taxonomize personal sharing behaviors and facilitation strategies in the corpus . |
| Outcome: | The proposed framework can be used to analyze facilitated dialogues and parse spoken conversations . the data can be applied to other fields, including civic use in governance and social science . |
Using In-Context Learning to Improve Dialogue Safety (2023.findings-emnlp)
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Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Recent work has highlighted safety issues with large neural-based conversational models. |
| Approach: | They propose a retrieval-based approach for reducing bias and toxicity in chatbot responses . they retrieve demonstrations of safe responses to similar dialogue contexts to generate a response . |
| Outcome: | The proposed method reduces bias and toxicity in three chatbot models . it can be used in compliment to existing dialogue safety approaches, such as RLHF. |
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming (2023.acl-long)
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| Challenge: | a recent study shows that open-domain dialogue systems are not able to perform well in fast-growing scenarios such as live streaming due to the domain gap between online-post constructed data and those required in downstream conversational tasks. |
| Approach: | They propose to train a conversational agent based on large social media datasets with multiple domains to improve response in live streaming scenarios. |
| Outcome: | The proposed model improves response modeling and addressee recognition in live open-domain scenarios. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues (2025.findings-acl)
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Jon Cai, Brendan King, Peyton Cameron, Susan Windisch Brown, Miriam Eckert, Dananjay Srinivas, George Arthur Baker, V Kate Everson, Martha Palmer, James Martin, Jeffrey Flanigan
| Challenge: | Understanding speaker intentions remains a challenge in NLP . a number of corpora annotated using theoretical frameworks of dialogue focus on utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue. |
| Approach: | They propose to annotate a corpus of 33 dialogues and over 9,000 utterance units using the Dependency Dialogue Acts framework. |
| Outcome: | The proposed corpus spans four genres of multi-party conversations from different modalities. |