Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (P19-1)
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| Challenge: | Neural generative models are becoming more popular when building conversational agents. |
| Approach: | They propose to study the sensitivity of neural dialog models to unnatural perturbations . they experiment with 10 different types of perturbations on 4 multi-turn dialog datasets . |
| Outcome: | The proposed model is sensitive to unnatural changes or perturbations on 4 multi-turn dialog datasets. |
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Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
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| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
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. |
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition (2021.tacl-1)
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| Challenge: | Existing punctuation in the transcripts has a massive effect on the models’ performance, and specific label set specificity does not affect dialog act segmentation performance. |
| Approach: | They apply two pre-trained transformer models to a conversation transcript as a sequence of dialog acts and achieve strong results on Switchboard Dialog Act and Meeting Recorder Dialog Act corpora. |
| Outcome: | The proposed models achieve 8.4% and 14.2% error rates on the Switchboard Dialog Act and Meeting Recorder Dialog Act corpora. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
| Approach: | They propose an Adaptive Neural Dialogue generation model which manages various conversations with conversation-specific parameterization. |
| Outcome: | The proposed model performs better on a large-scale conversational dataset. |
Improving Neural Conversational Models with Entropy-Based Data Filtering (P19-1)
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| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
| Approach: | They propose an unsupervised method of filtering dialog datasets by removing generic utterances from training data using an entropy-based approach that does not require human supervision. |
| Outcome: | The proposed method improves dialog quality as chatbots learn to output more diverse responses to open-ended utterances. |
Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models (2025.findings-acl)
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| Challenge: | Recent advances in multi-turn voice interaction models have improved user-model communication, but whether open-source models share this ability remains unexplored. |
| Approach: | They propose to use ContextDialog to evaluate open-source interaction models' ability to recall past utterances to identify key limitations. |
| Outcome: | The proposed model retains and recalls past utterances better than closed-source models, but still struggles with questions about past . findings highlight key limitations in open-source model and suggest ways to improve memory retention and retrieval robustness. |
A Dynamic Speaker Model for Conversational Interactions (N19-1)
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| Challenge: | a neural model for characterizing individual differences in speakers is shown to be useful in human-computer interaction and dialog act prediction. |
| Approach: | They propose a neural model for learning a dynamically updated speaker embedding in a conversational context. |
| Outcome: | The proposed model is used for content ranking and dialog act prediction in human-human conversations. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
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| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |
Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries (2021.acl-short)
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| Challenge: | despite advances in task-oriented and chit-chat based dialogue systems, many systems rely on static and unnatural responses. |
| Approach: | They propose a neural approach which generates contextually aware responses to user queries . they perform automatic and manual evaluations to demonstrate the efficacy of the system . |
| Outcome: | The proposed approach generates responses which are contextually aware with the user query and say no to the user. |
Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
| Approach: | They propose an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. |
| Outcome: | The proposed architecture improves the next utterance prediction in chit-chat type of generative dialogue models by incorporating external knowledge from Wikipedia summaries and the NELL knowledge base. |