Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models with seq2seq framework lack ability to effectively manage concept transitions . lack of concept management strategies might lead to incoherent dialogue due to loosely connected concepts . |
| Approach: | They propose a concept-guided non-autoregressive model for open-domain dialogue generation that learns to identify multiple associated concepts from a conceptual graph and a customized Insertion Transformer to perform concept-directed generation to complete a response. |
| Outcome: | The proposed model outperforms state-of-the-art models in automatic and human evaluations with substantially faster inference speed. |
Similar Papers
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog (2021.naacl-main)
Copied to clipboard
| Challenge: | Semantic parsing using sequence-to-sequence models is stymied by higher compute requirements and higher latency. |
| Approach: | They propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. |
| Outcome: | The proposed architecture achieves an 81% reduction in latency on TOP dataset and retains competitive performance over non-pretrained models on three different semantic parsing datasets. |
NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding. |
| Approach: | They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks. |
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
Copied to clipboard
| 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. |
Non-Autoregressive Sequence Generation (2022.acl-tutorials)
Copied to clipboard
| Challenge: | Non-autoregressive sequence generation (NAR) models generate output sequences in parallel to speed up generation process. |
| Approach: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to generate the entire or partial output sequences in parallel to speed up the generation process . |
| Outcome: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to reduce the performance gap between state-of-the-art models due to lack of modeling power . |
Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)
Copied to clipboard
| Challenge: | Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation. |
| Approach: | They propose a way to diversify dialogue generation by leveraging non-conversational text . they collect large-scale corpus from forum comments, idioms and book snippets . |
| Outcome: | The proposed model produces significantly more diverse responses without sacrificing relevance with context. |
Stylized Dialogue Generation with Feature-Guided Knowledge Augmentation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods synthesize pseudo data through back translation but lack guidance on target style features. |
| Approach: | They propose a knowledge-augmented stylized dialogue generation model with a feature-guided style knowledge selection module that utilizes context and response features. |
| Outcome: | The proposed model produces a satisfactory performance on two public benchmarks on both semantic and stylized levels. |
Learning to Plan and Realize Separately for Open-Ended Dialogue Systems (2020.findings-emnlp)
Copied to clipboard
Sashank Santhanam, Zhuo Cheng, Brodie Mather, Bonnie Dorr, Archna Bhatia, Bryanna Hebenstreit, Alan Zemel, Adam Dalton, Tomek Strzalkowski, Samira Shaikh
| Challenge: | Existing approaches to natural language generation are construed as end-to-end systems . however, some issues persist, such as coherence of output and repetition/hallucination of tokens . |
| Approach: | They propose to decouple natural language generation into two phases: planning and realization. |
| Outcome: | The proposed approach performs better than an end-to-end approach. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Non-Autoregressive Models for Fast Sequence Generation (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | Autoregressive (AR) models can only generate target sequence word-by-word due to the AR mechanism and suffer from slow inference. |
| Approach: | This tutorial provides an introduction to non-autoregressive sequence generation. |
| Outcome: | This tutorial explains how to generate non-autoregressive sequence generation models. |
A Study of Non-autoregressive Model for Sequence Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Non-autoregressive (NAR) models generate all tokens in parallel, resulting in faster generation speed compared to autoregressive models. |
| Approach: | They propose to use knowledge distillation and source-target alignment to bridge the gap between NAR and autoregressive models in various tasks. |
| Outcome: | The proposed techniques can speed up NAR models in some tasks but not all . the proposed techniques reduce target token dependency while allowing for faster inference . |