CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling

• URL: http://arxiv.org/abs/2109.11541v1
• Date: Thu, 23 Sep 2021 07:47:28 GMT
• Title: CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
• Authors: Han Wu, Kun Xu, Linqi Song
• Abstract summary: We present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure-aware graph network which explicitly encodes the speaker dependent information.
• Score: 27.528361001332264
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure-aware graph network which explicitly encodes the speaker dependent information. We also propose a multi-task learning method to further improve the model. Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.

Related papers

• Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model [57.78191634042409]
  We propose Pseudo-Word HuBERT (PW-HuBERT), a framework that integrates pseudo word-level targets into the training process. Our experimental results on four spoken language understanding (SLU) benchmarks suggest the superiority of our model in capturing semantic information.
  arXiv  Detail & Related papers  (2024-02-08T16:55:21Z)
• History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling [28.27368343751272]
  We propose a History Semantic Graph Enhanced KBQA model (HSGE) that is able to effectively model long-range semantic dependencies in conversation history. We evaluate HSGE on a widely used benchmark dataset for complex sequential question answering.
  arXiv  Detail & Related papers  (2023-06-12T05:10:58Z)
• Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
  We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
  arXiv  Detail & Related papers  (2023-05-04T16:04:41Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph [40.43625257213158]
  We propose to automatically induce a predicate-oriented latent graph (POLar) with a predicate-centered Gaussian mechanism. The POLar structure is then dynamically pruned and refined so as to best fit the task need. We additionally introduce an effective dialogue-level pre-trained language model, CoDiaBERT, for better supporting multiple utterance sentences.
  arXiv  Detail & Related papers  (2022-10-06T16:42:00Z)
• Zero-shot Cross-lingual Conversational Semantic Role Labeling [30.28068025468505]
  We present an effective approach to perform zero-shot cross-lingual CSRL. Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders. We confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese.
  arXiv  Detail & Related papers  (2022-04-11T07:29:39Z)
• Conversational Query Rewriting with Self-supervised Learning [36.392717968127016]
  Conversational Query Rewriting (CQR) aims to simplify the multi-turn dialogue modeling into a single-turn problem by explicitly rewriting the conversational query into a self-contained utterance. Existing approaches rely on massive supervised training data, which is labor-intensive to annotate. We propose to construct a large-scale CQR dataset automatically via self-supervised learning, which does not need human annotation.
  arXiv  Detail & Related papers  (2021-02-09T08:57:53Z)
• Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference [50.768682650658384]
  Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document. Recent Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE task, and it has obtained competitive performance on various benchmarks. In this paper, we propose to adopt the Dynamic Graph Convolutional Networks (DGCN) to solve the above two problems simultaneously.
  arXiv  Detail & Related papers  (2020-10-24T08:11:23Z)
• Semantic Role Labeling Guided Multi-turn Dialogue ReWriter [63.07073750355096]
  We propose to use semantic role labeling (SRL) to highlight the core semantic information of who did what to whom. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.
  arXiv  Detail & Related papers  (2020-10-03T19:50:04Z)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)

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.