Autoregressive Entity Generation for End-to-End Task-Oriented Dialog

• URL: http://arxiv.org/abs/2209.08708v1
• Date: Mon, 19 Sep 2022 01:49:29 GMT
• Title: Autoregressive Entity Generation for End-to-End Task-Oriented Dialog
• Authors: Guanhuan Huang, Xiaojun Quan, and Qifan Wang
• Abstract summary: Most current end-to-end TOD systems either retrieve the KB information explicitly or embed it into model parameters for implicit access. In either approach, the systems may generate a response with conflicting entity information. We propose to generate the entity autoregressively first and leverage it to guide the response generation in an end-to-end system.
• Score: 24.75603780275969
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Task-oriented dialog (TOD) systems often require interaction with an external knowledge base to retrieve necessary entity (e.g., restaurant) information to support the response generation. Most current end-to-end TOD systems either retrieve the KB information explicitly or embed it into model parameters for implicit access.~While the former approach demands scanning the KB at each turn of response generation, which is inefficient when the KB scales up, the latter approach shows higher flexibility and efficiency. In either approach, the systems may generate a response with conflicting entity information. To address this issue, we propose to generate the entity autoregressively first and leverage it to guide the response generation in an end-to-end system. To ensure entity consistency, we impose a trie constraint on entity generation. We also introduce a logit concatenation strategy to facilitate gradient backpropagation for end-to-end training. Experiments on MultiWOZ 2.1 single and CAMREST show that our system can generate more high-quality and entity-consistent responses.

Related papers

• Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents [0.0]
  We present a question-and-answer (Q&A) application designed to support the contract management process. Data is processed by a large language model (LLM) to provide precise and relevant answers.
  arXiv  Detail & Related papers  (2024-12-23T19:54:28Z)
• ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  We propose a pioneering generAtive Cross-modal rEtrieval framework (ACE) for end-to-end cross-modal retrieval. ACE achieves state-of-the-art performance in cross-modal retrieval and outperforms the strong baselines on Recall@1 by 15.27% on average.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)
• PICK: Polished & Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems [59.1250765143521]
  Current knowledge-grounded dialogue systems often fail to align the generated responses with human-preferred qualities. We propose Polished & Informed Candidate Scoring (PICK), a generation re-scoring framework. We demonstrate the effectiveness of PICK in generating responses that are more faithful while keeping them relevant to the dialogue history.
  arXiv  Detail & Related papers  (2023-09-19T08:27:09Z)
• Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems [53.38517204698343]
  We propose a novel paradigm that uses a textual interface to align external knowledge and eliminate redundant processes. We demonstrate our paradigm in practice through MultiWOZ-Remake, including an interactive textual interface built for the MultiWOZ database.
  arXiv  Detail & Related papers  (2023-05-23T05:48:21Z)
• Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation [33.56304858796142]
  Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve evidences separately and then use a language model to generate an answer based on the retrieved evidences. We propose a Structured Knowledge and Unified Retrieval-Generation (RG) approach to address these issues.
  arXiv  Detail & Related papers  (2022-12-16T18:12:04Z)
• Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems [71.33737787564966]
  End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called 'likelihood trap' We propose a reranking method which aims to select high-quality items from the lists of responses initially overgenerated by the system. Our methods improve a state-of-the-art E2E ToD system by 2.4 BLEU, 3.2 ROUGE, and 2.8 METEOR scores, achieving new peak results.
  arXiv  Detail & Related papers  (2022-11-07T15:59:49Z)
• Towards End-to-End Open Conversational Machine Reading [57.18251784418258]
  In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. We model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework.
  arXiv  Detail & Related papers  (2022-10-13T15:50:44Z)
• EchoEA: Echo Information between Entities and Relations for Entity Alignment [1.1470070927586016]
  We propose a novel framework, Echo Entity Alignment (EchoEA), which leverages self-attention mechanism to spread entity information to relations and echo back to entities. The experimental results on three real-world cross-lingual datasets are stable at around 96% at hits@1 on average.
  arXiv  Detail & Related papers  (2021-07-07T07:34:21Z)
• Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing [52.71007876803418]
  Existing methods for Dialogue Response Generation (DRG) in Task-oriented Dialogue Systems ( TDSs) can be grouped into two categories: template-based and corpus-based. We propose a prototype-based, paraphrasing neural network, called P2-Net, which aims to enhance quality of the responses in terms of both precision and diversity.
  arXiv  Detail & Related papers  (2020-08-07T22:25:36Z)
• Unsupervised Learning of KB Queries in Task-Oriented Dialogs [21.611723342957887]
  Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent. Existing approaches require dialog datasets to explicitly annotate these KB queries. We define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation.
  arXiv  Detail & Related papers  (2020-04-30T22:10:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.