Papers with dialogue task
Pretraining the Noisy Channel Model for Task-Oriented Dialogue (2021.tacl-1)
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| Challenge: | Current research on task-oriented dialogue models suffers from the explaining-away effect, manifested in models that favor short and generic responses. |
| Approach: | They propose to factorize the dialogue task into two models, the distribution of the context given the response, and the prior for the response itself, using Bayes' theorem. |
| Outcome: | The proposed model mitigates the explaining-away effect and allows the principled incorporation of large pretrained models for the response prior. |
Hierarchical Inductive Transfer for Continual Dialogue Learning (2022.findings-acl)
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| Challenge: | Existing frameworks for learning and deployment of neural dialogue models have been used for online chit-chat scenarios. |
| Approach: | They propose a hierarchical inductive transfer framework to learn and deploy dialogue skills continually and efficiently. |
| Outcome: | The proposed framework achieves comparable performance under deployment-friendly model capacity. |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
QualEval: Qualitative Evaluation for Model Improvement (2024.naacl-long)
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Vishvak Murahari, Ameet Deshpande, Peter Clark, Tanmay Rajpurohit, Ashish Sabharwal, Karthik Narasimhan, Ashwin Kalyan
| Challenge: | Quantitative evaluation metrics are inadequate for large language models due to complexity of tasks and cannot provide actionable diagnostics. |
| Approach: | They propose a quantitative evaluation tool called QualEval that uses automated qualitative evaluation as a vehicle for model improvement. |
| Outcome: | The proposed method improves the performance of the Llama 2 model by 15% compared to baselines. |
Automatically Learning Data Augmentation Policies for Dialogue Tasks (D19-1)
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| Challenge: | Recent advances in automatic data augmentation have focused on computer vision tasks where it is easy to apply imperceptible perturbations without changing an image’s semantic meaning. |
| Approach: | They adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation. |
| Outcome: | The proposed algorithm reduces data-level model bias by using a controller trained on the target task. |
Reference-Centric Models for Grounded Collaborative Dialogue (2021.emnlp-main)
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| Challenge: | Using a structured referent grounding module, we can effectively ground and inform a partner's utterances to their own context. |
| Approach: | They propose a grounded neural dialogue model that works with people in a partially-observable reference game. |
| Outcome: | The proposed model outperforms state-of-the-art models on a spatial grounding dialogue task and achieves a 20% relative improvement in human evaluations. |
Dialogue-oriented Pre-training (2021.findings-acl)
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| Challenge: | Pre-trained language models (PrLMs) have shown impressive improvements for various downstream tasks including various dialogue related ones. |
| Approach: | They propose to use pre-trained language models to simulate dialogue features on general plain text with common language model training objectives to improve performance. |
| Outcome: | The proposed method is fine-tuned on three public multi-turn dialogue datasets and achieves significant and consistent improvement over the plain PrLMs. |
Extremely Small BERT Models from Mixed-Vocabulary Training (2021.eacl-main)
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| Challenge: | Existing knowledge distillation methods cannot be directly applied to train student models with reduced vocabulary and embedding dimensions. |
| Approach: | They propose a method to align teacher and student embeddings via mixed-vocabulary training. |
| Outcome: | The proposed method compresses BERT-LARGE to a task-agnostic model with smaller vocabulary and hidden dimensions, which is an order of magnitude smaller than other distilled models. |
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. |
Symbolic Planning and Code Generation for Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Large language models excel at processing and generating text and code, but lack a grounded task-oriented dialogue system that can handle grounding. |
| Approach: | They propose a modular and interpretable grounded dialogue system that integrates a reader and planner to convert partner utterances into executable code and a symbolic planner to determine the next appropriate response. |
| Outcome: | The proposed system outperforms the existing state-of-the-art on a one-common dialogue task and improves task success in human evaluations from 56% to 69% in the most challenging setting. |
Collection and Analysis of Travel Agency Task Dialogues with Age-Diverse Speakers (2022.lrec-1)
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| Challenge: | Using deep neural networks, task-oriented dialogue systems can be used to generate an appropriate response to users' inputs. |
| Approach: | They collected a multimodal dialogue corpus with a wide range of speaker ages and set up a dialogue task based on travel . results suggest adult speakers have more independent opinions, older speakers express opinions more frequently compared with other age groups, and operators expressed a smile more frequently to minor speakers. |
| Outcome: | The results show that adult speakers have more independent opinions, the older speakers express their opinions more frequently compared with other age groups, and the operators expressed a smile more frequently to the minor speakers. |