Dialogue Meaning Representation for Task-Oriented Dialogue Systems

• URL: http://arxiv.org/abs/2204.10989v1
• Date: Sat, 23 Apr 2022 04:17:55 GMT
• Title: Dialogue Meaning Representation for Task-Oriented Dialogue Systems
• Authors: Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, Zheng Zhang
• Abstract summary: We propose Dialogue Meaning Representation (DMR), a flexible and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges with inheritance hierarchy to represent rich semantics for compositional semantics and task-specific concepts. We propose two evaluation tasks to evaluate different machine learning based dialogue models, and further propose a novel coreference resolution model GNNCoref for the graph-based coreference resolution task.
• Score: 51.91615150842267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically follows the intent-slot framework, which is easy for annotation yet limited on scalability for complex linguistic expressions. A line of works alleviates the representation issue by introducing hierarchical structures but challenging to express complex compositional semantics, such as negation and coreference. We propose Dialogue Meaning Representation (DMR), a flexible and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges with inheritance hierarchy to represent rich semantics for compositional semantics and task-specific concepts. We annotated DMR-FastFood, a multi-turn dialogue dataset with more than 70k utterances, with DMR. We propose two evaluation tasks to evaluate different machine learning based dialogue models, and further propose a novel coreference resolution model GNNCoref for the graph-based coreference resolution task. Experiments show that DMR can be parsed well with pretrained Seq2Seq model, and GNNCoref outperforms the baseline models by a large margin.

Related papers

• WEBDial, a Multi-domain, Multitask Statistical Dialogue Framework with RDF [0.0]
  We present a dialogue framework that relies on a graph formalism by using RDF triples instead of slot-value pairs. We show its applicability from simple to complex applications, by varying the complexity of domains and tasks.
  arXiv  Detail & Related papers  (2024-01-08T14:08:33Z)
• Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis [4.768182075837568]
  DiaASQ aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue. Existing work independently encodes each utterance, thereby struggling to capture long-range conversational context. We propose a novel Dynamic Multi-scale Context Aggregation network (DMCA) to address the challenges.
  arXiv  Detail & Related papers  (2023-09-27T08:17:28Z)
• InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models [9.611864685207056]
  We propose a novel approach, InstructERC, to reformulate the emotion recognition task from a discriminative framework to a generative framework based on Large Language Models (LLMs) InstructERC makes three significant contributions: (1) it introduces a simple yet effective retrieval template module, which helps the model explicitly integrate multi-granularity dialogue supervision information; (2) we introduce two additional emotion alignment tasks, namely speaker identification and emotion prediction tasks, to implicitly model the dialogue role relationships and future emotional tendencies in conversations; and (3) Pioneeringly, we unify emotion labels across benchmarks through the feeling wheel to fit real application scenarios.
  arXiv  Detail & Related papers  (2023-09-21T09:22:07Z)
• 'What are you referring to?' Evaluating the Ability of Multi-Modal Dialogue Models to Process Clarificational Exchanges [65.03196674816772]
  Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee. Addressees usually detect such ambiguities immediately and work with the speaker to repair it using meta-communicative, Clarification Exchanges (CE): a Clarification Request (CR) and a response. Here, we argue that the ability to generate and respond to CRs imposes specific constraints on the architecture and objective functions of multi-modal, visually grounded dialogue models.
  arXiv  Detail & Related papers  (2023-07-28T13:44:33Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Guiding the PLMs with Semantic Anchors as Intermediate Supervision: Towards Interpretable Semantic Parsing [57.11806632758607]
  We propose to incorporate the current pretrained language models with a hierarchical decoder network. By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks. We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines.
  arXiv  Detail & Related papers  (2022-10-04T07:27:29Z)
• Representation Learning for Conversational Data using Discourse Mutual Information Maximization [9.017156603976915]
  We argue that the structure-unaware word-by-word generation is not suitable for effective conversation modeling. We propose a structure-aware Mutual Information based loss-function DMI for training dialog-representation models. Our models show the most promising performance on the dialog evaluation task DailyDialog++, in both random and adversarial negative scenarios.
  arXiv  Detail & Related papers  (2021-12-04T13:17:07Z)
• Semantic Representation for Dialogue Modeling [22.80679759491184]
  We exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge. We are the first to leverage a formal semantic representation into neural dialogue modeling.
  arXiv  Detail & Related papers  (2021-05-21T07:55:07Z)
• Discovering Dialog Structure Graph for Open-Domain Dialog Generation [51.29286279366361]
  We conduct unsupervised discovery of dialog structure from chitchat corpora. We then leverage it to facilitate dialog generation in downstream systems. We present a Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover a unified human-readable dialog structure.
  arXiv  Detail & Related papers  (2020-12-31T10:58:37Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)

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.